MMWAVE Cell Discovery in Ultra-Dense Networks

Abstract

A wireless transmit / receive unit (WTRU) may perform cell selection and / or beam association as described herein. The WTRU may use context information, such as location, to enable cell selection and / or beam association. The WTRU may be configured with resources for reporting the WTRU context information. In response to the context information, the WTRU may receive assistance information associated with one or more cells to enable cell selection and / or beam association. For example, the WTRU may receive a defined subset of measurement resources in response to a transmission of a location on the resources. The WTRU may determine a base station (BS) and / or a beam pair (BP) based on the assistance information. The WTRU may perform transmission to a BS using at least one beam of the BP.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority to U.S. Provisional Patent Application No. 63 / 314,782, filed in the United States on February 28, 2022, and U.S. Provisional Patent Application No. 63 / 421,456, filed in the United States on November 1, 2022, the entire contents of which are incorporated by reference herein. [Background technology]

[0002] Millimeter wave (MMWAVE) cell discovery in emerging mobile networks may have relatively high signal directivity. Exhaustive periodic beam sweeping (e.g., may have relatively high latency) may be used. Hierarchical beam sweeping may be used to reduce latency. Additionally and / or alternatively, artificial intelligence (AI) may be used to reduce latency.

[0003] To address the capacity crunch problem faced by existing networks, emerging networks may use frequency spectrum within the MMWAVE range. However, MMWAVE may have a relatively short coverage range due to path loss occurring at MMWAVE frequencies. To compensate for this, MMWAVE systems may rely on directional antennas, which may make cell discovery more difficult compared to using more traditional omnidirectional antennas. Furthermore, MMWAVE signals may be susceptible to environmental variations that result in blockages. For these reasons, successful MMWAVE base station (BS) discovery may depend on proper beam alignment between a wireless transmit / receive unit (WTRU) and an MMWAVE BS, which refers to the process of finding the best beamforming direction between the WTRU and the BS for transmission and reception during initial access to establish an MMWAVE link before data can be transmitted. Summary of the Invention

[0004] A wireless transmit / receive unit (WTRU) may perform cell selection and / or beam association as described herein. The WTRU may perform cell reselection and / or beam association in one or more phases. For example, the WTRU may perform cell reselection and / or beam association using a two-phase procedure as described herein. The WTRU may use context information, such as the location of the WTRU, to enable a first phase of cell selection and / or beam association. The WTRU may refine the cell selection and / or beam association during a second phase of the procedure.

[0005] Systems, methods, and apparatuses for performing cell selection and / or beam association are described herein. In an example, a WTRU may receive system information for one or more cells (e.g., a set of cells). The WTRU may be configured with resources for reporting WTRU context information, such as the location of the WTRU (e.g., via System Information (SI), Radio Resource Control (RRC), Medium Access Control (MAC) Control Element (CE), and / or Downlink Control Information (DCI)). The resources may be indicated in the system information. Additionally or alternatively, the resources may be configured by the RRC (e.g., to enable mobility to different cells). The WTRU may be configured to determine and / or report the context information. The WTRU may transmit the determined context information (e.g., regarding the resources). The WTRU may receive a defined subset of measurement resources associated with one or more cells to enable cell selection and / or beam association based on the context information. For example, the WTRU may receive a defined subset of measurement resources in response to a transmission of a location on the resources. The WTRU may determine a cell and / or beam pair (BP) of a base station (BS) based on measurements performed on the defined subset of measurement resources. The WTRU may perform a first transmission to the BS using one or more of the beams (e.g., uplink (UL)) of the BP.

[0006] The WTRU may be configured to improve beam pairing quality. The WTRU may be able to determine and / or indicate whether fine-tuning is enabled (e.g., as a function of WTRU service requirements). In an example, the WTRU may report one or more first measurements and / or report one or more service requirements. For example, the WTRU may report one or more first measurement results to the BS (e.g., via one or more of the beams of the BP). For example, the WTRU may report one or more service requirements to the BS (e.g., via one or more of the beams of the first BP). The WTRU may receive a second defined subset of measurement resources associated with the BS (e.g., in response to the one or more first measurements and / or the service requirements). The WTRU may determine a second BP having a beamwidth narrower than the beamwidth of the first BP (e.g., based on a second measurement performed on the second defined subset of measurement resources). The WTRU may perform a second transmission to the BS using one or more of the beams of the second BP (e.g., UL transmission).

[0007] Hierarchical low latency low power initial access may be used. One or more coverage quality indicators (e.g., reference signal received power (RSRP)) and / or WTRU location obtained (e.g., via Minimization of Drive Test (MDT) traces) may be used by the AI. A first of two or more phases may use AI techniques to predict WTRU-base station (BS) association with a relatively wide beam. The first phase may provide a single wide beam and / or an ordered list of candidates to the remaining phases. The remaining phases may use different techniques (e.g., conventional beam sweeping) to sort the candidates and / or transition to a narrower beam if user demand requires.

[0008] The WTRU may report its location to enable AI-based cell detection. For example, the WTRU may perform one or more methods, receive one or more resources, and / or receive one or more triggers to report its location. The WTRU may perform AI-based cell detection in response to measurements and / or broadcasted information. The WTRU may receive a configuration for a cell / beam before accessing a cell. The WTRU may use a DL beam pair for UL transmission. The WTRU may report a preferred cell, beam, and / or beam pair. [Brief description of the drawings]

[0009] [Figure 1A] FIG. 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented. [Figure 1B] FIG. 1B is a system diagram illustrating an example wireless transmit / receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A, according to one embodiment. [Figure 1C] FIG. 1C is a system diagram illustrating an example radio access network (RAN) and an example core network (CN) that may be used within the communications system illustrated in FIG. 1A, according to one embodiment. [Figure 1D] FIG. 1D is a system diagram illustrating a further exemplary RAN and a further exemplary CN that may be used within the communications system illustrated in FIG. 1A, according to one embodiment. [Figure 1E] FIG. 1E is a schematic illustration of an exemplary system environment for training and applying artificial intelligence (AI) / machine learning (ML) models. [Diagram 2] FIG. 2 illustrates an example network topology showing base station (BS) locations, array orientations, sweep directions, and sample WTRU distribution. [Diagram 3] FIG. 3 illustrates exemplary simulation parameters. [Figure 4] FIG. 4 illustrates an example process that may be implemented to perform cell selection and / or beam association using AI / ML. [Diagram 5] FIG. 5 illustrates exemplary accurate and inaccurate predictions using the k-nearest neighbor (KNN) algorithm. [Figure 6] FIG. 6 illustrates an exemplary deep neural network (DNN) architecture, where ReLU (rectified linear unit) activation may be used for layers dense_1 and dense_2, the output layer may have a sigmoid activation, the loss function used may be a sparse categorical entropy function, and the optimizer may be an Adam optimization function. [Figure 7] FIG. 7 illustrates an exemplary average precision of the ML algorithm. [Figure 8] FIG. 8 illustrates example receiver operating characteristics (ROC) for a KNN classifier for one or more classes. [Figure 9] FIG. 9 illustrates an example impact of ML-based prediction on RSRP. [Figure 10A] FIG. 10A illustrates a system flow diagram depicting an example of a communication procedure for determining a configuration for cell selection and / or beam association (e.g., beam pair). [Figure 10B] FIG. 10B illustrates a system flow diagram depicting an example of a communication procedure for improving configuration for cell selection and / or beam association (e.g., beam pair). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. Communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcasts, etc., to multiple wireless users. Communications system 100 may enable multiple wireless users to access such content through sharing of system resources, including wireless bandwidth. For example, the communications system 100 may use one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0011] 1A, communications system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, Internet 110, and other networks 112, although it will be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a “station” and / or “STA,” may be configured to transmit and / or receive wireless signals and may include user equipment (UE), mobile stations, fixed or mobile subscriber units, subscription-based units, pagers, mobile phones, personal digital assistants (PDAs), smartphones, laptops, netbooks, personal computers, wireless sensors, hotspots or Mi-Fi devices, Internet of Things (loT) devices, watches or other wearable, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., for remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial and / or automated processing chain context), consumer electronics devices, devices operating in commercial and / or industrial wireless networks, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be referred to interchangeably as a UE.

[0012] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base station transceiver station (BTS), a Node B, an eNode B, a Home Node B, a Home eNode B, a gNB, an NR Node B, a site controller, an access point (AP), a wireless router, etc. Although the base stations 114a, 114b are each depicted as a single element, it will be understood that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0013] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), a relay node, etc. The base station 114a and / or the base station 114b may be configured to transmit and / or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide wireless service coverage for a particular geographic area, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In one embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell, for example, using beamforming to transmit and / or receive signals in a desired spatial direction.

[0014] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d via an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0015] More specifically, as noted above, the communications system 100 may be a multiple access system, but may use one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, etc. For example, the base stations 114a and the WTRUs 102a, 102b, 102c in the RAN 104 / 113 may implement a radio technology, such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 115 / 116 / 117 using wideband CDMA (WCDMA). WCDMA may include communications protocols such as High-Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High-Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High-Speed ​​Uplink Packet Access (HSUPA).

[0016] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and / or LTE-Advanced (LTE-A) and / or LTE-Advanced Pro (LTE-A Pro).

[0017] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology, such as New Radio (NR) radio access, which may establish the air interface 116 using NR technology.

[0018] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, e.g., using dual connectivity (DC) principles. Thus, the air interface utilized by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., eNBs and gNBs).

[0019] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement a wireless technology such as IEEE 802.11 (i.e., Wireless Fidelity, WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access, WiMAX), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), or the like.

[0020] The base station 114b of FIG. 1A may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in a localized area, such as an office, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, etc. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology, such as IEEE 802.11, to establish a wireless local area network (WLAN). In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology, such as IEEE 802.15, to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or femtocell. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not need to access the Internet 110 via the CN 106 / 115.

[0021] The RAN 104 / 113 may communicate with the CN 106 / 115, which may be any type of network configured to provide voice, data, application, and / or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have various quality of service (QoS) requirements, such as, for example, different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform high-level security functions such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may communicate directly or indirectly with other RANs employing the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may utilize NR radio technology, the CN 106 / 115 may also communicate with another RAN (not shown) using GSM, UMTS, CDMA2000, WiMAX, E-UTRA, or WiFi radio technology.

[0022] The CN 106 / 115 may also act as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network providing plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP), and / or the internet protocol (IP) of the TCP / IP Internet protocol suite. The networks 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may use the same RAT as the RANs 104 / 113 or a different RAT.

[0023] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with a base station 114a, which may employ a cellular-based wireless technology, and a base station 114b, which may employ an IEEE 802 wireless technology.

[0024] Figure 1B is a system diagram illustrating an example WTRU 102. As shown in Figure 1B, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be understood that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0025] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an Application Specific Integrated Circuits (ASIC), a Field Programmable Gate Arrays (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. The processor 118 may perform signal coding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although FIG. 1B depicts the processor 118 and the transceiver 120 as separate components, it will be understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0026] The transmit / receive element 122 may be configured to transmit or receive signals to or from a base station (e.g., base station 114a) over the air interface 116. For example, in one embodiment, the transmit / receive element 122 may be an antenna configured to transmit and / or receive RF signals. In one embodiment, the transmit / receive element 122 may be an emitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In yet another embodiment, the transmit / receive element 122 may be configured to transmit and / or receive both RF and light signals. It will be appreciated that the transmit / receive element 122 may be configured to transmit and / or receive any combination of wireless signals.

[0027] 1B as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0028] The transceiver 120 may be configured to modulate signals transmitted by the transmit / receive element 122 and demodulate signals received by the transmit / receive element 122. As mentioned above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers to enable the WTRU 102 to communicate via multiple RATs, such as, for example, NR and IEEE 802.11.

[0029] The processor 118 of the WTRU 102 may be coupled to and may receive user-entered data from a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as non-removable memory 130 and / or removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, etc. In other embodiments, the processor 118 may access information from and store data in memory that is not physically located on the WTRU 102, such as on a server or home computer (not shown).

[0030] The processor 118 may receive power from the power source 134 and may be configured to distribute and / or control the power to other components in the WTRU 102. The power source 134 may be any suitable device for providing power to the WTRU 102. For example, the power source 134 may include one or more dry batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0031] The processor 118 may also be coupled to a GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, information from the GPS chipset 136, the WTRU 102 may receive location information from base stations (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may obtain location information by way of any suitable location determination method while remaining consistent with an embodiment.

[0032] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or videos), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a virtual reality and / or augmented reality (VR / AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, which may be one or more of a gyroscope, an accelerometer, a Hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor, a geolocation sensor, an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0033] The WTRU 102 may include a full-duplex radio where transmission and reception of some or all of the signals associated with a particular subframe (e.g., for both the UL (e.g., for transmission) and the downlink (e.g., for reception) may be parallel and / or simultaneous. The full-duplex radio may include an interference management unit 139 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., via a separate processor (not shown) or processor 118). In one embodiment, the WRTU 102 may include a half-duplex radio for transmission and reception of some or all of the signals (e.g., associated with a particular subframe for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0034] 1C is a system diagram illustrating the RAN 104 and the CN 106, according to one embodiment. As mentioned above, the RAN 104 may employ E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also communicate with the CN 106.

[0035] The RAN 104 may include eNodeBs 160a, 160b, 160c, although it will be understood that the RAN 104 may include any number of eNodeBs while remaining consistent with an embodiment. The eNodeBs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In an embodiment, the eNodeBs 160a, 160b, 160c may implement MIMO technology. Thus, the eNodeB 160a may, for example, use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0036] Each of the eNodeBs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, etc. As shown in FIG 1C, the eNodeBs 160a, 160b, 160c may communicate with one another via an X2 interface.

[0037] 1C may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. Although each of the foregoing elements is depicted as part of the CN 106, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0038] The MME 162 may be connected to each of the eNodeBs 162a, 162b, 162c in the RAN 104 via an S1 interface and may function as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, activating / deactivating bearers, selecting a particular serving gateway during initial attachment of the WTRUs 102a, 102b, 102c, etc. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0039] The SGW 164 may be connected to each of the eNodeBs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to and from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during inter-eNodeB handover, triggering paging when DL data is available to the WTRUs 102a, 102b, 102c, and managing and storing the context of the WTRUs 102a, 102b, 102c.

[0040] The SGW 164 may be connected to a PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0041] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include or communicate with an IP gateway (e.g., an IP multimedia subsystem (IMS) server) that serves as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers.

[0042] Although the WTRU is illustrated in FIGS. 1A-1D as a wireless terminal, it is contemplated that in certain representative embodiments such a terminal may use a wired communications interface (e.g., temporarily or permanently) with the communications network.

[0043] In an exemplary embodiment, the other network 112 may be a WLAN.

[0044] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) of the BSS and one or more stations (STAs) associated with the AP. The AP may have access or interface to a distribution system (DS) or another type of wired / wireless network that carries traffic into and / or out of the BSS. Traffic originating outside the BSS to the STA may arrive through the AP and be sent to the STA. Traffic originating from the STA to a destination outside the BSS may be sent to the AP to be sent to the respective destination. Traffic between STAs within the BSS may be sent, for example, through the AP, where the source STA may send traffic to the AP, and the AP may send traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as peer-to-peer traffic. Peer-to-peer traffic may be sent between (e.g., directly between) the source STA and the destination STA using a direct link setup (DLS). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The IBSS mode of communication may be referred to herein as an "ad-hoc" communication mode.

[0045] When using an 802.11ac infrastructure mode of operation or a similar mode of operation, an AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., a 20 MHz wide bandwidth) or a width that is dynamically set via signaling. The primary channel may be the operating channel of the BSS, but may be used by STAs to establish a connection with the AP. In certain representative embodiments, for example, in an 802.11 system, Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) may be implemented. With CSMA / CA, STAs (e.g., all STAs), including the AP, may sense the primary channel. If the primary channel is sensed / detected and / or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0046] A High Throughput (HT) STA may use a 40 MHz wide channel for communication, which may be formed, for example, through a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels.

[0047] A Very High Throughput (VHT) STA may support 20 MHz, 40 MHz, 80 MHz, and / or 160 MHz wide channels. A 40 MHz and / or 80 MHz channel may be formed by combining multiple adjacent 20 MHz channels. A 160 MHz channel may be formed by combining eight consecutive 20 MHz channels or by combining two non-adjacent 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, after channel encoding, the data may pass through a segment parser that may split the data into two streams. Inverse Fast Fourier Transform (IFFT) processing and time domain processing may be performed separately on each stream. The streams may be mapped to two 80 MHz channels and the data may be transmitted by the transmitting STA. At the receiver of the receiving STA, the operations described above for the 80+80 configuration may be reversed and the combined data may be sent to the Medium Access Control (MAC).

[0048] Sub-1 GHz operating modes are supported by 802.11af and 802.11ah. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah compared to those used in 802.11n and 802.11ac. 802.11af supports 5 MHz, 10 MHz, and 20 MHz bandwidths in TV White Space (TVWS) spectrum, and 802.11ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support meter-type control / machine-type communication, such as MTC devices in macro coverage areas. MTC devices may have limited capabilities, including certain capabilities, such as support for certain and / or limited bandwidths (e.g., only supporting these). MTC devices may include batteries with above-threshold battery life (e.g., to maintain very long battery life).

[0049] WLAN systems that may support multiple channels and channel bandwidths, such as 802.11n, 802.11ac, 802.11af, and 802.11ah, include a channel that may be designated as a primary channel. The primary channel may have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be configured and / or limited by the STAs among all STAs operating in the BSS that support the smallest bandwidth operating mode. In an 802.11ah embodiment, the primary channel may be 1 MHz wide for STAs (e.g., MTC-type devices) that support (e.g., only) the 1 MHz mode, even if the AP and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or Network Allocation Vector (NAV) configuration may depend on the status of the primary channel. For example, if the primary channel is active due to a STA (that only supports the 1 MHz mode of operation) transmitting to the AP, the entire available frequency band may be considered active even though most of the frequency band may remain dormant and available.

[0050] In the United States, the available frequency bands that can be used by 802.11ah are 902MHz to 928MHz. In South Korea, the available frequency bands are 917.5MHz to 923.5MHz. In Japan, the available frequency bands are 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz depending on the country code.

[0051] 1D is a system diagram illustrating the RAN 113 and the CN 115, according to one embodiment. As mentioned above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also communicate with the CN 115.

[0052] The RAN 113 may include gNBs 180a, 180b, 180c, although it will be understood that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In an embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 108b may utilize beamforming to transmit signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a may transmit and / or receive wireless signals to and from the WTRU 102a, for example, using multiple antennas. In one embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, the gNB 180a may transmit multiple component carriers to the WTRU 102a (not shown). A subset of these component carriers may be on an unlicensed spectrum, while the remaining component carriers may be on a licensed spectrum. In one embodiment, the gNBs 180a, 180b, 180c may implement Coordinated Multi-Point (CoMP) technology. For example, the WTRU 102a may receive coordinated transmissions from the gNBs 180a and 180b (and / or gNB 180c).

[0053] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with scalable numerology. For example, the OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or transmission time intervals (TTIs) of different or scalable lengths (e.g., including different numbers of OFDM symbols and / or lasting different lengths of absolute time).

[0054] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c without accessing another RAN (e.g., eNodeBs 160a, 160b, 160c, etc.). In a standalone configuration, the WTRUs 102a, 102b, 102c may utilize one or more of the gNBs 180a, 180b, 180c as mobility anchor points. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in unlicensed bands. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate with and connect to a gNB 180a, 180b, 180c while also communicating with and connecting to another RAN, such as an eNodeB 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement DC principles to communicate with one or more gNBs 180a, 180b, 180c and one or more eNodeBs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNodeBs 160a, 160b, 160c may act as mobility anchors for the WTRUs 102a, 102b, 102c, and the gNBs 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0055] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and / or DL, support for network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data to User Plane Functions (UPFs) 184a, 184b, routing of control plane information to Access and Mobility Management Functions (AMFs) 182a, 182b, etc. As shown in FIG. 1D, the gNBs 180a, 180b, 180c may communicate with each other via an Xn interface.

[0056] 1D may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one Session Management Function (SMF) 183a, 183b, and possibly a Data Network (DN) 185a, 185b. Although each of the foregoing elements is depicted as part of the CN 115, it will be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0057] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N2 interface and may function as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, supporting network slicing (e.g., handling different PDU sessions with different requirements), selecting a particular SMF 183a, 183b, managing registration areas, terminating NAS signaling, mobility management, etc. The network slices may be used by the AMF 182a, 182b to customize the CN support of the WTRUs 102a, 102b, 102c based on the type of service utilizing the WTRUs 102a, 102b, 102c. For example, different network slices may be established for different use cases such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced massive mobile broadband (eMBB) access, services for machine type communication (MTC) access, etc. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies such as WiFi.

[0058] The SMFs 183a, 183b may be connected to the AMFs 182a, 182b in the CN 115 via an N11 interface. The SMFs 183a, 183b may also be connected to the UPFs 184a, 184b in the CN 115 via an N4 interface. The SMFs 183a, 183b may select and control the UPFs 184a, 184b and configure the routing of traffic through the UPFs 184a, 184b. The SMFs 183a, 183b may perform other functions such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, etc. The PDU session types may be IP-based, non-IP-based, Ethernet-based, etc.

[0059] The UPFs 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via an N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPFs 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, etc.

[0060] The CN 115 may facilitate communication with other networks. For example, the CN 115 may include or communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that serves as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired and / or wireless networks owned and / or operated by other service providers. In an embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b through the UPFs 184a, 184b via an N3 interface to the UPFs 184a, 184b and an N6 interface between the UPFs 184a, 184b and the DNs 185a, 185b.

[0061] 1A-1D and the corresponding description thereof, one or more or all of the functions described herein with respect to one or more of the WTRUs 102a-d, base stations 114a-b, eNode-Bs 160a-c, MME 162, SGW 164, PGW 166, gNBs 180a-c, AMFs 182a-ab, UPFs 184a-b, SMFs 183a-b, DNs 185a-b, and / or any other devices described herein may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more or all of the functions described herein. For example, the emulation devices may be used to test other devices and / or to simulate network and / or WTRU functions.

[0062] The emulation device may be designed to implement one or more tests of other devices in a lab environment and / or an operator network environment. For example, one or more emulation devices may perform one or more or all functions while fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices in the communication network. One or more emulation devices may perform one or more or all functions while temporarily implemented / deployed as part of a wired and / or wireless communication network. The emulation device may be directly coupled to another device for testing purposes and / or may use terrestrial wireless communication to implement the tests.

[0063] The one or more emulation devices may perform one or more functions, inclusive, while not being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices may be utilized in test scenarios in a test lab and / or in an undeployed (e.g., test) wired and / or wireless communication network to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (which may include, for example, one or more antennas) may be used by the emulation devices to transmit and / or receive data.

[0064] The systems, methods, and / or apparatus described herein may implement artificial intelligence (AI) and / or machine learning (ML). For example, one or more devices in the communications system 100 may implement AI / ML. One or more of the WTRUs 102a, 102b, 102c, 102d, the RAN 104 / 113, and / or the CN 106 / 115 may implement AI / ML. Additionally, other WTRUs, base stations, and / or network elements may implement AI / ML.

[0065] The AI / ML may implement one or more algorithms configured to learn from data received as input. The AI / ML may implement supervised learning or unsupervised learning. When implementing supervised learning, the AI / ML may receive training data as input, and the parameters of the AI / ML may be trained towards a particular target output. The training data may be labeled to teach the AI / ML to learn from the labeled data and test the accuracy of the AI / ML to be implemented on unlabeled input data during generation. Supervised learning may be implemented for various types of AI / ML algorithms, including algorithms implementing linear regression, logistic regression, neural networks, decision trees, Bayesian logic, random forests, and / or support vector machines (SVMs). Supervised learning may be routinely utilized and / or implemented for classification and / or regression algorithms. Classification algorithms may be used to classify data into classes and / or categories. Exemplary classification algorithms may include a logistic regression algorithm, a naive Bayes algorithm, a k-nearest neighbor (KNN) algorithm, a decision tree algorithm, and / or a support vector machine. Regression algorithms may include a linear regression algorithm, a ridge regression algorithm, a neural network regression algorithm, a decision tree regression algorithm, a random forest algorithm, a KNN regression model, a support vector machine (SVM), a Gaussian regression algorithm, and / or a polynomial regression algorithm.

[0066] AI / ML may implement deep learning based models. Neural networks (NNs) and / or deep neural networks (DNNs) may be common examples of AI / ML models that may be trained using supervised training. Various examples of NNs include perceptrons, multilayer perceptrons (MLPs), feed-forward NNs, fully connected NNs, convolutional neural networks (CNNs), recurrent NNs (RNNs), long-short term memory (LSTM) NNs, and / or residual NNs (ResNets). A perceptron is a NN that includes a function that multiplies the input of the NN by learned weighting factors to generate an output value. A feed-forward NN is a NN that receives inputs at one or more nodes in an input layer and moves directional information through one or more hidden layers to one or more nodes in an output layer. In a feed-forward NN, one or more nodes in a given layer may be connected to one or more nodes in another layer. A fully connected NN is a NN that includes an input layer, one or more hidden layers, and an output layer. In a fully connected NN, each node in a layer is connected to each node in another layer of the NN. An MLP is a fully connected class of feed-forward NN. A CNN is a NN that has one or more convolutional layers configured to perform convolutions. Various types of NNs may have elements that include one or more CNNs or convolutional layers, such as Generative Adversarial Networks (GANs), Conditional Generative Adversarial Networks (CGANs), and / or Cycle Consistent Generative Adversarial Networks (CycleGANs). An RNN is an inherently recursive NN because the nodes include feedback connections and an internal hidden state (e.g., memory) that allows the output from a node in the NN to affect subsequent inputs to the same node.LSTM NNs can be similar to RNNs in that nodes have feedback connections and an internal hidden state (e.g., memory). However, LSTM NNs can include additional gates to allow the LSTM NN to learn longer-term dependencies between sequences of data. A ResNet is a NN that can include skip connections to skip one or more layers of the NN.

[0067] FIG. 1E is a schematic illustration of an exemplary system environment 101 for training and applying an AI / ML model implementing a NN 109a. However, other types of AI / ML models may be trained and / or implemented as well. The NN 109a may be trained to determine and / or update parameters (e.g., hyperparameters) of the NN 109a. The raw data 103a may be generated from one or more sources. For example, the raw data 103a may include a set of information, such as image data, a set of text, or a set of network information related to a communication network, and / or other types of data. The raw data 103a may be pre-processed at 105a to generate training data 107. The pre-processing may include formatting or other types of processing to generate the training data 107 in a format for input to the NN 109a.

[0068] The NN 109a may include one or more layers 111a. The one or more layers 111a may include one or more input layers for receiving training data 107, one or more hidden layers, and / or one or more output layers for generating outputs 121. Each layer 111a may include one or more nodes that may be trained as described herein. The training data 107 may be in one or more formats, such as an image format, a tensor format (e.g., including a multidimensional array), etc.

[0069] During the training process 123, the training data 107 may be input to the NN 109a and used to learn parameters. The parameters may include weights and / or biases of the NN 109a. The NN 109a may also include hyperparameters. The hyperparameters may include, for example, the number of epochs, the batch size, the number of layers, and / or the number of nodes in each layer. Parameters and hyperparameters may be used interchangeably. The parameters and / or hyperparameters may be adjusted during the training process. Training may be performed by initializing the parameters and / or hyperparameters of the NN 109a, accessing the training data 107, generating and inputting the training data 107 to the NN 109a, calculating the loss from the output of the neural network 109a to the target output 115 via the loss function 113 to update the parameters and / or hyperparameters (e.g., via gradient descent and associated backpropagation), and / or repeating the training process until a termination condition is achieved. The termination condition may be achieved when the output of the neural network 109a is within a predefined threshold of the target output 115.

[0070] Loss function 113 may be implemented using back-propagation based gradient update and / or gradient descent techniques, such as stochastic gradient descent (SGD), synchronous SGD, asynchronous SGD, batch gradient descent, and / or mini-batch gradient descent. An optimizer may be implemented along with loss function 113. The optimizer may be implemented to update parameters and / or hyperparameters of neural network 109a.

[0071] After the training process 123 is completed, the trained parameters and / or hyperparameters 117 may be implemented by the neural network 109b in an operational or production process 125. During the operational or production process 125, the neural network 109b may receive input data 119 and generate output 121 using the trained parameters and / or hyperparameters 117. The input data 119 may be pre-processed from the raw data 103b at 105b. For example, the raw data 103b may include similar types of data as the raw data 103a, which may be pre-processed similarly to the training data 107 used as input during the training process 123. The pre-processing may include formatting or other types of processing to generate the input data 119 in a format for input to the NN 109b. The output 121 may be within a pre-defined threshold of the target output 115 used during the training process 123. The output 121 may be one or more images, tensors, or other formatted outputs. Neural network 109b may include one or more layers 111b having a similar configuration as layer 111a after a training process 123. During an operational or production process 125, parameters and / or hyperparameters may be refined or optimized by being updated based on the output 121.

[0072] In contrast to supervised learning of AI / ML as described herein, AI / ML may be used to implement unsupervised learning. AI / ML may receive training data as input and learn from the data without being trained towards a particular target output. For example, during unsupervised learning, AI / ML may receive unlabeled training data and determine patterns and / or similarities in the training data without being trained towards a particular target output. Unsupervised learning may be implemented to perform clustering, anomaly detection, and / or association of different types of input data. AI / ML may implement hierarchical clustering algorithms, k-means clustering algorithms, anomaly detection algorithms, principal component analysis algorithms, and / or prior algorithms.

[0073] As described herein, AI / ML may be implemented on one or more devices. For example, AI / ML may be implemented in whole or in part on one or more devices, such as one or more WTRUs, one or more base stations, and / or one or more other network entities, such as a network server. An example of a network in which AI / ML may be distributed may include a federated network. A federated network may include a distributed group of devices, each including an AI / ML. AI / ML may be implemented for collaborative learning, in which the AI / ML is trained across multiple devices. In another example, AI / ML may be trained at a centralized location or device, and one or more portions of the AI / ML may be distributed to non-centralized locations. For example, updated parameters or hyperparameters may be sent to one or more devices to update the AI / ML implemented thereon. Federated learning allows multiple actors to build a common robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights, and / or access to disparate data. Its applications may be spread across several industries, including defense, telecommunications, IoT, and / or pharmaceuticals. A major open question at this time may be how inferior a model trained through federated data is to a model whose data is pooled. Another open question may relate to the trustworthiness of edge devices and / or the influence of malicious actors on the trained model.

[0074] The AI / ML described herein may be implemented as described herein using software and / or hardware. The AI / ML may be stored as computer-executable instructions on a computer-readable medium accessible by a processor to perform as described herein. Exemplary AI / ML environments and / or libraries may include TENSORFLOW, TORCH, PYTORCH, MATLAB, GOOGLE CLOUD AI and AUTOML, AMAZON SAGEMAKER, AZURE MACHINE LEARNING STUDIO, and / or ORACLE MACHINE LEARNING.

[0075] Cell discovery in Millimeter Wave (MMWAVE) networks may include legacy systems (e.g., legacy cell discovery). The legacy systems may include a WTRU performing one or more permutations to obtain information on which the WTRU operates in a given MMWAVE band. For example, the WTRU may sweep one or more (e.g., several) beams / beam pairs to determine an optimal cell / beam. Context information-based techniques / methods may be employed to reduce the complexity / latency for cell discovery in MMWAVE networks. The context information-based techniques / methods may be based on additional information. For example, the context information may include the WTRU position and / or location. The context information-based techniques may help reduce the complexity / latency. A hierarchical method may include a base station (BS) performing an exhaustive search across one or more wide beams. The hierarchical method may include evolving (e.g., iteratively evolving) to narrower beams.

[0076] Described herein are systems, methods, and apparatus for performing low latency cell discovery (e.g., in a beam-based environment). The systems, methods, and apparatus may improve beam / cell selection complexity / latency by combining hierarchical search methods with AI / machine learning (ML). Described herein are embodiments for performing cell selection and / or beam association using two phases. The two-phase cell selection / beam association may include obtaining various coverage quality indicators (e.g., Reference Signal Received Power (RSRP) and / or WTRU location) (e.g., via a Minimization of Drive Test (MDT) trace). The various coverage quality indicators may be used by the BS (e.g., via the AI / ML portion of the two-phase cell selection / beam association). The first phase of the two-phase cell selection / beam association may use context information to reduce beam pairing latency. The first phase may provide either a single wide beam and / or an ordered list of candidates to the remaining phase or phases. One or more remaining phases may use different techniques (e.g., beam sweeping) to sort candidates and / or transition to a narrower beam (e.g., in response to user demand).

[0077] The WTRU may use its location and / or other context information to enable a first phase of a two-phase cell selection / beam association. The WTRU may report its location and / or other context information to enable the BS to perform cell discovery (e.g., including a method, resource, and / or trigger for reporting location). The context information may include service requirements (e.g., reliability requirements, latency requirements, data type, amount of data to transmit, etc.) that may be reported. The report may include sending an indication of the information and / or other context information indicating the WTRU's location. The indication may be explicit, to explicitly identify the WTRU's location and / or other context information. The indication may implicitly indicate the WTRU's location and / or other context information. The indication may be explicit and / or implicit. The WTRU may receive assistance information from the BS in response to the location information and / or other information to enable configuration for a cell / beam (e.g., before accessing a cell). The assistance information may include an indication of a BS (e.g., a cell or set of cells) and / or at least one beam of a beam pair (BP). In one example, the assistance information may include a defined subset of measurement resources for one or more cells of the BS and / or at least one beam of a beam pair to enable cell selection and / or beam association.

[0078] The WTRU may update the cell selection and / or beam association. The updated cell selection and / or beam association may be performed in a second phase to allow refinement of a previously identified cell / beam pair. For example, the WTRU may update the cell selection / beam association to improve the beam pairing quality in response to one or more service requirements of the WTRU. The WTRU may report the measurement and / or service requirements to the BS. The reporting may include sending an indication of information indicative of the one or more measurements and / or one or more service requirements. The indication may be explicit, to explicitly indicate the one or more measurements and / or one or more service requirements. The indication may be implicit, to implicitly indicate the one or more measurements and / or one or more service requirements. The indication may be both explicit and implicit. The WTRU may receive a defined subset of measurement resources for performing cell selection and / or beam association (e.g., in response to the measurement and / or service requirements). The defined subset of measurement resources may enable the WTRU to determine an updated beam pair (e.g., narrower) compared to the original cell / beam pair. Although a two-phase cell selection / beam association procedure may be described herein, the cell selection and / or beam association may be implemented in one or more phases. For example, the cell selection / beam association procedure may include a first phase of cell selection / beam association described herein. In another example, one or more portions of the first phase and / or second phase of the cell selection / beam association procedure may be implemented in a single phase or multiple phases.

[0079] There may be one or more techniques described herein that address cell discovery in MMWAVE networks. The techniques may implement AI / ML using one or more of naive non-ML techniques, ML-based techniques, and non-ML techniques that utilize context information (e.g., based on measurements of one or more possible cells and / or beams and / or beam pairs). The context information may include user position and / or location, channel gain, user spatial distribution, angles of arrival and departure, past multipath fingerprints, radar signals, sub-6 GHz band information in a control data plane split architecture, and / or antenna configuration. The terms WTRU position and WTRU location may be used interchangeably herein.

[0080] One or more naive non-ML techniques may be used. These techniques may be used to search for the optimal beam pair. For example, a sequential search pattern technique may be used. The sequential search pattern may rely on an exhaustive search through many beam pair combinations between the WTRU and the base station (BS) to find the optimal beam pair with the highest reference signal received power (RSRP). Alternatively, or additionally, a linear rotation pattern may be used. The linear rotation pattern may be an exhaustive search method that sweeps many beam pairs in either a counterclockwise or clockwise direction. Another technique may be a random starting point method in which the BS begins the sweeping process by randomly selecting one direction. This technique may then continue by using a sequential sweeping method from the randomly selected starting point, or by randomly selecting from the remaining directions until one or more (e.g., all) directions have been swept. While these techniques may be simple and / or require minimal external information, one or more of these techniques may perform an exhaustive sweep without incorporating any intelligence and / or additional information, resulting in slow cell discovery.

[0081] One or more data-driven machine learning (ML) based techniques may be used. These techniques may include one or more approaches that leverage recurrent neural networks (RNNs) where call detail record (CDR) data may be used to predict optimal beam pairs. In some approaches, pseudo-omni antennas (e.g., rather than directional antennas) may be used at the BS, and each square grid (e.g., bin) in the coverage area may be considered as one sector. In other approaches, random forest (RF) and multi-layer perceptron ML algorithms may be used to predict optimal BS and WTRU beam pairs using GPS coordinates or another location of the user, and these algorithms may be comparable to one or more context information methods, such as naive methods that select nearby BSs and beams given the location of the WTRU, and inverse fingerprinting techniques. Ray tracing software (e.g., Wireless InSite) may be used to simulate an urban outdoor environment.

[0082] As described herein, deep learning based methods may be used. Omnidirectional sounding signals from multiple BSs may be used to train a deep learning model to predict the optimal beam. In one example, a prototype validation of the proposed deep learning may be performed. However, this approach may use less than 6 GHz for the MMWAVE channel by assuming that both channels have strong spatio-temporal correlation under certain conditions. A deep learning based solution in a switched beam multi-user (MU)-MIMO system may be used. A deep learning based beam selection strategy using user location and / or orientation information may be implemented. There may be several support vector machine (SVM) based algorithms to address optimal beam prediction in MMWAVE networks. SVM may be combined with an iterative sequential minimal optimization algorithm. SVM may be comparable to k-nearest neighbors and multi-layer perceptrons using angle-of-arrival information.

[0083] Beam training can be formulated in a multi-armed bandit framework to select optimal beam pairs, which can be comparable to exhaustive search methods. Machine learning tools of random forest classifiers combined with situational awareness can be used to learn beam information (e.g., power, optimal beam index, etc.) from past observations in the vehicular network.

[0084] One or more context information based non-ML techniques may be used. Non-ML techniques that rely on using some additional context information to improve the exhaustive search method may be used. This additional information may be user position and / or location, channel gain, user spatial distribution, angles of arrival and departure, past multipath fingerprints, radar signals, sub-6 GHz band information in a control data plane split architecture, and / or antenna configuration. Context information based non-ML techniques may include analytical solutions (e.g., because analytical-based solutions usually rely on predefined assumptions). To improve the pure random search algorithm, a greedy search technique called Discovery Greedy Search may be used. The serving MMWAVE BS may know information about the user's location from the C-plane of the macro BS to calculate the optimal beamwidth and / or pointing direction to reach it. However, if the serving MMWAVE BS does not detect the user (e.g., due to positioning inaccuracies), the MMWAVE BS may start scanning over various directions while maintaining the same beamwidth. If the user is still not found, the MMWAVE BS may resume the circular sweep with reduced beam width and repeatedly scan a larger set of pointing directions. This approach may be comparable to an enhanced discovery procedure. In the presence of positioning inaccuracies, the BS may rely on n circular sectors to scan the surrounding environment. In the first scanned sector, i.e., the sector pointing to the user position, the BS may begin to search for beam directions adjacent to the user position, alternating between clockwise and counterclockwise directions, with a fixed beam width to cover the sector.

[0085] Enhanced discovery may provide a trade-off between opposing methods. For example, performing discovery by scanning a first large azimuth angle and then extending the range by narrowing the beam may provide a trade-off against performing discovery by searching a first narrow azimuth angle until a maximum range is reached and then changing the pointing direction. A control and data separation architecture may be used by deriving and optimizing the network coverage probability to evaluate the beam misalignment problem. A method that considers only the system throughput may be used (e.g., does not consider the frequent beam handoff problem). The sum rate in a switched beam-based MIMO system operating in the MMWAVE frequency band may be maximized. Location information from a train control system in high-speed train communication for beam alignment may be considered. An integrated consideration of beam width selection and scheduling may be implemented to maximize the effective network throughput. Using sub-6 GHz out-of-band information to assist MMWAVE beam steering, compressed beam selection may be formulated as a weighted sparse signal recovery problem, and weighting information from the sub-6 GHz channel may be obtained. The training overhead for beam selection can be reduced by exploiting the spatial clustering of multipaths in the channel.

[0086] Signal processing-based methods may be used. These methods may include Kalman filter-based methods for tracking angle-of-arrival and angle-of-departure information, and / or extended Kalman filter-based methods that use a joint minimum mean square error (MMSE) beamforming and extended Kalman filter tracking strategy to minimize beamforming angle misalignment. The extended Kalman filter estimation approach may be combined with a conditional beam switching scheme. However, this approach may assume that the device can switch the beam pattern to any arbitrary direction when the system loses tracking, which may not be possible with analog beamforming (e.g., the number of unique beam patterns is limited to the number of antenna elements in the beamforming array). Another signal processing approach that may be used is a particle filter-based method in which the BS tracks the WTRU based on a particle filter and adaptively expands or contracts the beamwidth via partial activation of the antenna array.

[0087] A compressed sensing approach may be used in which an estimation of the spatial frequency associated with the launch direction of the dominant ray from the base station and the associated complex gain is made by transmitting a compressed beacon. However, the compressed beacon may be omnidirectional in nature and may not benefit from the signal-to-noise ratio (SNR) and spatial reuse of beamforming obtained during data transmission. The compressed sensing approach may be comparable to an approach that utilizes knowledge of previous angle of departure (AoD) / angle of arrival (AoA) estimates to asymmetrically scan the beam space by projecting a pseudo-random sequence onto the beam space. One or more approaches may be used to select the optimal beam pair using past multipath fingerprints. In one example, the position and / or location of the WTRU may be used to query a multipath fingerprint database from the BS, which gives prior knowledge of potential pointing directions for beam alignment. One or more types of fingerprinting databases may be used. For example, the first type may have the top M beam pairs ranked according to the RSRP level, and the second type may store the average RSRP for each beam pair. The first approach to select the optimal beam may be heuristic, while the other may minimize the mislocation probability by maximizing the received power of the optimal selected beam pair. However, since this method relies on a historical database, some paths may not be present in the database due to obstructions.

[0088] A hierarchical search method may be used to reduce latency compared to an exhaustive search method. For example, the BS may first perform an exhaustive sequential search over wider beams and then iteratively progress to narrower beams. A hierarchical search method may be combined with AI / ML.

[0089] Achieving initial access using directional transmission and reception may involve high latency and / or high processing power. For example, an initial robust and reliable link may be found by simply performing a wide beam sweep, which may require a relatively long time and / or high processing power. Methods may be disclosed herein that may reduce both the latency and processing power required for directional transmission and reception systems.

[0090] AI-assisted initial access schemes may be used to reduce the latency and / or processing power utilized in directional transmit and receive based systems. As used herein, the terms "low latency initial access," "low latency cell detection," "low latency cell discovery," and / or "low latency measurement" may be used interchangeably.

[0091] An AI-assisted low latency initial access algorithm may be used. For example, AI / ML may be used to learn the optimal beam / beam pair and / or optimal cell / BS from the wider beam (e.g., rather than using an exhaustive search to search across the wider beams as may be performed in a hierarchical search method). The narrow beam may be used to fine-tune the optimal beam pair predicted by the exhaustive search. Using AI / ML to learn the optimal beam pair and using an exhaustive search to fine-tune the predicted optimal beam pair may reduce latency compared to using an exhaustive search for both and may take into account user requirements to select whether fine-tuning is needed.

[0092] Learning optimal beam pairs and BSs using AI / ML may utilize fewer resources since the search space may be reduced when wider beams are used compared to narrower beams. Furthermore, in new ultra-dense network scenarios, BS density may be much greater than macrocells, resulting in a large number of cell identification / identifier (ID) options or BS classes for WTRU association. Thus, training on wider beams may also help narrow down the number of additional classes resulting from multiple beam pairs, since fewer wider beams may cover the same coverage area compared to narrower beams. Training on wider beams (e.g., compared to training on narrow beams) may reduce the probability of overfitting during the model training phase. Developing ML models on wider beams rather than narrower beams may result in greater tolerance to errors due to characteristics of the cellular environment, such as shadowing and multipath or imprecise user positioning.

[0093] A system model may be used and data collection may be performed. Historical Minimized Drive Test (MDT)-based reports from MMWAVE base stations may be used to train the ML algorithm. The MDT reports may include network coverage-related key performance indicators (e.g., RSRP) measured at the WTRU. These reports may be tagged with the WTRU's geographic location information (e.g., WTRU position, WTRU location, etc.) and then transmitted to the WTRU's serving base station. Synthetic data generated through a 3GPP-compliant simulator may be used to investigate the techniques disclosed herein. For example, a 3GPP-defined indoor scenario of a 5G indoor office may be used. Scenario parameters for channel modeling implemented in the simulator may include, among others, parameters related to delay spread, angle of arrival and departure spread, shadow fading, k-factor, cross-correlation, number of clusters, and / or rays per cluster. These parameters may be defined for both LoS and NLoS paths.

[0094] FIG. 2 illustrates an image 200 of an example network topology showing base station (BS) locations, array orientations, sweep directions, and sample WTRU distribution for channel modeling implemented by a simulator. In this example, a total of 15,000 WTRUs may be dropped within the simulation area. Five-fold cross-validation may be used to avoid overfitting. There may be six BSs within the simulation area, each with four arrays rotated around the z-axis by 0 degrees, 90 degrees, 180 degrees, and / or 270 degrees, resulting in a total of 24 BSs. Each array may be composed of 2×1 antenna elements, which may result in a wide beamwidth of approximately 60 degrees. The antenna element pattern may be 3GPP defined using a 3D Gaussian element generation method, azimuth and elevation 3 dB beamwidth of 65 degrees, maximum gain of 8 dB, front-to-back and side lobe ratios of 25 dB, and a tilt angle of 12 degrees. The WTRUs may have omnidirectional antennas. The RSRP steering angle can take values ​​of 0 and 45 in the azimuth direction, which means that for every BS, beam sweeping is performed in these two directions. As shown in Figure 2, there can be a total of two beam pairs for each of the 24 BSs.

[0095] The remaining simulation parameters may be as shown in Figure 3. Figure 3 includes a table 300 that includes system parameters 302 and corresponding values ​​304 that are implemented in the simulation.

[0096] The two-phase cell selection and / or beam association process may be implemented using an AI-aided framework as described herein. For example, the framework may include AI-aided cell discovery in emerging networks (AIDEN) implementing AI / ML. In the AIDEN framework, MMWAVE user history drive test minimization (MDT) traces including coverage quality indicators (e.g., RSRP and / or location) may be collected. The AIDEN framework may include using AI techniques on wider beams and transitioning to narrower beams to further fine-tune the optimal beam pair predicted in the first phase (e.g., taking into account user requirements). AIDEN may be more robust to overfitting during the model training phase and / or provide more tolerance to errors arising from the propagation characteristics of the environment compared to other AI-based search methods (e.g., which may use AI on narrower beams and use a larger search space). Additionally or alternatively, AIDEN may avoid phase 1 latency while also maintaining accuracy comparable to hierarchical search-based methods.

[0097] 4 illustrates an example process 400 that may be implemented to perform cell selection and / or beam association using AI / ML. For example, process 400 may be implemented utilizing the AIDEN framework. One or more portions of process 400 may be implemented in a network entity, such as a BS or a network server in communication with a BS, to implement AI / ML for cell selection and / or beam association. Additionally, although one or more portions of process 400 may be described as being implemented by a BS or another network entity, one or more portions of process 400 may be implemented by another device on the network, such as a WTRU, another BS, or another network server.

[0098] As shown in FIG. 4, the process 400 may include a training phase 402 and / or an operational phase 404 (e.g., also referred to as a production phase). During the training phase 402, a training data set may be built and in the operational phase 404, an AI / ML model may be implemented. As shown in FIG. 4, at 406, historical reports from MMWAVE cells consisting of WTRU GPS locations and RSRPs of nearby cells may be logged. The historical reports may be used as raw data sets that may be preprocessed as described herein. An optimal cell / BS and an optimal beam / beam pair may be labeled at 408 for each WTRU location in the training data. In the simulation, cell association and beam pair association may be made based on the highest RSRP. The labeled data set may be preprocessed at 410 to generate data in a format for input to ML. Using the labeled preprocessed data set, at 412, an associated cell ID and beam pair map may be created for each WTRU location. Each user location may be labeled with a 2-tuple (associated cell ID, associated beam pair). Further pre-processing of the training data may be performed (e.g., depending on the function of the ML algorithm). The training stage 402 may include training performed by inputting (e.g., at 406) one or more WTRU locations and / or one or more RSRP values. The training stage may include determining cell / gNB and / or beam associations via the AL / ML model and / or comparing the gNB or beam associations with optimal cell / gNB and / or optimal beam associations (e.g., obtained via exhaustive search). The training stage may include a cost function. The cost function may include minimizing a (e.g., total) cost when performing the comparison.For example, minimizing cost may include minimizing the difference between the RSRP value measured by the WTRU of a signal transmitted from the strongest (e.g., best) beam selected by the AI / ML and the strongest (e.g., best) beam optimally determined (e.g., via exhaustive search) selected by the AI / ML.

[0099] For AI / ML algorithms that do not directly support multi-class multi-output classification, multi-class single-label classification can be used (e.g., using the label power set method), where a unique integer is assigned to each 2-tuple of (associated cell ID, associated beam pair). In this way, one multi-class classifier is trained for one or more (e.g., all) unique label combinations found in the training data.

[0100] Outliers in the training data (e.g., WTRUs far away from the particular BS associated with that BS) may be filtered out based on a distance threshold between the WTRU and the BS that defines the range of the BS. An outlier may be defined as a WTRU-BS distance value with an interquartile range of more than 1.5 above the upper quartile (75 percent) or below the lower quartile (25 percent).

[0101] The operation phase 404 may include a first phase and / or a second phase. As shown in FIG. 4, given a WTRU location 416 (e.g., x and y geographic coordinates, Global Navigation Satellite System (GNSS) or GPS location, and / or another geolocation of the WTRU) as an input, one or more AI / ML algorithms 414 may be used to predict an optimal beam pair at 418 and / or predict an optimal cell / BS at 420. The optimal BS and / or optimal beam pair (BP) may be output at 422. One or more of the AI / ML models may be trained (e.g., during the training phase 402 and prior to the operation phase 404 procedures described herein) to take the input location coordinates (e.g., possibly along with other inputs such as service requirements, WTRU identity, interference, etc.). One or more AI / ML models may be trained to output one or more sets of cells, gNB Tx beams, WTRU Rx beams, beam pairs, gNB Rx beams, and / or WTRU Tx beams. Training may be done with one or more (e.g., multiple) WTRU reporting locations (e.g., and / or any of the inputs described herein). Training may include performing a (e.g., exhaustive) cell / beam search to determine the strongest cell / beam. Training data using wide beams may be used to train several ML algorithms, including support vector machines (SVM), k-nearest neighbors (KNN), random forests (RF), and / or deep neural networks (DNN). KNN may be chosen because it is easy to implement, fast, and requires hyperparameter tuning of only three variables. KNN classifies new data points based on a similarity measure of historical data points. KNN may not cope well with a large number of input variables and may require uniform features. Two input variables (eg, WTRU x and y coordinates) may be used, which may be on a homogeneous scale (eg, meters).KNN's sensitivity to outliers and noise can be addressed through data pre-processing (e.g., as described herein). SVM can be chosen because classification decisions are made by knowing the decision boundary / hyperplane. For example, the margins or boundaries separating the classes can be visualized (e.g., as shown in FIG. 5). Data non-linearity can be addressed (e.g., via kernel tricks).

[0102] SVMs may be memory intensive since they may need to store one or more (e.g., all) support vectors, which grow with the training dataset size. RFs may be a collection of decision trees, and the majority vote of the forests may be selected as the predicted output. Compared to decision trees, RFs may be less susceptible to overfitting and may yield more robust solutions. Compared to KNNs, random forests may support automatic feature interactions and may be (e.g., typically) faster. Compared to SVMs (e.g., using kernels to solve nonlinear problems), each decision tree in a random forest may derive a hypercuboid in the input space to solve such problems. Furthermore, decision trees may tackle collinearity better than SVMs.

[0103] DNNs may be able to directly support multi-class multi-output classification tasks, but those tasks often have non-linear activation functions, and computing the gradients of those functions can be computationally expensive during backpropagation of errors. DNNs may be used (e.g., directly) to predict two output variables (e.g., optimal BS and optimal beam pair), but different strategies may be adopted to utilize KNN, SVM, and / or RF. In the case of KNN, 24 classes for BS and 2 classes of beam pairs for each BS may be transformed into 48 unique classes. SVM is essentially a binary classification algorithm and may require modification for both multi-output and multi-class classification tasks. Multi-class classifications may be subdivided into multiple binary classifications via one-versus-one and one-versus-all methods, and these binary classifications may be additional hyperparameter options used to tune the SVM model. In the case of decision trees, one classifier per output may be fitted.

[0104] The operational phase 404 shown in FIG. 4 may include a second phase. In the second phase, the predicted beams / beam pairs may be fine-tuned at 424 to generate optimal beams / beam pairs and / or cells / BSs 426. The second phase may be performed based on requested user service demand at 428. For example, if the user request demand is determined to be higher at 428, a narrower beam may be determined. If the user request demand is determined to be low, a wide beam may be used. Depending on the user demand requested at 428, one or more narrow beams may be used. An exhaustive beam sweep may be performed, for example, roughly constrained within the beamwidth of the predicted beam from the first phase. This may further refine the predicted beam from the first phase, as shown in FIG. 4. The second phase may refine the cell / beam determined by the AI / ML. The AI / ML model may return a beam with a wide beamwidth. The second phase (e.g., a refinement step) may include determining beams having narrow beamwidths. The second phase may use one or more AI / ML models.

[0105] The term "beam" or "beam pair" as used herein may refer to any of an uplink (UL)-transmit (Tx) beam, a downlink (DL)-Tx beam, a UL-receive (Rx) beam, and / or a DL-Rx beam. The term "beam" or "beam pair" may refer to one or more of a DL-Tx beam (e.g., a beam used by a gNB in ​​DL transmission), a DL-Rx beam (e.g., a beam used by a WTRU in DL reception), a UL-Tx beam (e.g., a beam used by a WTRU in UL transmission), a UL-Rx beam (e.g., a beam used by a gNB in ​​UL reception), a DL Tx-Rx beam pair (e.g., a beam used by a WTRU and a gNB for DL ​​transmission), and a UL Tx-Rx beam pair (e.g., a beam used by a WTRU and a gNB for UL transmission). It may be assumed that a UL Tx-Rx beam pair may use the same beam as a DL Tx-Rx beam pair. In such a case, the UL Tx beam at the WTRU may be the same as the DL Rx beam at the WTRU (e.g., both beams use the same spatial filtering), and the UL Rx beam at the gNB may be the same as the DL Tx beam at the gNB (e.g., both beams use the same spatial filtering).

[0106] The beam pair determined in one direction (e.g., DL) may be used by the WTRU to transmit in the UL. For example, the WTRU may determine the UL Tx beam to be the same as the determined DL Rx beam, and the WTRU may determine the UL transmission resource according to the DL Tx beam used by the gNB.

[0107] The WTRU location report may include coordinates indicating a given position and / or location of the WTRU. The WTRU location report may be configured with a given granularity. For example, the WTRU may be configured with one or more areas and the WTRU may determine its location as being within a given area. The WTRU may report the area or area index to the gNB. The report may include sending an indication of information indicating the WTRU location, as described herein. The indication may be explicit, to explicitly indicate the WTRU location. The indication may be implicit, to implicitly indicate the location of the WTRU. The indication may be both explicit and implicit.

[0108] The WTRU may determine its location relative to one or more cells or DL-Tx beams and / or beam pairs. The WTRU may report its relative location, for example, along with the DL-TX beam or beam pair or reference signal (RS) index to which the WTRU's relative location is applicable.

[0109] The WTRU location report may include one or more positions and / or locations of the WTRU, including coordinates, area or area index, and / or one or more measurements performed on one or more signals (e.g., signals configured to enable WTRU location determination).

[0110] Signaling may be implemented to support cell discovery. The signaling to support cell discovery may be used to determine the Rx beam on the pair and / or may use the UL beam corresponding to the beam pair. The WTRU may report its location to the network to enable, for example, AI-assisted cell discovery. In an example, the WTRU may detect a first cell and DL-Tx beam combination, for example, from a subset of all possible cell and DL-Tx beam combinations. The WTRU may detect the first cell and DL-Tx beam combination by receiving a DL transmission from the first cell and DL-Tx beam combination. The WTRU may determine a UL resource to transmit the WTRU's location from the first cell's DL transmission and DL-Tx beam combination. For example, the WTRU may detect a broadcasted transmission that provides resources for transmitting its location. For example, the transmission may include system information (SI) indicating one or more resources for transmitting its location.

[0111] The WTRU may report its location along with a temporary identification tag. For example, the WTRU may report its location using a preamble. The WTRU may report its location by transmitting a signal in a resource. Parameters of such a signal transmission may indicate the WTRU's location and / or a WTRU ID. The WTRU may report other context information. For example, other context information may be implemented here (e.g., antenna configuration, etc.). The WTRU's location may be indicated by one or more of the resource (e.g., time or frequency) on which the WTRU transmits the signal, the signal structure, the associated preamble, and / or the signal content.

[0112] The location of the WTRU may be indicated by the resources from which the WTRU transmits signals. For example, the WTRU may be configured with a mapping between signal resources and geographical regions (e.g., indicated via a broadcast transmission). The time of the resources may be defined in terms of symbols or slots. The frequency of the resources may be defined in terms of Physical Resource Blocks (PRBs) or subcarriers or Bandwidth Parts (BWPs).

[0113] The location of the WTRU may be indicated by the signal structure, for example, the WTRU may select a transmission sequence depending on its location.

[0114] The location of the WTRU may be indicated by an associated preamble. For example, a WTRU may be configured with one or more sets of preambles or PRACH occasions, where a (e.g., each) set may be associated with a different WTRU location (e.g., or a different WTRU location area). The WTRU may select a preamble or PRACH occasion associated with the location or area in which the WTRU is located.

[0115] The location of the WTRU may be indicated by the content of the signal. For example, the WTRU may encode its location into the signal. The encoding may be done via scrambling of a set of bits (e.g., CRC bits).

[0116] A WTRU may report its location to a serving cell. A WTRU may be triggered to report its location when one or more of the following occur: The WTRU location changes by more than a threshold amount. The WTRU is triggered to report L3 measurements. The WTRU is triggered for a conditional handover (HO). The RSRP is less than a threshold. The serving cell RSRP is less than the neighbor cell RSRP value plus an offset. The WTRU's transmission requirements change. The WTRU's best Rx beam changes. The WTRU's best UL panel changes. The WTRU detects a new cell or DL-Tx beam or a combination thereof. The WTRU determines N or more NACKs for DL ​​transmissions (e.g., within a predefined time period). A configurable, periodic, or predefined trigger event occurs. The WTRU's velocity changes by more than a threshold amount and / or the WTRU determines that it has failed channel access more than N times (e.g., within a predefined time period).

[0117] The WTRU may be triggered to report its location when it detects a new cell or DL-Tx beam or a combination thereof. For example, if the WTRU detects a new cell and / or DL-Tx beam with an RSRP greater than a threshold, the WTRU may report its location.

[0118] The WTRU may be configured to perform neighbor cell discovery / measurement based on low latency cell detection using the assistance information. The WTRU may be configured with one or more neighbor cells for which low latency cell detection may be enabled. For example, the WTRU may be configured to trigger a location report to the network when a measurement is triggered for at least one neighbor for which low latency cell detection is enabled. The WTRU may receive the assistance information from the gNB. For example, the WTRU may receive the assistance information from the gNB (e.g., in response to transmission of the WTRU's location and / or other context information) as described herein. The WTRU may determine the assistance information (e.g., in response to the WTRU's location and / or other context information as described herein). The functionality of determining the assistance information may include AI / ML. The functionality of determining the assistance information may include the WTRU being configured (e.g., via broadcasted SI and / or via radio resource control (RRC)) with an association between one or more locations and one or more sets of assistance information. The assistance information may include a defined subset of measurement resources for at least one beam in a cell and / or beam pair based on the location of the WTRU. For example, the assistance information may include one or more of: one or more measurement objects, a set of resources for monitoring discovery signals or synchronization signal blocks (SSBs), and / or a mini-measurement gap configuration (e.g., the gaps may be shorter in duration compared to normal / legacy measurement gaps).

[0119] The assistance information that the WTRU receives from the gNB may include one or more measurement objects. For example, the WTRU may be configured with a prioritized list of one or more neighboring cells and / or frequencies. The WTRU may prioritize these neighbors for performing measurements and / or reporting.

[0120] The assistance information that the WTRU receives from the gNB may include a set of resources for monitoring discovery signals or SSBs. For example, the set of resources may be configured as an SMTC window specific to low latency cell detection. For example, the WTRU may be configured with information such as an SSB index (e.g., or any implicit / explicit beam identification) expected to be received within the SMTC window.

[0121] The assistance information that the WTRU receives from the gNB may include a mini measurement gap configuration. For example, the WTRU may be configured to monitor discovery signals and / or SSBs from neighboring cells in this mini measurement gap configuration. The WTRU may apply the mini measurement gap configuration as an alternative to a regular measurement gap configuration. The WTRU may apply the mini measurement gap configuration for measurements in addition to the regular measurement gap configuration.

[0122] The assistance information may be signaled via a radio resource control (RRC) message and / or a MAC control element (CE). The medium access control (MAC) control element may activate or deactivate a preconfigured measurement configuration. The WTRU may be configured to use the assistance information to perform neighbor cell discovery and / or measurements (e.g., measurement resources). The WTRU may be configured to report the neighbor cell measurements to the gNB. The WTRU may be configured to report the cell discovery (e.g., including one or more associated measurements) to the gNB. The WTRU may be configured to report the neighbor cell measurements via preconfigured UL resources. The reporting may include sending an indication of information indicating the neighbor cell measurements. The indication may be explicit to explicitly indicate the neighbor cell measurements. The indication may be implicit to implicitly indicate the neighbor cell measurements. The indication may be both implicit and explicit. The preconfigured UL resources may be determined as an offset from the timing of the assistance information, or may be configured as part of the assistance information, or may be implicitly determined based on the content of the assistance information.

[0123] WTRU center cell discovery may be performed. The WTRU may be provided with an association of the WTRU location and resources for transmitting signals or associated cells or DL-Tx beams. The WTRU may transmit signals on resources determined by its location and / or service requirements. The WTRU may monitor for responses. The resources on which the WTRU monitors for responses may be determined by the WTRU location and / or service requirements.

[0124] The granularity of the received association between the WTRU location and the resource for transmitting the signal, or the associated cell or DL-Tx beam, may be such that the WTRU may need to interpolate to determine the appropriate resource of the cell or DL-Tx beam. The WTRU may use AI / ML to perform the interpolation. In an example, there may be an association with the location and the resource for transmitting the signal. The association may have a certain granularity. If the WTRU is located between two locations (e.g., two locations where associated transmission resources exist), the WTRU may input its location into the AI / ML model (e.g., possibly with other inputs) and / or the output may provide the transmission resource.

[0125] The WTRU may determine a preferred cell (e.g., of a BS) or beam or beam pair. For example, the WTRU may determine a preferred cell (e.g., of a BS) or beam or beam pair based on its location. The WTRU may report the preferred cell (e.g., of a BS) or beam or beam pair to a gNB. The reporting may include sending an indication of information indicating the preferred cell (e.g., of a BS) or beam or beam pair. The indication may be explicit, to explicitly indicate the preferred cell (e.g., of a BS) or beam or beam pair. The indication may be implicit, to implicitly indicate the preferred cell (e.g., of a BS) or beam or beam pair. The indication may be both explicit and implicit. The WTRU may be configured with resources for reporting the preferred cell (e.g., of a BS) or beam or beam pair to at least one gNB. The WTRU may report the preferred cell (e.g., of a BS) or beam or beam pair on the same resources received for reporting location information. The WTRU may report a preferred cell (e.g., of a BS) or beam or beam pair on one or more different resources. The WTRU may report a preferred cell (e.g., of a BS) or beam or beam pair on resources specifically configured for reporting location information. The WTRU may be configured with multiple resources for transmitting signals. The WTRU may select resources depending on the preferred cell (e.g., of a BS) or beam or beam pair. For example, the WTRU may be configured with one or more sets of reporting resources. The one or more sets of reporting resources may be associated with one or more preferred cells (e.g., of a BS) and / or beams and / or beam pairs. The WTRU may transmit an UL signal on the resources. The UL signal may include information or measurements about one or more cells or beams or beam pairs (e.g., including at least the preferred cells or beams or beam pairs of the BS).

[0126] Upon sending the BS preferred cell or beam or beam pair indication, the WTRU may monitor for an acknowledgement from the gNB, which may be received via one or more of a Random Access Response (RAR), a Scheduling Grant, an RRC (re)configuration, a DL RS, or a Downlink Control Information (DCI).

[0127] The WTRU may indicate its service requirements to the network. This may indicate to the network whether it can be served by a narrow beam or a wide beam. The WTRU may be indicated the cell and DL-Tx wide beam (e.g., determined by the AI-ML method) and may immediately begin operation. In an example, the WTRU may be indicated the cell and DL-Tx wide beam and may be triggered to perform refined beam selection, e.g., to narrow the serving DL-Tx beam.

[0128] The WTRU may indicate one or more of its service requirements (e.g., data volume, reliability requirement, latency requirement, data type) to the gNB. The service requirements may include one or more thresholds (e.g., latency threshold, RSRP threshold, etc.). The gNB may use the one or more service requirements of the WTRU to determine an appropriate beam for serving the WTRU. The service requirements may indicate the WTRU's serving cell DL-Tx beam requirement (e.g., narrow beam or wide beam). The WTRU may indicate its service requirements via transmission of a signal prior to completion of cell discovery. For example, the WTRU may encode its service requirements using a scheme similar to the location reporting method described herein. Alternatively, the WTRU may indicate its service requirements via transmission of a signal after completion of coarse cell discovery. For example, once coarse cell discovery is completed, the WTRU may be provided with resources on which the WTRU may transmit its service requirements. For example, such a transmission may use a scheduling request (SR)-like operation whereby the WTRU is configured with one or more, possibly periodic, resources on which the WTRU may indicate changes (e.g., updates) to one or more service requirements. The transmission may use a SR-like operation whereby the WTRU is configured with one or more (e.g., possibly periodic) resources on which the WTRU may indicate a new set of service requirements.

[0129] Once the coarse cell discovery is complete, the WTRU may receive a transmission to enable finer beam selection. If the WTRU determines that fine beam selection is appropriate for its service requirements, it may perform fine beam selection. The WTRU may skip fine beam selection and / or indicate to the network that fine beam selection is not required. Beam refinement may be performed via exhaustive search. To implement exhaustive search, the WTRU may be configured with a set of resources (e.g., a set of reference signals) for measuring one or more beams in a set of beams. For example, the set of resources may include a set of reference signals for measuring each of the possible finer beams. The WTRU may report an appropriate value within the wider or narrower beam. The appropriate value may include one or more measurements as described herein. Additionally or alternatively, the WTRU may use other means (e.g., AI / ML based) to receive a transmission to enable finer beam selection.

[0130] The WTRU may receive an indication from the network that it is associated with a cell and / or DL-Tx beam. For example, the WTRU may monitor DL ​​transmissions from the network (e.g., after providing its location to the network). The WTRU may monitor a set of discovery signals or SSBs. The set of discovery signals or SSBs to be monitored by the WTRU may be determined depending on the location that the WTRU provided to the network and / or depending on the ID that the WTRU provided to the network. The WTRU may ignore any other discovery signals or SSBs that are not associated with its reported location or ID.

[0131] The WTRU reporting the first WTRU location may be configured to monitor a first set of resources on which it may expect at least one discovery signal or SSB. Upon receiving the at least one discovery signal or SSB, the WTRU may proceed with random access. The WTRU may perform random access using the determined cell, beam, and / or beam pair. The WTRU may attempt to decode the discovery signal or SSB using its reported ID or location (or location ID or tag). For example, the SSB may be scrambled with the WTRU ID or location tag. The WTRU may select the SSB as the one with the WTRU's location or ID appropriately descrambled.

[0132] The WTRU may decode an information block that may include a set of resources associated with the WTRU location. The WTRU may perform random access to the resources indicated in the information block associated with its location.

[0133] The WTRU may be configured with measurement resources (e.g., associated with the location of the WTRU). For example, after transmitting its location, the WTRU may be configured with a set of measurement resources. The WTRU may perform measurements and / or report measurements for at least one cell or beam or beam pair. The measurements may include one or more of RSRP, Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), Signal to interference and noise ratio (SINR), Rank Indicator (RI), Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), Layer Indicator (LI), CRI, Doppler shift, Doppler spread, Angle of Arrival (AoA), Angle of Departure (AoD), Delay spread, and / or average delay. The WTRU may report measurements using resources associated with at least one measurement resource. The WTRU may report measurements using resources associated with the measurement resource that provides the best measurement (e.g., highest RSRP, or SINR, or RSRQ). The WTRU may perform transmissions (e.g., to report measurements) using a cell or beam or beam pair selected by the results of measurements performed on configured measurement resources. The reporting may include sending an indication of information indicating one or more measurement resources. The indication may be explicit, to explicitly indicate one or more measurement resources. The indication may be implicit, to implicitly indicate one or more measurement resources. The indication may be both explicit and implicit.

[0134] The WTRU may determine the set of cells, beams, and / or measurement resources depending on the WTRU's location and / or broadcast information, e.g., system information block (SIB). The WTRU may be configured with an association between the location and SIB information and the set of cells, beams, and / or measurement resources (e.g., via SI, or RRC, or MAC CE, or DCI). The WTRU may perform measurements on the set of cells and / or beams and / or measurement resources and may report the measurements together with an identifier for the set of cells and / or beams and / or measurement resources to the gNB.

[0135] The WTRU may receive an indication of a cell or DL-Tx beam with which it may be associated. Upon receiving an indication of the best cell or DL-Tx beam, the WTRU may perform a random access (RA) to that cell. The indication of the cell or DL-Tx beam may include a reference signal (RS) configuration associated with the cell / beam. The WTRU may perform measurements on the RS. The WTRU may receive resources for performing an RA or a first transmission to the indicated cell / beam. In such a RA or first transmission, the WTRU may report measurements performed on the associated RS.

[0136] If the measurements obtained by the WTRU are below a threshold and / or another measurement (e.g., measurements), the WTRU may reject the cell / beam combination and / or decide to perform legacy cell discovery. In an example, the WTRU may simultaneously perform enhanced cell discovery (e.g., by indicating the WTRU's location to the network) and / or legacy cell discovery (e.g., by detecting a discovery signal or SSB). The WTRU may receive an indication of a cell / DL-Tx beam combination from the network. The WTRU may perform measurements on the indicated cell / DL-Tx beam combination. The WTRU may report measurements of the indicated cell / DL-Tx beam combination, possibly in combination with measurements obtained for a discovery signal or SSB detected by the WTRU. This may enable the BS or other network entity to refine its cell discovery algorithm to provide better cell association results. In an example where the cell discovery algorithm includes an AI / ML algorithm, the cell discovery algorithm may be refined to provide better cell association results. If the measurements (e.g., RSRP, RSSI, RSRQ, SINR, RI, CQI, PMI, LI, CRI, Doppler shift, Doppler spread, Angle of Arrival (AoA), Angle of Departure (AoD), delay spread, average delay, etc.) of the indicated cell / DL-Tx beam combination are less than the WTRU-discovered cell / DL-Tx beam plus an offset, the WTRU may ignore the indicated cell / DL-Tx beam and proceed to access the WTRU-discovered cell / DL-Tx beam. The WTRU may indicate to the WTRU-discovered cell that it has ignored the network-determined cell / DL-Tx beam. The WTRU may provide measurements on the network-determined cell / DL-Tx beam to the WTRU-discovered cell.

[0137] The AI / ML parameters (e.g., weights, biases, and / or other hyperparameters) may be refined by updating the parameters as described herein. Hyperparameter tuning of the AI / ML model may be performed through Bayesian optimization. For example, tuning of the AI / ML model used in the first phase of the procedure to determine cells / beams from location, service requirements, and / or other context information may include hyperparameters that may be refined by tuning through Bayesian optimization. The resulting optimal hyperparameters may be as follows: for KNN, number of neighbors = 1, distance metric = Euclidean, distance weight = inverse squared; for SVM, kernel function = Gaussian, kernel scale = 1.5255, box constraint level = 3.4541, multiclass method = 1 vs all; for RF, number of trees = 65, maximum tree depth = 50, maximum features for best split = 2, bootstrap = true, out-of-bag score = true.

[0138] An AI-aided cell discovery framework for ultra-dense emerging networks with high BS density (AIDEN) may be implemented as disclosed herein. Compared to hierarchical search algorithms (e.g., may utilize exhaustive search to search for optimal cells and beam pairs using wide and narrow beams), AIDEN may use AI in the search phase with wide beams. AIDEN may be implemented with or without exhaustive search in the first phase of cell reselection and / or beam association to reduce latency. Narrow beams may then be used to further fine-tune the predicted beams from the AI ​​(e.g., depending on user requirements). AIDEN may be trained on wider beams (e.g., only wider beams), which may make AIDEN robust to overfitting and outliers and reduce the number of BS classes in ultra-dense network scenarios.

[0139] FIG. 6 illustrates an example of a DNN architecture 600 and hyperparameters that may be configured for the DNN architecture. The DNN architecture 600 may be implemented by AI / ML operating on one or more devices as described herein. For example, one or more portions of the DNN architecture 600 may be implemented on a network entity, such as a BS or a network server in communication with a BS, to implement AI / ML for cell selection and / or beam association. Additionally, although one or more portions of the DNN architecture 600 may be described as being implemented by a BS or another network entity, one or more portions of the DNN architecture 600 may be implemented by other devices on the network, such as a WTRU, another BS, or another network server.

[0140] As shown in FIG. 6, the DNN architecture 600 may receive an input 602 at an input layer 604. The input 602 may include location information and / or other context information as described herein. The input layer 604 may receive an input 606. The input 606 may have a tensor configuration of [(none, 2)]. The input layer 604 may generate an output 608. The output 608 may be passed to a dense layer 612. The dense layer 612 may receive the output 608 from the input layer 604 as an input. The dense layer 612 may generate an output 610. The output 610 from the dense layer 612 may be passed to a dense layer 614. The dense layer 614 may receive the output 610 from the dense layer 612 as an input. The dense layer 614 may generate an output 616. The dense layer may provide the output 616 to one or more output layers.

[0141] As shown in FIG. 6, the DNN architecture 600 may include an output layer 618 and / or an output layer 620. The output layer 618 may be a dense layer that receives the output 616 of the dense layer 614 as an input. The output layer 618 may provide an output 622. The output 622 may include an identifier of a base station and / or a cell (or set of cells) that may be provided to the WTRU in response to the location. The output layer 620 may be a dense layer that receives the output 616 of the dense layer 614 as an input. The output layer 620 may provide an output 624. The output 624 may include a set of resources that may be provided to the WTRU in response to the location.

[0142] A 5-fold cross-validation strategy may be implemented to minimize overfitting during the model training stage. The 5-fold cross-validation average accuracy of the aforementioned models may be as shown in FIG. 7. For RF and DNN, which predict two explicit outputs (unlike SVM and KNN, where multi-output labels are converted to a single output), the accuracy reported in FIG. 7 may be a close match (e.g., subset accuracy), which may indicate the proportion of samples in which each of the sample's labels is correctly classified. KNN may perform best in terms of accuracy since the number of features in the data is small compared to the training data. SVM may generally perform better than KNN, for example, when there are large features and less training data. DNN may perform worse than SVM. There may be well-defined boundaries, but they are nonlinear (e.g., as can be visualized from the results shown in FIG. 5). These boundaries drawn by neural networks may be somewhat arbitrary, as they may depend on several factors that are random, such as weight initialization.

[0143] SVM may draw the optimal boundary in a more structured way by using support vector points. Datasets with two input features may not require DNN-type architectures to extract hidden features from the many input features. However, the maximum accuracy may be limited by the fact that there are many classes (e.g., 48). The receiver operating characteristics (ROC) of KNN, the best performing AI / ML algorithm, are shown in Figure 8. Multi-class predictions can be reduced to multiple sets of binary predictions by considering one positive class at a time and all others as negative. The minimum value of the area under the ROC curve (AUC) may be 0.90, while the maximum value may be 1.00. The macro-averaged AUC is 0.977, which is close to the ideal 1.00, indicating that ML techniques can be used to reduce latency during phase 1 of AI / ML algorithms such as AIDEN (e.g., even when the number of classes is relatively large).

[0144] Referring again to FIG. 5, the graph shown in FIG. 5 illustrates an example of a WTRU location with an accurate predicted class and an example of a WTRU location with an inaccurately predicted class. As shown in FIG. 5, the inaccurately predicted points may be mainly on the cell edge. One way to measure or evaluate the impact of accurate prediction may be to investigate how much the key performance indicators (e.g., RSRP, SINR, throughput) corresponding to the target class label differ from the predicted class label. FIG. 9 shows the distribution of the difference between the RSRP of the WTRU corresponding to the predicted BS and beam pair and the RSRP corresponding to the target / actual BS and beam pair for the unseen test data of a sample of 1000 WTRUs. As shown in FIG. 9, 80% of the WTRUs may have an RSRP difference of 0 (e.g., accurate prediction of the optimal BS and beam pair), 2.7% of the WTRUs may have less than 5 dB difference in RSRP due to misclassification, and there may be no WTRUs with more than 10 dB difference.

[0145] One or more of the embodiments disclosed herein may reduce latency compared to exhaustive search. Prediction at the cell edge may be improved. Phase 1 of the AI / ML (e.g., AIDEN) framework may be skipped (e.g., for time-critical use cases). The AI / ML (e.g., AIDEN) framework may be applied to NLoS paths and may be applied when obstructions are present in the environment (e.g., because MMWAVE propagation is susceptible to obstructions). The framework may be applied to WTRUs with directional antenna arrays, which may result in more beam pairs and thus more classes for AI / ML algorithm training. The reported WTRU location may be inaccurate due to location errors (e.g., GPS positioning errors) or to protect user privacy. Location errors (e.g., GPS positioning errors) may be compensated for in the AI / ML (e.g., AIDEN) framework.

[0146] MDT-based data may be sparse, for example, in scenarios with low WTRU density or in small cells where there may be fewer users compared to macro cells. Techniques for data enrichment may be incorporated into existing frameworks to obtain sufficient amounts of training data. Accuracy may be limited by the large number of classes (e.g., 48 classes). AI / ML techniques may be developed that can handle classification of a large number of classes compared to the available data. The AI / ML (e.g., AIDEN) framework may be tested in other 3GPP-defined environmental scenarios where multiple frequency bands operate simultaneously. The AI / ML (e.g., AIDEN) framework may be extended to scenarios with WTRU transition mobility (e.g., instead of initial cell discovery).

[0147] 10A illustrates a system flow diagram depicting an example of a communication procedure 1000 for cell selection and / or beam association. The communication procedure 1000 may be performed between one or more network entities. For example, the communication procedure 1000 may be performed between one or more WTRUs and one or more network entities (e.g., gNBs, BSs, network servers, etc.). If the network entity is a gNB, the gNB may communicate with other network entities, such as a network server, to provide information to the WTRUs, as described herein.

[0148] At 1006, for example, the WTRU 1002 may receive system information (e.g., SIB) from the network entity 1004. The system information may include one or more resources for reporting the location of the WTRU 1002. The system information may include one or more resources for the WTRU to communicate its location to the network entity 1004. The system information may include an indication of one or more channels or sub-channels. The WTRU 1002 may determine UL resources for transmitting the WTRU's location from the DL transmission 1006 and DL-Tx beam combination for which the system information is provided. The WTRU 1002 may detect a broadcasted transmission that provides resources for transmitting its location.

[0149] At 1008, the WTRU may determine context information to be reported to the network entity 1004. For example, the WTRU may determine its location mobility (e.g., speed or direction), antenna configuration, and / or service requirements. The WTRU 1002 may determine its location based on the received system information (e.g., system information received at 1006). The WTRU 1002 may determine its location relative to one or more cells or DL-Tx beams and / or beam pairs as described herein. The WTRU's location may include geographic location information, geographic coordinates (e.g., x and / or y geographic coordinates), GNSS location, GPS location / GPS coordinates, measurements made on one or more signals (e.g., signals configured to enable WTRU location determination), and / or location information for being within a given area and / or area index (e.g., for a gNB). The WTRU may generate a location report to transmit the WTRU's location as described herein. For example, the WTRU 1002 may be configured with one or more areas. The WTRU 1002 may determine its location to be within an area. The WTRU 1002 may transmit the area and / or an area index to the network entity 1004.

[0150] At 1010, the WTRU may transmit the context information determined at 1008. For example, the WTRU 1002 may transmit its location to the network entity 1004 on one or more resources received from the network entity 1004. The WTRU 1002 may be triggered to report its location. The WTRU 1002 may be triggered to report its location when the WTRU 1002 detects a new cell or DL-Tx beam or a combination thereof, as described herein. The WTRU 1002 may transmit its location in a location report. The location report may include one or more of coordinates, an area or area index, and / or measurements performed on one or more signals (e.g., signals configured to enable location determination of the WTRU 1002). The one or more measurements in the location report may be based on measurements on resources from one or more neighboring cells and / or a serving cell. The WTRU 1002 may report its location by transmitting a signal on a resource as described herein.

[0151] At 1012, the network entity 1004 may determine assistance information to be transmitted to the WTRU 1002 based on the context information it receives from the WTRU 1002. For example, the network entity may determine a preferred cell (or set of cells) of the BS and / or at least one beam of a beam pair associated with a base station based on the location of the WTRU. The cell and / or at least one beam may be determined to be the best cell and / or at least one beam based on the location of the WTRU 1002. The assistance information may indicate the base station and / or cell (or set of cells) determined from the location of the WTRU 1002. The assistance information may also, or alternatively, indicate the at least one beam of the beam pair determined from the location of the WTRU 1002. The at least one beam may be a DL-Tx beam with which the WTRU 1002 may be associated.

[0152] The assistance information may include a defined subset of measurement resources for at least one beam in a cell and / or beam pair based on the location of the WTRU 1002. For example, the assistance information may include one or more of a measurement object, a set of resources for monitoring a discovery signal or synchronization signal block (SSB), and / or a mini-measurement gap configuration. The subset of measurement resources may include a reference signal (RS) configuration associated with the cell / beam. The subset of measurement resources may include other measurement resources as described herein.

[0153] The network entity 1004 may use AI / ML to identify a cell (or set of cells) and / or at least one beam in a beam pair of the BS based on the location of the WTRU 1002. The network entity 1004 may use AI / ML to define a subset of measurement resources to be transmitted to the WTRU 1002. In other embodiments, the subset of measurement resources, the cell, and / or at least one beam in a beam pair may be pre-defined based on the location of the WTRU.

[0154] At 1014, the WTRU 1002 may receive assistance information. At 1016, the WTRU 1002 may determine, based on the assistance information, a cell of a BS and / or at least one beam of a beam pair to which it is associated for performing transmission. For example, the WTRU 1002 may identify a BS (one or more cells) and / or at least one beam of a beam pair from the assistance information. The WTRU 1002 may perform measurements on one or more measurement resources indicated in the assistance information to identify the BS (one or more cells) and / or at least one beam of a beam pair as described herein. The one or more measurements may include an exhaustive sweep. The measurements may include one or more of RSRP, RSSI, RSRQ, SINR, RI, CQI, PMI, LI, CRI, Doppler shift, Doppler spread, angle of arrival of AoA, angle of departure of AoD, delay spread, and / or average delay. At 1018, the WTRU 1002 may perform communication on the cell (or multiple cells) to the BS using at least one beam of the beam pair. For example, the WTRU 1002 may perform a random access (RA) or first transmission on the cell (or multiple cells) to the BS using at least one beam of the beam pair. The WTRU 1002 may transmit on a UL-Tx beam that corresponds to an identified serving DL-Tx beam of the BS.

[0155] 10B depicts a flowchart illustrating an example of a communication procedure 1050 for updating the configuration of cells and / or beams (e.g., beam pairs) utilized by one or more WTRUs 1002. One or more portions of the procedure 1050 may be performed by one or more WTRUs 1002 and / or one or more network entities 1054. The network entities 1054 may be the same as or different from the network entities 1004 shown in FIG. 10A. For example, the one or more network entities 1054 may include a gNB, a BS, a network server, and / or another network entity. If the network entity is a gNB, the gNB may communicate with other network entities, such as a network server, to provide information to the WTRUs, as described herein.

[0156] Procedure 1050 may be performed as a second phase of procedure 1000. However, procedures 1000, 1050, and / or one or more portions therein may be performed independently.

[0157] As shown in FIG. 10B, the WTRU 1002 may perform refined beam selection. In one example, the WTRU 1002 may be configured to perform and / or perform communications on a cell and a DL-Tx wide beam and may be triggered to perform refined beam selection to narrow the serving DL-Tx beam. The trigger may include a change in one or more service requirements. The trigger may be based on one or more measurements, such as, for example, interference. The trigger may be based on performance of one or more transmissions (e.g., if a block error ratio (BLER) is higher than required). In another example, the WTRU 1002 may be configured to perform and / or perform communications on a cell and a DL-Tx narrow beam and may be triggered to perform refined beam selection to widen the serving DL-Tx beam (e.g., based on mobility within a cell, etc., as described herein).

[0158] At 1056, the WTRU 1002 may transmit / report measurements and / or service requirements to a network entity 1054 (e.g., BS, gNB, etc.). The measurements may include one or more of RSRP, RSSI, RSRQ, SINR, RI, CQI, PMI, LI, CRI, Doppler shift, Doppler spread, Angle of Arrival (AoA), Angle of Departure (AoD), delay spread, and / or average delay. As described herein, the network entity 1054 may be a BS of a cell identified during the procedure 1000 shown in FIG. 10A or may be another network entity. The WTRU 1002 may transmit the one or more measurements and / or service requirements via one or more of the beams of a beam pair for communicating with the network entity 1054. For example, the WTRU 1002 may transmit the one or more measurements and / or service requirements via a UL-Tx beam of the WTRU 1002 that corresponds to a DL-Tx beam of the network entity 1054. The WTRU 1002 may indicate service requirements to the network 1004 as described herein. The WTRU 1002 may indicate to the network entity 1054 whether the WTRU 1002 may be served by a narrow beam or a wide beam. The WTRU 1002 may be configured to communicate via a cell and a DL-Tx wide beam (e.g., as determined by the procedure 1000 or a portion thereof) and / or may initiate operation based on the configuration. The WTRU 1002 may be triggered to perform refined beam selection, for example, to narrow the serving DL-Tx beam when the WTRU 1002 is capable.

[0159] At 1058, the network entity 1004 may send information to the WTRU 1002 for updating the beam configuration. For example, the information for updating the beam configuration may include a defined subset of measurement resources that may be used by the WTRU 1004 when performing an exhaustive search to identify narrower beams from which DL information may be received on DL-Tx beams from the network entity 1054. The defined subset of resources may include a cell identifier and / or a set of reference signals for measuring each of the finer beams that may be during the exhaustive search. In another example, the network entity 1054 may determine one of the cells and / or updated beams / beam pairs based on the reported measurements and / or service requirements and explicitly indicate the cell and / or updated beams / beam pairs in the communication 1058 to the WTRU 1002. For example, the network entity 1054 may implement AI / ML and / or another refinement algorithm to determine the cell and / or refined beams / beam pairs based on the measurements or service requirements. In another example, cells and / or beams / beam pairs may be pre-defined in information stored in the network entity 1054 based on the range of measurements to be reported.

[0160] At 1060, the WTRU 1002 may determine an updated beam / beam pair based on the information received from the network entity 1054. The updated beam / beam pair may be narrower than the previous beam / beam pair. The updated beam / beam pair may be explicit in the information or the WTRU 1002 may perform measurements to determine the updated beam / beam pair at 1060 based on the received information. For example, the WTRU 1002 may perform measurements on a set of reference signals received in the information from the network entity 1054 during the exhaustive search. The WTRU 1002 may also determine an updated cell for conducting communication based on the information received from the network entity 1054.

[0161] At 1062, the WTRU 1002 may report at least one of the performed measurements, the cell, and / or the updated beam / beam pair to the network entity 1054. The WTRU 1002 may be configured with resources to report the cell and / or the updated beam / beam pair to the network entity 1054 as described herein. The WTRU 1002 may report an appropriate value within a wider or narrower beam. The appropriate value may include one or more measurements as described herein. At 1064, the WTRU 1002 may perform communication with the network entity 1054 using the updated beam configuration.

[0162] The processes and techniques described herein may be applied to other wireless technologies and to other services.

[0163] The WTRU may refer to a physical device identity or subscription-related identity, e.g., a user identity such as MSISDN, SIP URI, etc., and the WTRU may refer to an application-based identity, e.g., a username that may be used per application.

[0164] The processes described above may be implemented in a computer program, software, and / or firmware embodied in a computer readable medium for execution by a computer and / or a processor. Examples of computer readable media include, but are not limited to, electronic signals (transmitted over wired and / or wireless connections) and / or computer readable storage media. Examples of computer readable storage media include, but are not limited to, read only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media such as, but not limited to, internal hard disks and removable disks, magneto-optical media, and / or optical media such as CD-ROM disks and / or digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, and / or any host computer.

Claims

1. 1. A method implemented by a wireless transmit / receive unit (WTRU), the method comprising: receiving system information for a set of cells, the WTRU being configured with resources for reporting a WTRU location; determining a location of the WTRU; transmitting the determined location on the resource; and receiving, in response to the transmission of the location on the resources, information indicative of a defined subset of measurement resources associated with one or more cells for enabling cell selection and beam association; determining a base station (BS) cell and beam pair (BP) based on measurements performed on the defined subset of measurement resources; and performing a first transmission to the BS using an uplink (UL) beam of the BP.

2. The measurement performed on a defined subset of the measurement resources includes a first measurement, the transmission is a first transmission, the defined subset is a first defined subset, the BP is a first BP, and the method further comprises: reporting at least one of a first measurement or service requirement to the BS via one or more of the beams of the first BP; receiving a second defined subset of measurement resources associated with the BS in response to the at least one of the first measurement or the service requirements; determining a second BP for the BS having a beam width narrower than a beam width of the first BP based on second measurements performed on a second defined subset of the measurement resources; The method of claim 1 , further comprising: performing a second transmission to the BS using an UL transmission beam of the second BP.

3. 3. The method of claim 2, further comprising: reporting to the BS one or more first measurements or one or more second measurements in response to the measurements performed on a downlink (DL) transmission beam of the second BP that is narrower than a DL transmission beam of the first BP in response to the measurements performed on a second defined subset of the measurement resources.

4. 4. The method of claim 3, further comprising: performing an exhaustive search based on the second defined subset of measurement resources to refine the first BP and determine the narrower second BP.

5. The method of claim 2 , wherein the first BP is a wide beam BP and the second BP is a narrow beam BP.

6. 2. The method of claim 1, wherein the defined subset of measurements includes one or more measurement objects, a set of resources to monitor for a discovery signal or a synchronization signal block (SSB), or a mini-measurement gap configuration.

7. The method of claim 1 , wherein the defined subset of measurements is received via a Radio Resource Control (RRC) message or a Medium Access Control (MAC) control element.

8. performing neighbor cell discovery or measurements using the defined subset of measurement resources; The method of claim 1 , further comprising: reporting the neighbor cell measurement resource or the neighbor cell discovery via an uplink (UL) resource.

9. determining at least one preferred beam of a preferred cell or BP; The method of claim 1 , further comprising: reporting at least one preferred beam of the preferred cell or BP.

10. The method of claim 1 , wherein the service requirements include at least one of a data volume, a reliability requirement, a latency requirement, or a data type.

11. 1. A wireless transmit / receive unit (WTRU), comprising: A transceiver; a processor, the processor comprising: receiving, via the transceiver, system information for a set of cells, the processor being configured with resources for reporting WTRU location; determining a location of the WTRU; transmitting the determined location via the transceiver over the resource; receiving, via the transceiver, information indicative of a defined subset of measurement resources associated with one or more cells to enable cell selection and beam association in response to the transmission of the location on the resources; determining a base station (BS) cell and beam pair (BP) based on measurements performed on the defined subset of measurement resources; A wireless transmit / receive unit (WTRU) configured to perform a first transmission to the BS using an uplink (UL) beam of the BP.

12. The measurement performed on the defined subset of measurement resources includes a first measurement, the transmission is a first transmission, the defined subset is a first defined subset, the BP is a first BP, and the processor: reporting at least one of a first measurement or service requirement to the BS via one or more of the beams of the first BP; receiving, via the transceiver and in response to the at least one of the first measurement or the service requirements, a second defined subset of measurement resources associated with the BS; determining a second BP for the BS having a beam width narrower than a beam width of the first BP based on second measurements performed on a second defined subset of the measurement resources; The WTRU of claim 11 , further configured to perform a second transmission to the BS using a UL transmission beam of the second BP.

13. The processor, The WTRU of claim 12, further configured to report to the BS one or more first measurements or one or more second measurements for reception on a downlink (DL) transmission beam of the second BP that is narrower than the DL transmission beam of the first BP in response to the measurements performed on a second defined subset of the measurement resources.

14. The processor, 14. The WTRU of claim 13, further configured to perform an exhaustive search based on a second defined subset of the measurement resources to refine the first BP and determine the narrower second BP.

15. The WTRU of claim 12 , wherein the first BP is a wide beam BP and the second BP is a narrow beam BP.

16. The WTRU of claim 11 , wherein the defined subset of measurements includes one or more measurement objects, a set of resources to monitor for a discovery signal or a synchronization signal block (SSB), or a mini-measurement gap configuration.

17. The WTRU of claim 11 , wherein the defined subset of measurements is received via a radio resource control (RRC) message or a medium access control (MAC) control element.

18. The processor, performing neighbor cell discovery or measurements using the defined subset of measurement resources; The WTRU of claim 11, further configured to report the neighbor cell measurement resources or the neighbor cell discovery via the transceiver on an uplink (UL) resource.

19. The processor, determining at least one preferred beam of the preferred cell or BP; The WTRU of claim 11 , further configured to report at least one preferred beam of the preferred cell or BP.

20. The WTRU of claim 11 , wherein the service requirements include at least one of a data volume, a reliability requirement, a latency requirement, or a data type.

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