Lane control on a central cruise control unit for outer and rear passenger compartments

Description

Technical Field

[0001] Various embodiments relate generally to methods and devices for wireless communications.Background

[0002] Developments in radio communication networks have led to various new types of network architectures. Some of these network architectures relate to heterogenous networks, where both larger macro cells and small cells are deployed in a coverage area. The macro cells may serve large coverage areas while the small cells serve more limited spaces. Other network architectures including moving cells, such as cells that can use mobility to improve coverage to their served terminal devices. Additional networks may use vehicular communication devices, where vehicles can be equipped with connectivity functionality to wirelessly communicate with each other and the underlying network.

[0003] Wang Xiaoli et al. "Networked Drone Cameras for Sports Streaming". In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, 2017-06-05, IEEE Computer Society, US, pages 308-318. The document relates to a network of drone cameras that can be deployed to cover live events, such as high-action sports game played on a large field. To compensate for network round-trip latencies, the centralized controller uses a predictive approach to predict which locations the drones should cover next.

[0004] Gao Yajun et al. "Autonomous WiFi-relay control with mobile robots". In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2016-06-06, IEEE, pages 198-203. The document relates to, to ensure stable wireless communication, the need to eliminate the bottleneck of data transmission in order to make the communication ability best amongst base station, mobile robots and clients by equipping WiFi routers on robots to enable and enhance communication ability.

[0005] Carfang Anthony J. et al. "Improving data ferrying by iteratively learning the radio frequency environment". In: 2014 IEEE / RSJ International Conference on Intelligent Robots and Systems, 2014-09-14, IEEE, pages 1182-1188. The document relates to use of a data ferry in sparse sensor networks by combining physical movement with wireless relaying of data, in which an unmanned aircraft is used as a data ferry.

[0006] Ladosz Pawel et al. "Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments". In: 2017 IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS), 2017-09-24, IEEE, pages 6831-6836. The document relates to a use of a learning approach to predict air-to-ground communication strength in support of the communication relay mission using UAVs in an urban environment.

[0007] Additionally, US2017 / 111102 A1 discloses a drone controller that receives status information for a wireless communication link being received by an wireless device from an access point (e.g., a WiFi access point, a 3G / 4G femtocell, carrier base station, etc.) The drone controller then evaluates the status information to determine whether a wireless extension service should be invoked for the wireless device. In response to determining the reported communication link status (e.g., measured signal quality) meets the trigger condition (e.g., reaches an unacceptable threshold level), the drone controller deploys a signal extender drone automatically. The drone controller determines an initial target location for the signal extender drone to be intermediate the location of the wireless device and the access point. The drone controller then deploys the signal extender drone by instructing the drone to navigate to the initial target location. When the initial target location is reached, the signal extender drone activates its signal extender to initiate signal extension service. At this point, the wireless communication link will be relayed between the wireless device and the access point via the signal extender drone.

[0008] The invention related to this particular application is defined in the claims.Brief Description of the Drawings

[0009] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments of the invention are described with reference to the following drawings. In particular, the invention is best understood in view of figures 7-11. The remaining embodiments, aspects and examples disclosed below are included for illustrative purposes and for facilitating the understanding of the invention. FIG. 1 shows an exemplary radio communication network according to some aspects; FIG. 2 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 3 shows an exemplary internal configuration of a network access node according to some aspects; FIG. 4 shows an exemplary radio communication network with a core network according to some aspects; FIG. 5 shows an exemplary vehicular communication device according to some aspects; FIG. 6 shows an exemplary internal configuration of vehicular communication device according to some aspects; FIG. 7 shows an exemplary network scenario with backhaul and outer moving cells according to some aspects; FIG. 8 shows an exemplary internal configuration of an outer moving cell according to some aspects; FIG. 9 shows an exemplary internal configuration of a backhaul moving cell according to some aspects; FIG. 10 shows an exemplary internal configuration of a central trajectory controller according to some aspects; FIG. 11 shows an exemplary trajectory control procedure for backhaul and outer moving cells according to some aspects; FIG. 12 shows an exemplary radio map according to some aspects; FIG. 13 shows an exemplary network scenario with backhaul moving cells according to some aspects; FIG. 14 shows an exemplary trajectory control procedure for backhaul moving cells according to some aspects; FIG. 15 shows an exemplary method for a central trajectory controller according to some aspects; FIG. 16 shows an exemplary method for an outer moving cell according to some aspects; FIG. 17 shows an exemplary method for a backhaul moving cell according to some aspects; FIG. 18 shows an exemplary method for a central trajectory controller according to some aspects; FIG. 19 shows an exemplary method for a backhaul moving cell according to some aspects; FIG. 20 shows an exemplary indoor coverage area according to some aspects; FIG. 21 shows a diagram for mobile access nodes and an anchor access point according to some aspects; FIG. 22 shows an exemplary internal configuration of a mobile access node according to some aspects; FIG. 23 shows an exemplary internal configuration of an anchor access point according to some aspects; FIG. 24 shows an exemplary procedure for mobile access nodes and an anchor access point according to some aspects; FIG. 25 shows an exemplary method for identify usage patterns according to some aspects; FIG. 26 shows an exemplary scenario of adjusting a trajectory of a mobile access node according to some aspects; FIG. 27 shows an exemplary scenario for adjusting a trajectory of a mobile access node based on a trajectory departure according to some aspects; FIG. 28 shows an exemplary method for a mobile access node according to some aspects; FIG. 29 shows an exemplary method for a mobile access node according to some aspects; FIG. 30 shows an exemplary method for a mobile access node according to some aspects; FIG. 31 shows an exemplary method for an anchor access point according to some aspects; FIG. 32 shows an exemplary scenario of an indoor coverage area according to some aspects; FIG. 33 shows an exemplary internal configuration of a mobile access node according to some aspects; FIG. 34 shows an exemplary internal configuration of a central trajectory controller according to some aspects; FIG. 35 shows an exemplary procedure for determining trajectories for mobile access nodes according to some aspects; FIG. 36 shows an exemplary procedure for determining trajectories for mobile access nodes according to some aspects; FIG. 37 shows an exemplary network scenario for beamsteering according to some aspects; FIG. 38 shows an exemplary procedure for determining trajectories of mobile access nodes based on capacity according to some aspects; FIG. 39 shows an exemplary method for a central trajectory controller according to some aspects; FIG. 40 shows an exemplary method for a mobile access node according to some aspects; FIG. 41 shows an exemplary method for a mobile access node according to some aspects; FIG. 42 shows an exemplary method for a central trajectory controller according to some aspects; FIG. 43 shows an exemplary diagram of a virtual network according to some aspects; FIG. 44 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 45 shows an exemplary procedure for forming and using a virtual network according to some aspects; FIG. 46 shows an exemplary procedure for using a virtual network with a virtual master terminal device according to some aspects; FIG. 47 shows an exemplary diagram of various VEFs for a virtual network according to some aspects; FIGs. 48 and 49 show examples of distributing VEFs in a virtual network according to some aspects; FIG. 50 shows an exemplary procedure for executing VEFs according to some aspects; FIG. 51 shows an exemplary method of allocating VEFs according to some aspects; FIG. 52 shows an exemplary procedure for forming and using a virtual cell according to some aspects; FIG. 53 shows an exemplary network diagram of a virtual cell according to some aspects; FIG. 54 shows an example illustrating allocation and execution of virtual cell VEFs at terminal devices according to some aspects; FIG. 55 shows an exemplary diagram of virtual cell VEF allocation and execution according to some aspects; FIG. 56 shows an exemplary procedure for managing members of a virtual cell according to some aspects; FIG. 57 shows an exemplary network scenario of handover for a virtual cell according to some aspects; FIG. 58 shows an exemplary method of operating a terminal device according to some aspects; FIG. 59 shows an exemplary method of operating a terminal device according to some aspects; FIG. 60 shows an exemplary method of operating a terminal device according to some aspects; FIG. 61 shows an exemplary network scenario for a virtual cell according to some aspects; FIG. 62 shows an exemplary internal configuration of a terminal device for a virtual cell according to some aspects; FIG. 63 shows an exemplary procedure for creating a virtual cell according to some aspects; FIG. 64 shows an exemplary diagram of a virtual cell with different regions according to some aspects; FIG. 65 shows an exemplary diagram of a virtual cell according to some aspects; FIG. 66 shows an example where a virtual cell is divided into multiple subareas according to some aspects; FIGs. 67 and 68 show examples of virtual cell VEF allocation according to some aspects; FIG. 69 shows an exemplary division of a virtual cell coverage area according to some aspects; FIGs. 70 and 71 show examples of virtual cell VEF allocation according to some aspects; FIG. 72 shows an example of mobility for served terminal devices of virtual cells according to some aspects; FIG. 73 shows an exemplary virtual cell VEF allocation with a mobility layer according to some aspects; FIGs. 74-79 show exemplary methods of operating communication devices according to some aspects; FIG. 80 shows an exemplary diagram of dynamic local server processing offload according to some aspects; FIG. 81 shows an exemplary internal configuration of a network access node according to some aspects; FIG. 82 shows an exemplary internal configuration of a local server according to some aspects; FIG. 83 shows an exemplary internal configuration of a user plane server according to some aspects; FIG. 84 shows an exemplary internal configuration of a cloud server according to some aspects; FIG. 85 shows an exemplary procedure for dynamic local server processing offload according to some aspects; FIG. 86 shows an exemplary procedure for dynamic local server processing offload according to some aspects; FIG. 87 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 88 shows an exemplary procedure for dynamic local server processing offload according to some aspects; FIG. 89 shows an exemplary procedure for dynamic local server processing offload according to some aspects; FIGs. 90-93 show exemplary methods for performing processing functions at a local server according to some aspects; FIG. 94 shows an exemplary method for filtering and routing data according to some aspects; FIGs. 95 and 96 show exemplary methods for execution at a cloud server according to some aspects FIG. 97 shows an exemplary network configuration for a cell association function according to some aspects; FIG. 98 shows an exemplary internal configuration of cell association controller according to some aspects; FIGs. 99 and 100 show exemplary procedures for a cell association function according to some aspects; FIGs. 101-103 show various exemplary network scenarios for cell association according to some aspects; FIGs. 104-106 show exemplary selections of MEC servers according to some aspects; FIG. 107 shows an exemplary internal configuration of a bias control server according to some aspects; FIG. 108 shows an exemplary procedure for determining bias values according to some aspects; FIGs. 109 and 110 show exemplary procedures for controlling cell association according to some aspects; FIG. 111 shows an exemplary method of determining bias values according to some aspects; FIG. 112 shows an exemplary radio communication network employing CSMA according to some aspects; FIG. 113 shows an exemplary method according to which terminal devices may communicate following a CSMA scheme according to some aspects; FIG. 114 shows an exemplary radio communication network relating to full duplex communication according to various aspects of the present disclosure; FIG. 115 shows a further exemplary radio communication network relating to full duplex communication according to various aspects of the present disclosure; FIG. 116 shows a further exemplary radio communication network relating to full duplex communication according to various aspects of the present disclosure; FIG. 117 shows an exemplary internal configuration of a communication device in accordance with various aspects of the present disclosure; FIG. 118 shows an exemplary method, which a communication device may execute using the internal configuration of FIG. 117 in accordance with some aspects; FIGs. 119A and 119B show exemplary timing diagrams in accordance with certain aspects; and FIGs. 120A and 120B, illustrate exemplary frequency resources that may in certain aspects be used for broadcasting scheduling messages. FIG. 121 shows an exemplary method for a communication device according to some aspects; FIGs. 122-125 show exemplary illustrations implementing full duplex (FD) methods in some aspects. FIG. 126 shows an exemplary device configuration for low power Δ between transmitter and receiver for FD in some aspects. FIG. 127 shows an exemplary device configuration for high power Δ between transmitter and receiver for FD in some aspects. FIG. 128 shows an exemplary configuration of a terminal device in some aspects. FIG. 129 shows an exemplary Message Sequence Chart (MSC) for Cluster ID creation / allocation in some aspects. FIG. 130 shows an exemplary flowchart describing a method for communicating between a first device and a second device in some aspects. FIG. 131 shows an exemplary flowchart describing a method for wireless communications in some aspects. FIG. 132 illustrates problems identified in V2X communications in some aspects. FIG. 133 shows an exemplary network configuration and frequency, time, and power graph in some aspects FIG. 134 shows an exemplary internal configuration for a low-complexity broadcasting repeater (LBR) in some aspects. FIG. 135 shows an exemplary flowchart describing a method for wireless communications in some aspects, FIG. 136 shows an exemplary small cell deployment problem scenario in some aspects. FIG. 137 shows exemplary small cell configurations in some aspects. FIG. 138 shows an exemplary scenario in which a node may be configured as a relay to execute transformation / translation services between different RATs in some aspects. FIG. 139 shows an exemplary internal configuration for a terminal device in some aspects. FIG. 140 shows an exemplary internal configuration for a device configured to process different RAT signals in some aspects. FIG. 141 shows an exemplary flowchart describing a method for deploying a small cell communication arrangement in some aspects. FIG. 142 shows an exemplary flowchart describing a method for translating a first radio access technology (RAT) signal into a second RAT signal in some aspects. FIG. 143 shows an exemplary RRC state transition chart in some aspects. FIG. 144 shows an exemplary message sequence chart (MSC) illustrating a terminal device RX calibration in some aspects. FIG. 145 shows an exemplary message sequence chart (MSC) illustrating a terminal device TX calibration in some aspects. FIG. 146-147 show exemplary diagrams for an software reconfiguration based replacement of defective source components in some aspects. FIG. 148 shows an exemplary diagram illustrating a hardware replacement of defective source components in a terminal device in some aspects. FIG. 149 shows an exemplary diagram for a hardware reconfiguration based replacement of defective source components in some aspects. FIG. 150 shows an exemplary flowchart describing a method for calibrating a communication device in some aspects. FIG. 151 shows an exemplary flowchart describing replacing a component of a communication device in some aspects. FIG. 152 shows an exemplary flowchart describing a method for selecting a RAT link for transmitting a message in some aspects. FIG. 153 shows an exemplary MSC with a corresponding small cell network in some aspects. FIG. 154-155 show exemplary diagrams for small cell reconfiguration in some aspects. FIG. 156 shows an exemplary small cell network with a plurality of specialized small cells in some aspects. FIG. 157 shows an exemplary MSC for the signaling of a small cell network in some aspects. FIG. 158 shows an exemplary flowchart describing a method for a network access node to interact with users in some aspects. FIG. 159 shows an exemplary flowchart describing management of a network access node arrangement including a master network access node and one or more dedicated network access nodes in some aspects. FIG. 160 shows a diagram highlighting differences between reconfiguring a single terminal device compared to reconfiguring a small cell in some aspects. FIG. 161 shows an exemplary small cell architecture according to some aspects. FIG. 162 shows an exemplary overall system architecture for providing updates to the small cell in some aspects. FIG. 163 shows an exemplary small cell priority determiner in some aspects. FIG. 164 shows an exemplary MSC describing a signaling process for a small cell network in some aspects. FIG. 165 shows an exemplary flowchart describing a method for configuring a network access node in some aspects. FIG. 166 shows an exemplary an exemplary V2X network environment in some aspects. FIG. 167 shows an exemplary diagram describing an exemplary hierarchical setup in some aspects. FIG. 168A shows an exemplary internal configuration for a hierarchy determiner of a terminal device in some aspects. FIG. 168B shows an exemplary an exemplary MSC describing a method for identifying capabilities of one or more small cells for determining a small cell hierarchy in some aspects. FIG. 168C shows an exemplary diagram describing a process for meeting latency requirements in some aspects. FIG. 168D shows an exemplary small cell network configuration in some aspects. FIG. 169 shows an exemplary flowchart describing a method for creating a hierarchy of nodes for use in wireless communications in some aspects. FIG. 170 shows an example of a transmitting and receiving streams of user plane data according to some aspects; FIG. 171 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 172 shows an exemplary network scenario of dynamic compression selection with multiple network access nodes according to some aspects; FIGs. 173 and 174 show exemplary procedures for dynamic compression selection in uplink and downlink according to some aspects; FIG. 175 shows an exemplary network scenario of dynamic compression selection with one network access node according to some aspects; FIGs. 176 and 177 show exemplary procedures for dynamic compression selection in uplink and downlink according to some aspects; FIG. 178 shows an exemplary internal configuration of a terminal device according to some aspects; FIGs. 179-181 show exemplary methods of transferring a data stream at a communication device according to some aspects; FIG. 182 shows an example of a network communication scenario according to some aspects; FIG. 183 shows an exemplary internal configuration of a network access node according to some aspects; FIG. 184 shows an exemplary procedure for a modulation scheme selection function according to some aspects; FIG. 185 shows an exemplary procedure for a modulation scheme selection function with additional control variables according to some aspects; FIG. 186 shows an exemplary procedure for a modulation scheme selection function with spectrum offload according to some aspects; FIG. 187 shows an exemplary network scenario for a modulation scheme selection function with multiple terminal devices according to some aspects; FIG. 188 shows an exemplary procedure for a modulation scheme selection function with multiple terminal devices according to some aspects; FIG. 189 shows an exemplary procedure for a modulation scheme selection function at a terminal device according to some aspects; FIG. 190 shows an exemplary procedure for operating a network access node according to some aspects; FIG. 191 shows an exemplary procedure for operating a terminal device according to some aspects; FIG. 192 shows an exemplary procedure for operating a network access node according to some aspects; FIG. 193 shows an exemplary internal configuration of a radio communication arrangement, and an antenna system according to some aspects. FIG. 194 shows an exemplary network scenario in accordance with some aspects. FIG. 195 shows an exemplary flow diagram for a device under test according to some aspects. FIG. 196 shows an exemplary flow diagram for a device under test according to some aspects. FIG. 197 shows an exemplary process for performing a conformance test of a device under test according to some aspects. FIG. 198 shows an exemplary process for performing an OTA update process according to some aspects. FIG. 199 is an exemplary message sequence chart according to some aspects. FIG. 200 shows an exemplary method for communicating over a radio communication network in accordance with some aspects. FIG. 201 shows an exemplary method for communicating over a radio communication network in accordance with some aspects. FIG. 202 shows an exemplary decision chart for an in-field diagnostic process according to some aspects. FIG. 203 shows an exemplary evaluation of an in-field diagnostic process in accordance with some aspects. FIG. 204 shows an exemplary internal configuration of a radio communication arrangement, and an antenna system according to some aspects. FIG. 205 shows an exemplary network scenario in accordance with some aspects. FIG. 206 shows an exemplary logical architecture of a radio communication arrangement in accordance with some aspects. FIG. 207 shows an exemplary logical architecture of a radio communication arrangement in accordance with some aspects. FIG. 208 is an exemplary message sequence chart in accordance with some aspects. FIG. 209 is an exemplary message sequence chart in accordance with some aspects. FIG. 210 is an exemplary message sequence chart in accordance with some aspects. FIG. 211 shows an exemplary method for communicating over a radio communication network in accordance with some aspects. FIG. 212 shows an exemplary method for communicating over a radio communication network in accordance with some aspects. FIG. 213 shows an exemplary unmanned aerial vehicle according to some aspects; FIG. 214 shows an exemplary unmanned aerial vehicle with a flight structure according to some aspects; FIG. 215 shows an exemplary change in a target zone and target location according to some aspects; FIG. 216 shows an exemplary change in a target zone and target location according to some aspects; FIG. 217 shows an exemplary flight path according to some aspects; FIG. 218 shows an exemplary flight path according to some aspects; FIG. 219 shows an exemplary flight path according to some aspects; FIG. 220 shows an exemplary method for flying on a flight path according to some aspects; FIG. 221 shows an exemplary method for flying on a flight path according to some aspects; FIG. 222 shows an exemplary flight formation according to some aspects; FIG. 223 shows an exemplary flight formation according to some aspects; FIG. 224 shows an exemplary flight formation according to some aspects; FIG. 225 shows an exemplary method for arranging a flight formation according to some aspects; FIG. 226 shows an exemplary relay for a network access node according to some aspects; FIG. 227 shows an exemplary relay for a network access node according to some aspects; FIG. 228 shows an exemplary method for controlling a relay for a network access node according to some aspects; FIG. 229 shows an exemplary two-dimensional cell network according to some aspects; FIG. 230 shows an exemplary three-dimensional cell network according to some aspects; FIG. 231 shows an exemplary unmanned aerial vehicle according to some aspects; FIG. 232 shows an exemplary flight path for charging an unmanned aerial vehicle according to some aspects; FIG. 233 shows an exemplary method for charging an unmanned aerial vehicle according to some aspects; FIG. 234 shows an exemplary structure for charging an unmanned aerial vehicle according to some aspects; FIG. 235 shows an exemplary method for charging an unmanned aerial vehicle according to some aspects; FIG. 236 shows an exemplary arrangement for charging an unmanned aerial vehicle according to some aspects; FIG. 237 shows an exemplary method for charging an unmanned aerial vehicle according to some aspects; FIG. 238 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 239 shows an exemplary network scenario in a network tracking area according to some aspects; FIG. 240 show a first exemplary message sequence chart involving a core network signaling procedure according to some aspects; FIGs. 241A and 241B show a second exemplary message sequence chart involving a core network signaling procedure according to some aspects; FIG. 242 shows an exemplary network scenario in multiple network tracking areas according to some aspects; FIG. 243 shows an exemplary message sequence chart for a core network signaling procedure according to some aspects; FIG. 244 shows an exemplary network scenario with a fake cell according to some aspects; FIG. 245 shows an exemplary message sequence chart for a core network signaling procedure with a fake cell according to some aspects; FIG. 246 shows an exemplary message sequence chart for a core network signaling procedure with a rejection according to some aspects; FIG. 247 shows an exemplary network scenario in multiple tracking areas with a fake cell according to some aspects; FIG. 248 shows an exemplary internal configuration of a terminal device according to some aspects; FIG. 249 shows an exemplary message sequence chart for a failed registration attempt according to some aspects; FIGs. 250A and 250B shows an exemplary message sequence chart for multiple failed registration attempts according to some aspects; FIG. 251 shows an exemplary procedure for failed registration attempts according to some aspects; FIG. 252 shows an exemplary diagram illustrating terminal device registration according to some aspects; FIG. 253 shows a first exemplary method of operating a communication device according to some aspects; FIG. 254 shows a first exemplary method of operating a communication device according to some aspects; FIG. 255 shows a first exemplary method of operating a communication device according to some aspects; and FIG. 256 shows a first exemplary method of operating a communication device according to some aspects. Description

[0010] The following detailed description refers to the accompanying drawings that show, by way of illustration, specific details and aspects of embodiments in which the invention may be practiced.

[0011] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

[0012] The words "plurality" and "multiple" in the description or the claims expressly refer to a quantity greater than one. The terms "group (of)", "set [of]", "collection (of)", "series (of)", "sequence (of)", "grouping (of)", among others, and the like in the description or in the claims refer to a quantity equal to or greater than one, i.e. one or more. Any term expressed in plural form that does not expressly state "plurality" or "multiple" likewise refers to a quantity equal to or greater than one. The terms "proper subset", "reduced subset", and "lesser subset" refer to a subset of a set that is not equal to the set, i.e. a subset of a set that contains less elements than the set.

[0013] Any vector and / or matrix notation utilized herein is exemplary in nature and is employed solely for purposes of explanation. Accordingly, aspects of this disclosure accompanied by vector and / or matrix notation are not limited to being implemented solely using vectors and / or matrices, and that the associated processes and computations may be equivalently performed with respect to sets, sequences, groups, among others, of data, observations, information, signals, samples, symbols, elements, among others.

[0014] As used herein, "memory" are understood as a non-transitory computer-readable medium in which data or information can be stored for retrieval. References to "memory" included herein may thus be understood as referring to volatile or non-volatile memory, including random access memory (RAM), read-only memory (ROM), flash memory, solid-state storage, magnetic tape, hard disk drive, optical drive, among others, or any combination thereof. Furthermore, registers, shift registers, processor registers, data buffers, among others, are also embraced herein by the term memory. A single component referred to as "memory" or "a memory" may be composed of more than one different type of memory, and thus may refer to a collective component comprising one or more types of memory. Any single memory component may be separated into multiple collectively equivalent memory components, and vice versa. Furthermore, while memory may be depicted as separate from one or more other components (such as in the drawings), memory may also be integrated with other components, such as on a common integrated chip or a controller with an embedded memory.

[0015] The term "software" refers to any type of executable instruction, including firmware.

[0016] The term "terminal device" utilized herein refers to user-side devices (both portable and fixed) that can connect to a core network and / or external data networks via a radio access network. "Terminal device" can include any mobile or immobile wireless communication device, including User Equipments (UEs), Mobile Stations (MSs), Stations (STAs), cellular phones, tablets, laptops, personal computers, wearables, multimedia playback and other handheld or body-mounted electronic devices, consumer / home / office / commercial appliances, vehicles, and any other electronic device capable of user-side wireless communications. Without loss of generality, in some cases terminal devices can also include application-layer components, such as application processors or other general processing components, that are directed to functionality other than wireless communications. Terminal devices can optionally support wired communications in addition to wireless communications. Furthermore, terminal devices can include vehicular communication devices that function as terminal devices.

[0017] The term "network access node" as utilized herein refers to a network-side device that provides a radio access network with which terminal devices can connect and exchange information with a core network and / or external data networks through the network access node. "Network access nodes" can include any type of base station or access point, including macro base stations, micro base stations, NodeBs, evolved NodeBs (eNBs), Home base stations, Remote Radio Heads (RRHs), relay points, Wi-Fi / WLAN Access Points (APs), Bluetooth master devices, DSRC RSUs, terminal devices acting as network access nodes, and any other electronic device capable of network-side wireless communications, including both immobile and mobile devices (e.g., vehicular network access nodes, moving cells, and other movable network access nodes). As used herein, a "cell" in the context of telecommunications may be understood as a sector served by a network access node. Accordingly, a cell may be a set of geographically co-located antennas that correspond to a particular sectorization of a network access node. A network access node can thus serve one or more cells (or sectors), where the cells are characterized by distinct communication channels. Furthermore, the term "cell" may be utilized to refer to any of a macrocell, microcell, femtocell, picocell, among others Certain communication devices can act as both terminal devices and network access nodes, such as a terminal device that provides network connectivity for other terminal devices.

[0018] Various aspects of this disclosure may utilize or be related to radio communication technologies. While some examples may refer to specific radio communication technologies, the examples provided herein may be similarly applied to various other radio communication technologies, both existing and not yet formulated, particularly in cases where such radio communication technologies share similar features as disclosed regarding the following examples. Various exemplary radio communication technologies that the aspects described herein may utilize include, but are not limited to: a Global System for Mobile Communications (GSM) radio communication technology, a General Packet Radio Service (GPRS) radio communication technology, an Enhanced Data Rates for GSM Evolution (EDGE) radio communication technology, and / or a Third Generation Partnership Project (3GPP) radio communication technology, for example Universal Mobile Telecommunications System (UMTS), Freedom of Multimedia Access (FOMA), 3GPP Long Term Evolution (LTE), 3GPP Long Term Evolution Advanced (LTE Advanced), Code division multiple access 2000 (CDMA2000), Cellular Digital Packet Data (CDPD), Mobitex, Third Generation (3G), Circuit Switched Data (CSD), High-Speed Circuit-Switched Data (HSCSD), Universal Mobile Telecommunications System (Third Generation) (UMTS (3G)), Wideband Code Division Multiple Access (Universal Mobile Telecommunications System) (W-CDMA (UMTS)), High Speed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), High Speed Packet Access Plus (HSPA+), Universal Mobile Telecommunications System-Time-Division Duplex (UMTS-TDD), Time Division-Code Division Multiple Access (TD-CDMA), Time Division-Synchronous Code Division Multiple Access (TD-CDMA), 3rd Generation Partnership Project Release 8 (Pre-4th Generation) (3GPP Rel. 8 (Pre-4G)), 3GPP Rel. 9 (3rd Generation Partnership Project Release 9), 3GPP Rel. 10 (3rd Generation Partnership Project Release 10) , 3GPP Rel. 11 (3rd Generation Partnership Project Release 11), 3GPP Rel. 12 (3rd Generation Partnership Project Release 12), 3GPP Rel. 13 (3rd Generation Partnership Project Release 13), 3GPP Rel. 14 (3rd Generation Partnership Project Release 14), 3GPP Rel. 15 (3rd Generation Partnership Project Release 15), 3GPP Rel. 16 (3rd Generation Partnership Project Release 16), 3GPP Rel. 17 (3rd Generation Partnership Project Release 17) and subsequent Releases (such as Rel. 18, Rel. 19, among others), 3GPP 5G, 3GPP LTE Extra, LTE-Advanced Pro, LTE Licensed-Assisted Access (LAA), MuLTEfire, UMTS Terrestrial Radio Access (UTRA), Evolved UMTS Terrestrial Radio Access (E-UTRA), Long Term Evolution Advanced (4th Generation) (LTE Advanced (4G)), cdmaOne (2G), Code division multiple access 2000 (Third generation) (CDMA2000 (3G)), Evolution-Data Optimized or Evolution-Data Only (EV-DO), Advanced Mobile Phone System (1st Generation) (AMPS (1G)), Total Access Communication System / Extended Total Access Communication System (TACS / ETACS), Digital AMPS (2nd Generation) (D-AMPS (2G)), Push-to-talk (PTT), Mobile Telephone System (MTS), Improved Mobile Telephone System (IMTS), Advanced Mobile Telephone System (AMTS), OLT (Norwegian for Offentlig Landmobil Telefoni, Public Land Mobile Telephony), MTD (Swedish abbreviation for Mobiltelefonisystem D, or Mobile telephony system D), Public Automated Land Mobile (Autotel / PALM), ARP (Finnish for Autoradiopuhelin, "car radio phone"), NMT (Nordic Mobile Telephony), High capacity version of NTT (Nippon Telegraph and Telephone) (Hicap), Cellular Digital Packet Data (CDPD), Mobitex, DataTAC, Integrated Digital Enhanced Network (iDEN), Personal Digital Cellular (PDC), Circuit Switched Data (CSD), Personal Handy-phone System (PHS), Wideband Integrated Digital Enhanced Network (WiDEN), iBurst, Unlicensed Mobile Access (UMA), also referred to as also referred to as 3GPP Generic Access Network, or GAN standard), Zigbee, Bluetooth(r), Wireless Gigabit Alliance (WiGig) standard, mmWave standards in general (wireless systems operating at 10-300 GHz and above such as WiGig, IEEE 802.11ad, IEEE 802.11ay, among others), technologies operating above 300 GHz and THz bands, (3GPP / LTE based or IEEE 802.11p and other) Vehicle-to-Vehicle (V2V) and Vehicle-to-X (V2X) and Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communication technologies, 3GPP cellular V2X, DSRC (Dedicated Short Range Communications) communication systems such as Intelligent-Transport-Systems and others, the European ITS-G5 system (i.e. the European flavor of IEEE 802.11p based DSRC, including ITS-G5A (i.e., Operation of ITS-G5 in European ITS frequency bands dedicated to ITS for safety related applications in the frequency range 5,875 GHz to 5,905 GHz), ITS-G5B (i.e., Operation in European ITS frequency bands dedicated to ITS non- safety applications in the frequency range 5,855 GHz to 5,875 GHz), ITS-G5C (i.e., Operation of ITS applications in the frequency range 5,470 GHz to 5,725 GHz)), among others. Aspects described herein can be used in the context of any spectrum management scheme including dedicated licensed spectrum, unlicensed spectrum, (licensed) shared spectrum (such as LSA = Licensed Shared Access in 2.3-2.4 GHz, 3.4-3.6 GHz, 3.6-3.8 GHz and further frequencies and SAS = Spectrum Access System in 3.55-3.7 GHz and further frequencies). Applicable spectrum bands include IMT (International Mobile Telecommunications) spectrum as well as other types of spectrum / bands, such as bands with national allocation (including 450 - 470 MHz, 902-928 MHz (e.g., allocated for example in US (FCC Part 15)), 863-868.6 MHz (e.g., allocated for example in European Union (ETSI EN 300 220)), 915.9-929.7 MHz (e.g., allocated for example in Japan), 917-923.5 MHz (e.g., allocated for example in South Korea), 755-779 MHz and 779-787 MHz (e.g., allocated for example in China), 790 - 960 MHz, 1710 - 2025 MHz, 2110 - 2200 MHz, 2300 - 2400 MHz, 2.4-2.4835 GHz (e.g., it is an ISM band with global availability and it is used by Wi-Fi technology family (11b / g / n / ax) and also by Bluetooth), 2500 - 2690 MHz, 698-790 MHz, 610 - 790 MHz, 3400 - 3600 MHz, 3400 - 3800 MHz, 3.55-3.7 GHz (e.g., allocated for example in the US for Citizen Broadband Radio Service), 5.15-5.25 GHz and 5.25-5.35 GHz and 5.47-5.725 GHz and 5.725-5.85 GHz bands (e.g., allocated for example in the US (FCC part 15), consists four U-NII bands in total 500 MHz spectrum), 5.725-5.875 GHz (e.g., allocated for example in EU (ETSI EN 301 893)), 5.47-5.65 GHz (e.g., allocated for example in South Korea, 5925-7125 MHz and 5925-6425MHz band (e.g., under consideration in US and EU, respectively, where next generation Wi-Fi system may also include the 6 GHz spectrum as operating band), IMT-advanced spectrum, IMT-2020 spectrum (expected to include 3600-3800 MHz, 3.5 GHz bands, 700 MHz bands, bands within the 24.25-86 GHz range, among others), spectrum made available under FCC's "Spectrum Frontier" 5G initiative (including 27.5 - 28.35 GHz, 29.1 - 29.25 GHz, 31 - 31.3 GHz, 37 - 38.6 GHz, 38.6 - 40 GHz, 42 - 42.5 GHz, 57 - 64 GHz, 71 - 76 GHz, 81 - 86 GHz and 92 - 94 GHz, among others), the ITS (Intelligent Transport Systems) band of 5.9 GHz (typically 5.85-5.925 GHz) and 63-64 GHz, bands currently allocated to WiGig such as WiGig Band 1 (57.24-59.40 GHz), WiGig Band 2 (59.40-61.56 GHz) and WiGig Band 3 (61.56-63.72 GHz) and WiGig Band 4 (63.72-65.88 GHz), 57-64 / 66 GHz (e.g., where this band has near-global designation for Multi-Gigabit Wireless Systems (MGWS) / WiGig . In US (FCC part 15) allocates total 14 GHz spectrum, while EU (ETSI EN 302 567 and ETSI EN 301 217-2 for fixed P2P) allocates total 9 GHz spectrum), the 70.2 GHz - 71 GHz band, any band between 65.88 GHz and 71 GHz, bands currently allocated to automotive radar applications such as 76-81 GHz, and future bands including 94-300 GHz and above. Furthermore, the scheme can be used on a secondary basis on bands such as the TV White Space bands (typically below 790 MHz) where in particular the 400 MHz and 700 MHz bands are promising candidates. Besides cellular applications, specific applications for vertical markets may be addressed such as PMSE (Program Making and Special Events), medical, health, surgery, automotive, low-latency, drones, among others applications.

[0019] Aspects described herein can also implement a hierarchical application of the scheme is possible, e.g. by introducing a hierarchical prioritization of usage for different types of users (e.g., low / medium / high priority, among others), based on a prioritized access to the spectrum e.g. with highest priority to tier-1 users, followed by tier-2, then tier-3, and so forth users. Aspects described herein can also be applied to different Single Carrier or OFDM flavors (CP-OFDM, SC-FDMA, SC-OFDM, filter bank-based multicarrier (FBMC), OFDMA, among others) and in particular 3GPP NR (New Radio) by allocating the OFDM carrier data bit vectors to the corresponding symbol resources.]. Some of the features in this disclosure are defined for the network side, such as Access Points, eNodeBs, among others In some cases, a User Equipment (UE) may also take this role and act as an Access Points, eNodeBs, or the like. some or all features defined for network equipment may be implemented by a UE.

[0020] For purposes of this disclosure, radio communication technologies may be classified as one of a Short Range radio communication technology or Cellular Wide Area radio communication technology. Short Range radio communication technologies may include Bluetooth, WLAN (e.g., according to any IEEE 802.11 standard), and other similar radio communication technologies. Cellular Wide Area radio communication technologies may include Global System for Mobile Communications (GSM), Code Division Multiple Access 2000 (CDMA2000), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), General Packet Radio Service (GPRS), Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), High Speed Packet Access (HSPA; including High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), HSDPA Plus (HSDPA+), and HSUPA Plus (HSUPA+)), Worldwide Interoperability for Microwave Access (WiMax) (e.g., according to an IEEE 802.16 radio communication standard, e.g., WiMax fixed or WiMax mobile), for example, and other similar radio communication technologies. Cellular Wide Area radio communication technologies also include "small cells" of such technologies, such as microcells, femtocells, and picocells. Cellular Wide Area radio communication technologies may be generally referred to herein as "cellular" communication technologies.

[0021] The terms "radio communication network" and "wireless network" as utilized herein encompasses both an access section of a network (e.g., a radio access network (RAN) section) and a core section of a network (e.g., a core network section). The term "radio idle mode" or "radio idle state" used herein in reference to a terminal device refers to a radio control state in which the terminal device is not allocated at least one dedicated communication channel of a mobile communication network. The term "radio connected mode" or "radio connected state" used in reference to a terminal device refers to a radio control state in which the terminal device is allocated at least one dedicated uplink communication channel of a radio communication network.

[0022] Unless explicitly specified, the term "transmit" encompasses both direct (point-to-point) and indirect transmission (via one or more intermediary points). Similarly, the term "receive" encompasses both direct and indirect reception. Furthermore, the terms "transmit", "receive", "communicate", and other similar terms encompass both physical transmission (e.g., the transmission of radio signals) and logical transmission (e.g., the transmission of digital data over a logical software-level connection). For example, a processor or controller may transmit or receive data over a software-level connection with another processor or controller in the form of radio signals, where the physical transmission and reception is handled by radio-layer components such as RF transceivers and antennas, and the logical transmission and reception over the software-level connection is performed by the processors or controllers. The term "communicate" encompasses one or both of transmitting and receiving, i.e. unidirectional or bidirectional communication in one or both of the incoming and outgoing directions. The term "calculate" encompass both 'direct' calculations via a mathematical expression / formula / relationship and 'indirect' calculations via lookup or hash tables and other array indexing or searching operations.General Network and Device Description

[0023] FIGs. 1 and 2 depict an exemplary network and device architecture for wireless communications. In particular, FIG. 1 shows exemplary radio communication network 100 according to some aspects, which may include terminal devices 102 and 104 and network access nodes 110 and 102. Radio communication network 100 may communicate with terminal devices 102 and 104 via network access nodes 110 and 102 over a radio access network. Although certain examples described herein may refer to a particular radio access network context (e.g., LTE, UMTS, GSM, other 3rd Generation Partnership Project (3GPP) networks, WLAN / WiFi, Bluetooth, 5G, mmWave, etc.), these examples are demonstrative and may therefore be readily applied to any other type or configuration of radio access network. The number of network access nodes and terminal devices in radio communication network 100 is exemplary and is scalable to any amount.

[0024] In an exemplary cellular context, network access nodes 110 and 102 may be base stations (e.g., eNodeBs, NodeBs, Base Transceiver Stations (BTSs), or any other type of base station), while terminal devices 102 and 104 may be cellular terminal devices (e.g., Mobile Stations (MSs), User Equipments (UEs), or any type of cellular terminal device). Network access nodes 110 and 102 may therefore interface (e.g., via backhaul interfaces) with a cellular core network such as an Evolved Packet Core (EPC, for LTE), Core Network (CN, for UMTS), or other cellular core networks, which may also be considered part of radio communication network 100. The cellular core network may interface with one or more external data networks. In an exemplary short-range context, network access node 110 and 102 may be access points (APs, e.g., WLAN or WiFi APs), while terminal device 102 and 104 may be short range terminal devices (e.g., stations (STAs)). Network access nodes 110 and 102 may interface (e.g., via an internal or external router) with one or more external data networks.

[0025] Network access nodes 110 and 102 (and, optionally, other network access nodes of radio communication network 100 not explicitly shown in FIG. 1) may accordingly provide a radio access network to terminal devices 102 and 104 (and, optionally, other terminal devices of radio communication network 100 not explicitly shown in FIG. 1). In an exemplary cellular context, the radio access network provided by network access nodes 110 and 102 may enable terminal devices 102 and 104 to wirelessly access the core network via radio communications. The core network may provide switching, routing, and transmission, for traffic data related to terminal devices 102 and 104, and may further provide access to various internal data networks (e.g., control nodes, routing nodes that transfer information between other terminal devices on radio communication network 100, etc.) and external data networks (e.g., data networks providing voice, text, multimedia (audio, video, image), and other Internet and application data). In an exemplary short-range context, the radio access network provided by network access nodes 110 and 102 may provide access to internal data networks (e.g., for transferring data between terminal devices connected to radio communication network 100 ) and external data networks (e.g., data networks providing voice, text, multimedia (audio, video, image), and other Internet and application data).

[0026] The radio access network and core network (if applicable, such as for a cellular context) of radio communication network 100 may be governed by communication protocols that can vary depending on the specifics of radio communication network 100. Such communication protocols may define the scheduling, formatting, and routing of both user and control data traffic through radio communication network 100, which includes the transmission and reception of such data through both the radio access and core network domains of radio communication network 100. Accordingly, terminal devices 102 and 104 and network access nodes 110 and 102 may follow the defined communication protocols to transmit and receive data over the radio access network domain of radio communication network 100, while the core network may follow the defined communication protocols to route data within and outside of the core network. Exemplary communication protocols include LTE, UMTS, GSM, WiMAX, Bluetooth, WiFi, mm Wave, etc., any of which may be applicable to radio communication network 100.

[0027] FIG. 2 shows an internal configuration of terminal device 102 according to some aspects, which may include antenna system 202, radio frequency (RF) transceiver 204, baseband modem 206 (including digital signal processor 208 and protocol controller 210 ), application processor 212, and memory 214. Although not explicitly shown in FIG. 2, in some aspects terminal device 102 may include one or more additional hardware and / or software components, such as processors / microprocessors, controllers / microcontrollers, other specialty or generic hardware / processors / circuits, peripheral device(s), memory, power supply, external device interface(s), subscriber identity module(s) (SIMs), user input / output devices (display(s), keypad(s), touchscreen(s), speaker(s), external button(s), camera(s), microphone(s), etc.), or other related components.

[0028] Terminal device 102 may transmit and receive radio signals on one or more radio access networks. Baseband modem 206 may direct such communication functionality of terminal device 102 according to the communication protocols associated with each radio access network, and may execute control over antenna system 202 and RF transceiver 204 to transmit and receive radio signals according to the formatting and scheduling parameters defined by each communication protocol. Although various practical designs may include separate communication components for each supported radio communication technology (e.g., a separate antenna, RF transceiver, digital signal processor, and controller), for purposes of conciseness the configuration of terminal device 102 shown in FIG. 2 depicts only a single instance of such components.

[0029] Terminal device 102 may transmit and receive wireless signals with antenna system 202, which may be a single antenna or an antenna array that includes multiple antennas. In some aspects, antenna system 202 may additionally include analog antenna combination and / or beamforming circuitry. In the receive (RX) path, RF transceiver 204 may receive analog radio frequency signals from antenna system 202 and perform analog and digital RF front-end processing on the analog radio frequency signals to produce digital baseband samples (e.g., In-Phase / Quadrature (IQ) samples) to provide to baseband modem 206. RF transceiver 204 may include analog and digital reception components including amplifiers (e.g., Low Noise Amplifiers (LNAs)), filters, RF demodulators (e.g., RF IQ demodulators)), and analog-to-digital converters (ADCs), which RF transceiver 204 may utilize to convert the received radio frequency signals to digital baseband samples. In the transmit (TX) path, RF transceiver 204 may receive digital baseband samples from baseband modem 206 and perform analog and digital RF front-end processing on the digital baseband samples to produce analog radio frequency signals to provide to antenna system 202 for wireless transmission. RF transceiver 204 may thus include analog and digital transmission components including amplifiers (e.g., Power Amplifiers (PAs), filters, RF modulators (e.g., RF IQ modulators), and digital-to-analog converters (DACs), which RF transceiver 204 may utilize to mix the digital baseband samples received from baseband modem 206 and produce the analog radio frequency signals for wireless transmission by antenna system 202. In some aspects baseband modem 206 may control the radio transmission and reception of RF transceiver 204, including specifying the transmit and receive radio frequencies for operation of RF transceiver 204.

[0030] As shown in FIG. 2, baseband modem 206 may include digital signal processor 208, which may perform physical layer (PHY, Layer 1) transmission and reception processing to, in the transmit path, prepare outgoing transmit data provided by protocol controller 210 for transmission via RF transceiver 204, and, in the receive path, prepare incoming received data provided by RF transceiver 204 for processing by protocol controller 210. Digital signal processor 208 may be configured to perform one or more of error detection, forward error correction encoding / decoding, channel coding and interleaving, channel modulation / demodulation, physical channel mapping, radio measurement and search, frequency and time synchronization, antenna diversity processing, power control and weighting, rate matching / de-matching, retransmission processing, interference cancelation, and any other physical layer processing functions. Digital signal processor 208 may be structurally realized as hardware components (e.g., as one or more digitally-configured hardware circuits or FPGAs), software-defined components (e.g., one or more processors configured to execute program code defining arithmetic, control, and I / O instructions (e.g., software and / or firmware) stored in a non-transitory computer-readable storage medium), or as a combination of hardware and software components. In some aspects, digital signal processor 208 may include one or more processors configured to retrieve and execute program code that defines control and processing logic for physical layer processing operations. In some aspects, digital signal processor 208 may execute processing functions with software via the execution of executable instructions. In some aspects, digital signal processor 208 may include one or more dedicated hardware circuits (e.g., ASICs, FPGAs, and other hardware) that are digitally configured to specific execute processing functions, where the one or more processors of digital signal processor 208 may offload certain processing tasks to these dedicated hardware circuits, which are known as hardware accelerators. Exemplary hardware accelerators can include Fast Fourier Transform (FFT) circuits and encoder / decoder circuits. In some aspects, the processor and hardware accelerator components of digital signal processor 208 may be realized as a coupled integrated circuit.

[0031] Terminal device 102 may be configured to operate according to one or more radio communication technologies. Digital signal processor 208 may be responsible for lower-layer processing functions (e.g., Layer 1 / PHY) of the radio communication technologies, while protocol controller 210 may be responsible for upper-layer protocol stack functions (e.g., Data Link Layer / Layer 2 and / or Network Layer / Layer 3). Protocol controller 210 may thus be responsible for controlling the radio communication components of terminal device 102 (antenna system 202, RF transceiver 204, and digital signal processor 208 ) in accordance with the communication protocols of each supported radio communication technology, and accordingly may represent the Access Stratum and Non-Access Stratum (NAS) (also encompassing Layer 2 and Layer 3) of each supported radio communication technology. Protocol controller 210 may be structurally embodied as a processor configured to execute protocol stack software (retrieved from a controller memory) and subsequently control the radio communication components of terminal device 102 to transmit and receive communication signals in accordance with the corresponding protocol stack control logic defined in the protocol stack software. Protocol controller 210 may include one or more processors configured to retrieve and execute program code that defines the upper-layer protocol stack logic for one or more radio communication technologies, which can include Data Link Layer / Layer 2 and Network Layer / Layer 3 functions. Protocol controller 210 may be configured to perform both user-plane and control-plane functions to facilitate the transfer of application layer data to and from radio terminal device 102 according to the specific protocols of the supported radio communication technology. User-plane functions can include header compression and encapsulation, security, error checking and correction, channel multiplexing, scheduling and priority, while control-plane functions may include setup and maintenance of radio bearers. The program code retrieved and executed by protocol controller 210 may include executable instructions that define the logic of such functions.

[0032] In some aspects, terminal device 102 may be configured to transmit and receive data according to multiple radio communication technologies. Accordingly, in some aspects one or more of antenna system 202, RF transceiver 204, digital signal processor 208, and protocol controller 210 may include separate components or instances dedicated to different radio communication technologies and / or unified components that are shared between different radio communication technologies. For example, in some aspects protocol controller 210 may be configured to execute multiple protocol stacks, each dedicated to a different radio communication technology and either at the same processor or different processors. In some aspects, digital signal processor 208 may include separate processors and / or hardware accelerators that are dedicated to different respective radio communication technologies, and / or one or more processors and / or hardware accelerators that are shared between multiple radio communication technologies. In some aspects, RF transceiver 204 may include separate RF circuitry sections dedicated to different respective radio communication technologies, and / or RF circuitry sections shared between multiple radio communication technologies. In some aspects, antenna system 202 may include separate antennas dedicated to different respective radio communication technologies, and / or antennas shared between multiple radio communication technologies. Accordingly, while antenna system 202, RF transceiver 204, digital signal processor 208, and protocol controller 210 are shown as individual components in FI, in some aspects antenna system 202, RF transceiver 204, digital signal processor 208, and / or protocol controller 210 can encompass separate components dedicated to different radio communication technologies.

[0033] Terminal device 102 may also include application processor 212 and memory 214. Application processor 212 may be a CPU, and may be configured to handle the layers above the protocol stack, including the transport and application layers. Application processor 212 may be configured to execute various applications and / or programs of terminal device 102 at an application layer of terminal device 102, such as an operating system (OS), a user interface (UI) for supporting user interaction with terminal device 102, and / or various user applications. The application processor may interface with baseband modem 206 and act as a source (in the transmit path) and a sink (in the receive path) for user data, such as voice data, audio / video / image data, messaging data, application data, basic Internet / web access data, etc. In the transmit path, protocol controller 210 may therefore receive and process outgoing data provided by application processor 212 according to the layer-specific functions of the protocol stack, and provide the resulting data to digital signal processor 208. Digital signal processor 208 may then perform physical layer processing on the received data to produce digital baseband samples, which digital signal processor may provide to RF transceiver 204. RF transceiver 204 may then process the digital baseband samples to convert the digital baseband samples to analog RF signals, which RF transceiver 204 may wirelessly transmit via antenna system 202. In the receive path, RF transceiver 204 may receive analog RF signals from antenna system 202 and process the analog RF signals to obtain digital baseband samples. RF transceiver 204 may provide the digital baseband samples to digital signal processor 208, which may perform physical layer processing on the digital baseband samples. Digital signal processor 208 may then provide the resulting data to protocol controller 210, which may process the resulting data according to the layer-specific functions of the protocol stack and provide the resulting incoming data to application processor 212. Application processor 212 may then handle the incoming data at the application layer, which can include execution of one or more application programs with the data and / or presentation of the data to a user via a user interface.

[0034] Memory 214 may embody a memory component of terminal device 102, such as a hard drive or another such permanent memory device. Although not explicitly depicted in FIG. 2, the various other components of terminal device 102 shown in FIG. 2 may additionally each include integrated permanent and non-permanent and / or volatile & non-volatile memory components, such as for storing software program code, buffering data, etc..

[0035] In accordance with some radio communication networks, terminal devices 102 and 104 may execute mobility procedures to connect to, disconnect from, and switch between available network access nodes of the radio access network of radio communication network 100. As each network access node of radio communication network 100 may have a specific coverage area, terminal devices 102 and 104 may be configured to select and re-select between the available network access nodes in order to maintain a strong radio access connection with the radio access network of radio communication network 100. For example, terminal device 102 may establish a radio access connection with network access node 110 while terminal device 104 may establish a radio access connection with network access node 112. In the event that the current radio access connection degrades, terminal devices 102 or 104 may seek a new radio access connection with another network access node of radio communication network 100; for example, terminal device 104 may move from the coverage area of network access node 112 into the coverage area of network access node 110. As a result, the radio access connection with network access node 112 may degrade, which terminal device 104 may detect via radio measurements such as signal strength, signal quality, or error rate-related measurements of network access node 112. Depending on the mobility procedures defined in the appropriate network protocols for radio communication network 100, terminal device 104 may seek a new radio access connection (which may be, for example, triggered at terminal device 104 or by the radio access network), such as by performing radio measurements on neighboring network access nodes to determine whether any neighboring network access nodes can provide a suitable radio access connection. As terminal device 104 may have moved into the coverage area of network access node 110, terminal device 104 may identify network access node 110 (which may be selected by terminal device 104 or selected by the radio access network) and transfer to a new radio access connection with network access node 110. Such mobility procedures, including radio measurements, cell selection / reselection, and handover are established in the various network protocols and may be employed by terminal devices and the radio access network in order to maintain strong radio access connections between each terminal device and the radio access network across any number of different radio access network scenarios.

[0036] FIG. 3 shows an exemplary internal configuration of a network access node, such as network access node 110, according to some aspects. As shown in FIG. 3, network access node 110 may include antenna system 302, radio transceiver 304, and baseband subsystem 306 (including physical layer processor 308 and protocol controller 310 ). In an abridged overview of the operation of network access node 110, network access node 110 may transmit and receive wireless signals via antenna system 302, which may be an antenna array including multiple antennas. Radio transceiver 304 may perform transmit and receive RF processing to convert outgoing baseband samples from baseband subsystem 306 into analog radio signals to provide to antenna system 302 for radio transmission and to convert incoming analog radio signals received from antenna system 302 into baseband samples to provide to baseband subsystem 306. Physical layer processor 308 may be configured to perform transmit and receive PHY processing on baseband samples received from radio transceiver 304 to provide to controller 310 and on baseband samples received from controller 310 to provide to radio transceiver 304. Controller 310 may control the communication functionality of network access node 110 according to the corresponding radio communication technology protocols, which may include exercising control over antenna system 302, radio transceiver 304, and physical layer processor 308. Each of radio transceiver 304, physical layer processor 308, and controller 310 may be structurally realized with hardware (e.g., with one or more digitally-configured hardware circuits or FPGAs), as software (e.g., as one or more processors executing program code defining arithmetic, control, and I / O instructions stored in a non-transitory computer-readable storage medium), or as a mixed combination of hardware and software. In some aspects, radio transceiver 304 may be a radio transceiver including digital and analog radio frequency processing and amplification circuitry. In some aspects, radio transceiver 304 may be a software-defined radio (SDR) component implemented as a processor configured to execute software-defined instructions that specify radio frequency processing routines. In some aspects, physical layer processor 308 may include a processor and one or more hardware accelerators, wherein the processor is configured to control physical layer processing and offload certain processing tasks to the one or more hardware accelerators. In some aspects, controller 310 may be a controller configured to execute software-defined instructions that specify upper-layer control functions. In some aspects, controller 310 may be limited to radio communication protocol stack layer functions, while in other aspects controller 310 may also be configured for transport, internet, and application layer functions.

[0037] Network access node 110 may thus provide the functionality of network access nodes in radio communication networks by providing a radio access network to enable served terminal devices to access communication data. For example, network access node 110 may also interface with a core network, one or more other network access nodes, or various other data networks and servers via a wired or wireless backhaul interface.

[0038] As previously indicated, network access nodes 112 and 114 may interface with a core network. FIG. 4 shows an exemplary configuration in accordance with some aspects where network access node 110 interfaces with core network 402, which may be, for example, a cellular core network. Core network 402 may provide a variety of functions to manage operation of radio communication network 100, such as data routing, authenticating and managing users / subscribers, interfacing with external networks, and various other network control tasks. Core network 402 may therefore provide an infrastructure to route data between terminal device 104 and various external networks such as data network 404 and data network 406. Terminal device 104 may thus rely on the radio access network provided by network access node 110 to wirelessly transmit and receive data with network access node 110, which may then provide the data to core network 402 for further routing to external locations such as data networks 404 and 406 (which may be packet data networks (PDNs)). Terminal device 104 may therefore establish a data connection with data network 404 and / or data network 406 that relies on network access node 110 and core network 402 for data transfer and routing.

[0039] Terminal devices may in some cases be configured as vehicular communication devices (or other movable communication devices). FIG. 5 shows an exemplary internal configuration of a vehicular communication device 500 according to some aspects. As shown in FIG. 5, vehicular communication device 500 may include steering and movement system 502, radio communication arrangement 504, and antenna system 506. One or more components of vehicular communication device 500 may be arranged around a vehicular housing of vehicular communication device 500, mounted on or outside of the vehicular housing, enclosed within the vehicular housing, and / or any other arrangement relative to the vehicular housing where the components move with vehicular communication device 500 as it travels. The vehicular housing, such as an automobile body, plane or helicopter fuselage, boat hull, or similar type of vehicular body dependent on the type of vehicle that vehicular communication device 500 is. Steering and movement system 502 may include components of vehicular communication device 500 related to steering and movement of vehicular communication device 500. In some aspects where vehicular communication device 500 is an automobile, steering and movement system 502 may include wheels and axles, an engine, a transmission, brakes, a steering wheel, associated electrical circuitry and wiring, and any other components used in the driving of an automobile. In some aspects where vehicular communication device 500 is an aerial vehicle, steering and movement system 502 may include one or more of rotors, propellers, jet engines, wings, rudders or wing flaps, air brakes, a yoke or cyclic, associated electrical circuitry and wiring, and any other components used in the flying of an aerial vehicle. In some aspects where vehicular communication device 500 is an aquatic or sub-aquatic vehicle, steering and movement system 502 may include any one or more of rudders, engines, propellers, a steering wheel, associated electrical circuitry and wiring, and any other components used in the steering or movement of an aquatic vehicle. In some aspects, steering and movement system 502 may also include autonomous driving functionality, and accordingly may also include a central processor configured to perform autonomous driving computations and decisions and an array of sensors for movement and obstacle sensing. The autonomous driving components of steering and movement system 502 may also interface with radio communication arrangement 504 to facilitate communication with other nearby vehicular communication devices and / or central networking components that perform decisions and computations for autonomous driving.

[0040] Radio communication arrangement 504 and antenna system 506 may perform the radio communication functionalities of vehicular communication device 500, which can include transmitting and receiving communications with a radio communication network and / or transmitting and receiving communications directly with other vehicular communication devices and terminal devices. For example, radio communication arrangement 504 and antenna system 506 may be configured to transmit and receive communications with one or more network access nodes, such as, in the exemplary context of Dedicated Short Range Communications (DSRC) and LTE Vehicle to Vehicle (V2V) / Vehicle to Everything (V2X), Roadside Units (RSUs) and base stations.

[0041] FIG. 6 shows an exemplary internal configuration of antenna system 506 and radio communication arrangement 504 according to some aspects. As shown in FIG. 6, radio communication arrangement 504 may include RF transceiver 602, digital signal processor 604, and controller 606. Although not explicitly shown in FIG. 6, in some aspects radio communication arrangement 504 may include one or more additional hardware and / or software components (such as processors / microprocessors, controllers / microcontrollers, other specialty or generic hardware / processors / circuits, etc.), peripheral device(s), memory, power supply, external device interface(s), subscriber identity module(s) (SIMs), user input / output devices (display(s), keypad(s), touchscreen(s), speaker(s), external button(s), camera(s), microphone(s), etc.), or other related components.

[0042] Controller 606 may be responsible for execution of upper-layer protocol stack functions, while digital signal processor 604 may be responsible for physical layer processing. RF transceiver 602 may be responsible for RF processing and amplification related to transmission and reception of wireless radio signals via antenna system 506.

[0043] Antenna system 506 may be a single antenna or an antenna array that includes multiple antennas. Antenna system 506 may additionally include analog antenna combination and / or beamforming circuitry. In the receive (RX) path, RF transceiver 602 may receive analog radio signals from antenna system 506 and perform analog and digital RF front-end processing on the analog radio signals to produce baseband samples (e.g., In-Phase / Quadrature (IQ) samples) to provide to digital signal processor 604. In some aspects, RF transceiver 602 can include analog and digital reception components such as amplifiers (e.g., a Low Noise Amplifiers (LNAs)), filters, RF demodulators (e.g., RF IQ demodulators)), and analog-to-digital converters (ADCs), which RF transceiver 602 may utilize to convert the received radio signals to baseband samples. In the transmit (TX) path, RF transceiver 602 may receive baseband samples from digital signal processor 604 and perform analog and digital RF front-end processing on the baseband samples to produce analog radio signals to provide to antenna system 506 for wireless transmission. In some aspects, RF transceiver 602 can include analog and digital transmission components such as amplifiers (e.g., Power Amplifiers (PAs), filters, RF modulators (e.g., RF IQ modulators), and digital-to-analog converters (DACs) to mix the baseband samples received from baseband modem 206, which RF transceiver 602 may use to produce the analog radio signals for wireless transmission by antenna system 506.

[0044] Digital signal processor 604 may be configured to perform physical layer (PHY) transmission and reception processing to, in the transmit path, prepare outgoing transmit data provided by controller 606 for transmission via RF transceiver 602, and, in the receive path, prepare incoming received data provided by RF transceiver 602 for processing by controller 606. Digital signal processor 604 may be configured to perform one or more of error detection, forward error correction encoding / decoding, channel coding and interleaving, channel modulation / demodulation, physical channel mapping, radio measurement and search, frequency and time synchronization, antenna diversity processing, power control and weighting, rate matching / de-matching, retransmission processing, interference cancelation, and any other physical layer processing functions. Digital signal processor 604 may include one or more processors configured to retrieve and execute program code that algorithmically defines control and processing logic for physical layer processing operations. In some aspects, digital signal processor 604 may execute processing functions with software via the execution of executable instructions. In some aspects, digital signal processor 604 may include one or more hardware accelerators, where the one or more processors of digital signal processor 604 may offload certain processing tasks to these hardware accelerators. In some aspects, the processor and hardware accelerator components of digital signal processor 604 may be realized as a coupled integrated circuit.

[0045] While digital signal processor 604 may be responsible for lower-layer physical processing functions, controller 606 may be responsible for upper-layer protocol stack functions. Controller 606 may include one or more processors configured to retrieve and execute program code that algorithmically defines the upper-layer protocol stack logic for one or more radio communication technologies, which can include Data Link Layer / Layer 2 and Network Layer / Layer 3 functions. Controller 606 may be configured to perform both user-plane and control-plane functions to facilitate the transfer of application layer data to and from radio communication arrangement 504 according to the specific protocols of the supported radio communication technology. User-plane functions can include header compression and encapsulation, security, error checking and correction, channel multiplexing, scheduling and priority, while control-plane functions may include setup and maintenance of radio bearers. The program code retrieved and executed by controller 606 may include executable instructions that define the logic of such functions.

[0046] In some aspects, controller 606 may be coupled to an application processor, which may handle the layers above the protocol stack including transport and application layers. The application processor may act as a source for some outgoing data transmitted by radio communication arrangement 504 and a sink for some incoming data received by radio communication arrangement 504. In the transmit path, controller 606 may therefore receive and process outgoing data provided by the application processor according to the layer-specific functions of the protocol stack, and provide the resulting data to digital signal processor 604. Digital signal processor 604 may then perform physical layer processing on the received data to produce baseband samples, which digital signal processor may provide to RF transceiver 602. RF transceiver 602 may then process the baseband samples to convert the baseband samples to analog radio signals, which RF transceiver 602 may wirelessly transmit via antenna system 506. In the receive path, RF transceiver 602 may receive analog radio signals from antenna system 506 and process the analog RF signal to obtain baseband samples. RF transceiver 602 may provide the baseband samples to digital signal processor 604, which may perform physical layer processing on the baseband samples. Digital signal processor 604 may then provide the resulting data to controller 606, which may process the resulting data according to the layer-specific functions of the protocol stack and provide the resulting incoming data to the application processor.

[0047] In some aspects, radio communication arrangement 504 may be configured to transmit and receive data according to multiple radio communication technologies. Accordingly, in some aspects one or more of antenna system 506, RF transceiver 602, digital signal processor 604, and controller 606 may include separate components or instances dedicated to different radio communication technologies and / or unified components that are shared between different radio communication technologies. For example, in some aspects controller 606 may be configured to execute multiple protocol stacks, each dedicated to a different radio communication technology and either at the same processor or different processors. In some aspects, digital signal processor 604 may include separate processors and / or hardware accelerators that are dedicated to different respective radio communication technologies, and / or one or more processors and / or hardware accelerators that are shared between multiple radio communication technologies. In some aspects, RF transceiver 602 may include separate RF circuitry sections dedicated to different respective radio communication technologies, and / or RF circuitry sections shared between multiple radio communication technologies. In some aspects, antenna system 506 may include separate antennas dedicated to different respective radio communication technologies, and / or antennas shared between multiple radio communication technologies. Accordingly, while antenna system 506, RF transceiver 602, digital signal processor 604, and controller 606 are shown as individual components in FIG. 6, in some aspects antenna system 506, RF transceiver 602, digital signal processor 604, and / or controller 606 can encompass separate components dedicated to different radio communication technologies.Trajectory control for forward sensing / access and backhaul moving cells

[0048] Many radio access networks deploy their cells as stationary entities. Examples include base stations deployed at fixed locations throughout a mobile broadband coverage area and access points placed at a fixed location in a residential or commercial are. Given their fixed locations, these cells may not be able to move to dynamically respond to the positioning of their served terminal devices. While various types of aerial cells (such as cell-equipped drones) have been proposed, these aerial cells are still developing.

[0049] In accordance with aspects of this disclosure, a set of moving cells providing sensing, access, and / or backhaul services may optimize their positioning within a coverage area. As further described herein, in some aspects, there may be a set of backhaul moving cells that provide backhaul to outer moving cells, where trajectories of both the backhaul and outer moving cells can be controlled by a central trajectory controller. In other aspects, the set of backhaul moving cells may provide backhaul to end devices (e.g., outer moving cells or terminal devices) that do not have trajectories which are controllable by the central controller.

[0050] FIG. 7 shows an exemplary network diagram according to some aspects, which relates to aspects where there are both backhaul and outer moving cells with trajectories that are controllable by a central trajectory controller. As shown in FIG. 7, a set of outer moving cells 702-706 may be configured to perform an outer task for their respective target areas. The outer task can be sensing, where the outer moving cells 702-706 perform sensing with local sensors (e.g., audio, video, image, position, radar, light, environmental, or any other type of sensing component) to obtain sensing data for their respective target areas. Additionally or alternatively, the outer task can be access, where outer moving cells 702-706 provide fronthaul access to terminal devices (as shown in FIG. 7) located in their respective target areas. In some aspects, each of moving cells 702-706 may perform the same outer task, while in other aspects some of moving cells 702-706 may perform different outer tasks (e.g., some perform sensing while others perform access). The number of outer moving cells in FIG. 7 is exemplary and is scalable to any quantity.

[0051] The outer moving cells 702-706 may generate uplink data for transmission back to the network. In the case of sensing outer moving cells, the sensing outer moving cells may generate sensing data that is sent back to a server for storage and / or processing (e.g., to evaluate and interpret the sensing data, such as for surveillance / monitoring, control of moving vehicles, or other analytics). In the case of access outer moving cells, their respectively served terminal devices may generate communication data (e.g., control and user data) that is sent back to the radio access, core, and / or external data networks. This sensing and communication data may be the uplink data.

[0052] As shown in FIG. 7, outer moving cells 702-706 may use backhaul moving cells 708 and 710 for backhaul. Accordingly, outer moving cells 702-706 may transmit their uplink data to backhaul moving cells 708 and 710 on fronthaul links 716-720. Backhaul moving cells 708 and 710 may then receive this uplink data and transmit the uplink data to network access node 712 on backhaul links 722 and 724 (e.g., may relay the uplink data to network access node 712, which can include any type of relaying scheme including those with decoding and error correction). Network access node 712 may then use and / or route the uplink data as appropriate. For example, network access node 712 may locally use uplink communication data related to access stratum control data (e.g., at its protocol stack), route uplink communication data related to non-access stratum control data to core network control nodes, and route sensing data and uplink communication data through the core network on the path towards its destination (e.g., a cloud server for processing sensing data, or an external data network associated with user data). In some aspects, network access node 712 may be stationary, while in other aspects network access node 712 may be mobile. The number of backhaul moving cells in FIG. 7 is exemplary and is scalable to any quantity.

[0053] The positions of outer moving cells 702-706 and backhaul moving cells 708 and 710 could impact communication and / or sensing performance. For example, when performing sensing or access, outer moving cells 702-706 may each have target areas to perform sensing on or to provide access to (where their respective target areas can be geographically fixed or dynamic). Outer moving cells 702-706 may therefore not be completely free to move to any location, as they may be expected to stay at a position that allows them to effectively serve their respective target areas. However, in some cases the optimal position to serve the target area may not be the optimal position to transmit uplink data to backhaul moving cells 708-710. This can occur, for example, when the line-of-sight (LOS) path from the optimal serving position to backhaul moving cells 708 and 710 is blocked by some object, or when the optimal serving position is far from backhaul moving cells 708 and 710. This could in turn lead to low link strength for fronthaul links 712-720.

[0054] Backhaul moving cells 708 and 710 may experience similar positioning issues. For example, as shown in FIG. 7, backhaul moving cell 710 may provide backhaul to outer moving cells 704 and 706. As outer moving cells 704 and 706 serve different target areas, they may be located in different positions. The optimal backhaul position for backhaul moving cell 710 to serve outer moving cell 704 (e.g., a position that maximizes link strength for fronthaul link 718 ), however, is unlikely to be the same as the optimal backhaul position for backhaul moving cell 710 to serve outer moving cell 706 (e.g., a position that optimizes fronthaul link 720 ). Furthermore, even though backhaul moving cell 710 may be able to obtain better reception performance from outer moving cells 704 and 706 when positioned closer to them, this positioning may mean that backhaul moving cell 710 is located far from network access node 712. The relaying transmission from backhaul moving cell 710 to network access node 712 may therefore suffer with this positioning, as backhaul links 722 and 724 may be longer in distance.

[0055] Accordingly, as shown in FIG. 7, central trajectory controller 714 may also be deployed as part of the network architecture. In some aspects central trajectory controller 714 may be deployed as part of network access node 712. In other aspects, central trajectory controller 714 may be deployed separately and could be proximate to network access node 712, such as in a Mobile Edge Computing (MEC) platform. In other aspects, central trajectory controller 714 may be deployed as a server in the core network, or as a server in an external data network (e.g., part of the Internet or cloud). Although shown as a single component of FIG. 7, in some aspects central trajectory controller 714 may be deployed as multiple separate physical components that are logically interconnected with each other to form a virtualized central trajectory controller.

[0056] As will be described, central trajectory controller 714 may be configured to determine trajectories (e.g., fixed position or dynamic movement path) for outer moving cells 702-706 and backhaul moving cells 708 and 710. Outer moving cells 702-706 and backhaul moving cells 708 and 710 may cooperate in this trajectory determination to locally optimize their trajectories. As used herein, the term "optimize" refers to attempting to move towards an optimal value and / or reaching an optimal value, and may or may not include actually reaching the optimal value. Optimizing thus includes incrementing a function towards a maximum value (e.g., a local or absolute maximum value) or decrementing a function towards a minimum value (e.g., a local or absolute minimum value), such as by using incremental or decremental steps. As further described below, the underlying logic of this trajectory determination can be embodied in trajectory algorithms, where central trajectory controller 714 may execute a central trajectory algorithm, outer moving cells 702-706 may execute an outer trajectory algorithm, and backhaul moving cells 708-710 may execute a backhaul trajectory algorithm. These trajectory algorithms can determine trajectories for outer moving cells 702-706 and backhaul moving cells 708-710 may therefore be based on multiple factors, such as the current locations of outer moving cells 702-706 and their respective target areas, the current locations of backhaul moving cells 708 and 710 and their respective target areas, the location of network access node 712, and the channel conditions and transmit capabilities of the involved devices. The logic of these trajectory algorithms is described in detail below.

[0057] FIGs. 8-10 show exemplary internal configurations of outer moving cells 702-706, backhaul moving cells 708 and 710 and central trajectory controller 714 according to some aspects. With initial reference to FIG. 8, outer moving cells 702-706 may include antenna system 802, radio transceiver 804, baseband subsystem 806 (including physical layer processor 808 and protocol controller 810), trajectory platform 812, and movement system 822. Antenna system 802, radio transceiver 804, and baseband subsystem 806 may be configured in a similar or same manner as antenna system 302, radio transceiver 304, and baseband subsystem 306 as shown and described for network access node 110 in FIG. 3. Antenna system 802, radio transceiver 804, and baseband subsystem 806 may therefore be configured to perform radio communications to and from outer moving cells 702-706, which can include wirelessly communicating with other moving cells, terminal devices, and network access nodes.

[0058] Trajectory platform 812 may be responsible for determining the trajectories of outer moving cells 702-706, including communicating with other moving cells and central trajectory controller 714 to obtain input data and executing an outer trajectory algorithm on the input data to obtain trajectories for outer moving cells 702-706. As shown in FIG. 8, trajectory platform 812 may include central interface 814, cell interface 816, and trajectory processor 818, and outer task subsystem 820. In some aspects, central interface 814 and cell interface 816 may each be application-layer processors that are configured to transmit and receive data (on logical software-level connections) with central trajectory processor 714 and other moving cells, respectively. For example, when transmitting data to central trajectory controller 714, central interface 814 may be configured to generate packets from the data (e.g., according to a predefined format used by central interface 814 and its peer interface at central trajectory controller 714 ) and provide the packets to the protocol stack running at protocol controller 810. Protocol controller 810 and physical layer processor 808 may then process the packets according to the protocol stack and physical layer protocols and transmit the data as wireless radio signals via radio transceiver 804 and antenna system 802. When receiving data from central trajectory controller 714, antenna system 802 and radio transceiver 804 may receive the data in the form of wireless radio signals, and provide corresponding baseband data to baseband modem. Physical layer processor 808 and protocol controller 810 may then process the baseband data to recover packets transmitted by the peer interface at central trajectory controller 714, which protocol controller 810 may provide to central interface 814. Cell interface 816 may similarly transmit data to a peer interface at other moving cells.

[0059] Trajectory processor 818 may be a processor configured to execute an outer trajectory algorithm that determines the trajectory for outer moving cells 702-706. As used herein, trajectories can refer to static positions, sequences of static positions (e.g., a time-stamped sequence of static positions), or paths or contours. Trajectory processor 818 may be configured to retrieve executable instructions defining the outer trajectory algorithm from a memory (not explicitly shown) and to execute these instructions. Trajectory processor 818 may be configured to execute the outer trajectory algorithm on input data to determine updated trajectories for outer moving cells 702-706. The logic of this outer trajectory algorithm is described herein both in prose below and visually by the drawings.

[0060] Outer task subsystem 820 may be configured to perform the outer task for outer moving cells 702-706. In some aspects where outer moving cells 702-706 are configured to perform sensing, outer task subsystem 820 may include one or more sensors. These sensors can be, without limitation, audio, video, image, position, radar, light, environmental, or another type of sensor. Outer task subsystem 820 may also include at least one processor configured to provide sensing data obtained from the sensors to baseband subsystem 806 for transmission. In some aspects where outer moving cells 702-706 are configured to provide access to terminal devices, outer task subsystem 820 may include one or more processors configured to transmit, receive, and relay data from the terminal devices (via baseband subsystem 806, which may handle the protocol stack and physical layer communication functionality). While FIG. 8 shows outer task subsystem 820 as part of trajectory platform 812, in some aspects outer task subsystem 820 may be included as part of baseband subsystem 806.

[0061] Movement system 822 may be responsible for controlling and executing movement of outer moving cells 702-706. As shown in FIG. 8, movement system 822 may include movement controller 824 and steering and movement machinery 826. Movement controller 824 may be configured to control the overall movement of outer moving cells 702-706 (e.g., through execution of a movement control function), and may provide control signals to steering and movement machinery 826 that specify the movement. In some aspects, movement controller 824 may be autonomous, and therefore may be configured to execute an autonomous movement control function where movement controller 824 directs movement of outer moving cells 702-706 without primary human / operator control. Steering and movement machinery 826 may then execute the movement specified in the control signals. In some aspects where outer moving cells 702-706 are terrestrial vehicles, steering and movement machinery 826 may include, for example, wheels and axles, an engine, a transmission, brakes, a steering wheel, associated electrical circuitry and wiring, and any other components used in the driving of an automobile or other land-based vehicle. In some aspects where outer moving cells 702-706 are aerial vehicles, including but not limited to drones, steering and movement machinery 826 may include, for example, one or more of rotors, propellers, jet engines, wings, rudders or wing flaps, air brakes, a yoke or cyclic, associated electrical circuitry and wiring, and any other components used in the flying of an aerial vehicle. In some aspects where outer moving cells 702-706 are aquatic or sub-aquatic vehicles, steering and movement machinery 826 may include, for example, any one or more of rudders, engines, propellers, a steering wheel, associated electrical circuitry and wiring, and any other components used in the steering or movement of an aquatic vehicle.

[0062] FIG. 9 shows an exemplary internal configuration of backhaul moving cells 708 and 710 according to some aspects. As shown in FIG. 9, backhaul moving cells 708 and 710 may include similar components to outer moving cells 702-706. Antenna system 902, radio transceiver 904, baseband subsystem 906, central interface 914, cell interface 916, movement controller 924, and steering and movement machinery 926 may be respectively configured in the manner of antenna system 802, radio transceiver 804, baseband subsystem 806, central interface 814, cell interface 816, movement controller 824, and steering and movement machinery 826 as shown and described for FIG. 8.

[0063] Trajectory processor 918 may be configured to execute a backhaul trajectory algorithm that controls the trajectory of backhaul moving cells 708 and 710. This backhaul trajectory algorithm is described herein in prose and visually by the figures.

[0064] As shown in FIG. 9, backhaul moving cells 708 and 710 may also include relay router 920. As previously indicated, backhaul moving cells 708 and 710 may be configured to provide backhaul services to outer moving cells 702-706, which can include receiving uplink data from outer moving cells 702-706 (on fronthaul links 716-720) and relaying the uplink data to the radio access network (e.g., to network access node 712 on backhaul links 722 and 724). Relay router 920 may be configured to handle this relaying functionality, and may interact with cell interface 916 to identify the uplink data for relaying and subsequently transmit the uplink data to the radio access network via baseband subsystem 906. Although shown as part of trajectory platform 912 in FIG. 9, in some aspects relay router 920 may also be part (e.g., fully or partially) of baseband subsystem 906.

[0065] FIG. 10 shows an exemplary internal configuration of central trajectory controller 714 according to some aspects. As shown in FIG. 10, central trajectory controller 714 may include cell interface 1002, input data repository 1004, and trajectory processor 1006. In some aspects, cell interface 1002 may be an application-layer processor configured to transmit and receive data (on logical software-level connections) with its peer central interfaces 814 and 914 in outer moving cells 702-706 and backhaul moving cells 708 and 710. Cell interface 1002 may therefore send packets on the interface shown in FIG. 10, which may pass through an Internet backhaul, core network, and / or radio access network (depending on the deployment location of central trajectory controller 714). The radio access network (e.g., network access node 712) may transmit the packets as wireless radio signals. Outer moving cells 702-706 and backhaul moving cells 708 and 710 may be configured to receive and process the wireless radio signals to recover the data packets at their peer central interfaces 814 and 914.

[0066] Input data repository 1004 may be a server-type component including a controller and a memory. Input data repository 1004 may be configured to collect input data for input to a central trajectory algorithm executed by trajectory processor 1006. The central trajectory algorithm may be configured to determine coarse trajectories for outer moving cells 702-706 and backhaul moving cells 708 and 710. These coarse trajectories may be the high-level, planned trajectories provided by central trajectory controller 714, and may be determined by central trajectory controller 714 to optimize the fronthaul and backhaul links provided by backhaul moving cells 708 and 710 while also enabling outer moving cells 702-706 to perform their respective forward tasks. Outer moving cells 702-706 and backhaul moving cells 708 and 710 may refine these coarse trajectories using their outer and backhaul trajectory algorithms to obtain updated trajectories. In some aspects, the central trajectory algorithm may also be configured to determine initial routings for outer moving cells 702-706 and backhaul moving cells 708 and 710. These initial routings may specify the backhaul path between outer moving cells 702-706 and the radio access network via backhaul moving cells 708 and 710, or in other words, which of backhaul moving cells 708 and 710 outer moving cells 702-706 should transmit their uplink data to. This central trajectory algorithm is described herein in prose and visually by the figures.

[0067] The signaling flow and operation involved in trajectory control for outer and backhaul moving cells will now be described. FIG. 11 shows exemplary message sequence chart 1100 according to some aspects. As shown in FIG. 11, outer moving cells 702-706, backhaul moving cells 708 and 710, and central trajectory controller 714 may be involved in the trajectory control for outer and backhaul moving cells. Central trajectory controller 714 may first perform initialization and setup with backhaul moving cells 708 and 710 and outer moving cells 702-706 in stages 1102 and 1104, respectively. For example, in stage 1102, cell interface 1002 of central trajectory controller 714 may exchange signaling (according to a predefined initialization and setup procedure) with the central interfaces 914 of backhaul moving cells 708 and 710. Cell interface 1002 may therefore may establish signaling connections with backhaul moving cells 708 and 710. Likewise, in stage 1104, cell interface 1002 of central trajectory controller 714 may exchange signaling (according to a predefined initialization and setup procedure) with the central interfaces 814 of outer moving cells 702-706, and therefore establishes signaling connections with backhaul moving cells 702-706. As previously discussed regarding FIG. 7, central trajectory controller 714 may interface with network access node 712 (e.g., as part of network access node 712, as an edge computing component, as part of the core network behind network access node 712, or from an external network location), and may exchange this signaling with central interfaces 814 and 914 over data bearers that use the radio access network provided by network access node 712. Further references to communication between cell interface 1002 and central interfaces 814 and 914 are understood as referring to data exchange over such data bearers.

[0068] In addition to establishing signaling connections with outer moving cells 702-706 and backhaul moving cells 708 and 710 in stages 1102 and 1104, central trajectory controller 714 also obtains input data for computing coarse trajectories and may also obtain initial routings as part of the initialization and setup with outer moving cells 702-706 and backhaul moving cells 708 and 710. For example, as part of stages 1102 and 1104, central interfaces 814 and 914 may send input data including data rate requirements (e.g., for sending sensing data or access data from served terminal devices) of outer moving cells 702-706, the positions (e.g., geographical locations) of outer moving cells 702-706 and backhaul moving cells 708 and 710, the target areas assigned to outer moving cells 702-706 (e.g., for sensing or access), recent radio measurements obtained by outer moving cells 702-706 and backhaul moving cells 708-710 (e.g., obtained by their respective baseband subsystems 806 and 906), and / or details about the radio capabilities of outer moving cells 702-706 and backhaul moving cells 708-710 (e.g., transmit power capabilities, effective operation range, etc.). Cell interface 1002 of central trajectory controller 714 may receive this input data and provide it to input data repository 1004, which may store the input data for subsequent use by trajectory processor 1006. In some aspects, cell interface 1002 may also be configured to communicate with network access node 712, and may, for example, receive input data such as radio measurements by network access node 712 (e.g., of signals transmitted by outer moving cells 702-706 and backhaul moving cells 708-710).

[0069] Central trajectory controller 714 may be configured to use this input data for the central trajectory algorithm executed by trajectory processor 1006. In some aspects, the central trajectory algorithm may also use, as input data, a statistical model of the radio environment between outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network (e.g., network access node 712 optionally in addition to one or more additional network access nodes). Various aspects of this disclosure may use statistical models of varying complexity. For example, in some aspects the statistical model can be a basic propagation model (e.g., a free-space pathloss model) that evaluates the distance between devices and their current radio conditions to estimate the channel conditions between the devices (e.g., that models the radio environment based on the distance between devices and their current radio conditions). In other aspects, the statistical model can be based on a radio map (e.g., a radio environment map (REM)) that indicates channel conditions over a mapped area. This type of statistical model can therefore use more advanced geographic data to model the radio environment over geographic areas having different propagation characteristics.

[0070] FIG. 12 shows a basic example illustrating the concept of a radio map according to some aspects. Radio map 1200 shown in FIG. 12 assigns a channel condition rating to each of a plurality of geographic units, where lighter-shaded geographic units indicate better channel conditions (estimated) than darker-shaded geographic units. The shades of the geographic units can indicate, for example, estimated pathloss of radio signals traveling through the geographic unit, where each shade can be assigned a specific pathloss value (e.g., in dBs or a similar metric). The configuration of radio map 1200 is exemplary. Accordingly, other radio maps using uniform and non-uniform grids with different types of geographic unit shapes and sizes can likewise be used without limitation. While radio map 1200 depicts a single radio parameter (as indicated by the shading of the geographic units), this is also exemplary, and radio maps can be applied that assign multiple radio parameters to the geographic units.

[0071] Input data repository 1004 may store the underlying radio map data for such a radio map. In some aspects, input data repository 1004 may download part or all of this radio map data from a remote location, such as a remote server that stores radio map data (e.g., a REM server). In some aspects, input data repository 1004 may generate part or all of the radio map data locally (e.g., based on the input data provided by outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network).

[0072] In some aspects, input data repository 1004 may update the radio map data based on the input data provided in stages 1102 and 1104 by outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network. For example, input data repository 1004 may be configured to match radio measurements (of the input data) with the corresponding positions of the device that made the measurement. Input data repository 1004 may then update the radio parameters in the geographic unit of the radio map in which the position is located based on the radio measurement. This type of updating may therefore adapt the radio map data based on measurements provided by devices in the radio environment.

[0073] The input data obtained by input data repository 1004 can therefore include the input data provided by outer moving cells 702-706 and backhaul moving cells 708 and 710 as well as other input data related to the statistical model of the radio environment (e.g., for basic propagation models or radio map data). After obtaining this input data, central trajectory controller 714 may compute the coarse trajectories and initial routings for outer moving cells 702-706 and backhaul moving cells 708 and 710 in stage 1106. For example, input data repository 1004 may provide the input data to trajectory processor 1006, which may then execute the central trajectory algorithm using the input data as input.

[0074] As previously indicated, the outputs of the central trajectory algorithm may be coarse trajectories (e.g., static positions, sequences of static positions, or paths or contours) that central trajectory controller 714 assigns to outer moving cells 702-706 and backhaul moving cells 708 and 710. The outputs can also include initial routings that govern the flow of data between outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network. In some aspects, the central trajectory algorithm may be configured to compute these coarse trajectories and initial routings to optimize an optimization criteria according to the statistical model. As previously indicated, the statistical model may provide a probabilistic characterization of the radio environment between outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network. Accordingly, the central trajectory algorithm may evaluate the statistical model to estimate the radio environment over a range of possible coarse trajectories and / or routings, and may determine coarse trajectories and / or initial routings that optimize an optimization criteria related to the radio environment.

[0075] For example, in some aspects the optimization criteria may be a supported data rate. In this example, outer moving cells 702-706 may have minimum data rate requirements. Outer moving cells 702-706 may be generating uplink data related to sensing (e.g., sensing data generated by outer moving cells 702-706) or related to access (e.g., uplink data generated by the terminal devices served by outer moving cells 702-706), and this uplink data may have a certain minimum data rate that is capable of supporting transmission of this sensing data. If the backhaul relaying path (including a fronthaul link from outer moving cell to backhaul moving cell, and a backhaul link from backhaul moving cell to network access node) has a data rate that is at least this minimum data rate, the uplink data may be successfully transmitted to the network.

[0076] Accordingly, the central trajectory algorithm may determine coarse trajectories and initial routings in stage 1106 that increase or maximize a function of the supported data rate using the statistical model to approximate the data rate. This can use any type of suitable optimization algorithm, such as gradient descent (used herein to collectively refer to both gradient descent and ascent) or another optimization algorithm that incrementally 'steps' over different possible coarse trajectories and / or initial routings to find a coarse trajectory or initial routing that increases or maximizes the supported data rate. In some aspects, the central trajectory algorithm may increase or maximize the overall supported data rate of each backhaul relaying path outgoing from outer moving cells 702-706 (e.g., an aggregate across all backhaul relaying paths from outer moving cells 702-706 to the radio access network). In other aspects the central trajectory algorithm may increase or maximize the probability that each backhaul relaying path outgoing from outer moving cells 702-706 has a supported data rate above a predefined data rate threshold.

[0077] Additionally or alternatively, in some aspects the optimization criteria may be a link quality metric. The link quality metric can be signal strength, signal quality, signal-to-noise ratio (SNR or another related metric such as signal-to-interference-plus-noise ratio (SINR)), error rate (e.g., bit error rate (BER), block error rate (BLER), packet error rate (PER), or any other type of error rate), distance between communication devices, estimated pathloss between communication devices, or any other type of link quality metric. The central trajectory algorithm can similarly be configured to determine coarse trajectories and / or initial routings for outer moving cells 702-706 and backhaul moving cells 708 and 710 by optimizing a link quality metric as the optimization criteria. For example, the central trajectory algorithm can increase or maximize a function of the link quality metric using the statistical model to approximate the link quality metric. As in the case above, the function can be a function of the link quality metric itself (e.g., an aggregate over the backhaul relaying paths) or a function of the probability that the link quality metric is above a link quality metric threshold (e.g. a probability that each backhaul relaying path has a link quality metric above the link quality metric threshold).

[0078] Although the above examples identify individual optimization criteria, in some aspects the central trajectory algorithm may be configured to evaluate multiple optimization criteria simultaneously. For example, a weighted combination of the individual functions of the optimization criteria can be defined and subsequently used as the function to be increased or maximized with the optimization algorithm.

[0079] As the backhaul relaying paths from each outer moving cell includes both a fronthaul link (to a backhaul moving cell) and a backhaul link (from the backhaul moving cell to the network), the coarse trajectories may attempt to balance between strong fronthaul links 716-720 and strong backhaul links 722-724. For example, if the central trajectory algorithm determines coarse trajectories that place backhaul moving cells 708 and 710 very close to outer moving cells 702-706, this may yield strong fronthaul links 716-720. However, this may position backhaul moving cells 708 and 710 further from network access node 712, which may yield weaker backhaul links 722-724. The supported data rate and / or link quality metric of the backhaul relaying paths may therefore not be as high as if the central trajectory algorithm determines coarse trajectories that place backhaul moving cells 708 and 710 in the middle between outer moving cells 702-706 and network access node 712. As the central trajectory algorithm models the supported data rate and / or link quality metric with an optimization criteria, increasing or maximizing the function of the optimization criteria may yield coarse trajectories that appropriately place backhaul moving cells 708 and 710 between outer moving cells 702-706 and network access node 712.

[0080] As indicated above, the central trajectory algorithm may be configured to use the statistical model of the radio environment to approximate the function of the optimization criteria. For example, in cases where the statistical model is a basic propagation model, the central trajectory algorithm may be configured to approximate the optimization criteria using the basic propagation model, such as by using a supported data rate function that takes into consideration the relative distances between outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network (where, for example, closer relative positions may yield higher supported data rates than far relative positions). The central trajectory algorithm may then attempt to find trajectories for outer moving cells 702-706 and backhaul moving cells 708 and 710 that increase this supported data rate function (e.g., according to gradient descent or another optimization algorithm). As there are multiple moving cells, this may include determining individual trajectories outer moving cells 702-706 and backhaul moving cells 708 and 710, where the individual trajectories (when executed together) increase the supported data rate function.

[0081] In cases where the statistical model is based on radio map data, the central trajectory algorithm may be configured to approximate the optimization criteria using a propagation model that also depends on the radio parameters for the geographic units of the radio map. The supported data rate function can therefore take into consideration the relative distances between outer moving cells 702-706, backhaul moving cells 708 and 710, and the radio access network as well as the radio parameters of the geographic units of the radio map that fall between their respective positions. The central trajectory algorithm can then likewise attempt to find trajectories for outer moving cells 702-706 and backhaul moving cells 708 and 710 that increase or maximize this supported data rate function. As indicated above, this can include determining individual trajectories for outer moving cells 702-706 and backhaul moving cells 708 and 710 that when executed together increase or maximize the supported data rate function.

[0082] In some aspects, the function of the optimization criteria may also depend on the routing, where some routings may yield higher approximated optimization criteria than others. For example, with reference to the exemplary context of FIG. 7, outer moving cell 702 may be able to achieve a higher supported data rate for its uplink data when using backhaul moving cell 708 for backhaul than compared to backhaul moving cell 710. Additionally or alternatively, backhaul moving cells 708 and 710 may be able to provide backhaul relaying paths with higher supported data rates when they relay the uplink data to a particular network access node of the radio access network. As part of stage 1106, the central trajectory algorithm may therefore also treat the routings as adjustable parameters that can be used to increase the function of the optimization criteria. The central trajectory algorithm can therefore determine initial routings in stage 1106, which can include selecting which of backhaul moving cells 708 and 710 for forward moving cells 702-706 to transmit their uplink data to and / or selecting which network access node for backhaul moving cells 708 and 710 to relay this uplink data to.

[0083] In some aspects, the central trajectory algorithm may also consider constraint parameters when determining the coarse trajectories and initial routings. For example, target areas assigned to outer moving cells 702-706 may act as constraints, where outer moving cells 702-706 are expected to perform their assigned outer tasks (sensing or routing) in certain target areas. Accordingly, in some cases the coarse trajectories assigned to outer moving cells 702-706 may be constrained to being within or near the target areas (e.g., to be proximate enough to the target area to perform the assigned outer task with outer task subsystem 820). When attempting to increase the function of the optimization criteria, the central trajectory algorithm may therefore consider, and in some aspects consider exclusively, coarse trajectories of outer moving cells 702-706 that are constrained by their respectively assigned target areas. In some aspects, backhaul moving cells 708 and 710 may also have geographical constraints that the central trajectory algorithm may consider when determining the coarse trajectories.

[0084] In some aspects, the central trajectory algorithm may determine the target areas for outer moving cells 702-706 as part of the coarse trajectory determination. For example, the central trajectory algorithm may identify an overall target area (e.g., as reported by outer moving cells 702-706 as input data) that defines the overall geographic area in which the outer moving cells 702-706 are assigned to perform their outer tasks. Instead of treating the target area of each outer moving cell as the area to which each individual outer moving cell is assigned to, the central trajectory algorithm may determine coarse trajectories for outer moving cells that increase the optimization criteria while also covering the overall target area.

[0085] After determining the coarse trajectories and initial routings in stage 1106, central trajectory controller 714 may send the coarse trajectories and initial routings to backhaul moving cells 708 and 710 and outer moving cells 702-706 in stages 1108 and 1110, respectively. For example, trajectory processor 1006 may provide the coarse trajectories and initial routings to cell interface 1002, which may then send the coarse trajectories and initial routings to its peer central interfaces 814 and 914 at outer moving cells 702-706 and backhaul moving cells 708 and 710. In some aspects, cell interface 1002 may identify the coarse trajectory and initial routing individually assigned to each of outer moving cells 702-706 and backhaul moving cells 708 and 710, and may then transmit the coarse trajectory and initial routing to each moving cell to the corresponding central interface 814 or 914 of the moving cells.

[0086] Backhaul moving cells 708 and 710 and outer moving cells 702-706 may then receive the coarse trajectories and initial routings at central interfaces 814 and 914, respectively. As shown in FIG. 11, backhaul moving cells 708 and 710 may then establish connectivity with outer moving cells 702-706 in stage 1112. For example, backhaul moving cells 708 and 710 may set up a backhaul relaying path with outer moving cells 702-706 that outer moving cells 702-706 can use to transmit and receive data with the radio access network (including network access node 712). This can include, for example, setting up fronthaul links 716-720 between outer moving cells 702-706 and backhaul moving cells 708 and 710 and setting up backhaul links 722 and 724 between backhaul moving cells 708 and 710 and the radio access network (although in some aspects the backhaul links may already be established). In some aspects, backhaul moving cells 708 and 710 may also set up a link with each other, with which they can, for example, coordinate their updated trajectories.

[0087] In some aspects, backhaul moving cells 708 and 710 and outer moving cells 702-706 may execute stage 1112 at their cell interfaces 816 and 916. For example, with reference to outer moving cell 702, its central interface 814 may receive the coarse trajectory and initial routing assigned to outer moving cell 702 in stage 1110. Central interface 814 of outer moving cell 702 may then provide the coarse trajectory to trajectory processor 818 and the initial routing to cell interface 816. The initial routing may specify that outer moving cell 702 is assigned to use one of backhaul moving cells 708 and 710, such as backhaul moving cell 708. Accordingly, cell interface 816 of outer moving cell 702 may identify that it is assigned to establish a backhaul relaying path to the radio access network via backhaul moving cell 708. Cell interface 816 of outer moving cell 702 may therefore establish connectivity with cell interface 916 of backhaul moving cell 708, such as by exchanging wireless signaling (via baseband subsystem 806 of outer moving cell 702 and baseband subsystem 906 of backhaul moving cell 708) with each other that establishes a fronthaul link between outer moving cell 702 and backhaul moving cell 708. Outer moving cells 702-706 may similarly establish connectivity with the backhaul moving cells assigned to them by their respective initial routings.

[0088] In some aspects, the central trajectory algorithm may determine coarse trajectories but not initial routings. Accordingly, outer moving cells 702-706 and backhaul moving cells 708 and 710 may be configured to determine the routings (e.g., to determine backhaul relaying paths). For example, the cell interfaces 814 of outer moving cells 702-706 may perform a discovery process to identify nearby backhaul moving cells, and may then select a backhaul moving cell to use as a backhaul relaying path. These routings may therefore be the initial routings. Outer moving cells 702-706 and backhaul moving cells 708 and 710 may then establish connectivity with each other according to these initial routings.

[0089] After establishing connectivity, outer moving cells 702-706 may perform their outer tasks while moving according to their respectively assigned coarse trajectories in stage 1114. For example, with exemplary reference to outer moving cell 702, trajectory processor 818 may provide the coarse trajectory to movement controller 824. Movement controller 824 may then provide control signals to steering and movement machinery 826 that direct steering and movement machinery 826 to move outer moving cell 702 according to its coarse trajectory. If configured to perform sensing as its outer task, one or more sensors (not explicitly shown in FIG. 8) of outer task subsystem 820 may obtain sensing data. If configured to perform access as its outer task, outer task subsystem 820 may use baseband subsystem 806 to wirelessly provide radio access to terminal devices in the coverage area of outer moving cell 702.

[0090] As previously indicated, the coarse trajectories may be static positions, sequences of static positions, or a paths or contours. If the coarse trajectory is a static position, movement controller 824 may control steering and movement machinery 826 to position outer moving cell 702 at the static position and to remain at the static position. If the coarse trajectory is a sequence of static positions, movement controller 824 may control steering and movement machinery 826 to sequentially move outer moving cell 702 to each of the sequence of static positions. The sequence of static positions can be time-stamped, and movement controller 824 may control steering and movement machinery 826 to move outer moving cell 702 to each of the sequence of static positions at the according to the time stamps. If the coarse trajectory is a path or contour, movement controller 824 may control steering and movement machinery 826 to move outer moving cell 702 along the path or contour.

[0091] As shown in FIG. 11, outer moving cells 702-706 and backhaul moving cells 708 and 710 may perform data transmission in stages 1116 and 1118. For example, outer moving cells 702-706 (e.g., at their respective cell interfaces 816) may transmit uplink data from the outer task on their respective fronthaul links 716-720 to backhaul moving cells 708 and 710 as assigned by the initial routings. Backhaul moving cells 708 and 710 may then receive the uplink data at their respective cell interfaces 916. Relay routers 920 may then identify the uplink data received at the cell interfaces 916 and transmit the uplink data to the radio access network on respective backhaul links 722 and 724 via the baseband subsystems 906. In some aspects, fronthaul moving cells 702-706 may also use the backhaul relaying paths for downlink data transmission. Accordingly, backhaul moving cells 708 and 710 may receive downlink data addressed to outer moving cells 702-706 from the radio access network at their baseband subsystems 906. Relay routers 920 may identify this downlink data and provide it to the cell interfaces 916, which may then transmit the downlink data (via baseband subsystem 906) on the fronthaul link to outer moving cells 702-706.

[0092] Similar to outer moving cells 702-706, backhaul moving cells 708 and 710 may move according to their assigned coarse trajectories during stages 1116 and 1118. Accordingly, with exemplary reference to backhaul moving cell 708, trajectory processor 918 (after receiving the coarse trajectory from central interface 914) may specify the coarse trajectory to movement controller 924. Movement controller 924 may then direct steering and movement machinery 926 to move backhaul moving cell 708 according to the coarse trajectory.

[0093] These coarse trajectories and initial routings determined by central trajectory controller 714 can be considered a high-level plan that forms the initial basis of the trajectories and routing of outer moving cells 702-706 and backhaul moving cells 708 and 710. Accordingly, in some aspects outer moving cells 702-706 and backhaul moving cells 708 and 710 may perform local optimization of the trajectories and routing. As shown in FIG. 11, outer moving cells 702-706 and backhaul moving cells 708 and 710 may perform parameter exchange in stage 1120, such as by using their cell interfaces 816 and 816 to exchange parameters over the signaling connections. These parameters may be related to the local input data used as input by trajectory processors 818 and 918 of outer moving cells 702-706 and backhaul moving cells 708 and 710 for their outer and backhaul trajectory algorithms, respectively. For example, the parameters can include similar information to the input data, such as data rate requirements of the moving cells, the positions of the moving cells, the target areas assigned to the moving cells, recent radio measurements obtained by the moving cells, and / or details about the radio capabilities of the moving cells. The parameters can also include the coarse trajectories assigned to the moving cells by the central trajectory algorithm. In some aspects, outer moving cells 702-706 and backhaul moving cells 708 and 710 may also receive parameters from other locations, such as from the radio access network (e.g., network access node 712). In some aspects, backhaul moving cells 708 and 710 may exchange parameters directly with each other.

[0094] After obtaining the parameters, cell interfaces 816 and 916 may provide the parameters to trajectory processors 818 and 918. With exemplary reference to trajectory processor 818 of outer moving cell 702, trajectory processor 818 may use the parameters as local input data for the outer trajectory algorithm. In some aspects, trajectory processor 818 may also use other information as the local input data, such as radio measurements performed by baseband subsystem 806 as well as its current coarse trajectory assigned by central trajectory controller 714. Trajectory processor 818 may then perform local optimization of its trajectory and routing in stage 1122 by executing the outer trajectory algorithm in stage 1122. Likewise, with exemplary reference to trajectory processor 918 of backhaul moving cell 708, trajectory processor 918 may use the parameters as local input data for the backhaul trajectory algorithm. Trajectory processor 918 may then perform local optimization of its trajectory and routing by executing the backhaul trajectory algorithm in stage 1122.

[0095] The outer and backhaul trajectory algorithms executed by outer moving cells 702- 706 and backhaul moving cells 708 and 710 may be similar to the central trajectory algorithm executed by central trajectory controller 714. For example, in some aspects, the outer and backhaul trajectory algorithms may also function by determining trajectories and / or routings that increase or otherwise maximize an optimization criteria. In some aspects, the optimization criteria used by the outer and backhaul trajectory algorithms may be the same as the optimization criteria used by the central trajectory algorithm. In some aspects, the outer and backhaul trajectory algorithms may similarly use a statistical model of the radio environment to approximate the optimization criteria, such as a basic propagation model or a propagation model based on a radio map.

[0096] For example, in some aspects, the outer and backhaul trajectory algorithms may determine an updated trajectory and / or updated routing for the moving cell executing the trajectory algorithm that increases the optimization criteria (e.g., by incrementally stepping parameters to guide a function of the optimization criteria toward a maximum value). Accordingly, in comparison to the central trajectory algorithm, which concurrently determines coarse trajectories and / or initial routings for multiple moving cells, the outer and backhaul trajectory algorithms may separately focus on the individual moving cell executing the trajectory algorithm. For example, trajectory processor 918 of backhaul moving cell 708 may attempt to determine an updated trajectory for backhaul moving cell 708 that increases or maximizes the function of the optimization criteria based on the position of backhaul moving cell 708. As the function of the optimization criteria (e.g., supported data rate and / or link quality metric of the backhaul relaying paths) depends on both fronthaul links 716-720 and backhaul links 722 and 724, trajectory processor 918 may determine an updated trajectory that yields an optimal balance between fronthaul and backhaul links (and thus increases or maximizes the function of the optimization criteria).

[0097] In some aspects, trajectory processors 818 and 918 of the moving cells may execute stage 1122 in an alternating manner. For example, dual-phased optimization can be used, where outer moving cells 702-706 and backhaul moving cells 708 and 710 may alternate between optimizing the trajectories of outer moving cells 702-706 and the trajectories of backhaul moving cells 708-710. In this example, the trajectory processors 818 of outer moving cells 702-706 may be configured to execute the outer trajectory algorithm using their current trajectory (e.g., the coarse trajectory), current routing, and relevant parameters from stage 1120 as the local input data for the outer trajectory algorithm. The outer trajectory algorithm may be configured to, using this local input data, determine an update to its current trajectory that steps the function of the optimization criteria toward a maximum value (e.g., by some incremental step). As described for the central trajectory algorithm, this can be done using gradient descent or another optimization algorithm. The outer trajectory algorithm can also determine an updated routing (e.g., if the updated trajectory would lead to a better routing for the optimization criteria).

[0098] Accordingly, each of outer moving cells 702-706 may determine a respective updated trajectory and / or updated routing. Then, outer moving cells 702-706 may perform another round of parameter exchange by sending the updated trajectories and / or routing to backhaul moving cells 708 and 710. Backhaul moving cells 708 and 710 may then use these updated trajectories and / or routing, in addition to any other relevant parameters, as local input data for the backhaul trajectory algorithm. Trajectory processors 916 of backhaul moving cells 708 and 710 may therefore execute the backhaul trajectory algorithm using this local input data to determine updated trajectories for backhaul moving cells 708 and 710. For example, as the trajectories of outer moving cells 702-706 have changed to the updated trajectories, the backhaul trajectory algorithm may be configured to determine updated trajectories for backhaul moving cells 708 and 710 that increase (e.g., maximize) the optimization criteria given the updated trajectories of outer moving cells 702-706. The backhaul trajectory algorithm may also be configured to change the routings, e.g., to change the updated routings determined by outer moving cells 702-710 to new updated routings that optimize the updated trajectories of backhaul moving cells 708 and 710.

[0099] After backhaul moving cells 708 and 710 have determined their own updated trajectories and / or updated routings, backhaul moving cells 708 and 710 may perform another round of parameter exchange and send their updated trajectories and / or updated routings to outer moving cells 702-706. Outer moving cells 702-706 may then again execute the outer trajectory algorithm using these updated trajectories and / or updated routings from backhaul moving cells 708 and 710 to determine new updated trajectories and / or routings that increase the optimization criteria. This dual-phased optimization may continue to repeat over time. In some aspects, an aggregate metric across both outer and backhaul can be used to steer the trajectories away from diverging in one direction. In some aspects, central trajectory controller 714 may periodically re-execute the central trajectory algorithm and provide new coarse trajectories and / or new initial routings to outer moving cells 702-706 and backhaul moving cells 708 and 710. This can be viewed as a type of periodic reorganization, where central trajectory controller 714 periodically reorganizes outer moving cells 702-706 and backhaul moving cells 708 and 710 in a centralized manner.

[0100] The local optimization is not limited to such dual-phased optimization approaches. In some aspects, outer moving cells 702-706 and backhaul moving cells 708 and 710 may execute their trajectory algorithms to update their trajectories and / or routing in an alternating or round-robin fashion, e.g., one of outer moving cells 702-706 and backhaul moving cells 708 and 710 at a time or other appropriate coordination implementation. In some aspects, one of outer moving cells 702-706, referred to here as a master outer moving cell, may assume the responsibility of determining updated trajectories and / or routing for one or more (or all) of the rest of outer moving cells 702-706. Accordingly, similarly to the central trajectory algorithm that concurrently evaluated trajectories for multiple outer moving cells, the master outer moving cell may execute an outer trajectory algorithm that concurrently determines updated trajectories and / or updated routings for multiple outer moving cells (e.g., by determining updated trajectories that maximize the optimization criteria). The master outer moving cell may then transmit the updated trajectories and / or routings to the other outer moving cells, which may then move according to the updated trajectories. This can similarly be applied for backhaul moving cells, where one of backhaul moving cells 708 or 710 may assume the role of master backhaul moving cell and determine updated trajectories and / or updated routings for multiple (or all) backhaul moving cells.

[0101] In some cases, the use of local optimization may lead to better performance. For example, as previously indicated outer moving cells 702-706 and backhaul moving cells 708 and 710 may be configured to exchange parameters prior to and between rounds of local optimization. These parameters can include current radio measurements, which can be more accurate indicators of the radio environment than the basic propagation model and / or radio maps used by central trajectory controller 714. Accordingly, in some cases, the local optimization may be based on a more accurate reflection of the actual radio environment, and may therefore lead to better optimization criteria (e.g., better values of the metric being used as the optimization criteria) in practice.

[0102] Furthermore, in some aspects the use of local optimization may result in a more advantageous division of processing. For example, outer moving cells 702-706 and backhaul moving cells 708 and 710 may not be able to support the same processing power as a server-type component such as central trajectory controller 714. Accordingly, depending on their design constraints, it may not be feasible for outer moving cells 702-706 and backhaul moving cells 708 and 710 to execute a full trajectory algorithm to locally determine their trajectories from scratch. The use of local optimization may enable central trajectory controller 714 to determine a high-level plan for trajectories while also allowing outer moving cells 702-706 and backhaul moving cells 708 and 710 to make local adjustments as needed (e.g., that are only adjustments as compared to determining new trajectories from the start).

[0103] Additionally, in some cases outer moving cells 702-706 and backhaul moving cells 708 and 710 may be able to adjust their trajectories with a lower latency than would occur if central trajectory controller 714 had full control over their trajectories (e.g., without any local optimization). For example, outer moving cells 702-706 and backhaul moving cells 708 and 710 can be configured to make local adjustments to their trajectories (e.g., based on their radio measurements and other parameter exchange) without having to first send data back to central trajectory controller 714 and subsequently waiting to receive a response.

[0104] In the exemplary context of FIG. 11, the central trajectory algorithm may exert positioning control over both outer moving cells 702-706 and backhaul moving cells 708 and 710. As previously indicated, other aspects of this disclosure are also directed to cases where central trajectory controller 714 exerts control over backhaul moving cells 708 and 710 but not outer moving cells 702-706. Other cases, for example, where backhaul moving cells 708 and 710 are present without any outer moving cells are also applicable. FIG. 13 shows one such example according to some aspects, where backhaul moving cells 708 and 710 may provide backhaul to various terminal devices and / or outer moving cells 734 and 736 (e.g., that are not controllable by central trajectory controller 714).

[0105] In these exemplary cases, central trajectory controller 714 may be able to provide coarse trajectories and / or routing to backhaul moving cells 708 and 710, but not to any of the served devices 734 and 736 (e.g., outer moving cells and / or terminal devices) as they may not be under the positional control of central trajectory controller 714. FIG. 14 shows exemplary message sequence chart 1400 according to some aspects, which relates to these cases. As shown in FIG. 14, central trajectory controller 714 and backhaul moving cells 708 and 710 may first perform initialization and setup in stage 1402 (e.g., in the same or similar manner as stage 1102). Central trajectory controller 714 may then compute coarse trajectories and initial routing using the input data and central trajectory algorithm in stage 1404.

[0106] As central trajectory controller 714 is only providing coarse trajectories for backhaul moving cells 708 and 810 in these aspects, the central trajectory algorithm may be different. For example, in the previous context of FIG. 11, central trajectory controller 714 could evaluate the optimization criteria using specific positions of outer moving cells 702-706 (e.g., approximate the supported data rate or link quality metric given specific locations of outer moving cells 702-706 using the statistical model of the radio environment). However, in the context of FIG. 14, the central trajectory algorithm may not be able to assume specific positions of the served devices, and may instead use statistical estimations of their positions.

[0107] For example, in some aspects, the central trajectory algorithm may use the concept of a virtual node to statistically estimate the position of served devices 734-736. For example, in some aspects input data repository 1004 of central trajectory controller 714 may be configured to collect statistical density information about served devices 734-736. In some cases, the statistical density information can be statistical geographic density information, such as basic information such as the reported positions of served devices 734-736 and / or more complex information such as a heat map indicating a density of served devices 734-736 over time. In some cases, the statistical density information can additionally or alternatively include statistical traffic density information, which indicates the geographic density of data traffic. For example, if there are only a few served devices in a given area but these served devices are generating considerable data traffic, the statistical traffic density information can indicate the increased data traffic in this area (whereas strictly geographic density information would indicate only that there are a few served devices). This statistical density information can be reported to central trajectory controller 714 by backhaul moving cells 708 and 710 (e.g., based on their own radio measurements or position reporting), from the radio access network, and / or from external network locations.

[0108] Accordingly, when executing the central trajectory algorithm in stage 1404, trajectory processor 1006 may use this statistical density information as input data. In some aspects, the central trajectory algorithm may utilize a similar optimization algorithm as described above for stage 1106. For example, this can include applying gradient descent (or another optimization algorithm) to determine coarse trajectories and / or routing for backhaul moving cells 708 and 710 that increase or maximize an optimization criteria, where the optimization criteria is represented by a function based on the statistical model of the radio environment. However, in contrast to the case of FIG. 11, the central trajectory algorithm may not have specific locations of served devices 734-736, and may instead use the statistical density information to characterize virtual served devices. For example, the central trajectory algorithm can approximate the positions of the virtual served devices using the statistical density information (e.g., the expected position of virtual served devices), and then use these positions when determining coarse trajectories and / or initial routings for backhaul moving cells 708 and 710. As served devices 734-736 are not under the positional control of central trajectory controller 714, the central trajectory algorithm may only determine coarse trajectories and / or initial routing for backhaul moving cells 708 and 710 (where the initial routings assign backhaul moving cells 708 and 710 to provide backhaul for certain of served devices 734-736). Similar to the case of FIG. 11, the optimization criteria can be, for example, supported data rate and / or link quality metric (including aggregate values and probabilities that the optimization criteria is above a predefined threshold for each backhaul relaying path).

[0109] As shown in FIG. 13, the backhaul relaying paths (on which the optimization criteria are based) may include a fronthaul link and a backhaul link. For example, backhaul moving cell 708 may have fronthaul links 726 with its served devices 734 and backhaul link 730 with network access node 712 while backhaul moving cell 708 may have fronthaul links 728 with its served devices 736 and backhaul link 732 with network access node 712. As the function of the optimization criteria depends on both fronthaul and backhaul links, the coarse trajectories determined by central trajectory controller 714 for backhaul moving cells 708 and 710 may therefore position backhaul moving cells 708 and 710 to jointly optimize fronthaul links 726-728 and backhaul links 730-732 (e.g., to yield fronthaul and backhaul links that increase or maximize the function of the optimization criteria). The coarse trajectories may therefore jointly balance between strong fronthaul and strong backhaul links.

[0110] After determining the coarse trajectories and / or initial routing in stage 1404, central trajectory controller 714 may send the coarse trajectories and / or initial routings to backhaul moving cells 708 and 710 (e.g., using signaling connections between cell interface 1002 of central trajectory controller 714 and its peer central interfaces 914 of backhaul moving cells 708 and 710). Backhaul moving cells 708 and 710 may then establish connectivity with served devices 734-736 in stage 1408 (e.g., using the initial routing provided by central trajectory controller 714, or by determining their own initial routings). If any of served devices 734-736 are outer moving cells, these served devices may perform an outer task in stage 1410. Served devices 734-736 may then transmit uplink data to backhaul moving cells 708 and 710 using fronthaul links 726-728 in stage 1412, and backhaul moving cells 708 and 710 may transmit the uplink data to the radio access network in stage 1414 on backhaul links 730 and 732. Stages 1412 and 1414 can also include transmission and relaying of downlink data from the radio access network to served devices 734-736 via the backhaul relaying path provided by backhaul moving cells 708 and 710. Backhaul moving cells 708 and 710 may move according to their respectively assigned coarse trajectories during stages 1412 and 1414.

[0111] Similar to the case of FIG. 11, the coarse trajectories and / or initial routings provided by central trajectory controller 714 may form a high-level plan that can be locally optimized. Accordingly, as shown in FIG. 14, backhaul moving cells 708 and 710 may perform parameter exchange with served devices 734-736 in stage 1416. In some aspects, served devices 734-736 may provide position reports to backhaul moving cells 708 and 710 in stage 1416, which backhaul moving cells can use to update the statistical density information of served devices 734-736. This updated statistical density information may be part of the local input data for the backhaul trajectory algorithm. The parameter exchange that forms the local input data can include any of data rate requirements of served devices 734-736, the positions of served devices 734-736, the target areas assigned to served devices 734-736, recent radio measurements obtained by served devices 734-736, and / or details about the radio capabilities of served devices 734-736.

[0112] Backhaul moving cells 708 and 710 may then perform local optimization of the trajectories and / or routing in stage 1418 by executing the backhaul trajectory algorithm on the local input data. The backhaul trajectory algorithm may calculate updated trajectories and / or updated routings based on the local input data. After determining the updated trajectories and / or updated routings, backhaul moving cells 708 and 710 may move according to the updated trajectories and / or perform backhaul relaying according to the updated routings. In some aspects, backhaul moving cells 708 and 710 may repeat stages 1412-1418 over time, and may thus repeatedly execute the backhaul trajectory algorithm using new local input data to update the trajectories and / or routings. As the local input data may reflect the actual radio environment, in some cases the local optimization can improve performance.

[0113] In some aspects, backhaul moving cells 708 and 710 may use dual-phased optimization to alternate between optimizing fronthaul links 726-728 and backhaul links 730-732. Using backhaul moving cell 708 as an example, trajectory processor 918 may alternate between determining an updated trajectory that optimizes fronthaul links 726 (e.g., based on link strength, supported data rate, and / or link quality metric) and determining an updated trajectory that optimizes backhaul links 730. By alternating between optimizing fronthaul and backhaul, trajectory processor 918 may optimize the function of the optimization criteria (which can depend on both fronthaul and backhaul links).

[0114] Various aspects of this disclosure consider one or more additional extensions to these systems. In some aspects, one or more of outer moving cells 702-706 and backhaul moving cells 708 and 710 may be configured to support multiple simultaneous radio links. Accordingly, instead of only using a single radio link for the fronthaul or backhaul link, one or more of the moving cells may be configured to transmit and / or receive using multiple radio links. In such cases, central trajectory controller 714 may have prior knowledge of the multi-link capabilities of the moving cells. The central trajectory algorithm may therefore use channel statistics representing the aggregate capacity across the multiple links when determining the coarse trajectories and / or initial routings. For example, if the data rate of a first available link of a moving cell is R 1 and the data rate of a second available link of the moving cell is R 2 , the central trajectory algorithm may assume that the data rate of both links together is R 1 + R 2 (e.g., treated independently, thus making the aggregate capacity additive). Similarly, if the moving cells support mmWave, the central trajectory algorithm can model the multiple beams from mmWave as multiple isolated links (e.g., by generating multiple antenna beams with mmWave antenna arrays).

[0115] In some aspects, the backhaul routing paths may introduce redundancy using multiple links. For example, outer moving cells 702-706 or the served devices may use multiple backhaul routing paths (e.g., with different fronthaul links and / or backhaul links), and may transmit the same data redundantly over the multiple backhaul routing paths. This could be done as packet-level redundancy.

[0116] In some aspects, outer moving cells 702-706 and / or backhaul moving cells 708 and 710 may use transmission or reception cooperation to improve radio performance. For example, the central trajectory algorithm may designate a cluster of outer moving cells or backhaul moving cells to cooperate as a single group, and can then determine coarse trajectories for the cluster to support transmit and / or receive diversity. The central trajectory algorithm can then treat the cluster as a composite node (e.g., using an effective rate representation). Once the central trajectory algorithm determines the coarse trajectory of the cluster, the moving cells in the cluster can use their outer or backhaul trajectory algorithms to adjust their trajectories so that the effective centroid location of the cluster remains constant.

[0117] In some aspects, the central, outer, and backhaul trajectory algorithms may use features described in J. Stephens et. al. "Concurrent control of mobility and communication in multi-robot system," (IEEE Transactions on Robotics, Oct., 2017), J. L. Ny et. al, "Adaptive communication constrained deployment of unmanned aerial vehicle," (IEEE JSAC, 2012), M. Zavlanos et. al. "Network integrity in mobile robotic network," (IEEE Trans. On Automatic Control, 2013), and / or J. Fink et. al., Motion planning for robust wireless networking," (IEEE Conf.. On Robotics & Automation, 2012).

[0118] FIG. 15 shows exemplary method 1500 for managing trajectories for moving cells according to some aspects. As shown in FIG. 15, method 1500 including establishing signaling connections with one or more backhaul moving cells and with one or more outer moving cells (1502), obtaining input data related to a radio environment of the one or more outer moving cells and the one or more backhaul moving cells (1504), executing, using the input data as input, a central trajectory algorithm to determine first coarse trajectories for the one or more backhaul moving cells and second coarse trajectories for the one or more outer moving cells (1506), and sending the first coarse trajectories to the one or more backhaul moving cells and the second coarse trajectories to the one or more outer moving cells (1508).

[0119] FIG. 16 shows exemplary method 1600 for operating an outer moving cell according to some aspects. As shown in FIG. 16, method 1600 includes receiving a coarse trajectory from a central trajectory controller (1602), performing an outer task according to the coarse trajectory, and sending data from the outer task to a backhaul moving cell for relay to a radio access network (1604), executing an outer trajectory algorithm with the coarse trajectory as input to determine an updated trajectory (1606), and performing the outer task according to the updated trajectory (1608).

[0120] FIG. 17 shows exemplary method 1700 for operating a backhaul moving cell according to some aspects. As shown in FIG. 17, method 1700 includes receiving a coarse trajectory from a central trajectory controller (1702), receiving data from one or more outer moving cells while moving according to the coarse trajectory, and relaying the data to a radio access network (1704), executing a backhaul trajectory algorithm with the coarse trajectory as input to determine an updated trajectory (1706), and receiving additional data from the one or more outer moving cells while moving according to the updated trajectory, and relaying the additional data to the radio access network (1708).

[0121] FIG. 18 shows exemplary method 1800 for managing trajectories for moving cells according to some aspects. As shown in FIG. 18, method 1800 includes establishing signaling connections with one or more backhaul moving cells (1802), obtaining input data related to a radio environment of the one or more backhaul moving cells and related to statistical density information of one or more served devices (1804), executing, using the input data as input, a central trajectory algorithm to determine coarse trajectories for the one or more backhaul moving cells (1806), and sending the coarse trajectories to the one or more backhaul moving cells (1808).

[0122] FIG. 19 shows exemplary method 1900 for operating a backhaul moving cell according to some aspects. As shown in FIG. 19, method 1900 includes receiving a coarse trajectory from a central trajectory controller (1902), receiving data from one or more served devices while moving according to the coarse trajectory, and relaying the data to a radio access network (1904), executing a backhaul trajectory algorithm with the coarse trajectory as input to determine an updated trajectory (1906), and receiving additional data from the one or more served devices while moving according to the updated trajectory, and relaying the additional data to the radio access network (1908). Mobile Access Nodes for Indoor Coverage

[0123] Similar techniques and trajectory algorithms can also be applied for indoor coverage use cases. For example, terminal devices may operate in private residences and commercial facilities. This can include terminal devices, such as handheld mobile phones, as well as connectivity-enable devices like televisions, printers, and appliances. In some cases, these terminal devices may follow predictable usage patterns within the indoor coverage areas. Several examples include users that congregate in a living room are of a private residence in the evening, meeting rooms that are frequently used during work hours in an office building, public transit stations that users wait at during commuting hours, or a stadium with many users of mobile access nodes.

[0124] FIG. 20 shows an exemplary scenario using building 2000 according to some aspects. In the example of FIG. 20, building 2000 may be a private residence. Users carrying terminal devices may exhibit predictable usage patterns inside building 2000. For example, the users may frequently be in building 2000 during evening hours and weekends and may leave building 2000 during work and / or school hours. Accordingly, user demand may be higher in evenings and weekends and lower during work and / or school hours. Furthermore, in some cases, the users may follow predictable usage patterns in terms of where and when they are located in building 2000. For example, the users may frequently congregate in dining room 2012 during early morning and early evening hours for breakfast and dinner. The users may also congregate in living room 2010 during late evening hours.

[0125] Users in various private and public coverage areas may similarly follow usage patterns that are predictable. Accordingly, in some aspects a network of mobile access nodes may follow trajectories that are based on these predictable usage patterns. Instead of positioning themselves in a purely responsive manner, the mobile access nodes may proactively position themselves according to where users are likely to be. In some cases, this type of trajectory control can improve coverage and service to users.

[0126] As shown in FIG. 20, mobile access nodes 2004, 2006, and 2008 can be deployed within building 2000. Mobile access nodes 2004-2008 may be configured to provide access to users within this target coverage area, and may therefore position themselves within building 2000 along trajectories that can effectively serve the users. Anchor access point 2002 may also be deployed within building 2000, and may be configured to provide control functions for mobile access nodes 2004-2008.

[0127] FIG. 21 shows a basic diagram illustrating the functionality of anchor access point 2002 and mobile access nodes 2004 and 2006 according to some aspects. As shown in FIG. 21, anchor access point 2002 may interface with backhaul link 2102. Backhaul link 2102 may provide anchor access point 2002 with a connection to a core network, through which anchor access point 2002 may connect with various external data networks. Backhaul link 2102 can be a wired or wireless link.

[0128] Anchor access point 2002 may interface with mobile access nodes 2004 and 2006 over anchor links 2104 and 2106. Anchor links 2104 and 2106 may be wired or wireless links. Accordingly, mobile access nodes 2004 and 2006 may be free to move and maintain anchor links 2104 and 2106 with anchor access point 2002.

[0129] As previously indicated, mobile access nodes 2004 and 2006 may provide access to various served terminal devices (e.g., users). As shown in FIG. 21, mobile access nodes 2004 and 2006 may interface with these served terminal devices over fronthaul links 2108 and 2110. Accordingly, in the downlink direction, mobile access nodes 2004 and 2006 may receive downlink data addressed to the served terminal devices from anchor access point 2002 over anchor links 2104 and 2106. Mobile access nodes 2004 and 2006 may then perform any applicable processing on the downlink data and subsequently transmit the downlink data to the served terminal devices, as appropriate, over fronthaul links 2108 and 2110. In the uplink direction, mobile access nodes 2004 and 2006 may receive uplink data originating from the served terminal devices over fronthaul links 2108 and 2110. Mobile access nodes 2004 and 2006 may then perform any applicable processing on the uplink data and then transmit the uplink data to anchor access point 2002 over anchor links 2104 and 2106.

[0130] As indicated in FIG. 21, anchor access point 2002 and mobile access nodes 2004 and 2006 may have certain functionalities related to the trajectory control. With reference to anchor access point 2002, anchor access point 2002 may provide, for example, central learning, central control, sensor hub, and central communication (the structure of which is further described below for FIG. 23). Mobile access nodes 2004 and 2006 may provide, for example, local learning, local control, local sensing, and local communication (the structure of which is further described below for FIG. 22).

[0131] FIGs. 22 and 23 show exemplary internal configurations of mobile access nodes 2004 and 2006 and anchor access point 2002 according to some aspects. As shown in FIG. 22, mobile access nodes 2004 and 2006 may include antenna system 2202, radio transceiver 2204, baseband subsystem 2206 (including physical layer processor 2208 and protocol controller 2210), application platform 2212, and movement system 2224. Antenna system 2202, radio transceiver 2204, and baseband subsystem 2206 may be configured in a similar or same manner as antenna system 302, radio transceiver 304, and baseband subsystem 306 as shown and described for network access node 110 in FIG. 3. Antenna system 2202, radio transceiver 2204, and baseband subsystem 2206 may therefore be configured to perform radio communications to and from anchor access point 2002.

[0132] As shown in FIG. 22, application platform 2212 may include anchor interface 2214, local learning subsystem 2216, local controller 2218, sensor 2220, and relay router 2222. In some aspects, anchor interface 2214 may be a processor configured to communicate with a peer mobile interface of an anchor access point (e.g., mobile interface 2314 as described below for anchor access point 2002). Anchor interface 2214 may therefore be configured to transmit data to anchor access points by providing the data to baseband subsystem 2206, which may then process the data to produce RF signals. RF transceiver 2204 may then wirelessly transmit the RF signals via antenna system 2202. The anchor access point may then receive and process the wireless RF signals to recover the data at its mobile interface. Anchor interface 2214 may receive data from the peer mobile interface through the reverse of this process. Anchor interface 2214 may therefore be configured to communicate with peer mobile interfaces of anchor access points over a logical connection that uses wireless transmission for physical transport. Further references to communication between mobile access nodes 2004 and 2006 and anchor access point 2002 may involve this type of transmission between anchor interface 2214 and the peer mobile interface.

[0133] Local learning subsystem 2216 may be a processor configured for learning-based processing. For example, local learning subsystem 2216 may be configured to execute program code for a pattern recognition algorithm, which can be, for example, an artificial intelligence (AI) algorithm that uses input data about served terminal devices to recognize predictable usage patterns. This can include sensing data that indicates the positions of served terminal devices. Local learning subsystem 2216 may comprises a processor that is capable of being configured to execute a propagation modeling algorithm for predicting radio conditions and / or an access usage prediction algorithm for predicting user behavior with radio access. The operation of these algorithms is described below and in the figures.

[0134] Local controller 2218 may be a processor configured to communicate with a counterpart central controller of anchor access point 2002. As further described below, local controller 2218 may be configured to receive and carry out control instructions provided by the central controller, execute a local trajectory algorithm to determine trajectories for the mobile access nodes, and determine scheduling and resource allocations, fronthaul radio access technology selections, and / or routings.

[0135] Sensor 2220 may be a sensor configured to perform sensing and to obtain sensing data. In some aspects, sensor 2220 may be a radio measurement engine configured to obtain radio measurements as sensing data. In some aspects, sensor 2220 can be image or video sensors or any type of proximity sensor (e.g., radar sensors, laser sensors, motion sensors, etc.) that can obtain sensing data that indicates positions of the served terminal devices.

[0136] Relay router 2222 may be a processor configured to communicate with a counterpart user router of anchor access point 2002. As further described below, the user router may send relay router 2222 downlink user data for the served terminal devices, which relay router may then transmit to the served terminal devices via baseband subsystem 2206. Relay router 2222 may also receive uplink user data from the served terminal devices, and may transmit the uplink user data to the user router of anchor access point 2002.

[0137] As shown in FIG. 23, anchor access point 2002 may include antenna system 2302, radio transceiver 2304, baseband subsystem 2306 (including physical layer processor 2308 and protocol controller 2310), and application platform 2312. Antenna system 2202, radio transceiver 2204, and baseband subsystem 2206 may be configured in a similar or same manner as antenna system 302, radio transceiver 304, and baseband subsystem 306 as shown and described for network access node 110 in FIG. 3. Antenna system 2302, radio transceiver 2304, and baseband subsystem 2306 may therefore be configured to perform radio communications to and from mobile access nodes 2004 and 2006.

[0138] As shown in FIG. 23, application platform 2312 may include mobile interface 2314, central learning subsystem 2316, central controller 2318, sensor hub 2320, and user router 2322. As previously introduced regarding anchor interface 2214, mobile interface 2314 may be a processor configured to communicate with anchor interface 2214 of mobile access nodes 2004 and 2006 on a logical connection that relies on wireless transmission for transport. Mobile interface 2314 may therefore transmit and receive signaling to and from its peer anchor interfaces 2214 at mobile access nodes 2004 and 2006.

[0139] Central learning subsystem 2316 may be a processor configured to execute, for example, a pattern recognition algorithm, propagation modeling algorithm, and / or access usage prediction algorithm. These algorithms can be AI algorithms that use input data about served terminal devices to predict user density, predict radio conditions, and predict user behavior for access usage. The operation thereof is further described below and by the figures.

[0140] Central controller 2318 may be a processor configured to determine control instructions for mobile access nodes 2004 and 2006. As further described below, the control instructions can include coarse trajectories, scheduling and resource allocations, fronthaul radio access technology selections, and / or initial routings. In some aspects, central controller 2318 may be configured to execute a central trajectory algorithm to determine coarse trajectories for mobile access nodes 2004 and 2006.

[0141] Sensor hub 2320 may be a server-type component configured to collect sensing data. The sensing data can be provided, for example, by the served terminal devices, mobile access nodes 2004 and 2006, and / or other remote sensors. Sensor hub 2320 may be configured to provide this sensing data to central learning subsystem 2316.

[0142] User router 2322 may be a processor configured to interface with relay router 2222 over a logical connection. User router 2322 may be configured to identify downlink user data addressed to served terminal devices, and to identify which mobile access node to send the downlink user data to. User router 2322 may then send the downlink user data to the relay router 2222 of the corresponding mobile access node. User router 2322 may also be configured to receive uplink user data from the relay routers 2222 of mobile access nodes 2004 and 2006, and to send the uplink user data along its configured path (e.g., through the core network and / or to an external network location).

[0143] Mobile access nodes 2004 and 2006 can have different capabilities in various aspects. For example, in some aspects, mobile access nodes 2004 and 2006 can have full cell functionality, including mobility control for terminal devices, scheduling and resource allocation, and physical layer processing. Accordingly, in these aspects, mobile access nodes 2004 and 2006 can act as full-service cells. For example, with reference to FIG. 22, protocol controller 2310 may be configured to handle the full cell protocol stack for both user and control planes. This can vary depending on the radio access technology or technologies supported by mobile access nodes. For example, in the case of LTE, protocol controller 2310 can be configured with PDCP, RLC, RRC, and MAC capabilities.

[0144] In other aspects, mobile access nodes 2004 and 2006 may have limited cell functionality (e.g., less than full cell functionality). As mobile access nodes 2004 and 2006 may therefore not have full cell functionality, anchor access point 2002 may provide the remaining cell functionality. For example, the protocol controllers 2210 of mobile access nodes 2004 and 2006 may be configured to handle some protocol stack layers and functions, while protocol controller 2310 of anchor access point 2002 may be configured to handle the remaining cell functionality. The specific distribution of cell functionality between mobile access nodes 2004 and 2006 versus anchor access point 2002 can vary in different aspects. For example, in some aspects protocol controllers 2210 of mobile access nodes 2004 and 2006 may handle scheduling and resource allocation (e.g., assignment of radio resources to served terminal devices for uplink and downlink) while protocol controller 2310 of anchor access point 2002 may handle mobility control (e.g., may handle handovers and other mobility management of terminal devices connected to mobile access nodes 2004 and 2006). In other aspects, protocol controllers 2210 of mobile access nodes 2004 and 2006 may be configured to handle some user plane functions (e.g., some of the radio access technology-dependent processing on user plane data) while protocol controller 2310 of anchor access point 2310 may be configured to handle the remaining user plane functions.

[0145] In other aspects, mobile access nodes 2004 and 2006 may only handle physical layer processing while anchor access point 2002 provides protocol stack cell functionality. Accordingly, protocol controller 2310 of anchor access point 2002 may be configured to handle mobility control and scheduling and resource allocation capabilities for the terminal devices served by mobile access nodes 2004 and 2006. Protocol controller 2310 of anchor access point 2002 may also be configured to handle user plane functions above the physical layer. Mobile access nodes 2004 and 2006 may therefore be configured to perform physical layer processing (with physical layer processors 2208) on data addressed to or originating from their respective served terminal devices, while protocol controller 2310 of anchor access point 2002 may be configured to perform the remaining user plane processing.

[0146] In some of these aspects, mobile access nodes 2004 and 2006 may therefore not include protocol controllers 2210. For example, as anchor access point 2002 may be configured to handle both the control and user plane protocol stack cell functionality, mobile access nodes 2004 and 2006 may not support protocol stack cell functionality and may therefore not include protocol controllers 2210. Instead, mobile access nodes 2004 and 2006 may include physical layer processors 2208 for performing physical layer processing.

[0147] In some aspects, anchor access point 2002 may handle physical layer and protocol stack cell functionality while mobile access nodes 2004 and 2006 handle only radio processing. Accordingly, protocol controller 2310 and physical layer processor 2308 of anchor access point 2002 may perform all of the physical layer and protocol stack processing, while radio transceivers 2204 and antenna systems 2202 of mobile access nodes 2004 and 2006 may perform radio processing. In some of these aspects, mobile access nodes 2004 and 2006 may therefore not include physical layer processors 2208 and protocol controllers 2210.

[0148] In some of these aspects, mobile access nodes 2004 and 2006 may function in a similar manner to remote radio heads (RRHs). These RRHs are normally deployed in distributed base station architectures, where a centralized baseband unit (BBU) performs baseband processing (including physical and protocol stack layers) and a remotely deployed RRH performs radio processing and wireless transmission. Accordingly, in these aspects, anchor access point 2002 may function in a manner similar to the BBUs (by performing physical and protocol stack cell processing.) while mobile access nodes 2004 and 2006 function in a manner similar to the RRHs (by performing radio processing and wireless transmission).

[0149] In some aspects, this distributed architecture for anchor access point 2002 and mobile access nodes 2004 and 2006 can use distributed RAN techniques, including Cloud RAN (C-RAN). For example, in C-RAN, the baseband processing for multiple base stations can be handled at a centralized location (e.g., at centralized core network servers). Similarly, anchor access point 2002 may be configured to handle the baseband processing for mobile access nodes 2004 and 2006 while mobile access nodes 2004 and 2006 perform radio processing and transmission.

[0150] Accordingly, as described above there are numerous possibilities for the distribution of cell functionality between anchor access point 2002 and mobile access nodes 2004 and 2006. Any of these cell functionality distributions can be utilized in the various aspects of this disclosure.

[0151] FIG. 24 shows exemplary message sequence chart 2400 illustrating the operation of anchor access point 2002 and mobile access nodes 2004-2006 according to some aspects. As shown in FIG. 24, anchor access point 2002 may first perform initialization and setup with mobile access nodes 2004-2006 and the terminal devices served by mobile access nodes 2004-2006 in stage 2402. In some aspects, stage 2402 may include a multi-phase procedure. This can include a first phase where the served terminal devices connect with mobile access nodes 2004-2006, a second phase where mobile access nodes 2004-2006 connect with anchor access point 2002, and a third phase where the served terminal devices connect with anchor access point 2002 (via mobile access nodes 2004-2006). For example, in the first phase, one or more terminal devices may connect with mobile access node 2004 by exchanging signaling (e.g., including a random access and registration procedure) with its protocol controller 2210, and one or more terminal devices may connect with mobile access node 2006 by exchanging signaling with its protocol controller 2210. In the second phase, mobile access nodes 2004 and 2006 may connect with anchor access point 2002 by exchanging signaling between their respective anchor interfaces 2214 and mobile interface 2314 of anchor access point 2002. In the third phase, the served terminal devices of mobile access nodes 2004-2006 may connect with anchor access point 2002 either by using mobile access nodes 2004-2006 as relays or by having mobile access nodes 2004-2006 register the served terminal devices with anchor access point 2002 on their behalf. For example, in some aspects the served terminal devices of mobile access node 2004 may transmit signaling, addressed to anchor access point 2002, to mobile access node 2004. Mobile access node 2004 may receive and process this signaling via its baseband subsystem 2206. Relay router 2222 of mobile access node 2004 may then relay the signaling to anchor access point 2002 by wirelessly transmitting it via baseband subsystem 2206. Anchor access point 2002 may then receive the signaling at its protocol processor 2310 and register the served terminal devices accordingly. In other aspects, the respective protocol controllers 2210 of mobile access nodes 2004 and 2006 may exchange signaling with protocol controller 2210 of anchor access point 2002 to register their respective served terminal devices.

[0152] The initialization and setup of stage 2402 may establish the wireless links between the involved devices. Accordingly, stage 2402 may establish fronthaul links 2108 and 2110 and anchor links 2104 and 2106. After the served terminal devices and mobile access nodes 2004 and 2006 are connected with anchor access point 2002, the served terminal devices may be able to use mobile access nodes 2004 and 2006 to transmit and receive user data. As shown in FIG. 24, the served terminal devices may perform data communications with mobile access nodes 2004 and 2006 in stage 2404a, and mobile access nodes may perform data communications with anchor access point 2002 in stage 2404b. For example, in the downlink direction, user router 2322 of anchor access point 2002 may receive user data addressed to a terminal device. User router 2322 may then determine which mobile access node is serving the terminal device, such as mobile access node 2004. User router 2322 may then provide the user data to baseband subsystem 2306, which may transmit the user data over the corresponding anchor link, such as anchor link 2104. Mobile access node 2004 may then wirelessly receive and process the user data at its baseband subsystem 2206, and provide the user data to relay router 2222 (which as previously indicated may have a logical connection with user router 2322). Relay router 2222 may then identify which served terminal device the user data is addressed to and subsequently transmit the user data to the served terminal device (over the corresponding fronthaul link) via baseband subsystem 2206.

[0153] In the uplink direction, a terminal device may transmit user data to its serving mobile access node, such as mobile access node 2004. Mobile access node 2004 may then wirelessly receive and process the user data via its baseband subsystem 2206, and provide the user data to relay router 2222. Relay router 2222 may then wirelessly transmit the user data to user router 2322 of anchor access point 2002 via its baseband subsystem 2206 and baseband subsystem 2306 of anchor access point 2002.

[0154] Mobile access nodes 2004 and 2006 may therefore provide access to their respective served terminal devices via the data communication of stages 2404a and 2404b. As denoted by the arrows in FIG. 24, mobile access nodes 2004 and 2006 may continue this data communication, and may therefore continue to provide access to their served terminal devices over time. As previously described, the cell functionalities of mobile access nodes 2004 and 2006 can differ in various different aspects, where some aspects may provide mobile access nodes 2004 and 2006 with full cell functionality, some aspects may provide mobile access nodes 2004 and 2006 with some but not all cell functionality, and some aspects may limit the mobile access nodes 2004 and 2006 to radio processing capabilities. Accordingly, mobile access nodes 2004 and 2006 may perform the data communications in stages 2418a and 2418b according to their cell functionality.

[0155] As mobile access nodes 2004 and 2006 are mobile, they may be able to adjust their trajectories over time to improve access performance. For example, mobile access nodes 2004 and 2006 may be able to position themselves relative to their served terminal devices to produce strong fronthaul links, which can yield higher data rates and reliability. Furthermore, as previously indicated, the served terminal devices may in some cases exhibit predictable usage patterns. This can include predictable positioning of terminal devices at specific times. For example, with reference back to FIG. 20, the served terminal devices may congregate in living room 2010 during late evening hours, or may congregate in dining room 2012 during breakfast and dinner times. Accordingly, by identifying predictable usage patterns such as these for the target coverage area, mobile access nodes 2004 and 2006 may be able to proactively position themselves in locations that can effectively provide access to their served terminal devices.

[0156] Mobile access nodes 2004 and 2006 and anchor access point 2002 may therefore attempt to determine these predictable usage patterns and subsequently use the predictable usage patterns to determine trajectories for mobile access nodes 2004 and 2006. In some aspects, mobile access nodes 2004 and 2006 and anchor access point 2002 may utilize sensing data to determine the predictable usage patterns. For example, mobile access nodes 2004 and 2006 and anchor access point 2002 may execute a pattern recognition algorithm (at local learning subsystems 2216 and central learning subsystem 2316) that uses sensing data to attempt to identify predictable usage patterns in their served terminal devices.

[0157] Accordingly, as shown in FIG. 24, mobile access nodes 2004 and 2006 may obtain and send sensing data to anchor access point 2002 in stage 2406. The sensing data can be any type of data that indicates the positions of terminal devices that are served by mobile access nodes 2004 and 2006. Sensors 2220 of mobile access nodes 2004 and 2006 may obtain the sensing data. For example, in some aspects, sensors 2220 may be radio measurement engines that are configured to measure wireless signals transmitted by the served terminal devices and to obtain corresponding radio measurements. Accordingly, the respective sensors 2220 of mobile access nodes 2004 and 2006 may be configured to obtain these radio measurements as the sensing data, and provide the radio measurements to anchor interfaces 2214. The anchor interfaces 2214 of mobile access nodes 2004 and 2006 may then transmit the radio measurements to mobile interface 2314 of anchor access point 2002, which may provide the radio measurements to sensor hub 2320. Although FIG. 24 shows sensors 2220 as part of application platform 2212, in some aspects sensors 2220 may be radio measurement engines that are part of baseband subsystem 2206.

[0158] In other aspects, sensors 2220 of mobile access nodes 2004 and 2006 may be another type of sensor that can obtain sensing data related to the positions of the served terminal devices. For example, sensors 2220 can be image or video sensors, or any type of proximity sensor (e.g., radar sensors, laser sensors, motion sensors, etc.), and can obtain sensing data that indicates positions of terminal devices and / or users potentially carrying terminal devices. Sensors 2220 may similarly send this sensing data to sensor hub 2320 of anchor access point 2318. In some aspects, sensors 2220 may include multiple types of sensors, and may send multiple types of sensing data to sensor hub 2320.

[0159] In some aspects, the served terminal devices may also send sensing data to anchor access point 2002 in stage 2408. For example, in some aspects the served terminal devices may include positional sensors (e.g., geopositional sensors, such as those based on satellite positioning systems) configured to estimate their positions, and may send the resulting position reports to sensor hub 2320. In some aspects, the served terminal devices may first send the position reports to mobile access nodes 2004 and 2006, which may then relay the position reports (e.g., via their relay routers 2222) to sensor hub 2320 of anchor access point 2002.

[0160] In some aspects, sensor hub 2320 may also maintain connections with remote sensors. These remote sensors can be deployed around the target coverage area, and may generate and send sensing data to sensor hub 2320 (e.g., via wireless or wireless links with anchor access point 2002, which can include direct links or IP-based internet links).

[0161] Sensor hub 2320 may therefore receive this sensing data that indicates the positions of the served terminal devices. As shown in FIG. 24, in some aspects mobile access nodes 2004-2006 and the served terminal devices may continue to provide sensing data to anchor access point 2002. Sensor hub 2320 may therefore collect and store the sensing data, such as in its local memory. In some aspects, the sensing data may be time-stamped. For example, sensor 2220 of mobile access nodes 2004 and 2006 may be configured to attach a timestamp to sensing data it generates. As referenced herein, these timestamps can be any information about time (e.g., are not limited to times expressed in hours in minutes). Additionally or alternatively, the served terminal devices may similarly attach timestamps to sensing data they generate and send to anchor access point 2002. Additionally or alternatively, sensor hub 2320 may attach timestamps to sensing data it receives.

[0162] As the sensing data indicates positions of served terminal devices, the timestamped sensing data may indicate positions of served terminal devices at certain times. It may therefore be possible to evaluate the timestamped sensing data to estimate predictable usage patterns by the served terminal devices. For example, referring back to the example of FIG. 20, the timestamped sensing data may indicate that the positions of the served terminal devices is probabilistically likely to be in living room 2010 during late evening hours, and probabilistically likely to be in dining room 2012 during lunch and dinner hours. Depending on the context, similar predictable usage patterns can also be derivable from the timestamped sensing data according to any type of repeated user behavior. Other examples include users congregating in office buildings during working hours (or, even more specifically, in particular offices or meeting rooms), users congregating in restaurants during mealtime hours, users congregating in shopping and retail areas during weekday evenings and weekends, users congregating in public transit areas (e.g., train or bus stations) during commuting hours, and any scenario in which users follow a repeating pattern. These predictable usage patterns may not be completely deterministic; in other words, there may not be an absolute certainty that the served terminal devices will always follow the predictable usage patterns. The predictable usage patterns instead refer to statistical data that indicates a probability that served terminal devices follow a particular usage pattern.

[0163] Anchor access point 2002 may then perform central trajectory and communication control processing in stage 2410. For example, sensor hub 2320 may provide the timestamped sensing data to central learning subsystem 2316. Central learning subsystem 2316 may then execute the pattern recognition algorithm on the timestamped sensing data to determine the predictable usage patterns. In various aspects, the pattern recognition algorithm can be an AI algorithm, such as a machine learning algorithm, neural network algorithm, or reinforcement learning algorithm. While any such algorithm capable of recognizing usage patterns can be employed, FIG. 25 shows flow chart 2500 illustrating a basic flow of an exemplary pattern recognition algorithm according to some aspects. As shown in FIG. 25, central learning subsystem 2316 may first evaluate the timestamped sensing data to identify locations that have dense user distributions at respective times in stage 2502. For example, central learning subsystem 2316 may be configured to use the timestamped sensing data to estimate terminal device positions over time, and may then generate a time-dependent density plot with the terminal device positions (e.g., such as a heat map for user density that is plotted over time). Using the time-dependent density plot, central learning subsystem 2316 may then evaluate the user densities over time to identify certain locations (e.g., two- or three-dimensional areas within the target coverage area) that have dense user distributions at a given time (e.g., a user distribution, expressed in users per unit area, exceeding a predefined threshold).

[0164] Then, central learning subsystem 2316 may be configured to pair each location with a time at which the dense user distribution occurred in stage 2504. The time can be, for example, a window of time during which the user distribution of the location was above a predefined threshold. Central learning subsystem 2316 may add the resulting location-time pairs to a pattern database (e.g., in its local memory) that records the occurrence of dense user distributions at certain times and locations.

[0165] In some aspects, sensor hub 2320 may collect timestamped sensing data over an extended period of time, such as over multiple days, weeks, or months. Accordingly, the timestamped sensing data may indicate terminal device positions that repeat over multiple days. Central learning subsystem 2316 may therefore determine whether any of the locations have dense user distributions at similar times on different days in stage 2506. For example, central learning subsystem 2316 may evaluate the pattern database to determine whether any of the location-time pairs (from stage 2504) from different days have matching locations and times (e.g., within a tolerance to account for small differences).

[0166] These matching time-location pairs may indicate that a dense user distribution in a location at a particular time on multiple different days. This may consequently indicate a predictable usage pattern. Central learning subsystem 2316 may then calculate a strength metric for each matching time-location pair in stage 2508. The strength metric may indicate a probabilistic likelihood that the matching time-location pair is a predictable usage pattern (e.g., that there exists some non-negligible probability that the dense user distribution will be repeated). In some aspects, central learning subsystem 2316 may determine the strength metric for a given matching location-time pair based on the number of days that produced matching time-location pairs. For example, matching location-time pairs that occurred more often than other matching location-time pairs may yield higher strength metrics, as the higher occurrence rate may indicate a higher likelihood that the dense user distribution will be repeated.

[0167] In some aspects, central learning subsystem 2316 may consider days of the week when calculating the strength metrics in stage 2508. For example, as previously referenced, there may be some predictable usage patterns that occur on, for example, workdays and others that occur on weekends. There may be other predictable usage patterns that occur only on, for example, one day per week (for example, a weekly meeting in a given conference room, or a weekly television show that a family watches every week). The strength metrics for location-time pairs may therefore not only depend on whether a dense user distribution occurs a high number of days, but also whether a dense user distribution regularly occurs on a same day of the week. In some aspects, central learning subsystem 2316 may associate one or more days of the week with the location-time pairs (e.g., as recorded in the pattern database) that specify which days of the week the corresponding dense user distribution occurs.

[0168] At the output of stage 2508, central learning subsystem 2316 may therefore obtain location-time pairs with corresponding strength metrics that indicate the probabilistic likelihood that the location-time pair is a usage pattern. The combinations of associated location-time pairs, strength metrics, and days of the week may each represent a predictable usage pattern related to predicted user density.

[0169] In some aspects, central learning subsystem 2316 can perform flow chart 2500 as a continuous procedure. For example, central learning subsystem 2316 may be configured to evaluate timestamped sensing data as it arrives (or, for example, at the end of each day or other predefined interval) to determine whether any dense user distributions occurred. If so, central learning subsystem 2316 may compare the location-time pair of the dense user distribution with the location-time pairs in the pattern database, and determine whether there are any matching location-time pairs. If so, central learning subsystem 2316 may calculate a strength metric for the location-time pair and use the location-time pair, strength metric, and any associated days of the week as a predictable usage pattern.

[0170] As previously indicated, the procedure of flow chart 2500 is exemplary, and central learning subsystem 2316 may equivalently use other pattern recognition algorithms to determine the predictable usage patterns. For example, in other aspects, instead of identifying discrete patterns such as location-time pairs of dense user distributions, central learning subsystem 2316 may generate a time-dependent density plot as the predictable usage patterns, where the time-dependent density plot shows a deterministic distribution of users over time. In these aspects, central learning subsystem 2316 may evaluate the sensing data, obtained over an extended period of time, to predict user density in the target coverage area over time. As previously indicated, this can be similar to a heat map that plots the density of users in the target coverage area over time. Accordingly, in contrast to identifying discrete patterns, central learning subsystem 2316 may develop a plot of user density over time, where the density of users in a particular location and time can be predicted using the density of the time-dependent density plot. In some aspects, central learning subsystem 2316 may develop a plot of user density over time and day, where the time-dependent density plot can predict the density of users in a given location at a given time and day of the week.

[0171] The predictable usage patterns described above for FIG. 25 relate to predicted user density (e.g., where terminal devices are likely to be located at certain times). In some aspects, central learning subsystem 2316 may also incorporate predicted access usage and / or predicted radio conditions into the predictable usage patterns. For example, the sensing data collected by sensor hub 2320 can include historical usage information that details the usage of the radio access network by the served terminal devices. This historical usage information can be information such as average data rate or throughput, total amount of downloaded or uploaded data, frequency / periodicity of active access (e.g., how often the served terminal devices download or upload user data on an active access connection), or any other information that indicates how often the served terminal devices use the radio access network or how much data the served terminal devices transfer. In some aspects, baseband subsystem 2306 may be configured to collect this historical usage information (e.g., by monitoring the access connections of served terminal devices, which run through baseband subsystem 2306 via mobile access nodes 2004 and 2006) and provide this historical usage information to sensor hub 2320. In some aspects, the served terminal devices may be configured to monitor their own access usage and to report the resulting historical usage information to sensor hub 2320. In some aspects, baseband subsystems 2206 of mobile access nodes 2004 and 2006 may be configured to monitor the access usage of their respective served terminal devices and to report the resulting historical usage information to sensor hub 2320.

[0172] In some aspects, the historical usage information can be timestamped and / or geotagged. Accordingly, central learning subsystem 2316 may be able to evaluate the historical usage information over time and / or area to predict access usage by the served terminal devices. For example, central learning subsystem 2316 may be configured to execute an access usage prediction algorithm on the historical usage information to predict access usage over time and / or area. In some aspects, central learning subsystem 2316 may be configured to use a similar algorithm flow to that of flow chart 2500 to predict the access usage. For example, when the historical usage information is timestamped and geotagged, central learning subsystem 2316 may be configured to evaluate the historical usage information to identify locations from which a heavy access usage occurs at certain times (e.g., data usage exceeding a data rate or throughput threshold). Central learning subsystem 2316 may then pair the locations with a time at which the heavy access usage occurred, and subsequently determine whether any locations have heavy access usage at similar times on different days. Central learning subsystem 2316 may then calculate a strength metric for the location-time pairs, and treat the location-time pairs, strength metrics, and associated days of the week as predictable usage patterns.

[0173] In another example where the predictable usage patterns also include predicted radio conditions, the sensing data can include radio measurements that characterize the radio environment in the target coverage area. These radio measurements can be made and reported to sensor hub 2320 of anchor access point 2002 by the served terminal devices of mobile access nodes 2004 and 2006, can be made and reported by sensors 2220 of mobile access nodes 2004 and 2006, or can be made at anchor access point 2002 (e.g., at its own sensors). In some aspects, the radio measurements can be geotagged, and can therefore indicate the position of the transmitting device (that transmits the wireless signal of which the radio measurement is made) or of the receiving device (that performs the radio measurement).

[0174] Sensor hub 2320 may then provide these radio measurements to central learning subsystem 2316, which may be configured to execute a propagation modeling algorithm to predict the radio environment of the target coverage area as part of stage 2410. For example, the propagation modeling algorithm may be configured to generate a radio map (e.g., an REM) by modeling the radio environment over the geographic area of the target coverage area using the radio measurements and associated geotags. The propagation modeling algorithm can use any type of propagation modeling technique, such as a basic propagation model (e.g., free-space pathloss model, as previously described) or a propagation model based on radio maps (e.g., based on a REM, as previously described). The predicted radio conditions may also form part of the predictable usage patterns, as it may estimate the radio environment around the served terminal devices (e.g., including estimation of the radio environment in the locations of the dense user distributions). The predicted radio conditions can also be time-dependent, and can approximate radio conditions at different times of day depending on observed changes in the radio measurements over time.

[0175] Accordingly, central learning subsystem 2316 may determine predictable usage patterns that relate to user density, access usage, and / or radio conditions. With reference back to FIG. 24, anchor access point 2002 may use the predictable usage patterns as part of the central trajectory and communication control processing of stage 2410. For example, central learning subsystem 2316 may provide the predictable usage patterns to central controller 2318.

[0176] In some aspects, central controller 2318 may be configured to execute a central trajectory algorithm, using the predictable usage patterns, that determines coarse trajectories for mobile access nodes 2004 and 2006. In some aspects, this central trajectory algorithm may be the same or similar to the central trajectory algorithm previously described for central trajectory controller 714 of FIGs. 7 and 10. For example, the central trajectory algorithm may use a statistical model of the radio environment in the target coverage area, where the statistical model is based on the predicted radio conditions of the predictable usage patterns (as determined by central learning subsystem 2316). The statistical model may also approximate the positions of the users with the predicted user density of the predictable usage patterns, and may approximate access usage (e.g., the extent to which the served terminal devices use the radio access network to transfer data) with the predicted access usage of the predictable usage patterns. Using this statistical model, the central trajectory algorithm may define a function of an optimization criteria related to the radio environment. The optimization criteria can be, for example, a supported data rate for the served terminal devices, a probability that the supported data for the served terminal devices is above a predefined data rate threshold, a link quality metric, or a probability that the link quality metric for the served terminal devices is above a predefined link quality threshold.

[0177] The function of the optimization criteria may depend on the trajectories of mobile access nodes 2004 and 2006. Accordingly, the central trajectory algorithm may be configured to determine coarse trajectories for mobile access nodes 2004 and 2006 that increase (e.g., maximize) the function of the optimization criteria. This can include using gradient descent (or another optimization algorithm) to iteratively step the coarse trajectories of mobile access nodes 2004 and 2006 in the direction that maximizes the function of the optimization criteria.

[0178] As the function of the optimization criteria also depends on the locations of the served terminal devices, the predicted user density (determined by central learning subsystem 2316) may enable the central trajectory algorithm to accurately estimate the locations of the served terminal devices. For example, when the predicted user density is a location-time pair associated with certain days of the week, the statistical model may approximate the locations of the served terminal devices as being at the location at the corresponding time. Accordingly, optimization of the function of the optimization criteria can include optimizing the function of the optimization criteria under the assumption that the served terminal devices are located at the location (of the location-time pair) at the corresponding time. The central trajectory algorithm can use the strength metric to govern how strong the assumption is that the served terminal devices are located at the location at the corresponding time. For example, for location-time pairs that have a very high strength metric (e.g., users are nearly always congregated at the location at the given time on the associated days of the week), the central trajectory algorithm may place a strong assumption that users will be congregated around the location at the corresponding time (and vice versa for weaker strength metrics). The resulting central trajectories may therefore be weighted toward optimizing the function of the optimization criteria given served terminal devices located according to the location-time pairs of the predicted user density.

[0179] In another example where the predicted user density is a time-dependent density plot, the central trajectory algorithm may approximate the locations of the served terminal devices with the time-dependent density plot. Accordingly, at a given time, the time-dependent density plot may estimate that some locations of the target coverage are denser than others (e.g., that users are congregated at a certain location). Accordingly, the central trajectory algorithm may calculate the coarse trajectories with a greater assumption that the served terminal devices are positioned around the denser areas of the time-dependent density plot. The coarse trajectories may therefore be weighted towards providing access to areas of the target coverage area that have higher density in the time-dependent density plot.

[0180] Anchor access point 2002 may therefore determine coarse trajectories for mobile access nodes 2004 and 2006 as part of the central trajectory and communication control processing of stage 2410. In some aspects, central controller 2318 may also perform communication control using the predictable usage patterns. This can include determining scheduling and resource allocations for the served terminal devices, selecting radio access technologies for the served terminal devices, and / or determining initial routings for the served terminal devices. For example, in some aspects central controller 2318 may use the predictable usage patterns to determine scheduling and resource allocations for mobile access nodes 2004 and 2006 to use for their served terminal devices. Although not so limited, this can be applicable when cell functionality (such as scheduling) is handled at anchor access point 2002 (on behalf of mobile access nodes 2004 and 2006 ). For example, central controller 2318 may evaluate the predicted user density, predicted radio conditions, and predicted access usage to determine scheduling and resource allocations for the served terminal devices to use when transmitting and receiving to mobile access nodes 2004 and 2006. In some aspects, central controller 2318 may determine the scheduling and resource allocations as part of the central trajectory algorithm, where central controller 2318 determines the scheduling and resource allocations to optimize a function of the optimization criteria.

[0181] Central controller 2318 may also select radio access technologies for the served terminal devices to use when transmitting and receiving to and from mobile access nodes 2004 and 2006. For example, in some aspects the served terminal devices and mobile access nodes 2004 and 2006 (e.g., their respective antenna systems 2202, RF transceivers 2204, and baseband subsystems 2206 ) may support multiple radio access technologies. These can include cellular radio access technologies (e.g., LTE or another 3GPP radio access technology, mmWave, or any other cellular radio access technology) and / or short-range radio access technologies (e.g., WiFi, Bluetooth, or any other short-range radio access technology). As they support multiple radio access technologies, the served terminal devices and mobile access nodes 2004 and 2006 may have several different options to select from for use on fronthaul links 2108 and 2110. Central controller 2318 can therefore be configured to select which radio access technologies for the served terminal devices and mobile access nodes 2004 and 2006 to use on fronthaul links 2108 and 2110 as part of stage 2410. In some aspects, central controller 2318 may be configured to select the radio access technologies as part of the central trajectory algorithm, where central controller 2318 selects radio access technologies for the fronthaul links that optimize the function of the optimization criteria.

[0182] In some aspects, central controller 2318 may be configured to select initial routings for the served terminal devices as part of stage 2410. For example, central controller 2318 may be configured to select which mobile access node the served terminal devices should use. In the example of FIG. 20, there may be two mobile access nodes (mobile access nodes 2004 and 2006 ) for central controller 2318 to select between for each served terminal device. In other examples, there can be any quantity of mobile access nodes for central controller 2318 to select between for the initial routings. In some aspects, central controller 2318 may select the initial routings as part of the central trajectory algorithm, where central controller 2318 selects the initial routings to optimize the function of the optimization criteria.

[0183] In some aspects, central controller 2318 may also use external context information, in addition to the sensing data, for the processing in stage 2410. This external context information can include, for example, information about the service profile of the served terminal devices, information about the user profile of the served terminal devices, information about capabilities of the served terminal devices (e.g., supported radio access technologies, supported data rates, transmit powers, etc.), or information about the target coverage area (e.g., such as maps or locations of obstacles).

[0184] In some aspects, anchor access point 2002 may use such context information as part of the central trajectory algorithm. For example, central controller 2318 may use context information about the target coverage area, such as maps or locations of obstacles, to define the statistical model used to approximate the radio environment. For instance, the statistical model can approximate propagation based on a map of the target coverage area and the locations of obstacles within the target coverage area. In another example, central controller 2318 may be configured to use context information about the capabilities of the served terminal devices as part of the statistical model. For instance, the capabilities of the served terminal devices may relate to the transmission and reception performance of the served terminal devices, and may therefore be relevant to propagation in the statistical model. In another example, central learning subsystem 2316 may use context information about the target coverage area to determine predictable usage patterns, such as by identifying rooms in a map of the target coverage area that are associated with a predictable usage pattern (e.g., that form a location at which users congregate at a certain time). In another example, central learning subsystem 2316 may use context information about service or user profiles when determining predictable usage patterns about predicted usage access (e.g., by using a service or user profile to estimate how users will use the served terminal devices).

[0185] In some aspects, mobile access nodes 2004 and 2006 and / or the served terminal devices may provide the context information to anchor access point 2002. In other aspects, anchor access point 2002 may receive the context information from an external location, such as a core network or external data server that stores the context information.

[0186] Anchor access point 2002 may therefore determine one or more of coarse trajectories, scheduling and resource allocations, radio access technologies for fronthaul links, or initial routings as part of the central trajectory and communication control processing in stage 2410. Then, anchor access point 2002 may send corresponding control instruction to mobile access nodes 2004 and 2006 in stage 2412. For example, central controller 2318 may provide the control instructions to mobile interface 2314, which may then transmit (via its baseband subsystem 2306 ) the control instructions to the respective peer anchor interfaces 2214 of mobile access nodes 2004 and 2004. The control instructions may specify any of coarse trajectories, scheduling and resource allocations, fronthaul radio access technologies selections, or initial routings.

[0187] After receiving the control instructions from anchor access point 2002, anchor interfaces 2214 of mobile access nodes 2004 and 2006 may provide the control instructions to their respective local controllers 2218. Local controllers 2218 may then perform local trajectory and communication control processing in stage 2414. For example, when the control instructions include a coarse trajectory, local controller 2218 may provide the coarse trajectory to movement controller 2226. Movement controller 2226 may then control steering and movement machinery 2228 to move mobile access nodes 2004 and 2006 according to their respective coarse trajectories in stage 2416.

[0188] In some cases where the control instructions include scheduling and resource allocations, local controller 2218 may provide the scheduling and resource allocations to protocol controller 2210 of mobile access nodes 2004 and 2006. Protocol controller 2210 may then use the scheduling and resource allocations to generate scheduling and resource allocation messages for the served terminal devices. Protocol controller 2210 may then send the scheduling and resource allocation messages to the served terminal devices.

[0189] In some cases where the control instructions include fronthaul radio access technology selections, local controller 2218 may provide the fronthaul radio access technology selections to protocol controller 2210. Protocol controller 2210 may then generate a fronthaul radio access technology selection message and transmit the fronthaul radio access technology selection message to the served terminal devices.

[0190] In some cases where the control instructions include initial routings, local controller 2218 may provide the initial routings to protocol controller 2210. Protocol controller 2210 may then generate an initial routing message and transmit the initial routing message to the served terminal devices.

[0191] Mobile access nodes 2004 and 2006 may then perform data communications with the served terminal devices in stage 2422a and perform data communications with anchor access point 2002 in stage 2418b. As previously described, mobile access nodes 2004 and 2006 may, in the downlink direction, receive user data addressed to their respective served terminal devices from anchor access point 2002 over anchor links 2104 and 2106 (e.g., at their respective relay routers 2222 from user router 2322 of anchor access point 2002 ). Mobile access nodes 2004 and 2006 may then wirelessly transmit the user data to the served terminal devices over fronthaul links 2108 and 2110 (e.g., by relay routers 2222 wirelessly transmitting the user data via baseband subsystems 2206 ). In the uplink direction, mobile access nodes 2004 and 2006 may wirelessly receive user data from their served terminal devices over fronthaul links 2108 and 2110 (e.g., at baseband subsystems 2206, which may provide the user data to relay routers 2222 ). Mobile access nodes 2004 and 2006 may then wirelessly transmit the user data to anchor access point 2002 over anchor links 2104 and 2106 (e.g., by relay routers 2222 sending the user data to user router 2322 of anchor access point 2002 via baseband subsystems 2206 ). Mobile access nodes 2004 and 2006 may therefore provide access to their served terminal devices.

[0192] Mobile access nodes 2004 and 2006 may perform these data communications in stages 2418a and 2418b according to the control instructions provided by anchor access point 2002. For example, mobile access nodes 2004 and 2006 may move according to the coarse trajectories while performing the data communications (e.g., by movement controller 2226 controlling steering and movement machinery 2228 to move mobile access nodes 2004 and 2006 according to their respective coarse trajectories). Mobile access nodes 2004 and 2006 may also use the scheduling and resource allocations (included in the control instructions) to schedule communications and allocate resources for communications with the served terminal devices over fronthaul links 2108 and 2110 (e.g., at their respective protocol controllers 2210 ). Mobile access nodes 2004 and 2006 may also use the fronthaul radio access technology selections to control which radio access technologies are used for fronthaul links 2108 and 2110 (e.g., by protocol controllers 2210 controlling which radio access technologies are used to transmit and receive over fronthaul links 2108 and 2110 ). Mobile access nodes 2004 and 2006 may also use the initial routings to control which of the served terminal devices they respectively serve (e.g., by protocol controllers 2210 controlling the mobility of the served terminal devices so that the served terminal devices use the selected mobile access node for their routing).

[0193] As denoted by the arrow in FIG. 24, in some aspects mobile access nodes 2004 and 2006 may repeat stages 2414-2418b. For example, in some aspects, local controllers 2218 and / or local learning subsystems 2216 may be configured to update the predictable usage patterns, coarse trajectories, scheduling and resource allocations, fronthaul radio access technology selections, and / or initial routings.

[0194] For example, central controller 2318 of anchor access point 2002 may be configured to provide the predictable usage patterns to mobile access nodes 2004 and 2006 as part of the control instructions in stage 2412. As previously indicated, the predictable usage patterns may be time-dependent. For example, predicted user densities may include location-time pairs and / or time-dependent density plots that characterize predicted user density over time. Predicted radio conditions may also be defined over time, where radio conditions may differ at different times of day. Predicted access usage may similarly vary over time. Accordingly, while the initial control instructions provided by central controller 2318 in stage 2412 may be relevant for the current time, the predictable usage patterns may indicate different user densities, radio conditions, and / or access usage at different times. Accordingly, in some aspects, local controllers 2218 of mobile access nodes 2004 and 2006 may be configured to use the predictable usage patterns to update the coarse trajectories, scheduling and resource allocations, fronthaul radio access technology selections, and / or initial routings over time (e.g., to determine updated trajectories, updated scheduling and resource allocations, updated fronthaul radio access technology selections, and / or updated routings).

[0195] In one example, the predictable usage patterns may indicate a different user density at a later time, different radio conditions at the later time, and / or different access usage at the later time. Local controllers 2218 of mobile access nodes 2004 and 2006 may therefore be configured to execute a local trajectory algorithm using the different user density, radio conditions, and / or access usage, and to determine updated trajectories for mobile access nodes 2004 and 2006. In some aspects, this local trajectory algorithm may function similarly to the central trajectory algorithm used by central controller 2318. For example, the local trajectory algorithm may be configured to re-define the statistical model using the different user density, radio conditions, and / or access usage for the later time, and to then determine updated trajectories for mobile access nodes 2004 and 2006 that optimize the function of the optimization criteria (e.g., using gradient descent or another optimization algorithm). In some aspects, local controllers 2218 may also be configured to determine updated scheduling and resource allocations, fronthaul radio access technology selections, and / or routings based on the different user density, radio conditions, and / or access usage. In some aspects, the respective local controllers 2218 of mobile access nodes 2004 and 2006 may operate independently of each other, while in other aspects the respective local controllers 2218 of mobile access nodes 2004 and 2006 may operate in a collaborative manner.

[0196] After determining updated trajectories, updated scheduling and resource allocations, updated fronthaul radio access technology selections, and / or updated routings, local controllers 2218 of mobile access nodes 2004 and 2006 may control mobile access nodes 2004 and 2006 to perform data communications accordingly. For example, local controllers 2218 may provide the respective updated trajectories to movement controllers 2226, which may then respectively control steering and movement machinery 2228 to move mobile access nodes 2004 and 2006 according to the updated trajectories. Local controllers 2218 may provide the updated scheduling and resource allocations to their respective protocol controllers 2210, which may then generate and send out scheduling and resource allocation messages for their respective served terminal devices. Local controllers 2218 may likewise provide the updated fronthaul radio access technology selections and / or updated routings to their protocol controllers 2210, which may generate and send out fronthaul radio access technology selection messages and / or routing messages for their respective served terminal devices. Mobile access nodes 2004 and 2006 may then provide access to the selected terminal devices over fronthaul links 2108 and 2110 and anchor links 2104 and 2106.

[0197] In some aspects, mobile access nodes 2004 and 2006 may use their local learning subsystems 2216 to execute its own pattern recognition algorithm, and to update the predictable usage patterns (originally determined by central learning subsystem 2316 ). For example, the respective sensors 2220 of mobile access nodes 2004 and 2006 may be configured to continue to obtain sensing data that indicates the positions of the served terminal devices. This sensing data can be related to current, past, or future positions of the served terminal devices, and can therefore include current positions, velocity, and / or acceleration measurements. Sensors 2220 may then provide the sensing data to the respective local learning subsystems 2216 of mobile access nodes 2004 and 2006. The served terminal devices may also send sensing data (e.g., position reports) to local learning subsystem 2216 ). The local learning subsystems 2216 may then execute a pattern recognition algorithm with the sensing data to update the predictable usage patterns. This can include updating any of predicted user densities, predicted access usage, or predicted radio conditions. In some aspects, the pattern recognition algorithm may function similarly to the pattern recognition algorithm used by central learning subsystem 2216. For example, local learning subsystem 2216 can use the pattern recognition algorithm to adapt the predictable usage patterns according to the most recent sensing data, such as by updating the location-time pairs or their corresponding strength metrics or by updating a time-dependent density plot.

[0198] In some aspects, local learning subsystem 2216 may additionally or alternatively be configured to update predicted access usage of the predictable usage patterns based on historical usage information of the sensing data. For example, the historical usage information may indicate changes in the access usage by the served terminal devices (e.g., as users of the served terminal devices have changed their behavior, or as new served terminal devices operated by new users are now present). Accordingly, local learning subsystem 2216 may be configured to execute an access usage prediction algorithm to update the predicted access usage by the served terminal devices. As this historical usage information is more recent than the historical usage information used by central learning subsystem 2316 in stage 2410, the predicted access usage may be updated.

[0199] In some aspects, local learning subsystem 2216 may additionally or alternatively be configured to update predicted radio conditions of the predictable usage patterns based on radio measurements of the sensing data. For example, local learning subsystem 2216 may be configured to execute a propagation modeling algorithm based on recent radio measurement (e.g., obtained by sensor 2220, or reported to local learning subsystem 2216 by the served terminal devices). As the radio measurements are more recent than those originally used by central learning subsystem 2216 in stage 2410, the resulting predicted radio conditions may be updated.

[0200] Local learning subsystem 2216 may then provide these updated predictable usage patterns to local controller 2218 of mobile access nodes 2004 and 2006. Local controller 2218 may then be configured to update the control instructions based on the updated predictable usage patterns. For example, in some aspects local controller 2218 may be configured to execute a local trajectory algorithm based on the updated predictable usage patterns. This local trajectory algorithm can be similar to the outer or backhaul trajectory algorithms previously described regarding outer moving cells 702-706 and backhaul moving cells 708 and 810. Accordingly, the local trajectory algorithm may be configured to use the updated predictable usage patterns to refine the coarse trajectories of outer moving cells 2004 and 2006. For example, as the updated predictable usage patterns are different from the predictable usage patterns originally used by central controller 2316 of anchor access point 2002 to determine the coarse trajectories, there may be new or alternative trajectories that can better optimize the function of the optimization criteria. Accordingly, local controllers 2218 of mobile access nodes 2004 and 2006 may be configured to execute respective local trajectory algorithms to determine updated trajectories that optimize the function of the optimization criteria (e.g., according to gradient descent, or another optimization algorithm) based on the updated predictable usage patterns. As previously described for the central trajectory algorithm, the predicted user densities, predicted radio conditions, and predicted access usage may influence the statistical model used by the local trajectory algorithm, such as by impacting the estimated positions of served terminal devices, estimated radio environment of the target coverage area, and estimated usage of the radio access network by the served terminal devices.

[0201] In some aspects, local controllers 2218 may then use the updated predictable usage patterns to update the other control instructions, such as scheduling and resource allocations, fronthaul radio access technology selections, and / or initial routings. Local controller 2218 may use a similar procedure as described above for central controller 2318 to update the scheduling and resource allocations, fronthaul radio access technology selections, and / or initial routings based on the updated predictable usage patterns.

[0202] After updating the control instructions, mobile access nodes 2004 and 2006 may then execute data communications with the updated control instructions. This can include sending scheduling and resource allocation messages, fronthaul radio access technology selection messages, and / or updated routing messages to their respective served terminal devices (e.g., from their protocol processors 2210 ). The local controllers 2218 may also provide updated trajectories to movement controllers 2226, which may then control steering and movement machinery 2228 to move mobile access nodes 2004 and 2006 according to the updated trajectories.

[0203] In some cases, the use of predictable usage patterns can produce performance benefits for the served terminal devices. For example, mobile access nodes 2004 and 2006 may be able to use trajectories that are determined based on predicted locations of the served terminal devices. Accordingly, by determining trajectories that optimize a function of the optimization criteria using the predictable usage patterns to approximate user location, mobile access nodes 2004 and 2006 may be able to intelligently position themselves in a manner that effectively serves the served terminal devices. Mobile access nodes 2004 and 2006 may similarly be able to use scheduling and resource allocations, fronthaul radio access selections, and / or routings based on predictable usage patterns, which can in turn increase performance.

[0204] In some aspects, mobile access nodes 2004 and 2006 may adjust their trajectories based on their power conditions. For example, in some cases mobile access nodes 2004 and 2006 may have definite power supplies, such as rechargeable batteries, that gradually deplete over the course of their operation. Accordingly, mobile access nodes 2004 and 2006 may periodically recharge their power supplies. This can include docking at a docking charging station or using a wireless charging station. In some cases where mobile access nodes 2004 and 2006 recharge by docking at a docking charging station, mobile access nodes 2004 and 2006 may move to the docking charging station and use a short-range charging interface to recharge their power supplies (e.g., a physical charging interface such as a wire or a short-range wireless charger). In some cases where mobile access nodes 2004 and 2006 recharge with wireless charging, the wireless charging station may be directional (e.g., may directionally steer wireless charging beams). Due to the potential presence of obstacles, mobile access nodes 2004 and 2006 may recharge with the wireless charging station by moving to a location for which the wireless charging station can direct a wireless charging beam.

[0205] Mobile access nodes 2004 and 2006 may therefore periodically move to certain locations to recharge. However, this movement may disrupt their provision of access to the served terminal devices. For example, moving to a docking charging station or to a wireless charging beam may move mobile access nodes 2004 and 2006 away from their served terminal devices.

[0206] Accordingly, in some aspects, mobile access nodes 2004 and 2006 may be configured to adjust their trajectories to allow for recharging. FIG. 26 shows an exemplary scenario in which mobile access node 2004 may adjust its trajectory to balance between recharging and providing access to its served terminal devices. As shown in FIG. 26, mobile access node 2004 may initially be using trajectory 2606. Trajectory 2606 can be a coarse trajectory (e.g., assigned by anchor access point 2002 ) or an updated trajectory (e.g., updated by local controller 2218 of mobile access node 2004 ), and may be plotted to provide access to the served terminal devices (e.g., based on optimization of a function of an optimization criteria related to the radio environment of the served terminal devices).

[0207] During movement of mobile access node 2004 along trajectory 2606, the battery power of mobile access node 2004 may gradually deplete. Mobile access node 2004 may then determine that mobile access node 2004 should recharge its power supply. For example, in some aspects, local controller 2218 may be configured to monitor the power supply of mobile access node 2004. When local controller 2218 determines that the power supply meets a predefined condition (e.g. when the remaining battery power falls below a battery power threshold), local controller 2218 may trigger adjustment of the trajectory of mobile access node 2004 to facilitate recharging.

[0208] For example, local controller 2218 may determine new trajectory 2604. As shown in FIG. 26, new trajectory 2604 may move mobile access node 2004 towards charging station 2602. In some aspects, local controller 2218 may determine new trajectory 2604 based on the served terminal devices and charging station 2602, such as by determining new trajectory 2604 as a trajectory that optimizes a function of the optimization criteria while moving mobile access node 2004 towards charging station 2602. In some aspects, local controller 2218 may use predictable usage patterns to model the served terminal devices when determining new trajectory 2604.

[0209] In some aspects where charging station 2602 is a wireless charging station, mobile access node 2004 may be able to recharge with the wireless charging beam while still providing access to the served terminal devices. However, there may be a tradeoff between the access and recharging rate, where mobile access node 2004 may be able to provide better access (e.g., a higher data rate or other link quality metric) when positioned closer to the served terminal devices and may be able to achieve a higher recharging rate when positioned closer to charging station 2602. In some aspects, local controller 2218 may therefore use a weighted function that depends on both the optimization criteria and a recharging rate (e.g., the rate at which the power supply of mobile access node 2004 ). Local controller 2218 may therefore determine new trajectory 2604 as a trajectory that maximizes the weighted function. New trajectory 2604 may therefore be balanced between optimizing access versus optimizing recharging rate.

[0210] In some aspects where charging station 2602 is a docking charging station, mobile access node 2004 may move to charging station 2602 (e.g., close enough to physically dock with charging station, or within a certain distance close enough to support a short-range wireless charger) to recharge. In some cases, mobile access node 2004 may be able to continue providing access to the served terminal devices (e.g., by relaying data between the served terminal devices and anchor access point 2002 ) when it is docked at charging station 2602. In other aspects, mobile access node 2004 may temporarily interrupt provision of access to the served terminal devices while it is docked at charging station 2602.

[0211] In some aspects, a mobile access node that departs from its trajectory may notify other mobile access nodes of the departure. The other mobile access nodes can then adjust their trajectories to compensate for the departure of the mobile access node. This can be used when mobile access nodes depart from their trajectory to recharge or for any other reason.

[0212] FIG. 27 shows an exemplary scenario where mobile access node 2004 may notify mobile access node 2006 that it is departing from its trajectory. For example, as shown in FIG. 27, mobile access node 2004 may initially be following trajectory 2706. Mobile access node 2004 may then adjust its trajectory to new trajectory 2706 (e.g., to move mobile access node 2004 towards charging station 2702 ). New trajectory 2706, however, may move mobile access node 2004 away from the served terminal devices, which negatively impact their radio access. Accordingly, mobile access node 2004 may notify mobile access node 2006 (and / or one or more other mobile access nodes that are nearby) that it has adjusted its trajectory. For example, local controller 2218 of mobile access node 2004 may transmit signaling (e.g., via wireless transmission using its baseband subsystem 2206 ) to local controller 2218 of mobile access node 2006 that notifies mobile access node 2006 of the trajectory adjustment.

[0213] Mobile access node 2006 may then adjust its trajectory to compensate for the trajectory adjustment of mobile access node 2004. For example, local controller 2218 of mobile access node 2006 may adjust the trajectory of mobile access node 2006 from trajectory 2710 to new trajectory 2708. As shown in FIG. 27, new trajectory 2708 may move mobile access node 2006 towards trajectory 2706 that mobile access node 2004 was originally following.

[0214] In some aspects, mobile access node 2004 may notify mobile access node 2006 of the trajectory departure prior to adjusting its trajectory. For example, local controller 2218 of mobile access node 2004 may be configured to monitor the remaining battery power of the power supply of mobile access node 2004. When the remaining battery power falls below a first threshold, local controller 2218 of mobile access node 2004 may be configured to notify local controller 2218 of mobile access node 2006 that mobile access node 2004 will adjust its trajectory. Local controller 2218 of mobile access node 2006 may therefore be able to determine its new trajectory 2708 prior to mobile access node 2004 actually departing from its trajectory. Then, when the remaining battery power of mobile access node 2004 falls below a second threshold, local controller 2218 of mobile access node 2004 may notify local controller 2218 of mobile access node 2006 that mobile access node 2004 will now change its trajectory. Local controller 2218 of mobile access node 2006 may then execute new trajectory 2708.

[0215] FIG. 28 shows method 2800 of operating a mobile access node. As shown in FIG. 28, method 2800 includes relaying data between one or more served terminal devices and an anchor access point (2802 ), receiving control instructions from the anchor access point that include a coarse trajectory and a predictable usage pattern of the one or more served terminal devices (2804 ), controlling the mobile access node to move according to the coarse trajectory while relaying data between the one or more served terminal devices and the anchor access point (2806 ), and updating the coarse trajectory based on the predictable usage pattern to obtain an updated trajectory (2808 ).

[0216] FIG. 29 shows method 2900 of operating a mobile access node. As shown in FIG. 29, method 2900 includes relaying data between one or more served terminal devices and an anchor access point (2902 ), obtaining sensing data that indicates positions of the one or more served terminal devices and sending the sensing data to the anchor access point (2904 ), receiving a coarse trajectory from the anchor access point that is based on the sensing data

[0217] (2906 ), and controlling the mobile access node to move according to the coarse trajectory while relaying data between the one or more served terminal devices and the anchor access point (2908 ).

[0218] FIG. 30 shows method 3000 of operating a mobile access node. As shown in FIG. 30, method 3000 includes relaying data between one or more served terminal devices and an anchor access point (3002 ), receiving a coarse trajectory from the anchor access point (3004 ), and controlling the mobile access node to move according to the coarse trajectory while relaying data between the one or more served terminal devices and the anchor access point (3006 ).

[0219] FIG. 31 shows exemplary method 3100 of operating an anchor access point according to some aspects. As shown in FIG. 31, method 3100 includes exchanging data with one or more served terminal devices via a mobile access node (3102 ), determining a predictable usage pattern of the one or more served terminal devices based on sensing data that indicates positions of the one or more served terminal devices (3104 ), and determining a coarse trajectory for the mobile access node based on the predictable usage pattern, and sending the coarse trajectory to the mobile access node (3106 ).Outdoor mobile access nodes for indoor coverage

[0220] Network providers have introduced the concept of customer-premises equipments (CPEs) for mobile broadband coverage. These CPEs are generally fixed devices similar to access points that are mounted on or outside of a building. The CPEs can have a backhaul link to the network, and can therefore provide radio access to various terminal devices inside of the building. These proposed CPEs are generally fixed in one location, and are therefore stationary. Accordingly, while the CPEs may improve access to indoor terminal devices due to their forward deployment, they may not be able to adapt to changing user positions and other dynamic conditions.

[0221] According to various aspects, mobile access nodes positioned outside of indoor coverage areas may utilize trajectories that can be dynamically optimized. As these mobile access nodes are both mobile and aware of dynamic conditions in the indoor coverage area, they can adapt their trajectories over time to maintain strong radio links with the terminal devices located in the indoor coverage area.

[0222] FIG. 32 shows an exemplary network scenario according to some aspects. As shown in FIG. 32, mobile access nodes 3202-3206 may be deployed outside of indoor coverage area 3212. Mobile access nodes 3202-3206 may be mobile CPEs or any other type of moving network access node or cell. Indoor coverage area 3212 can be, for example, a private residence, a commercial building, or any other type of indoor coverage area. Indoor coverage area 3212 can be completely or partially indoors (e.g., may or may not have walls on all sides and may or may not have a roof or other upper surface).

[0223] Mobile access nodes 3202-3206 may provide radio access to various served terminal devices located inside of indoor coverage area 3212. Mobile access nodes 3202-3206 may therefore act as relays to receive, process, and retransmit data between the served terminal devices and network access node 3208 over wireless backhaul links. Accordingly, in the uplink direction, mobile access nodes 3202-3206 may be configured to receive uplink data originating from the served terminal devices in indoor coverage area 3212. Mobile access nodes 3202-3206 may then process and retransmit the uplink data (e.g., using any type of relaying scheme) to network access node 3208 over wireless backhaul links. Network access node 3208 may then route the uplink data as appropriate, such as to external data networks via a core network to which network access node 3208 is connected to. In the downlink direction, network access node 3208 may obtain downlink data addressed to the served terminal devices in indoor coverage area 3212, such as by receiving it from the core network. Network access node 3208 may then transmit the downlink data to mobile access nodes 3202-3206 (e.g., to the mobile access node to which the destination terminal device is connected to) over wireless backhaul links. Mobile access nodes 3202-3206 may receive the downlink data addressed to their respective served terminal devices and then process and retransmit the downlink data to the corresponding served terminal devices.

[0224] The trajectories (e.g., positioning) of mobile access nodes 3202-3206 may impact the performance of the radio access provided to the served terminal devices in indoor coverage area 3212. For example, trajectories of mobile access nodes 3202-3206 that position them close to indoor coverage area 3212 may increase the link strength due to the reduced propagation distance. Furthermore, mobile access nodes 3202-3206 may be able to position themselves proximate to the actual positions of served terminal devices within indoor coverage area 3212, which can further improve link strength.

[0225] Additionally, in some cases the propagation pathloss of indoor coverage area 3212 (e.g., the outdoor-to-indoor propagation pathloss) may vary. FIG. 32 shows one example where indoor coverage area 3212 may have openings 3212a-3212f along its outer surface. Openings 3212a-3212f can be, for example, doors or windows. As openings 3212a-3212f have lower propagation pathloss than the remaining outer surface of indoor coverage area 3212 (e.g., the outer walls), wireless transmission through openings 3212a-3212f may yield higher link strength than wireless transmission through the remaining outer surface of indoor coverage area 3212. In addition to openings like doors and windows, there may be other areas of the outer surface of indoor coverage area 3212 that have lower propagation pathloss than others. For example, certain areas of the outer surface area may be made of different materials and / or have different layers (e.g., stone / brick versus sidewall, different levels of insulation, etc.), which may in turn yield different propagation pathlosses. The propagation pathloss of the outer surface may therefore vary.

[0226] Accordingly, in some aspects, mobile access nodes 3202-3206 may be configured to use trajectories that are based on information about the propagation pathloss of indoor coverage area 3212. As the varying propagation pathloss across the outer surface can produce some areas of the outer surface that have lower propagation pathloss than others, mobile access nodes 3202-3206 can position themselves in locations that can provide stronger links to served terminal devices inside of indoor coverage area 3212.

[0227] FIG. 33 shows an exemplary internal configuration of mobile access nodes 3202-3206 according to some aspects. While some examples in the following description may focus on describing the functionality of mobile access node 3202, these descriptions can also likewise apply to other mobile access nodes. Accordingly, in some aspects, multiple or all of mobile access nodes 3202-3206 can be configured according to any example presented using mobile access node 3202.

[0228] As shown in FIG. 32, in some aspects network access node 3208 may also interface with central trajectory controller 3210. Central trajectory controller 3210 may then be configured to determine coarse trajectories and provide the coarse trajectories to mobile access nodes 3202-3206. In other aspects, mobile access nodes 3202-3206 may be configured to determine their own trajectories, and may therefore not use a central trajectory controller to obtain coarse trajectories.

[0229] As shown in FIG. 33, mobile access node 3202 may include antenna system 3302, radio transceiver 3304, baseband subsystem 3306, application platform 3312, and movement system 3326. In some aspects, antenna system 3302, radio transceiver 3304, and movement system 3322 may be configured in the manner of antenna system 2202, radio transceiver 2204, and movement system 2224 described above for mobile access nodes 2004-2006 in FIG. 22.

[0230] As shown in FIG. 33, application platform 3312 may include central interface 3314, node interface 3316, local learning subsystem 3318, local controller 3320, sensor 3322, and relay router 3324. In some aspects, central interface 3314 may be a processor configured to maintain a signaling connection (e.g., a logical, software-level connection) with a peer node interface of central trajectory controller 3210. Central interface 3314 may therefore support a signaling connection between mobile access node 3202 and central trajectory controller 3210, where central interface 3314 may transmit and receive signaling over the signaling connection via baseband subsystem 3306. Central interface 3314 may therefore provide data addressed to central trajectory controller 3210 to baseband subsystem 3306, which may then wirelessly transmit the data (e.g., to network access node 3208, which may interface with central trajectory controller 3210 ). Baseband subsystem 3306 may also wirelessly receive data originating from central trajectory controller 3210 (e.g., that is wirelessly transmitted by network access node 3208 ), and may provide the data to central interface 3314. Further references to communications between mobile access node 3202 and central trajectory controller 3210 are understood as referring to such a communication arrangement.

[0231] Node interface 3316 may be a processor configured to maintain a signaling connection with a peer node interface of one or more other mobile access nodes, such as mobile access nodes 3204 and 3206. Node interface 3316 may therefore support a signaling connection between mobile access node 3202 and mobile access nodes 3204 and 3206, where node interface 3316 may transmit and receive signaling over the signaling connection via baseband subsystem 3306. Node interface 3316 may therefore provide data addressed to other mobile access nodes to baseband subsystem 3306, which may then wirelessly transmit the data to the other mobile access nodes. Baseband subsystem 3306 may also wirelessly receive data originating from other mobile access nodes, and may provide the data to node interface 3316. Further references to communications between mobile access node 3202 and other mobile access nodes are understood as referring to such a communication arrangement.

[0232] Local learning subsystem 3318 may be configured in the manner of local learning subsystem 2216 of FIG. 22, and may therefore be a processor configured to learning-based processing. In some local learning subsystem 3318 may be configured to execute a pattern recognition algorithm, propagation modeling algorithm, and / or an access usage prediction algorithm as described above for local learning subsystem 2216. These algorithms are described in detail below.

[0233] Local controller 3320 may be a processor configured to control the overall operation of mobile access node 3202 related to trajectories. In some aspects, local controller 3320 may be configured to receive and carry out instructions provided by central trajectory controller 3210, such as for coarse trajectories. Local controller 3320 may also be configured to execute a local trajectory algorithm to determine trajectories for mobile access node 3202.

[0234] Sensor 3322 may be configured in the manner of sensor 2220 of FIG. 22, and may therefore be a sensor configured to perform sensing and to obtain sensing data. In some aspects, sensor 3322 may be a radio measurement engine configured to obtain radio measurements as sensing data. In some aspects, sensor 2220 can be image or video sensors or any type of proximity sensor (e.g., radar sensors, laser sensors, motion sensors, etc.) that can obtain sensing data that indicates positions of the served terminal devices.

[0235] Relay router 3324 may be a processor configured to relay data between network access node 3208 and served terminal devices in indoor coverage area 3212. Accordingly, relay router 3324 may be configured to identify downlink data (received by baseband subsystem 3306 over the wireless backhaul link with network access node 3208 ) addressed to terminal devices served by mobile access node 3202, and to transmit the downlink data to the served terminal devices via baseband subsystem 3306. Relay router 3324 may also be configured to identify uplink data (received by baseband subsystem 3306 over wireless fronthaul links with served terminal devices) originating from the served terminal devices, and to transmit the uplink data to network access node 3208 via baseband subsystem 3306.

[0236] FIG. 34 shows an exemplary internal configuration of central trajectory controller 3210 according to some aspects. As shown in FIG. 34, central trajectory controller 3210 may include node interface 3402, input data repository 3404, trajectory processor 3406, and central learning subsystem 3408. In some aspects, node interface 3402 may be a processor configured to act as a peer to central interface 3314 of mobile access node 3202, and may therefore be configured to support a signaling connection between central trajectory controller 3210 and mobile access node 3202. As shown in FIG. 32, central trajectory controller 3210 may interface with network access node 3208. Node interface 3402 may therefore transmit signaling to mobile access node 3202 over this signaling connection by providing the signaling to network access node 3208, which may wirelessly transmit the signaling over the wireless backhaul link. Node interface 3402 may receive signaling from mobile access node 3202 by receiving the signaling from network access node 3208, which may in turn have initially received the signaling from mobile access node 3202 over the wireless backhaul link.

[0237] Input data repository 3404 and trajectory processor 3406 may be configured in the manner of input data repository 1004 and trajectory processor 1006 of central trajectory controller 714 in FIG. 10. Accordingly, input data repository 3404 may be a server-type component including a controller and the memory, where input data repository 3404 collects input data for a central trajectory algorithm executed by trajectory processor 3406. Trajectory processor 3406 may be configured to execute the central trajectory algorithm with the input data and to obtain coarse trajectories for mobile access nodes 3202-3206.

[0238] In some aspects, central learning subsystem 3408 may be configured in the manner of central learning subsystem 2316 of anchor access point 2002 in FIG. 23. Accordingly, central learning subsystem 3408 may be a processor configured to execute a pattern recognition algorithm, propagation modeling algorithm, and / or access usage prediction algorithm. These algorithms can be AI algorithms that use input data about served terminal devices to predict user density, predict radio conditions, and predict user behavior for access usage.

[0239] As previously indicated, in some aspects mobile access nodes 3202-3206 may operate in cooperation with central trajectory controller 3210 (e.g., may use trajectories determined in part by central trajectory controller 3210 ), while in other aspects mobile access nodes 3202-3206 may operate independently from a central trajectory controller (e.g., may determine their trajectories locally, optionally in cooperation with other mobile access nodes). FIG. 36 shows exemplary message sequence chart 3600 according to some aspects, which shows an example where mobile access nodes 3202-3206 may operate in coordination with central trajectory controller. In some aspects, the procedure of message sequence chart 3600 may be similar to that of message sequence chart 1400 of FIG. 14, in which central trajectory controller 714 and backhaul moving cells 708 and 710 determined coarse and updated trajectories (as well as initial routings) for various outer moving cells and / or terminal devices that were served by backhaul moving cells 708 and 710.

[0240] Accordingly, in some aspects message sequence chart 3600 may use a same or similar procedure as message sequence chart 1400 to determine coarse and updated trajectories (and, optionally, routings) for mobile access nodes 3202-3206 to serve indoor coverage area 3212. For example, mobile access nodes 3202-3206 may first perform initialization and setup with central trajectory controller 3210, which can include setting up the signaling connections between the respective central interfaces 3314 of mobile access nodes 3202-3206 and node interface 3402 of central trajectory controller 3210 (e.g., as previously described for stage 1402 of FIG. 14). Then, central trajectory controller 3210 may compute coarse trajectories and initial routing for mobile access nodes 3202-3206 in stage 3504. Similar to as described above for stage 1404, central trajectory controller 3210 may execute a central trajectory algorithm with its trajectory processor 3406. Trajectory processor 3406 may therefore use input data collected and provided by input data repository 3404 to develop a statistical model of the radio environment around indoor coverage area 3212. Then, using the statistical model to approximate the radio environment, trajectory processor 3406 (running the central trajectory algorithm) may determine coarse trajectories for mobile access nodes 3202-3206 that increase (e.g., maximize) a function of an optimization criteria. The optimization criteria can be, for example, a supported data rate for the served terminal devices, a probability that the supported data rate for all of served terminal devices is above a predefined data rate threshold, a link quality metric (e.g., SINR), or a probability that the link quality metric for all of the served terminal devices is above a predefined link quality threshold.

[0241] In some aspects, trajectory processor 3406 may balance the coarse trajectories of mobile access nodes 3202-3206 between fronthaul and backhaul. For example, the optimal position for mobile access node 3202 to provide access to served terminal devices in indoor coverage area 3212 may not be the optimal position for mobile access node 3202 to perform backhaul transmission or reception with network access node 3208. In some aspects, the function of the optimization criteria may depend on both fronthaul and backhaul (e.g., may consider both the fronthaul and backhaul link in representing the optimization criteria), and determining coarse trajectories to optimize the function of the optimization criteria may inherently consider both the fronthaul and backhaul. In other aspects, the function of the optimization criteria may, for example, only be based on the fronthaul (e.g., may represent supported data rate and / or link quality depending on the fronthaul but not the backhaul). In such cases, trajectory processor 3406 may be configured to use a dual-phase optimization approach. For example, trajectory processor 3406 may be configured to determine a coarse trajectory based on the function of the optimization criteria in the first phase, which only depends on the fronthaul. Trajectory processor 3406 may then update the coarse trajectory to improve the backhaul in the second phase (e.g., by adjusting the trajectory to optimize a function depending on the backhaul, such as to increase a function defining link strength of the backhaul link or decrease a function defining the distance between the mobile access nodes and network access node 3208 ). Trajectory processor 3406 may then return to the first phase to update the coarse trajectory to increase the function of the optimization criteria, and continue to alternate between the first and second phases to iteratively update the coarse trajectory. In one example, trajectory processor 3406 may perform these updates in an incremental manner, such as by updating the trajectories in limited steps with each update.

[0242] In some aspects, the central trajectory algorithm may be configured to use propagation pathloss data about indoor coverage area 3212 as input data. This propagation pathloss data can characterize the propagation pathloss on the outer surface of indoor coverage area. For example, the propagation pathloss data can be map-based data that geographically plots the propagation pathloss (e.g., with discrete values for each point or a continuous function along a line) along the outer surface of indoor coverage area 3212. This can be coordinate-based data, where the data includes coordinates along the outer surface and each coordinate is paired with a propagation pathloss value (that gives the propagation pathloss for wireless signals passing through the outer surface at the corresponding coordinate). The underlying propagation pathloss data can therefore be a set of map coordinates that are paired with a propagation pathloss value for the location corresponding to the map coordinates. The propagation pathloss data can be either two-dimensional (e.g., each coordinate having two values to identify a point on a 2D plane) or three-dimensional (e.g., each coordinate having three values to identify a point in a 3D area).

[0243] In some aspects, this map-based propagation pathloss data can be downloaded or preinstalled into central trajectory controller 3210. For example, a human operator can render the propagation pathloss data (e.g., with a computer-aided design tool, such as a mapping tool) for the outer surface of indoor coverage area 3212, and input data repository 3404 can download and store the propagation pathloss data for later use.

[0244] In other aspects, central trajectory controller 3210 may be configured to locally generate the propagation pathloss data. For example, the served terminal devices, mobile access nodes 3202-3206 (e.g., with sensor 3322 configured as a radio measurement engine), and or external sensors may perform and report radio measurements to input data repository 3404. The radio measurements can also be geotagged, such as with the location of the transmitting device for the radio measurement or the receiving device for the radio measurement. Input data repository 3404 may then provide the radio measurements to central learning subsystem 3408. Central learning subsystem 3408 may then execute a radio propagation modeling algorithm with radio measurements to estimate the propagation pathloss of the outer surface of indoor coverage area 3212 and to generate the propagation pathloss data. This can include using the geotagging information accompanying the radio measurements to estimate the propagation pathloss. For example, if a radio measurement is geotagged with both the transmitting and receiving device's locations (e.g., a location of a served terminal device and a mobile access node that performs a radio measurement on the served terminal device), the propagation modeling algorithm can determine approximately where the radio signal passed through the outer surface. Using the radio measurement and the distance between the transmitting and receiving devices (which is generally inversely proportional to signal strength), the propagation modeling algorithm can estimate the propagation pathloss at the point where the radio signal passed through the outer surface. In other cases where radio measurements are only geotagged at one side (e.g., with the location of only the transmitting device or the receiving device), the propagation modeling algorithm may still be able to estimate a region of the outer surface where the radio signal passed through the outer surface, and can thus derive propagation pathloss data from the radio measurements. The radio measurements can also be geotagged with Angle-of-Arrival (AoA) information about the angle at which the receiving device received the radio signal, which can similarly be used to estimate the point at which the radio signal passed through the outer surface. In some aspects, other context information, such as a map of indoor coverage area 3212, can be used to approximate, for example, where a served terminal device was when it transmitted a radio signal. The propagation modeling algorithm can then use this approximate location of the served terminal device to estimate the point where the radio signal passed through the outer surface, as well as the distance between the served terminal device and a mobile access node measuring the radio signal. The propagation modeling algorithm can then approximate the propagation pathloss for an approximate point on the outer surface. In some aspects, the propagation modeling algorithm can use other radio map data (e.g., such as an REM) that indicates the propagation pathlosses from other obstacles in the path between the served terminal device and the mobile access node to isolate the propagation pathloss that is due to the outer surface. In some aspects, central learning subsystem 3408 may use a large data set of such radio measurements to develop the propagation pathloss data for the outer surface of indoor coverage area 3212.

[0245] Central learning subsystem 3408 can therefore generate the propagation pathloss data as map-based data that plots propagation pathloss values along the outer surface of indoor coverage area 3212. In some aspects, central learning subsystem 3408 may use a map of indoor coverage area 3212, such as by tagging locations in the map (e.g., attaching data to the stored coordinates of these locations) that are located on the outer surface indoor coverage area 3212 with a propagation pathloss value.

[0246] Additionally or alternatively, in some aspects central learning subsystem 3408 may be configured to use propagation pathloss data that identifies low propagation pathloss areas along the outer surface of indoor coverage area 3212. In some cases, this propagation pathloss data can be less specific than the map-based propagation pathloss data, as it may only identify locations of finite number of low propagation pathloss areas instead of plotting out propagation pathloss values along the outer surface of indoor coverage area 3212. This is referred to herein as location-based propagation pathloss data. For example, with reference to FIG. 32, this location-based propagation pathloss data can identify the locations of openings 3212a-3212f as locations of low propagation pathloss areas. The underlying propagation pathloss data can therefore be map coordinates that identify the location of a low propagation pathloss area along the outer surface of indoor coverage area 3212. This location-based propagation pathloss data can be based on a map of indoor coverage area 3212, where locations (e.g., their coordinates) are tagged as being a low propagation pathloss area. Furthermore, in some aspects the low propagation pathloss areas can be paired with a propagation pathloss rating on a predefined scale, where the ratings indicate different propagation pathloss values. In an example where opening 3212d is a door and opening 3212a is a window, the coordinates for opening 3212d (in the propagation pathloss data) can be paired with a propagation pathloss rating that indicates more propagation pathloss than the coordinates for opening 3212a. These propagation pathloss ratings may be less specific than the propagation pathloss values described above for the map-based propagation pathloss data.

[0247] In some aspects, this location-based propagation pathloss data can be downloaded or preinstalled into central trajectory controller 3210. For example, a human operator can render the propagation pathloss data (e.g., with a computer-aided design tool, such as a mapping tool) for the outer surface of indoor coverage area 3212, such as by tagging a virtual map at the locations that are low propagation pathloss areas. Input data repository 3404 can then download and store the propagation pathloss data for later use.

[0248] In other aspects, central learning subsystem 3408 may execute a propagation modeling algorithm to generate the location-based propagation pathloss data. For example, similar to as described before, input data repository 3404 may collect radio measurements from around indoor coverage area 3212. Central learning subsystem 3408 may then execute the propagation modeling algorithm on the radio measurements and attempt to identify the locations of low propagation pathloss areas on the outer surface of indoor coverage area 3212. For example, as described above, central learning subsystem 3408 may be configured to estimate the positions of the transmitting and receiving devices based on the radio measurements (e.g., potentially using geotagging data), the point where the radio signal passed through the outer surface, and the distance between the transmitting and receiving devices. Using the inverse relationship between distance and signal strength, central learning subsystem 3408 may then estimate the propagation pathloss at the point on the outer surface and determine whether the point is has low propagation pathloss or not (e.g., propagation pathloss below a threshold). Central learning subsystem 3408 may do this with a large set of radio measurements, and therefore obtain determinations whether a corresponding large group of points on the outer surface have low propagation pathloss. Central learning subsystem 3408 may then evaluate the points on the outer surface that are identified as being low propagation pathloss, and identify areas of the outer surface that have a high density of points with low propagation pathloss (e.g., a density of points above a threshold) as being low propagation pathloss areas. In some aspects, central learning subsystem 3408 may also assign a propagation pathloss rating to the identified low propagation pathloss areas, where the rating can be based on the estimated propagation pathlosses of the points in the low propagation pathloss areas (e.g., based on an average or other combined metric of the estimated propagation pathlosses of the points).

[0249] The propagation pathloss data (e.g., map-based, location-based, or another type of propagation pathloss data) may therefore generally characterize propagation pathloss on the outer surface of indoor coverage area 3212. As previously indicated, in some aspects, indoor coverage area 3212 may only be partially indoors, such as a building with only three walls. In these cases, the propagation pathloss data may characterize openings resulting from partially indoor buildings (e.g., a missing wall, partially outdoor room and the like) as having a low propagation pathloss value and / or rating.

[0250] With reference back to message sequence chart 3500 in FIG. 35, the central trajectory algorithm running at trajectory processor 3406 can therefore use the propagation pathloss data as part of the statistical model to model the propagation pathloss through the outer surface during stage 3504. This can be particularly applicable when the statistical model is based on a radio map that models the radio environment over a mapped area, as the map-based or location-based propagation pathloss data can be inserted into the radio map along with other input data used to generate the radio map. Using the propagation pathloss data as part of the statistical model, trajectory processor 3406 may execute the central trajectory algorithm to determine coarse trajectories for mobile access nodes 3202-3206 that increase, which may include maximizing, the function of the optimization criteria in stage 3504. This can be done, for example, using gradient descent or another optimization approach.

[0251] As the statistical model is based on the propagation pathloss data, the coarse trajectories may help to position mobile access nodes 3202-3206 in locations from which they can serve terminal devices inside indoor coverage area 3212 with low propagation pathloss. For example, as the propagation pathloss data may provide an accurate characterization of the propagation pathlosses through the outer surface of indoor coverage area 3212, the central trajectory may be able to effectively determine coarse trajectories that yield radio links between mobile access nodes 3202-3206 that pass through low propagation pathloss areas in the outer surface. FIG. 32 shows an example of this, where mobile access nodes 3202-3206 may be able to use radio links that pass through low propagation pathloss areas (e.g., openings 3212a, 3212e, and 3212f ). As the propagation pathloss data may characterize the propagation pathloss of the outer surface at various different positions, the statistical model may be able to accurately approximate propagation pathloss between mobile access nodes, and thus can be used by the central trajectory algorithm to determine coarse trajectories that yield radio links having lower propagation pathloss.

[0252] In some aspects central trajectory controller 3210 may also determine initial routings (e.g., assign the terminal devices to one of mobile access nodes 3202-3206 ) that increase the function of the optimization criteria. Central trajectory controller 3210 may determine these initial routings using any processing technique described above for central trajectory controller 714. As central trajectory controller 3210 may also determine the initial routings based on the statistical model, the initial routings may also be dependent on the propagation pathloss data. For example, as the propagation pathloss data indicates areas on the outer surface of indoor coverage area 3212 that have low propagation pathloss, central trajectory controller 3210 may be configured to determine initial routings (e.g., select which of mobile access nodes 3202-3206 to assign to relay data for each served terminal device) that yield radio links between the mobile access nodes and served terminal devices that pass through the outer surface at areas with lower propagation pathloss.

[0253] In some aspects, central trajectory controller 3210 may use predictable usage patterns as part of the statistical model in stage 3504. Accordingly, central trajectory controller 3210 can use predictable usage patterns (e.g., generated by central learning subsystem 3408 ) in any manner described above for FIGs. 20-31. For example, central trajectory controller 3210 may be configured to use predicted user densities, predicted radio conditions, and / or predicted usage patterns as part of the statistical model when executing the central trajectory algorithm. Central trajectory controller 3210 may therefore determine the resulting coarse trajectories and / or initial routings determined in stage 3504 based on these predictable usage patterns. In some aspects, central trajectory controller 3210 may also use predictable usage patterns determining scheduling and resource allocations and / or selecting fronthaul radio access technologies.

[0254] Stages 3508-3514 may then generally follow the procedure previously described for message sequence chart 4100, and will be explained briefly here for purposes of conciseness. As shown in FIG. 35, central trajectory controller 3210 may send the coarse trajectories and initial routings to mobile access nodes 3202-3206 in stage 3506. Mobile access nodes 3202-3206 may establish connectivity with the served terminal devices in indoor coverage area 3212 (e.g., as specified by the initial routings). Mobile access nodes 3202-3206 may then relay data between the served terminal devices and the radio access network (e.g., network access node 3208 ) in stages 3510a-3510b while moving according to the coarse trajectories. As central trajectory controller 3210 determined the coarse trajectories using propagation pathloss data of the outer surface of indoor coverage area 3212, mobile access nodes 3202-3206 may use trajectories that position mobile access nodes 3202-3206 in positions that yield stronger links (through the outer surface of indoor coverage area 3212 ) with the served terminal devices. This can therefore help improve radio performance (e.g., reduce SNR)

[0255] Mobile access nodes 3202-3206 and the served terminal devices may then perform parameter exchange in stage 3512, such as where the served terminal devices report radio measurements back to mobile access nodes 3202-3206. With mobile access node 3202 as an example, local controller 3320 of mobile access node 3302 may receive the radio measurements from the served terminal devices via baseband subsystem 3306, and store them for use as input data in the local trajectory algorithm. Mobile access nodes 3202-3206 may also perform their own radio measurements on signals received from the served terminal devices. For example, sensor 3322 may be configured as a radio measurement engine, and may provide the resulting radio measurements to local controller 3320.

[0256] Mobile access nodes 3202-3206 may then perform local optimization of trajectories and / or routing in stage 3514. In an example using mobile access node 3202, local controller 3320 may be configured to execute the local trajectory algorithm to update the coarse trajectories based on input data. The input data can include the radio measurements. In some aspects, the local trajectory algorithm may determine an updated trajectory for mobile access node 3202 that increases, which may include maximizing, a function of the optimization criteria.

[0257] In some aspects, mobile access node 3202 may use local learning subsystem 3318 to update the propagation pathloss data. For example, central trajectory controller 3210 may have previously sent the propagation pathloss data for indoor coverage area 3212 to mobile access node 3202 (e.g., during stage 3506 ), which mobile access node 3202 may store at local learning subsystem 3318. Mobile access node 3202 may also provide the radio measurements to local learning subsystem 3318. Local learning subsystem 3318 may then update the propagation pathloss data using the radio measurements. For example, local learning subsystem 3318 may use geotagged radio measurements to estimate the point where the radio signal passed through the outer surface of indoor coverage area 3212, the distance between the transmitting and receive devices, and the corresponding propagation pathloss of the outer surface of indoor coverage area 3212. Local learning subsystem 3318 may then use this propagation pathloss to update the propagation pathloss data, such as by updating a propagation pathloss value of map-based propagation pathloss data at coordinates at or near the point, updating a propagation pathloss rating for location-based propagation pathloss data in a low propagation pathloss area in which the point falls, and / or by adding a new low propagation pathloss area to the existing low propagation pathloss areas of location-based propagation pathloss data.

[0258] Local controller 3320 may then execute the local trajectory algorithm using the updated propagation pathloss data, such as by determining an updated trajectory that increases the function of the optimization criteria (where the function of the optimization criteria is approximated with the statistical model that is based on the updated propagation pathloss data). Mobile access nodes 3202 may then move according to the updated trajectory while providing access to the served terminal devices (e.g., by relaying data between the served terminal devices and network access node 3208 ).

[0259] As the propagation pathloss data and the corresponding statistical model is updated, the updated trajectory produced by the local trajectory algorithm may be different from the coarse trajectory. In some cases, the updated trajectory may yield an improved link strength. In particular, as mobile access node 3202 may have a more accurate characterization of the propagation pathloss along the outer surface of indoor coverage area 3212, mobile access node 3202 may be able to more accurately determine an updated trajectory that has a strong link to the served terminal devices through the outer surface.

[0260] In some aspects, local controller 3320 may additionally update the initial routings to obtain updated routings, and then use the updated routings to control which served terminal devices that mobile access node 3202 provides access to. In various aspects, mobile access node 3202 may also use predictable usage patterns in stage 3514 (e.g., in any manner described above). This can include using predictable usage patterns to determine scheduling and resource allocations and / or to select fronthaul radio access technologies.

[0261] In some aspects, mobile access nodes 3202-3206 may repeat part of this procedure of message sequence chart 3500. For example, central trajectory controller 3210 may be configured to periodically re-determine new coarse trajectories, and to send the new coarse trajectories to mobile access nodes 3202-3206. Mobile access nodes 3202-3206 may then move according to the coarse trajectories and subsequently update the new coarse trajectories to obtain updated trajectories. Mobile access nodes 3202-3206 may then provide access to the served terminal devices while moving according to the updated trajectories.

[0262] As previously indicated, in some aspects mobile access nodes 3202-3206 may determine their trajectories independent of a central trajectory controller. FIG. 36 shows exemplary message sequence chart 3600 according to some aspects, which illustrates an example of this process. As shown in FIG. 36, the served terminal devices may first connect to mobile access nodes 3202-3206 in stage 3602a. This can include any connection procedure, such as a random access connection procedure. Mobile access nodes 3202-3206 may also connect to network access node 3208 in stage 3602a, and may therefore establish the wireless backhaul links used by mobile access nodes 3202-3206 to relay user data between the served terminal devices and network access node 3208.

[0263] Then, network access node 3208 may send mobile access nodes 3202-3206 context information about indoor coverage area 3212 in stage 3604. In some aspects, this context information can include, for example, map data for indoor coverage area 3212, or other information about the neighborhood environment. In some aspects, the context information can include propagation pathloss data, such as map-based propagation pathloss data or location-based propagation pathloss data. Network access node 3208 may receive this context information from an external data network, such as a server that stores preconfigured context information about indoor coverage area 3212. The context information can therefore be predefined.

[0264] Mobile access nodes 3202-3206 may then determine coarse trajectories in stage 3606. As mobile access nodes 3202-3206 are operating independently of a central trajectory controller, mobile access nodes 3202-3206 may perform the processing previously described for stage 3504 for central trajectory controller 3210 in FIG. 35. Accordingly, mobile access nodes 3202-3206 may execute a local trajectory algorithm with their local controllers 3320 to determine coarse trajectories that increase, which may include maximizing, a function of an optimization criteria. Mobile access nodes 3202-3206 may use any type of trajectory-related processing described above as part of the local trajectory algorithm.

[0265] In some aspects, mobile access nodes 3202-3206 may be configured to use a dual-phased optimization, such as where local controller 3320 alternates between iteratively updating the coarse trajectory based on the fronthaul in a first phase (e.g., to increase a function of the optimization criteria that depends on the fronthaul but not the backhaul) and updating the coarse trajectory based on the backhaul in a second phase (e.g., to optimize a function dependent on the backhaul).

[0266] In some aspects, mobile access nodes 3202-3206 may use propagation pathloss data as part of the statistical model used for the local trajectory algorithm. As noted above, in some aspects, network access node 3208 may transmit the propagation pathloss data as part of the context information in stage 3604. Local controller 3320 may receive this propagation pathloss data (via baseband subsystem 3306 ) and save it for execution of the local trajectory algorithm. In other aspects, network access node 3208 may transmit other context information about indoor coverage area 3212 as part of the context information in stage 3604. In some aspects where network access node 3208 does not provide the propagation pathloss data, mobile access nodes 3202-3206 may be configured to locally generate the propagation pathloss data.

[0267] In an example using mobile access node 3202, mobile access node 3202 may use local learning subsystem 3318 to generate the propagation pathloss data. In some aspects, local learning subsystem 3318 may use a same or similar technique to that described above for central learning subsystem 3408 regarding stage 3504. For example, mobile access node 3202 may collect radio measurements (e.g., provided as measurement reports by the served terminal devices or network access node 3208, or locally determined by sensor 3322 ) at local learning subsystem 3318. Local learning subsystem 3318 may then be configured to execute a propagation modeling algorithm to determine the propagation pathloss data based on the radio measurements (which can also be geotagged). This propagation pathloss data can be map-based propagation pathloss data or location-based propagation pathloss data. In some aspects where network access node 3208 provides other context information about indoor coverage area 3212, such as map data for indoor coverage area 3212, local learning subsystem 3318 may be configured to use the map data to generate the location-based propagation pathloss data (e.g., by using the map data to plot the outer surface of indoor coverage area 3212, and tagging different points on the outer surface with propagation loss values or identifying different areas as low propagation pathloss areas).

[0268] In some aspects, one of mobile access nodes 3202-3206 may be configured to generate the propagation pathloss data with its local learning subsystem 3318, and then to send the propagation pathloss data to the other of mobile access nodes 3202-3206 (e.g., using their node interfaces 3316 ). In some aspects mobile access nodes 3202-3206 may be configured to distribute the processing involved in the propagation modeling algorithm amongst themselves, and to each execute a different part of the processing. Mobile access nodes 3202-3206 may then compile the resulting data together the obtain the propagation pathloss data.

[0269] In some aspects, mobile access nodes 3202-3206 may also use predictable usage patterns (e.g., predicted user densities, predicted radio conditions, and / or predictable access usage) in stage 3606 as part of the statistical model used by the local trajectory algorithm. In some aspects, mobile access nodes 3202-3206 may also determine initial routings, determine scheduling and resource allocations and / or select fronthaul radio access technologies as part of stage 3606. This can include any related processing described above.

[0270] With reference back to FIG. 35, after determining coarse trajectories in stage 3606, mobile access nodes 3202-3206 may perform data transmission with the served terminal devices and network access node 3208 in stages 3608a-3608b. Accordingly, mobile access nodes 3202-3206 may provide access to the served terminal devices in indoor coverage area 3212 by relaying data between the served terminal devices and network access node 3208. Mobile access nodes 3202-3206 may follow their respective coarse trajectories while providing access to the served terminal devices (e.g., where local controller 3320 provides the coarse trajectory to movement controller 3328, which may then control steering and movement machinery 3330 to move the mobile access node according to the coarse trajectory).

[0271] As shown in FIG. 36, the served terminal devices may report parameters back to mobile access nodes 3202-3206 in stage 3610. This can include, for example, where the served terminal devices provide radio measurements, current positions, and / or geotagged radio measurements. In some aspects, mobile access nodes 3202-3206 may perform their own radio measurements with sensor 3322. These radio measurements, current positions, and geotagged radio measurements may form input data for the local trajectory algorithm.

[0272] Then, mobile access nodes 3202-3206 may then update the coarse trajectories to obtain updated trajectories in stage 3612. In an example using mobile access node 3202, local controller 3320 may update the statistical model with the input data and then, using the updated statistical model, determine an updated trajectory for mobile access node 3202 that increases the function of the optimization criteria. In some aspects, local learning subsystem 3318 may use the input data to update the propagation pathloss data, such as by using geotagged radio measurements to update propagation pathloss values for points on the outer surface and / or to identify or update low propagation pathloss areas. Local controller 3320 may then use this updated propagation pathloss data as part of the updated statistical model, and the updated trajectory may therefore be based on the updated propagation pathloss data.

[0273] After updating their trajectories to obtain update trajectories in stage 3612, mobile access nodes 3202-3206 may perform data transmission with the served terminal devices in indoor coverage area 3212 and network access node 3208 in stages 3614a and 3614b. Mobile access nodes 3202-3206 may move according to their respective updated trajectories while relaying data between the served terminal devices and network access node 3208, and may therefore provide access to the served terminal devices.

[0274] In some aspects, mobile access nodes 3202-3206 may repeat stages 3610-3614b, and may continue to receive parameters from the served terminal devices, update their trajectories, and provide access to the served terminal devices by relaying data between the served terminal devices and network access node 3208. As the updated trajectories may be based on propagation pathloss data that characterizes the propagation pathloss of indoor coverage area 3212, mobile access nodes 3202-3206 may be able to use trajectories that yield strong links (e.g., with lower propagation pathloss and / or higher SNR) to the served terminal devices. Supported data rate and other link quality metrics may therefore be improved.

[0275] In some aspects, mobile access nodes 3202-3206 may be configured to perform stage 3606 in coordination with each other. For example, mobile access nodes 3202-3206 may be able to cooperate to determine their coarse trajectories. Instead of determining their individual coarse trajectories independently, mobile access nodes 3202-3206 may therefore determine their coarse trajectories dependent on the coarse trajectories of each other.

[0276] For example, in some aspects mobile access nodes 3202-3206 may determine their coarse trajectories in stage 3506 in a sequential manner. For example, mobile access node 3202 may determine its coarse trajectory first. Namely, local controller 3320 of mobile access node 3202 may define a function of an optimization criteria (e.g., related to a supported data rate or a link quality metric) and determine a coarse trajectory for mobile access node 3202 that increases (e.g., maximizes) the function of the optimization criteria. The function of the optimization criteria can be based on a statistical model of the radio environment around indoor coverage area 3212, which can use propagation pathloss data, other radio map data, radio measurements, positions of served terminal devices, and / or predictable usage patterns to approximate the radio environment.

[0277] Then, after mobile access node 3202 has determined its coarse trajectory, local controller 3320 may send the coarse trajectory to mobile access node 3204 (e.g., via node interface 3316 and baseband subsystem 3306, which may use a device-to-device link to send the signaling to mobile access node 3204 ). Local controller 3320 of mobile access node 3204 may then determine its own coarse trajectory while considering the coarse trajectory of mobile access node 3202. For example, as part of the statistical model, local controller 3320 may estimate the radio coverage provided to terminal devices in indoor coverage area 3212 by mobile access node 3202 (e.g., by estimating the link strength between mobile access node 3202 and different points in indoor coverage area 3212 while mobile access node 3202 follows its coarse trajectory). Then, local controller 3320 may determine a coarse trajectory for mobile access node 3204 that increases the function of the optimization criteria given the estimated radio coverage provided by mobile access node 3202 with its coarse trajectory.

[0278] Local controller 3320 of mobile access node 3204 may then send its coarse trajectory and the coarse trajectory for mobile access node 3202 to mobile access node 3206. Local controller 3320 of mobile access node 3206 may then determine its own coarse trajectory using the coarse trajectories of mobile access nodes 3204 and 3206 (e.g., by estimating radio coverage provided by mobile access nodes 3204 and 3206 to indoor coverage area 3212, and determining a coarse trajectory for mobile access node 3206 that increases a function of the optimization criteria given this estimated radio coverage). Mobile access nodes 3202-3206 may then follow the coarse trajectories while relaying data between the served terminal device and network access node 3208. Mobile access nodes 3202-3206 may also receive parameters from the served terminal devices, update their trajectories (e.g., in coordination with each other as described immediately above), and relay data while moving according to the updated trajectories.

[0279] In some aspects, mobile access nodes 3202-3206 may be assigned to different geographic areas, and may be constrained to determine trajectories within their respectively assigned geographic areas. For example, mobile access node 3202 may be assigned to a first geographic area, mobile access node 3204 may be assigned to a second geographic area, and mobile access node 3206 may be assigned to a third geographic area. The geographic areas may be different (e.g., mutually exclusive, or without substantial overlap). Accordingly, when local controller 3320 determines a trajectory (coarse or updated) for mobile access node 3202, local controller 3320 may be conf...

Claims

1. A central trajectory controller (714) comprising: a cell interface (1002) configured to establish signaling connections with one or more backhaul moving cells (708, 710) and to establish signaling connections with one or more outer moving cells (702-706), wherein the one or more backhaul moving cells (708, 710) are configured to provide backhaul services to the one or more outer moving cells (702-706), which include receiving uplink data from outer moving cells (702-706) and relaying the uplink data to a radio access network (712); an input data repository (1004) configured to obtain input data based on a radio environment of the one or more outer moving cells (702-706) and the one or more backhaul moving cells (708, 710), the input data comprising a statistical model of the radio environment; and a trajectory processor (1006) configured to determine, based on the input data, first trajectories for the one or more backhaul moving cells (708, 710) and second trajectories for the one or more outer moving cells (702-706), wherein each determined trajectory of the first trajectories and the second trajectories is representative of a corresponding movement path of the one or more backhaul moving cells (708, 710) or the one or more outer moving cells (702-706) respectively, the cell interface (1002) further configured to send the first trajectories to the one or more backhaul moving cells (708, 710) and to send the second trajectories to the one or more outer moving cells (702-706).

2. The central trajectory controller (714) of claim 1, wherein the input data includes information about data rate requirements of the one or more outer moving cells (702-706), positions of the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710), a target area assigned to the one or more outer moving cells (702-706) for tasks which the one or more outer moving cells (702-706) are configured to perform, radio measurements by the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710), or the radio capabilities of the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710).

3. The central trajectory controller (714) of any one of claims 1 or 2, wherein the input data includes radio map data for the radio environment.

4. The central trajectory controller (714) of claim 3, wherein the input data repository (1004) is configured to generate the radio map data or to receive the radio map data from an external network.

5. The central trajectory controller (714) of any one of claims 1 to 4, wherein the trajectory processor (1006) is configured to determine the first and second trajectories by optimizing a function of optimization criteria as approximated by the statistical model.

6. The central trajectory controller (714) of claim 5, wherein the statistical model is a propagation model that approximates the radio environment.

7. The central trajectory controller (714) of claim 5 or claim 6, wherein the statistical model is a propagation model that approximates the radio environment based on a radio map.

8. The central trajectory controller (714) of any one of claims 5 to 7, wherein the optimization criteria is an aggregate supported data rate of backhaul relaying paths between the one or more outer moving cells (702-706) and a radio access network via the one or more backhaul moving cells (708, 710), or is a probability that the supported data rate of each of the backhaul relaying paths is above a predefined data rate threshold; or wherein the optimization criteria is an aggregate link quality metric of backhaul relaying paths between the one or more outer moving cells (702-706) and a radio access network via the one or more backhaul moving cells (708, 710), or is a probability that the link quality metric of each of the backhaul relaying paths is above a predefined link quality metric threshold.

9. The central trajectory controller (714) of any one of claims 5 to 8, wherein the trajectory processor (1006) is configured to determine the first and second trajectories to optimize the function of the optimization criteria as approximated by the statistical model by optimizing the function of the optimization criteria using gradient descent.

10. The central trajectory controller (714) of any one of claims 1 to 9, wherein the central trajectory controller (714) is further configured to determine initial routings between the one or more outer moving cells (702-706) and a radio access network via the one or more backhaul moving cells (708, 710).

11. A non-transitory computer readable medium storing instructions that when executed by a central trajectory controller cause the central trajectory controller to perform a method for managing trajectories for moving cells, the method comprising: establishing signaling connections (1102, 1104) with one more backhaul moving cells (708, 710) and with one or more outer moving cells (702-706), wherein the one or more backhaul moving cells (708, 710) are to provide backhaul services to the one or more outer moving cells (702-706), which include receiving uplink data from outer moving cells (702-706) and relaying the uplink data to a radio access network (712), obtaining input data comprising based on a radio environment of the one or more outer moving cells (702-706) and the one or more backhaul moving cells (708, 710), the input data comprising a statistical model of the radio environment, determining (1106), based on the input data, first trajectories for the one or more backhaul moving cells (708, 710) and second trajectories for the one or more outer moving cells (702-706), wherein each determined trajectory of the first trajectories and the second trajectories is representative of a corresponding movement path of the one or more backhaul moving cells (702-706) or the one or more moving cells (702-706) respectively, and sending (1108, 1110) the first trajectories to the one or more backhaul moving cells (708, 710) and the second trajectories to the one or more outer moving cells (702-706).

12. The non-transitory computer readable medium of claim 11, wherein the input data includes information about data rate requirements of the one or more outer moving cells (702-706), positions of the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710), a target area assigned to the one or more outer moving cells (702-706) for tasks which the one or more outer moving cells (702-706) are configured to perform, radio measurements by the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710), or the radio capabilities of the one or more outer moving cells (702-706) or the one or more backhaul moving cells (708, 710).

13. The non-transitory computer readable medium of any one of claims 11 or 12, wherein the input data includes radio map data for the radio environment.

14. The non-transitory computer readable medium of claim 13, wherein the method further comprises generating the radio map data or receiving the radio map data from an external network.

15. The non-transitory computer readable medium of any one of claims 11 to 14, wherein determining the first and second trajectories comprises determining the first and second trajectories to optimize a function of optimization criteria as approximated by the statistical model16. The non-transitory computer readable medium of claim 15, wherein the statistical model is a propagation model that approximates the radio environment.

17. The non-transitory computer readable medium of claim 15, wherein the statistical model is a propagation model that approximates the radio environment based on a radio map.

18. The non-transitory computer readable medium of any one of claims 11 to 17, wherein the optimization criteria is an aggregate supported data rate of backhaul relaying paths between the one or more outer moving cells (702-706) and a radio access network via the one or more backhaul moving cells (708, 710), or is a probability that the supported data rate of each of the backhaul relaying paths is above a predefined data rate threshold.