Sensor-assisted millimeter wave beam management method and user equipment for wireless communications

Abstract

The present disclosure relates to sensor-assisted millimeter wave beam management. Systems and methods for sensor-assisted beam selection for antennas and wireless networks are described. The systems and methods are configured to detect out-of-coverage (OoC) scenarios and perform beam management in response to detecting the OoC scenarios. The systems and methods are configured to perform beam management during baseband outage scenarios. In each scenario, a device (e.g., user equipment (UE)) is configured to determine whether the UE is stationary or mobile.

Description

Technical Field

[0001] The present disclosure relates generally to wireless communications. Background Art

[0002] Wireless devices may include phased array antennas for transmitting and receiving signals to and from remote devices (e.g., in wireless networks). A phased array includes a computer-controlled antenna array that generates a radio beam that can be electronically directed in different directions without moving the antenna.

[0003] Beamforming, or spatial filtering, is a signal processing technique used in antenna arrays to directionally transmit or receive signals. This is achieved by combining the elements in the antenna array in such a way that signals at certain angles experience constructive interference, while other signals experience destructive interference. Beamforming can be used on both the transmitting and receiving sides (e.g., with phased array antennas) to achieve spatial selectivity. Summary of the Invention

[0004] The present application describes systems and methods for sensor-assisted antenna and beam selection for wireless networks. The systems and methods are configured to detect out-of-coverage (OoC) scenarios and perform beam management in response to detecting an OoC scenario. The systems and methods are configured to perform beam management during baseband outage scenarios. In each scenario, a device (e.g., user equipment or UE) is configured to determine whether the UE is stationary or mobile. A UE is stationary when it has not moved in position or orientation relative to a base station or another device communicating with the UE within a given time period. A UE is mobile when it has changed position or orientation (collectively referred to as pose) within a given time period. The UE is configured to detect an OoC scenario and perform beamforming in response to the detection, as described below. The UE is configured to perform beam acquisition for beam tracking based on determining whether the UE is mobile or stationary, as described below.

[0005] In some cases, wireless networks include transmissions using millimeter wave (mmWave) spectrum. For example, mmWave spectrum can be used in cellular technologies such as 3rd Generation Partnership Project (3GPP), 5th Generation New Radio (5G NR), and / or Long Term Evolution (LTE) networks for transmissions in mmWave frequency ranges (e.g., Frequency Range 2 (FR2), Frequency Range 3 (FR3), etc.) from base stations (e.g., next-generation Node Bs or gNBs) or to and from client devices (e.g., the mobile devices described throughout this specification). Typically, FR2 transmissions are between 24.25 GHz and 52.6 GHz. Typically, mmWave high-bandwidth (e.g., approximately 400 MHz) transmissions have relatively high propagation losses. For example, mmWave transmissions may have a loss of 20 dB relative to frequency bands below 6 GHz, such as those used for Frequency Range 1 (FR1) transmissions.

[0006] To overcome this loss, the mmWave-enabled devices described herein are configured to perform beamforming, beam management, and antenna selection based on sensor feedback from one or more sensors of the mmWave-enabled device. Beamforming enables a device to steer radio frequency (RF) energy in a specific direction, thereby overcoming mmWave propagation losses. Beams are typically fixed and designed a priori in a codebook, such as phase-amplitude combinations of antenna elements. The transmitting device forms a beam by changing the amplitude and / or phase of one or more elements of a phased array antenna. Typically, the transmitting device generates a beam based on a predefined phase-amplitude combination for each antenna of the array to ensure that a relatively high-power narrow beam is transmitted in a desired direction relative to the phased array antenna. Beam management enables the device to identify the beam to be transmitted in each of the uplink and downlink directions. Beam selection enables a mmWave-enabled device (e.g., UE) to ensure high-speed connectivity by improving the wireless coverage of a given uplink or downlink transmission. Sensors on mmWave-enabled devices are configured to provide data indicating how the device moves in the environment. Systems and methods for sensor-assisted beam selection are configured to use feedback from sensors on mmWave-enabled devices to optimize beamforming and beam management, thereby mitigating propagation losses, improving the efficiency of beam determination with respect to time and / or resources, and improving link performance.

[0007] Beam management comprises the process by which the UE modifies the settings of the phase shifters of the phased antenna array. Beam management comprises receiving reference signals known to the UE from a remote device such as a base station. Typically, the base station sends multiple signals using the same transmit configuration including the same transmit (Tx) power, Tx antenna pattern and Tx precoding. The UE can measure link metrics using several different phase shifter settings (called beam scanning). The UE makes measurements on those reference signals. Typically, the UE makes multiple measurements of the reference signals using multiple phase shifter settings using the same transmit configuration. Based on these measurements, the UE attempts to optimize the phase shifter settings for the ongoing communication, as well as the specific transmit configuration used by the base station to obtain the beam with the best overall link metric.

[0008] The data processing system of the UE (e.g., one or more processing devices or computing devices) is configured to perform beam selection based on how the UE moves in the environment and one or more signal metrics associated with each orientation and / or position of the UE relative to the base station. The data processing system is configured to detect operating scenarios of the UE, such as out-of-coverage (OoC) scenarios. OoC scenarios are also called non-line-of-sight (NLOS) scenarios. OoC or NLOS scenarios include operating situations in which there is no clear or direct line-of-sight (LOS) path between the base station and the UE for a strong signal. Out-of-coverage scenarios can be attributed to obstruction or base station / UE antenna misalignment because there is no 360° beam coverage in 5G UEs. The data processing system is configured to improve beam selection of 5G mmWave devices in OoC / NLOS (hereinafter referred to as OoC) scenarios. The data processing system uses wireless signal metrics to determine that the UE is in an OoC scenario. The metrics include reference signal received power (RSRP), signal-to-noise ratio (SNR), or delay spread, among others. The data processing system also uses the accelerometer data or the gyroscope data or both to detect that the UE is experiencing an OoC scenario. The data processing system is configured to assist beam management in identifying an optimal beam based on the detection of the OoC scenario.

[0009] The data processing system is configured to assist in beam management based on the detection of an OoC scenario. For in-coverage scenarios, the beam management module of the data processing system scans only a few adjacent beams (depending on the beam pattern) to capture short-term channel dynamics. When the data processing system detects an OoC scenario in which the UE is stationary, the beam management module of the data processing system performs an extended hierarchical beam scan. The data processing system initiates a comprehensive scan of beams starting with a thinner beam (e.g., a maximum intensity beam) and adjacent beams. This is performed because the channel is expected to be less dynamic in a stationary scenario than in a mobility scenario. The data processing system terminates scanning potential beams when the measured RSRP is above a threshold. Extended beam scanning can be performed in a stationary setting because in a stationary setting, the wireless channel does not change rapidly. The UE has time to perform a wide scan before the channel changes. Extended beam search ensures that the data processing system selects the best performing beam.

[0010] When establishing a connection, the UE typically undergoes an initial beam acquisition period. During this period, the UE determines which beam is the optimal beam, or best beam. The optimal beam is associated with better link metrics compared to other beams. These link metrics include higher throughput, higher power, higher signal-to-noise ratio (SNR), lower delay spread, and so on. The UE performs adjustments during the ongoing beam tracking phase to maintain the optimal beam, where the beam configuration may change. During beam acquisition, the UE may experience periods of poor channel quality, which negatively impacts the user experience.

[0011] For mmWave networks, a UE may experience an mmWave baseband outage caused by, for example, a call on a secondary Subscriber Identity Module (SIM). A baseband outage interrupts all communications on the mmWave link for the duration of the outage. This reduces or eliminates the UE's ability to perform beam tracking. In some systems, the UE may require repeated beam acquisition cycles after an outage, even if the UE has not moved at all, unnecessarily impacting the overall user experience.

[0012] The system and method for sensor-assisted antenna and beam selection for wireless networks enables a UE to avoid beam acquisition periods when the UE is stationary. The UE can quickly re-establish a previous link using the previous best beam.

[0013] The systems and methods described in this document can achieve one or more of the following advantages. The data processing system is configured to reduce or eliminate the throughput drop experienced during an OoC scenario. For example, when the UE is stationary with an orientation of 140° in azimuth and 180° in elevation relative to the node, the cell power of the UE may be -100 dBm. This scenario is considered out of coverage (OoC) because there is no beam to adequately cover the UE orientation. In this case, the data processing system is configured to cause the UE to select a beam that avoids the 65% throughput drop that may occur in a conventional beam management method that does not detect OoC scenarios as described herein.

[0014] Typically, a UE can train a limited number of beams per synchronization signal block (SSB). Typically, the UE only trains a few "neighboring" beams and does not scan all available beam options. As a result, the UE may be "stuck" using a suboptimal beam, where the selected beam and its nearest neighbor are both suboptimal, but no superior beam is scanned due to their distance from the selected beam. The systems and methods described herein overcome this technical limitation by identifying OoC scenarios and expanding the beam search to select a better quality beam, increasing both beam throughput and beam power.

[0015] The method and system for sensor-assisted antenna and beam selection enables the UE to recover from baseband outages by bypassing the full beam acquisition process. The UE can recover from baseband outages within a shorter period during which channel quality is adversely affected, and re-establish a high-quality link more quickly when the UE is stationary. For example, if the UE is stationary and operating using a second SIM card, the UE can recover the previous beam without the delay of beam acquisition, thereby reducing or eliminating the negative impact on the user experience (e.g., watching a video or downloading data).

[0016] Methods and systems for sensor-assisted antenna and beam selection enable a UE or other similar device to account for changes in the UE's environment and allow for increased UE mobility (which may represent changes in the UE's environment). For example, a device using mmWave communications may frequently adjust beam and antenna selection in response to physical changes in the device's environment (e.g., a moving car or trees) or movement of the device. Changes in the environment may cause obstructions in the UE's communication path or changes in the location of a remote device (e.g., a node) communicating with the UE. This may cause the UE and / or node to frequently adjust its beam to achieve better performance of the communication link. Methods and systems for sensor-assisted antenna and beam selection enable mmWave devices in an environment to quickly (e.g., immediately or almost immediately) determine optimal beam selection and / or antenna selection (when applicable) to improve the performance of the communication link in response to these environmental changes and / or movement of one or both of the communicating devices.

[0017] One or more of the advantages described previously may be achieved through one or more implementations as described in the following sections.

[0018] In a general aspect, a method includes obtaining motion data from one or more motion sensors coupled to a wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device that communicates with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics for the beam, the link metrics associated with a time period during which the wireless device is stationary; determining, during the time period during which the wireless device is stationary, a link stability value associated with the beam based on the one or more link metrics; determining, based on the link stability value associated with the beam, that the wireless device is out of coverage (OoC) relative to the remote device; in response to determining that the wireless device is OoC relative to the remote device, selecting a first beam group comprising one or more first beams generated by the wireless device; determining, for each beam of the first beam group, a corresponding link stability value for the beam; selecting, based on the determination, a particular beam from the one or more first beams; and reestablishing communication with the remote device using the particular beam.

[0019] In some implementations, determining that a wireless device is out of coverage (OoC) relative to a remote device based on a link stability value associated with a beam includes: comparing the link stability value to a predetermined threshold; and determining that the link stability value fails to satisfy the threshold based on the comparison.

[0020] In some implementations, the threshold is determined using a machine learning model trained with link metric data that is labeled as an unstable link representing an OoC scenario or a stable link representing a line of sight (LOS) scenario.

[0021] In some implementations, the link stability value represents a link metric standard deviation of one or more link metrics.

[0022] In some implementations, the link stability value represents a link metric minimum value of one or more link metrics.

[0023] In some embodiments, the one or more link metrics include at least one of a signal-to-noise ratio (SNR) of a signal received from a remote device, a delay spread value of the signal, a change value of an angle of arrival (AoA) of the signal, a reference signal received power (RSRP) of the signal, or a received signal strength indicator (RSSI) (e.g., RSRP / RSSI / SINR).

[0024] In some implementations, selecting the particular beam includes selecting a first beam of a first beam group associated with a corresponding link stability value that satisfies a threshold link stability value.

[0025] In some embodiments, selecting a particular beam includes: determining that a first beam group does not include any beam associated with a link stability value that satisfies a link stability threshold; in response to the determination, selecting a second beam group, wherein a second beam of the second beam group is farther away from the beam than the first beam of the first beam group; and selecting a second beam from the second beam group.

[0026] In some implementations, the one or more motion sensors include at least an accelerometer or a gyroscope.

[0027] In some implementations, the wireless device and the remote device are configured for mm-wave communications using Frequency Range 2 (FR2).

[0028] In some implementations, the operations include periodically retrieving motion data to determine whether the wireless device is moving or stationary.

[0029] In some implementations, the wireless device includes an antenna array that includes at least a specified number of beam configurations, and wherein selecting a particular beam includes selecting one of the specified number of beam configurations.

[0030] In general terms, a method includes: receiving motion data from one or more motion sensors of a wireless device, the motion data indicating motion of the wireless device during an interruption of a baseband (BB) communication link between the wireless device and a remote device; retrieving a mapping of an amount of motion of the wireless device to a corresponding beam group of the wireless device; classifying the motion data as representing an amount of motion; based on the classification, identifying at least one of the mapped beam groups to perform beam acquisition; selecting a specific beam for the wireless device from the at least one beam group; and reestablishing communication with the remote device using the specific beam.

[0031] In some implementations, the operations include detecting a BB communication link interruption; and in response to the detection, initiating measurement of movement of the wireless device during the BB communication link interruption, wherein the movement data represents total movement of the wireless device during the BB communication link interruption.

[0032] In some embodiments, classifying the motion data as representing an amount of motion includes: determining that the wireless device has not moved during the BB communication link interruption; based on the determination, identifying an initial beam included in at least one beam group used when the wireless device detects the BB communication link interruption; and wherein the specific beam includes the initial beam.

[0033] In some implementations, the initial beam is selected independent of the beam acquisition process of the wireless device.

[0034] In some implementations, selecting a particular beam is based on one or more link metrics associated with each beam in at least one beam group during a beam acquisition process.

[0035] In some implementations, the mapping of the amount of motion of the wireless device to the corresponding beam group includes a relationship between a specific amount of motion and a beam configuration entry in a beamforming codebook.

[0036] In some implementations, classifying includes executing a machine learning model trained using labeled motion data.

[0037] In a general aspect, a user equipment (UE) includes at least one motion sensor; one or more antenna arrays, each antenna array configured for at least two beam configurations; one or more processors; and a non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations as described herein.

[0038] In a general aspect, a processor for a user equipment (UE) includes circuitry configured to communicate with a remote device; and circuitry for executing one or more instructions that, when executed, cause the processor to perform operations as described herein.

[0039] The details of one or more specific implementations are set forth in the following figures and description. The techniques described herein may be implemented by one or more wireless communication systems, components of wireless communication systems (e.g., stations, access points, user equipment, base stations, etc.), or other systems, devices, methods, or non-transitory computer-readable media. Additional features and advantages will be apparent from the detailed description and drawings, as well as from the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 An exemplary wireless communication system according to various embodiments herein is shown.

[0041] Figure 2 An example of a platform or device configured for sensor-assisted antenna and beam selection according to some implementations of the present disclosure is shown.

[0042] Figure 3 An exemplary device for motion and rotation detection for sensor-assisted antenna and beam selection according to some implementations of the present disclosure is shown.

[0043] Figure 4 An exemplary beam set for beam acquisition is shown.

[0044] Figure 5 An exemplary process of sensor-assisted antenna and beam selection by a UE is shown.

[0045] Figure 6 An exemplary environment for determining accumulated motion of a UE relative to a remote device is shown.

[0046] Figure 7 An example of selecting a beam based on sensor feedback according to some implementations of the present disclosure is shown.

[0047] Figure 8 An example of selecting a beam based on sensor feedback according to some implementations of the present disclosure is shown.

[0048] Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION

[0049] Techniques described herein enable a wireless device to perform beam selection in response to a change in the channel of a communication link. A device includes one or more sensors that provide motion data to the device. The device is configured to perform beamforming in response to receiving the motion data. This enables the device to perform beamforming to improve communication performance using less bandwidth overhead and less latency than performing a full beam scan of the device's antennas.

[0050] Typically, beam selection is performed to improve communication bandwidth in the context of mmWave systems (e.g., using FR2 frequencies, FR3 frequencies, or other mmWave frequencies). mmWave communication links have relatively high propagation losses over long distances (e.g., over tens or hundreds of meters) relative to the losses of FR1 links. To mitigate propagation losses and improve the performance of the communication link, mmWave-enabled devices are configured to perform beamforming, beam management, and antenna selection based on sensor feedback from one or more sensors of the mmWave-enabled device.

[0051] Beamforming enables a device to steer radio frequency (RF) energy in a specific direction. Transmitting devices form a beam by varying the amplitude and / or phase of one or more elements of a phased array antenna. Typically, the transmitting device generates a beam based on a predefined phase-amplitude combination for each antenna in the array to ensure a relatively high-power, narrow beam is transmitted in the desired direction relative to the phased array antenna.

[0052] The UE periodically (e.g., nearly continuously) receives reference signals from a base station or other remote device. The UE periodically (e.g., nearly continuously) optimizes the phase shifter settings used for beamforming. For example, a synchronization signal block (SSB) is a block of 4 symbols, each of which includes a reference symbol. The UE uses the demodulation reference signal (DMRS) that includes these symbols for beam management.

[0053] In some implementations, the UE performs beam management using a codebook. The codebook includes a set of phase shifter settings, each corresponding to a corresponding beam. The codebook enables the UE to perform beam management as follows. The UE tries beams on reference symbols and then uses the beam with the best corresponding measurement of the corresponding link metric. The codebook has a relatively small number of possible beams compared to the number of available measurement opportunities. In a given time, the number of measurement opportunities corresponds to the number of available measurement symbols with the same transmit configuration.

[0054] To comply with radio frequency (RF) requirements, the UE may include multiple phased arrays with (e.g., 4 elements, 8 elements, 16 elements, etc.). Therefore, the codebook size is greater than 30 beams. Typically, the base station provides an SSB signal for UE beam management, which enables 4 measurement changes every 20 milliseconds. Therefore, if no additional information is available, the UE performs an initial acquisition phase, through which the UE acquires the optimal beam in multiple steps. The UE then performs a tracking phase, in which the UE tracks the optimal UE beam by measuring a limited number of beams based on the optimal current beam.

[0055] Beam management enables a device to identify the beam to use for transmission in each of the uplink and downlink directions. In one example, for 5G NR mmWave transmissions, a node (e.g., a gNB) periodically (e.g., between 5 milliseconds and 160 milliseconds (monitoring system) period) transmits a synchronization signal to identify the best transmit beam and the best receive beam. This includes an initial beam training step using multiple beams. In this first step, a wider beam width is used to cover a wide sweep range. The second step includes a beam refinement step. In this step, the UE sweeps a narrower beam over a narrower range than in the first step. This enables the UE to adjust in the desired beam direction. In the third step, the device is configured for beam refinement. In the beam refinement step, the user equipment (UE) tunes the receive angle of the beam, and the node transmits using a fixed beam. The UE measures different signal strengths until the optimal configuration of beams is found. In one example, for 802.11ad / ay mmWave transmissions, an access point (AP) and a wireless device (e.g., UE) train their respective beams during sector-level scanning (SLS) and beam refinement procedure (BRP), as defined in the 802.11 standard.

[0056] Antenna selection enables a device (e.g., a UE) to ensure high-speed connectivity by improving the wireless coverage for a given uplink or downlink transmission. In one example, blocking of the first antenna or antenna misalignment can result in reduced throughput relative to an ideal transmission environment. In this case, the UE is configured to select from multiple phased antenna arrays (also known as antenna panels).

[0057] An mmWave-enabled device includes one or more sensors configured to provide motion data. The motion data indicates how the device moves in an environment. The motion data from the sensors enables the device to estimate beamforming parameters for an optimal connection based on previous data indicating a strong signal.

[0058] The data processing system of the UE is configured to determine whether the UE is in a stationary scenario or a moving scenario. A stationary scenario refers to a situation in which the UE does not move, rotate, or otherwise change position or orientation relative to the base station within a given time period. A moving scenario is a situation in which the UE is currently moving or rotating or has changed its position or orientation relative to the base station within a recent time period (e.g., within 5 seconds or less, but the threshold may be shorter or longer as needed). Based on determining whether the UE is stationary or moving, the UE performs beam selection. In a stationary scenario, the data processing system of the UE determines whether the UE is in an OoC scenario by analyzing one or more beam metrics. If the UE is in an OoC scenario, the data processing system performs increasingly intelligent searches of neighboring beams from the current beam to determine whether beam switching should be performed.

[0059] In some implementations, UE beam management and transmit operations on the phased array are temporarily interrupted. This can occur, for example, when performing dual SIM operations. Interruption of phased array communications occurs when the secondary SIM occupies baseband resources used for other operations.

[0060] During the outage, it is possible that the UE has moved (e.g., is mobile as described above) or that the UE has not moved (e.g., is stationary as described above). When the UE is stationary, the optimal beam may be the same beam used by the UE before the BB outage. When the UE is mobile, the UE may rotate to a position that requires a completely different beam than before the outage. In this case, the UE should perform a new initial acquisition.

[0061] In the event that a UE is stationary and a baseband outage has occurred, the UE is configured to reestablish the link without performing a beam acquisition cycle. The UE selects the previously used beam, which was in use before the baseband outage, as the best beam. If the UE is mobile, the UE performs beam acquisition. The UE may use mobility data to attempt to predict a new best beam. Alternatively or in addition, the UE may initiate beam acquisition based on the previously used beam before the outage.

[0062] Other examples of these methods are subsequently described in conjunction with the accompanying figures. The systems and methods described are compatible with any mmWave technology (e.g., 802.11ad / ay, 5G, etc.). The system is lightweight and configured to select beams, antennas, or both independently of any antenna or beam scanning.

[0063] Figure 1 An exemplary wireless communication system 100 is shown. For convenience and not limitation, the exemplary system 100 is described in the context of LTE and 5G NR communication standards defined by the 3rd Generation Partnership Project (3GPP) technical specifications. More specifically, the wireless communication system 100 is described in the context of a non-standalone (NSA) network that combines both LTE and NR, such as an E-UTRA (Evolved Universal Terrestrial Radio Access)-NR Dual Connectivity (EN-DC) network and a NE-DC network. However, the wireless communication system 100 may also be a standalone (SA) network that only combines NR. In addition, other types of communication standards are also possible, including future 3GPP systems (e.g., sixth generation (6G) systems), IEEE 802.16 protocols (e.g., WMAN, WiMAX, etc.), and the like.

[0064] System 100 includes UE 101a and UE 101b (collectively, "UE 101"). In this example, UE 101 is shown as a smartphone (eg, a handheld, touchscreen mobile computing device that can connect to one or more cellular networks). In other examples, any of the plurality of UEs 101 may include other mobile computing devices or non-mobile computing devices, such as consumer electronic devices, cellular phones, smart phones, feature phones, tablet computers, wearable computer devices, personal digital assistants (PDAs), pagers, wireless handheld devices, desktop computers, laptop computers, in-vehicle infotainment (IVI), in-car entertainment (ICE) devices, instrument clusters (ICs), heads-up display (HUD) devices, on-board diagnostic (OBD) devices, on-board mobility equipment (DME), mobile data terminals (MDTs), electronic engine management systems (EEMS), electronic / engine control units (ECUs), electronic / engine control modules (ECMs), embedded systems, microcontrollers, control modules, engine management systems (EMS), connected or “smart” appliances, machine type communication (MTC) devices, machine-to-machine (M2M) devices, Internet of Things (IoT) devices, or combinations thereof, etc.

[0065] In some examples, any of the plurality of UEs 101 may be an IoT UE, which may include a network access layer designed for low-power IoT applications utilizing short-term UE connections. The IoT UE may utilize technologies such as M2M or MTC to exchange data with an MTC server or device using, for example, a public land mobile network (PLMN), proximity services (ProSe), device-to-device (D2D) communications, sensor networks, IoT networks, or a combination thereof. M2M or MTC data exchanges may be machine-initiated data exchanges. The IoT network describes interconnected IoT UEs, which may include uniquely identifiable embedded computing devices (within the Internet infrastructure) with short-term connections. The IoT UE may execute background applications (e.g., keep-alive messages or status updates) to facilitate connectivity to the IoT network.

[0066] UE 101 is configured to connect (e.g., be communicatively coupled) to an access network (AN) or radio access network (RAN) 110. In some examples, RAN 110 can be a next-generation RAN (NG RAN), an evolved UMTS terrestrial radio access network (E-UTRAN), or a legacy RAN, such as a UMTS terrestrial radio access network (UTRAN) or a GSM EDGE radio access network (GERAN). As used herein, the term "NG RAN" can refer to the RAN 110 operating in a 5G NR system 100, while the term "E-UTRAN" can refer to the RAN 110 operating in an LTE or 4G system 100.

[0067] To connect to the RAN 110, multiple UEs 101 utilize connections (or channels) 103 and 104, respectively, each of which may include a physical communication interface or layer, as described below. In this example, connections 103 and 104 are shown as air interfaces to achieve communication coupling and may be consistent with cellular communication protocols, such as the Global System for Mobile Communications (GSM) protocol, the Code Division Multiple Access (CDMA) network protocol, the Push-to-Talk (PTT) protocol, the Cellular PTT (POC) protocol, the Universal Mobile Telecommunications System (UMTS) protocol, the 3GPP LTE protocol, the 5G NR protocol, or a combination thereof, as well as other communication protocols. In some examples, multiple UEs 101 may use an interface 105 such as a ProSe interface to directly exchange communication data. The interface 105 may alternatively be referred to as a sidelink interface 105 and may include one or more logical channels, such as a physical sidelink control channel (PSCCH), a physical sidelink shared channel (PSSCH), a physical sidelink downlink channel (PSDCH), or a physical sidelink broadcast channel (PSBCH), or a combination thereof.

[0068] UE 101b is shown configured to access access point (AP) 106 (also referred to as "WLAN node 106," "WLAN 106," "WLAN termination 106," "WT 106," etc.) using connection 107. Connection 107 may include a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, wherein AP 106 may include Wireless Fidelity. Router. In this example, AP 106 is shown connected to the Internet without being connected to the core network of the wireless system, as described in further detail below. In various examples, UE 101b, RAN 110, and AP 106 can be configured to use LTE-WLAN aggregation (LWA) operation or LTE / WLAN radio level operation integrated with IPsec tunneling (LWIP). LWA operation may involve RAN nodes 111a, 111b configuring UE 101b in an RRC_CONNECTED state to utilize radio resources of LTE and WLAN. LWIP operation may involve UE 101b using IPsec protocol tunneling to use WLAN radio resources (e.g., connection 107) to authenticate and encrypt packets (e.g., IP packets) sent over connection 107. IPsec tunneling may include encapsulating the entire original IP packet and adding a new packet header, thereby protecting the original header of the IP packet.

[0069] The RAN 110 may include one or more AN nodes or RAN nodes 111a and 111b (collectively referred to as "RAN nodes 111") that enable connections 103 and 104. As used herein, the terms "access node," "access point," and the like may describe equipment that provides radio baseband functionality for data or voice connections, or both, between a network and one or more users. These access nodes may be referred to as base stations (BSs), gNodeBs, gNBs, eNodeBs, eNBs, NodeBs, RAN nodes, roadside units (RSUs), transmit receive points (TRxPs or TRPs), and the like, and may include ground stations (e.g., terrestrial access points) or satellite stations that provide coverage within a geographic area (e.g., a cell). As used herein, the terms "NG RAN nodes" and the like may refer to RAN nodes 111 (e.g., gNBs) operating in a 5G NR system 100, while the term "E-UTRAN nodes" and the like may refer to RAN nodes 111 (e.g., eNBs) operating in an LTE or 4G system 100. In some examples, multiple RAN nodes 111 may be implemented as one or more dedicated physical devices such as macrocell base stations or low power (LP) base stations for providing femtocells, picocells, or other similar cells with smaller coverage areas, smaller user capacity, or higher bandwidth than macrocells.

[0070] In some examples, some or all of the multiple RAN nodes 111 may be implemented as one or more software entities running on a server computer as part of a virtual network that may be referred to as a cloud RAN (CRAN) or a virtual baseband unit pool (vBBUP). The CRAN or vBBUP may implement RAN functional partitioning, such as a packet data convergence protocol (PDCP) partitioning, where the radio resource control (RRC) and PDCP layers are operated by the CRAN / vBBUP, and other layer 2 (e.g., data link layer) protocol entities are operated by individual RAN nodes 111; a medium access control (MAC) / physical layer (PHY) partitioning, where the RRC, PDCP, MAC, and radio link control (RLC) layers are operated by the CRAN / vBBUP, and the PHY layer is operated by individual RAN nodes 111; or a "lower PHY" partitioning, where the RRC, PDCP, RLC, and MAC layers, as well as the upper portion of the PHY layer, are operated by the CRAN / vBBUP, and the lower portion of the PHY layer is operated by individual RAN nodes 111. This virtualization framework allows idle processor cores of the RAN nodes 111 to execute, for example, other virtualized applications. In some examples, individual RAN nodes 111 may represent nodes using individual F1 interfaces ( Figure 1 In some examples, the gNB-DU may include one or more remote radio heads or RFEMs (see, e.g., Figure 2 ), and the gNB-CU may be operated by a server (not shown) located in RAN 110 or by a server pool in a manner similar to CRAN / vBBUP. Additionally or alternatively, one or more of the RAN nodes 111 may be next-generation eNBs (ng-eNBs), including RAN nodes that provide E-UTRA user plane and control plane protocol terminations to UE 101 and connect to a 5G core network (e.g., core network 120) using a next-generation interface.

[0071] In a vehicle-to-everything (V2X) scenario, one or more of the RAN nodes 111 may be or act as an RSU. The term "roadside unit" or "RSU" refers to any traffic infrastructure entity used for V2X communications. The RSU may be implemented in or by a suitable RAN node or a stationary (or relatively stationary) UE, where an RSU implemented in or by a UE may be referred to as a "UE-type RSU," an RSU implemented in or by an eNB may be referred to as an "eNB-type RSU," an RSU implemented in or by a gNB may be referred to as a "gNB-type RSU," and so on. In some examples, the RSU is a computing device coupled to RF circuitry located on the roadside that provides connectivity support to passing vehicle UEs 101 (vUEs 101). The RSU may also include internal data storage circuitry for storing intersection map geometry, traffic statistics, media, and applications or other software for sensing and controlling ongoing vehicle and pedestrian traffic. The RSU may operate on the 5.9 GHz Direct Short Range Communication (DSRC) band to provide extremely low latency communications for high-speed events, such as collision avoidance, traffic warnings, and the like. Additionally or alternatively, the RSU may operate on the cellular V2X band to provide the aforementioned low latency communications as well as other cellular communication services. Additionally or alternatively, the RSU may operate as a Wi-Fi hotspot (2.4 GHz band) or provide connectivity to one or more cellular networks to provide uplink and downlink communications, or both. Some or all of the computing device and the RSU's RF circuitry may be packaged in a weatherproof enclosure suitable for outdoor installation, and may include a network interface controller to provide a wired connection (e.g., Ethernet) to a traffic signal controller or a backhaul network, or both.

[0072] Any one of the RAN nodes 111 may serve as the endpoint for the air interface protocol and may be the first point of contact for the UE 101. In some examples, any one of the multiple RAN nodes 111 may perform various logical functions of the RAN 110, including but not limited to functions of a radio network controller (RNC), such as radio bearer management, uplink and downlink dynamic radio resource management and data packet scheduling, and mobility management.

[0073] In some examples, multiple UEs 101 can be configured to communicate with each other or with any of multiple RAN nodes 111 using orthogonal frequency division multiplexing (OFDM) communication signals over a multi-carrier communication channel in accordance with various communication techniques, such as, but not limited to, OFDMA communication techniques (e.g., for downlink communication) or SC-FDMA communication techniques (e.g., for uplink and ProSe or sidelink communication), although the scope of the techniques described herein is not limited in this respect. The OFDM signal can include multiple orthogonal subcarriers.

[0074] In some examples, a downlink resource grid can be used for downlink transmissions from any of multiple RAN nodes 111 to multiple UEs 101, while uplink transmissions can utilize similar techniques. The grid can be a time-frequency grid, referred to as a resource grid or time-frequency resource grid, which represents the physical resources in the downlink during each time slot. This type of time-frequency plane representation is common for OFDM systems, making radio resource allocation intuitive. Each column and row of the resource grid corresponds to an OFDM symbol and an OFDM subcarrier, respectively. The duration of the resource grid in the time domain corresponds to a time slot in a radio frame. The smallest time-frequency unit in the resource grid is represented as a resource element. Each resource grid includes multiple resource blocks, which describe the mapping of certain physical channels to resource elements. Each resource block includes a collection of resource elements; in the frequency domain, this can represent the minimum amount of resources that can currently be allocated. Such resource blocks are used to transmit several different physical downlink channels.

[0075] In some examples, multiple UEs 101 and multiple RAN nodes 111 communicate (e.g., transmit and receive) data over a licensed medium (also referred to as a "licensed spectrum" or "licensed band") and an unlicensed shared medium (also referred to as an "unlicensed spectrum" and / or "unlicensed band"). The licensed spectrum may include channels operating in a frequency range of approximately 400 MHz to approximately 3.8 GHz, while the unlicensed spectrum may include a 5 GHz band. NR in unlicensed spectrum may be referred to as NR-U, and LTE in unlicensed spectrum may be referred to as LTE-U, License Assisted Access (LAA), or MulteFire.

[0076] The data processing system 202 includes circuitry such as, but not limited to, one or more processors (or processor cores), cache memory, and one or more of an LDO, an interrupt controller, a serial interface (such as SPI), an I2C or general programmable serial interface module, an RTC, a timer-counter (including an interval timer and a watchdog timer), general I / O, a memory card controller (such as an SD MMC or similar controller), a USB interface, a MIPI interface, and a JTAG test access port. The processor (or core) of the data processing system 202 may be coupled to or may include a memory / storage element and may be configured to execute instructions stored in the memory or storage device to enable various applications or operating systems to run on the system 200. In some examples, the memory or storage element may be an on-chip memory circuit that may include any suitable volatile or non-volatile memory, such as DRAM, SRAM, EPROM, EEPROM, flash memory, solid-state memory, or a combination thereof.

[0077] The processor of data processing system 202 may include, for example, one or more processor cores, one or more application processors, one or more GPUs, one or more RISC processors, one or more ARM processors, one or more CISC processors, one or more DSPs, one or more FPGAs, one or more PLDs, one or more ASICs, one or more microprocessors or controllers, a multi-threaded processor, an ultra-low voltage processor, an embedded processor, some other known processing element, or any suitable combination thereof. In some examples, data processing system 202 may include or may be a dedicated processor / controller for performing the techniques described herein.

[0078] For example, the processor of the data processing system 202 may include an Apple A series processor. The processor of the data processing system 202 may also be one or more of the following: Architecture Core TM Processors such as Quark TM 、Atom TM , i3, i5, i7 or MCU class processors, or available from Santa Clara, CA company( Another such processor is from Intel Corporation, Santa Clara, CA; Advanced Micro Devices (AMD) Processor or Accelerated Processing Unit (APU); from Snapdragon by Technologies, Inc.TM processors, Texas Instruments, Open Multimedia ApplicationsPlatform(OMAP) TM processors; MIPS-based designs from MIPS Technologies, Inc., such as the MIPS Warrior M-class, Warrior I-class, and Warrior P-class processors; ARM-based designs licensed from ARM Holdings, Ltd., such as the ARM Cortex-A, Cortex-R, and Cortex-M series processors; etc. In some implementations, data processing system 202 can be part of a system on a chip (SoC), in which data processing system 202 and other components are formed as a single integrated circuit.

[0079] Additionally or alternatively, data processing system 202 may include circuitry such as, but not limited to, one or more field programmable devices (FPDs) such as FPGAs; programmable logic devices (PLDs) such as complex PLDs (CPLDs), high-capacity PLDs (HCPLDs); ASICs such as structured ASICs; programmable SoCs (PSoCs), or combinations thereof. In some examples, data processing system 202 may include logic blocks or logic fabrics, as well as other interconnected resources that can be programmed to perform various functions, such as the processes, methods, and functions described herein. In some examples, data processing system 202 may include memory units (e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, static memory (e.g., static random access memory (SRAM), or antifuse)) for storing logic blocks, logic fabrics, data, or other data in lookup tables (LUTs).

[0080] The baseband module 210 may be implemented, for example, as a solder-in substrate including one or more integrated circuits, a single packaged integrated circuit soldered to a main circuit board, or a multi-chip module including two or more integrated circuits.

[0081] The antenna beam panel 212 (also referred to as a radio front end module (RFEM)) may include a mm-wave RFEM and one or more sub-mm-wave radio frequency integrated circuits (RFICs). In some examples, the one or more sub-mm-wave RFICs may be physically separate from the mm-wave antenna beam panel 212. These RFICs may include connections to one or more antennas or antenna arrays, and the antenna beam panel 212 may be connected to multiple antennas. In some examples, both mm-wave and sub-mm-wave radio functions may be implemented in the same physical antenna beam panel 212 that combines both mm-wave and sub-mm-wave antennas. In some embodiments, the mm-wave functionality implements the IEEE 802.11ad and 802.11ay standards.

[0082] The sensor module 204 includes a device, module, or subsystem whose purpose is to detect events or changes in its environment and send information about the detected events (e.g., sensor data) to one or more other devices, modules, or subsystems. Examples of such sensors include: an inertial measurement unit (IMU), such as an accelerometer, gyroscope, or magnetometer; a microelectromechanical system (MEMS) or nanoelectromechanical system (NEMS) including a three-axis accelerometer, a three-axis gyroscope, or a magnetometer; a liquid level sensor; a flow sensor; a temperature sensor (e.g., a thermistor); a pressure sensor; a barometric pressure sensor; a gravity meter; an altimeter; an image capture device (e.g., a camera or a lensless aperture); a light detection and ranging (LiDAR) sensor; a proximity sensor (e.g., an infrared radiation detector, etc.), a depth sensor, an ambient light sensor, an ultrasonic transceiver; a microphone or other audio capture device, or a combination thereof, etc. With respect to Figure 3 As the pose of the antennas of system 200 changes within the system environment, sensors 204 capture the motion of the system and send the motion data to motion detection module 206 .

[0083] The data processing system 202 is configured to host a motion detection module 206 and a beam selection module 208. The motion detection module 206 is configured to determine how the device is moving in the environment from the motion data of the sensor 204. The motion detection module 206 can determine the new position and orientation (e.g., pose) of the device relative to the previous pose of the device for which the optimal beam and antenna were selected. The updated pose can be provided to the beam selection module 208.

[0084] The beam selection module 208 is configured to select a beam of the panel 212 for transmitting or receiving data by the system 200 based on the updated pose provided from the motion detection module 206. The selection of the beam includes selecting a specific configuration of the phased antenna array to generate a directional beam from the array panel. In one example, the data processing system 202 continuously or nearly continuously performs the selection process when the device moves or is stationary in the environment. In some specific implementations, the data processing system 202 uses the beam selection module 208 to perform the selection process when one or more link metrics (e.g., SNR, RSRP, delay spread, etc.) drops below a threshold (e.g., when CoC is detected).

[0085] The beam selection module 208 is configured for OoC / NLOS scenarios and also assists beam management in identifying the optimal beam. The beam selection module 208 receives data from the motion detection module 206 indicating whether the UE is stationary or in motion. The beam selection module receives data from the baseband module 210, including data representing channel stability metrics of the wireless signal (such as SNR, RSRP, delay spread, etc.). The beam selection module 208 generates an indication of whether the UE is "in coverage" or "out of coverage." The beam selection module then sends the beam selection data to the antenna panel 212, which assists the UE in determining the optimal beam.

[0086] System 200 receives input from each of sensors 204 and baseband module 210. Baseband (BB) feedback may include signal-to-noise ratio (SNR), delay spread, RSRP, angle of arrival (AoA), and similar link metrics. Data processing system 202 uses the feedback data available from wireless baseband module 210 to infer the orientation of the dominant wireless path. The dominant wireless path comprises the path of a signal from a transmitter to a receiver. Data processing system 202 uses the baseband module 210 feedback data to identify whether the antenna panel or beam in use, or both, is optimal. Typically, if AoA data is not available to system 200, the AoA is estimated by data processing system 202.

[0087] In short, turn Figure 3 , devices 300 and 310 are shown by Figure 22. Example of data collected by sensor 204. For example, device 300 includes a gyroscope. The gyroscope is configured to measure the rate at which device 300 rotates around a spatial axis, including pitch, roll, and yaw, and movement (e.g., in degrees or radians) of device 300. Device 300 enables a data processing system receiving data from the gyroscope to determine a change in the orientation of one or more antennas (such as antenna 302), such as a change in orientation relative to a base station. Similarly, device 310 includes an accelerometer configured to measure changes in velocity (e.g., translational motion) of device 310 along the x, y, and z axes. Device 310 enables a data processing system receiving data from the accelerometer to determine a change in the orientation of one or more antennas (such as antenna 312), such as a change in orientation relative to a base station. Devices 300 and 310 can be combined into a single device that includes both a gyroscope and an accelerometer.

[0088] return Figure 2 , the motion detection module 206 receives the accelerometer motion data and identifies translational movement along each of the x, y, and z axes (e.g., in centimeters). The motion detection module 206 receives the gyroscope motion data and determines the rotation of the device about each of the x, y, and z axes. The result is an updated pose of the system 200 relative to the previous pose of the system. An initial pose of the system is determined relative to a remote device connected via a communication link. The initial pose can be determined based on AoA data (e.g., from the BB module 210). In some implementations, if AoA data is not available, a one-time beam scan is performed to determine the AoA, as described subsequently. The motion detection module 206 sends the motion data to the beam selection module 208, which is configured for beam selection.

[0089] The beam selection module 208 performs beam selection based on data from the BB module 210 and the updated pose provided by the motion detection module 206. Because the beam radiation pattern and antenna position are predefined for a given device (e.g., a device of the system 200), the beam selection module 208 includes coverage data. The coverage data includes the highest gain antenna panel and beam identification for each available device orientation and position.

[0090] The beam selection module 208 generally selects a new beam based on two scenarios. The first scenario is a blocking scenario, where OoC / NLOS occurs between the system 200 and the remote device, and the system 200 (e.g., UE) is considered to be stationary. The second scenario is a mobility scenario, where the mobile device movement (e.g., movement of the system or remote device) causes misalignment of the antenna or beam, or both.

[0091] In a blocked scenario, beam selection module 208 detects the presence of a blocked or NLOS scenario based on detecting an unstable wireless connection, even when system 200 is not moving in position or orientation (e.g., is stationary). Data processing system 202 detects an OoC scenario when one or more communication metrics (e.g., measured by baseband module 210) fail to meet a threshold, even when data processing system 202 has determined that the UE is stationary. In some cases, one or more communication metrics fail to meet a threshold because they have a value less than (or equal to) the threshold. In other cases, one or more communication metrics fail to meet a threshold because they have a value greater than the threshold.

[0092] Typically, a wireless channel is more stable in a LOS environment than in a blocked / NLOS environment. When channel stability is low, an NLOS setting or scenario may exist, and beam or antenna switching (or both) may improve channel stability. The data processing system 202 uses the following metrics to determine channel stability. The data processing system 202 determines low channel stability by measuring a drop in the SNR and contextualizing the drop using a standard deviation value associated with the SNR. For example, a standard deviation of more than 1 dB for the SNR may indicate an unstable link and OoC. Table 1 shows exemplary scenarios for LOS and NLOS detection.

[0093] Table 1: Example values for LOS and NLOS scenarios

[0094] LOS NLOS1 NLOS2 SNR(dB) 34 30.4 27.9 SNR standard deviation (dB) 0 1.14 2.22 RMS delay spread (ns) 0 3.76 7.6 The number of dominant paths 1 4 2

[0095] Blockage typically results in a significant initial SNR drop followed by a high SNR deviation. Data processing system 202 determines low channel stability by measuring the delay spread of the signal. Delay spread is typically higher in an NLOS / blocked setting compared to an LOS setting. Data processing system 202 determines low channel stability by measuring changes in AoA. When the wireless LOS path between two devices is blocked, the AoA of the dominant wireless path typically changes.

[0096] In some embodiments, the SNR or delay spread values may increase, and the AoA may change due to mobility rather than due to obstruction. The system 200 is configured to distinguish between obstruction and mobility scenarios by examining motion data from the sensor 204. When the sensor indicates that the system 200 is stationary, it is determined to be a blocking scenario. In some embodiments, the threshold value of the SNR deviation, delay spread, or any other channel metric is determined by training the model before runtime (for example, using a machine learning (ML) or similar model). For example, a machine learning model can be trained with data including various values of the metric to classify the signal from the remote device as blocked or unblocked for each of the various combinations of values. The machine learning model can be used to determine the appropriate threshold value for each of the one or more metrics to ensure that the characterization of the blocked or unblocked signal represents the correct scenario.

[0097] Once the data processing system 202 determines that the system 200 is in an OoC scenario, the beam selection module 208 assists in beamforming. Typically, for an in-coverage scenario, the beam selection module 208 scans only a few adjacent beams. Here, the adjacent beams are defined based on the beam pattern. The beam selection module 208 captures the short-term channel dynamics of the adjacent beams. When OoC is detected, the beam selection module 208 performs an extended hierarchical beam scan. The hierarchical beam scan of the selection module 208 includes an iterative process that combines Figure 4 Further description.

[0098] Figure 4 An exemplary beam set 400 for beam acquisition is shown. The hierarchy begins with the thinner (maximum intensity) beam 402 and the next adjacent beams 404a-404b. Although Figure 4 In the example, the initial beam is center beam 402, but any of beams 402 through 410 can be the initial beam for the scan. The scan begins with the first beam (e.g., beam 402). For the first beam, the link metric is compared to a predefined threshold. As previously described, the predefined threshold can be determined based on machine learning methods or through a heuristic model.

[0099] The beam selection module 208 classifies each beam in the set 400 into groups. Five groups are shown as an illustrative example in the beam set 400. These groups may be classified based on the distance from the current beam 402. Each group may include one or more beams (e.g., beam 402 in group 1, beams 404a and 404b in group 2, beams 406a and 406b in group 3, beams 408a and 408b in group 4, and beams 410a and 410b in group 5).

[0100] The beam selection module 208 is configured to scan consecutive groups to determine the best beam selection. The beam selection module 208 may start with group 1, then scan groups 1-2, then scan groups 1-3, and so on. In a first scenario, a limited number of beam groups (e.g., group 1 or groups 1-2) are scanned. In some second scenarios, all beams are scanned by the beam selection module 208.

[0101] The beam selection module scans beams close to group 1 (e.g., adjacent beams) and then moves to groups further away from group 1. Here, the larger the beam angle / azimuth of the beams of the group compared to the beams in the first beam group, the further away the beams are from the current beam (e.g., group 1). For example, group 5 is further away from group 1 than group 4, group 4 is further away from group 1 than group 3, and group 3 is further away from group 1 than group 2.

[0102] The beam selection module terminates scanning when a specific link metric (e.g., measured RSRP) exceeds a predetermined threshold. In some implementations, the metric may be a Layer 1 (physical layer) metric. For example, the metric may include throughput, received power, SNR, RSRP, RSRQ, SINR, RSSI, SIR, etc. Typically, the thresholds for these metrics are based on specific requirements and are not fixed.

[0103] return Figure 2 In a mobile scenario, the beam selection module 208 is configured to determine a new beam for the system 200 based on the updated pose provided by the motion detection module 206. The change in pose is determined based on the known pose of the system 200 relative to the remote device. The known pose can be determined (for example, once) using BB feedback data (such as the AoA of the signal). Typically, this can be determined based on the location in the antenna array where the highest received power exists. For example, the position associated with the highest power can be provided. Or the value of the angle θ. Typically, the system associates the determined AoA with the peak beam of the remote transmitter. In other words, the estimated strongest lobe is located at the location of the determined AoA. Based on the motion data and the AoA, an updated pose is determined and a new position in the coverage area is selected, as described subsequently. Based on the position in the coverage area, a particular beam of the panel 212 is selected for transmitting and / or receiving data in a communication link with the remote device. The method is described in U.S. Provisional Patent Application 63 / 135,503 filed on January 8, 2021, which is incorporated herein by reference in its entirety.

[0104] Figure 5 Shown is a UE (such as Figure 1 An exemplary process 500 for sensor-assisted antenna and beam selection by a UE 101). Figure 5The process 500 enables a UE to recover from a baseband outage and bypass beam acquisition periods when the UE is stationary or nearly stationary.

[0105] The UE is configured to track the physical movement of the UE, such as using an accumulated movement estimation module 520 (hereinafter referred to as movement module 520). The movement module 520 is configured to respond to data from the UE's data processing system (e.g., Figure 2 The data processing system 202 of the UE receives a request to track the movement of the UE. The mobility module 520 tracks the accumulated movement of the UE during the BB interruption. When the BB interruption is completed and the UE begins to re-establish the mmWave link, the UE refers to the accumulated mobility data to determine whether a new beam should be acquired or whether the previously used beam should be used to resume communication on the mmWave link. In some specific implementations, the mobility module 520 is part of the data processing system 202 of the UE. In some specific implementations, the mobility module 520 is a device separate from the data processing system 202 of the UE.

[0106] Process 500 includes detecting (502) by the data processing system 202 of the UE that a BB interruption is occurring. The UE may detect that a BB interruption is occurring in response to one or more events, such as the second SIM of a dual SIM system using Tx hardware (HW). For example, a user is downloading data on SIM 1 and receives a call (e.g., VoLTE) in SIM 2. The download in SIM 1 is interrupted until the call is completed in SIM 2. When switching back to SIM 1 to resume the download, the beam used may be suboptimal. The UE sends a request for motion data from the movement module 520. The movement module 520 receives the request from the data processing system 202 of the UE, and there is a wait period (514) in which the movement module is idle.

[0107] As previously described, the mobility module 520 is configured to acquire (516) motion data from the sensors of the sensor module 204. The motion data acquired by the mobility module 520 represents the cumulative movement of the UE (e.g., relative to the base station) while the BB outage is occurring. This includes any movement of the UE from the time the BB outage begins to the time the BB communication link is reestablished (e.g., when the BB mmWave call is being resumed). The data processing system 202 of the UE is configured to classify the cumulative movement as exceeding a threshold or not exceeding a threshold movement. In some embodiments, there may be several categories for determining the level of movement. Based on the classification, the data processing system of the UE is configured to determine whether a full new beam acquisition is required, whether to perform a partial acquisition in which an initial beam guess is performed, or whether a previously selected beam can be used to reestablish the communication link. Thus, the UE is configured to limit data stagnation and reduce the negative impact on the user experience when recovering from a baseband outage.

[0108] The UE's data processing system 202 is configured to detect (504) that the BB interruption has ended and that the BB communication link is to be reestablished. The UE requests (506) motion data from the mobility module 520. The mobility module receives the request from the UE's data processing system 202. In response to receiving the request, the mobility module 520 determines (518) the accumulated movement of the UE during the BB interruption. During the BB interruption, the mobility module 520 continuously monitors the motion data received from the sensor module 204.

[0109] The accumulated motion of the motion data includes the accumulated translation of the UE. The accumulated translation includes the distance the UE has moved in any lateral direction during the outage, such as Figure 3 4. The device 410 is shown. The accumulated translation is measured by one or more accelerometers.

[0110] The accumulated motion of the motion data includes accumulated rotation. The accumulated rotation indicates how much the UE has rotated relative to one or more reference domains (e.g., in degrees of pitch, roll, and yaw). For example, the first reference domain is the rotation relative to the direction of the main signal angle of arrival (AoA). In this example, the data processing system of the UE may determine the direction of signal arrival based on link metrics or other BB data. In some embodiments, the AoA data is determined using baseband and gyroscope data or only baseband data. In another example, the mobile module 520 of the UE determines the rotational movement (e.g., pitch, roll, and yaw) relative to the x, y, and z axes of the UE. These data are used when the signal AoA value is unknown and cannot be indirectly determined by the data processing system 202. The data processing system 202 of the UE receives (508) the motion data from the mobile module 520. The mobile module 520 then returns to the standby waiting period (514).

[0111] The data processing system 202 of the UE is configured to perform (510) movement classification based on the motion data received from the movement module 520. The movement classification includes determining how much movement the UE has undergone, wherein each class of the classification involves a corresponding search hierarchy, as previously described in connection with Figure 4 As described. Typically, if the UE has not moved at all, the beam search may be limited to group 1. If the UE has moved a small amount, the data processing system 202 classifies the motion data as a first layer of motion (e.g., less than a first minimum motion threshold). In this exemplary scenario, the data processing system 202 performs a search on group 1 (narrow beam) and one or more adjacent beams (e.g., groups 1-2). Each group 1-5 may be associated with a corresponding motion threshold. In one example, the UE has moved beyond a maximum threshold motion value. In this case, the translation value exceeds the maximum translation threshold, the rotation value exceeds the maximum rotation threshold, or a combination of the translation and rotation values exceeds the combined threshold. The data processing system 202 of the UE is then configured to search for Figure 4 A search is performed on all five groups of (eg, a complete beam acquisition process that searches all beams).

[0112] Figure 4 The group and corresponding search correspond to the beam codebook of the UE. The codebook of beam configuration is hierarchical because the codebook includes wide beams and narrow beams. For example, a wide beam includes a beam with wide coverage and low gain using a single antenna element. Wide beams support fast cell detection because the UE only needs to search a limited number of beams. Narrow beams include beams with narrow coverage and high gain using multiple antenna elements of a phased array. Narrow beams enable the UE to meet RF requirements with their high gain, but multiple narrow beams are required to cover a considerable portion of the UE's range.

[0113] For classification, the accumulated rotation is an input to the UE regarding which type of tiered beam the UE uses after a given BB outage. The rotation threshold is based on the beamwidth of the narrow beam and wide beam. The data processing system 202 uses the accumulated translation in a similar manner. The data processing system 202 determines the equivalent rotation and then selects the wide beam or narrow beam type. The data processing system 202 assumes that the UE determines the equivalent rotation relative to the base station. The data processing system 202 assumes the distance from the UE to the signal source. Typically, a "worst case" assumption of 2 meters can be applied, but a larger distance is typically used because the UE is unlikely to be closer than 2 meters from the base station.

[0114] In some embodiments, the classification includes using a classifier. The classifier can be the result of a machine learning model trained with motion data. For example, different motion data are labeled with an amount of motion. The machine learning model is trained to determine or classify the amount of motion in the motion data. Each amount of motion or classification can be mapped to an optimal beam configuration (e.g., from a codebook). The optimal beam configuration represents the beam that should be selected based on the detected amount of motion. The machine learning model can be trained with labeled motion data. For example, motion below a first threshold represents a stationary state in which beam acquisition is not performed. More specifically, the beam that was used when the BB interruption was first detected is selected to restore the connection. In another example, an amount of motion greater than a minimum threshold but less than a secondary threshold is associated with a second amount of motion. The second amount of motion corresponds to a second set of beam configurations, which may be the optimal beam configuration for the UE that has now moved to a new posture. Additional motion classifications and corresponding mappings to beam configurations (e.g., of a codebook) can be added. In another example, each motion classification can be associated with one or more beam groups (e.g., Figure 4 The threshold can be set based on the output of the trained machine learning model.

[0115] The data processing system is configured to determine (512) an optimal beam for resuming BB communication based on the motion data classification. The data processing system 202 causes the UE to resume transmission using a better beam type (e.g., wide or narrow) and thus proceed to the beam tracking phase more quickly than if another beam type had been selected. As previously described, if the UE has not undergone any rotation, the data processing system 202 causes the UE to reuse the same beam.

[0116] Figure 6 1 is shown for use by a UE 602 (e.g., Figure 1 FIG10 is an exemplary environment 600 in which a UE 101 (e.g., a UE 602) classifies accumulated motion. The UE 602 is configured for a narrow beam communication link range 608, a wide beam communication link range 604, and an intermediate beam communication link range 606 between the wide beam communication link and the narrow beam communication link. The intermediate beam range 606 is wider than the narrowest beam range 608 and narrower than the widest beam range 604. In this example, the narrowest beam range 608 is initially pointed at the signal source, but this is not necessarily the case in a given environment. As previously described, the UE 602 is configured to determine the equivalent rotation 610 and relative movement of the UE 602 relative to the signal source 614 before the BB interruption, and to determine the signal source position 616 after the BB interruption is complete. Figure 6 In the illustration of FIG, a middle beam range 606 is selected for communication based on the UE's classification of accumulated motion. The UE performs a search for beams in range 606 to reestablish a connection and does not need to search for beams in range 604 outside of range 606. This reduces beam acquisition time and reduces performance disruption to the user.

[0117] Figure 7 An exemplary process 700 for selecting a beam based on sensor feedback according to some implementations of the present disclosure is shown. In some examples, Figures 1 to 6 An electronic device, network, system, chip, or component, or a portion or implementation thereof, may be configured to perform process 700. Process 700 includes obtaining (702) motion data from one or more motion sensors coupled to a wireless device. In some implementations, the sensors may include one or both of an accelerometer and a gyroscope. The process includes determining (704) whether the wireless device is stationary or moving. The state of the wireless device may be determined based on a given time period (e.g., the last few seconds). The motion data indicates a change (or lack of change) in the position and / or orientation of the wireless device.

[0118] In response to determining that the wireless device is in motion (and not stationary), process 700 includes performing (706) beam selection based on motion data. Examples of estimating beams based on motion data are relatedly described in U.S. Provisional Patent Application 63 / 135,503, filed on January 8, 2021, which is incorporated herein by reference in its entirety. In some implementations, performing (706) beam selection based on motion data includes determining that the wireless device is experiencing blocking or BB interruption, such as described herein in conjunction with Figure 8 described.

[0119] Process 700 includes, in response to determining that the wireless device is stationary, obtaining (708) a link metric for a wireless mmWave communication link with a remote device, such as a base station (e.g., a gNB). In one example, the link metric may include SNR, delay spread, AoA, RSRP, a received signal strength indicator (RSSI), or any similar link metric indicative of link stability. The wireless device is configured to compare (710) the link metric value with a threshold value indicative of link stability. For example, the standard deviation of one or more of RSRP, SNR, delay spread, or similar values may be measured to measure the corresponding link metric over a time period. If the standard deviation value exceeds a threshold (e.g., 1 dB for SNR), the link is deemed unstable, even if the wireless device is not moving. Thus, despite the wireless device being stationary, the wireless device is determined to be in an OoC or NLOS scenario due to fluctuations in the link metric.

[0120] The process 700 includes, in response to determining that the link is unstable and the wireless device is in an Out-of-C (OoC) scenario, performing (712) a search of available beams. For example, beams adjacent to a current beam may be searched to determine whether another beam has a better link quality than the current beam. Because the wireless device is stationary, a broader search of the beam space may be performed, and the results of each beam test are comparable to each other. To initiate the search, the wireless device is configured to first search the nearest neighboring beam. In some implementations, the process 700 includes selecting the one or more corresponding thresholds by applying training data representing values of the one or more metrics to a machine learning model. The machine learning model is configured to classify the synchronization signal as blocked or unblocked.

[0121] Process 700 includes determining (714) whether any searched beam exceeds a link quality threshold. If no searched beam exceeds the link quality threshold, process 700 includes expanding (716) the search to additional beam groups. In this example, beam groups further away from the current beam may be searched. This process is repeated until a suitable link exceeding the quality threshold is found, or until all beams have been searched.

[0122] Process 700 includes performing (718), by the wireless device, data transmission using the selected beam that satisfies the link quality threshold. Thus, process 700 performs beam acquisition in a stationary context.

[0123] Figure 8 An exemplary process 800 for selecting a beam based on sensor feedback according to some implementations of the present disclosure is shown. In some examples, Figures 1 to 6 An electronic device, network, system, chip, or component, or a portion or implementation thereof, may be configured to perform process 800. Process 800 includes detecting (802), by a wireless device, an interruption in BB communication from the wireless device to a remote device, such as a base station (e.g., a gNB). The interruption may be detected directly (e.g., when the UE switches to a second SIM in dual SIM operation). The interruption may be detected indirectly (e.g., when the wireless device detects that the BB link is unavailable).

[0124] Process 800 includes, after detecting that a BB outage is occurring, initiating measurement (804) of motion data from one or more motion sensors coupled to the wireless device. In some implementations, these sensors may include one or both of an accelerometer and a gyroscope. The motion data is accumulated for the length of the BB outage. A motion module (e.g., module 520 described previously) is configured to determine the total translation and rotation of the wireless device during the outage. As previously described, the motion may be relative to a base station or beam direction.

[0125] Process 800 includes determining (806) that the BB outage has ended. The motion module sends motion data to a data processing system that performs process 800. Process 800 includes classifying the motion data (808). The classification includes determining how much the wireless device has moved during the BB outage. The classification associates different amounts of movement with different beam groups.

[0126] The process includes determining (810) whether the wireless device was stationary or moving during the BB outage. If the wireless device was stationary during the outage, the process 800 includes selecting (812) the beam used before the BB outage without performing a beam acquisition search. This enables rapid recovery of the mmWave link after the BB outage is complete. If the wireless device was mobile, the process 800 includes determining (814) a beam group to test during beam acquisition based on the classification of the movement. The amount of rotation or translation indicates that a beam at a given distance from the current beam (e.g., a wide beam) may be required to reestablish the mmWave link.

[0127] The process 800 includes performing (816) a search on the selected beam or beam group. In some implementations, the process 800 includes selecting the one or more corresponding thresholds by applying training data representing values of the one or more metrics to a machine learning model. The machine learning model is configured to classify the synchronization signal as blocked or unblocked.

[0128] Process 800 includes determining (818) whether any searched beam exceeds a link quality threshold. If no searched beam exceeds the link quality threshold, process 800 includes expanding (820) the search to additional beam groups. In this example, beam groups further away from the current beam may be searched. This process is repeated until a suitable link exceeding the quality threshold is found, or until all beams have been searched. Process 800 includes transmitting (822) data by the wireless device using the selected beam that meets the link quality threshold.

[0129] In some embodiments, process 800 includes determining an initial pose of the wireless device relative to the remote device using angle of arrival (AoA) data. The pose of the wireless device relative to the remote device is based on motion data indicating a change in position or orientation of the wireless device from the initial pose.

[0130] In some embodiments, the one or more metrics include at least one of a signal-to-noise ratio (SNR) of the synchronization signal, a delay spread value of the synchronization signal, and a variation value of the AoA of the synchronization signal.

[0131] In some embodiments, the wireless device and the remote device are configured for mm-wave communications using Frequency Range 2 (FR2).

[0132] In some embodiments, the wireless device includes at least three antenna arrays, and wherein each antenna array includes a specified number of beam configurations, which may be greater than ten.

[0133] In some embodiments, determining a pose of the wireless device relative to the remote device based on the motion data includes determining that one or more of translational motion or rotational motion of the wireless device exceeds a motion threshold. Process 800 includes selecting a particular beam set for beam acquisition in response to determining that the motion threshold is exceeded.

[0134] It is understood that the use of personally identifiable information should be subject to privacy policies and practices that are generally recognized to meet or exceed industry or government requirements for maintaining user privacy. Specifically, personally identifiable information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use, and the nature of authorized use should be clearly stated to users.

[0135] The specific implementation of the subject matter and functional operations described in this specification may be implemented in digital electronic circuits, in tangibly embodied computer software or firmware, in computer hardware (including the structures disclosed in this specification and their structural equivalents), or in a combination of one or more thereof. The software specific implementation of the subject matter may be implemented as one or more computer programs. Each computer program may include one or more modules of computer program instructions encoded on a tangible, non-transitory computer-readable computer storage medium for execution by a data processing device or for controlling the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded in / on an artificially generated propagation signal. In one example, the signal may be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver device for execution by a data processing device. The computer storage medium may be a combination of a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a computer storage medium.

[0136] The terms "data processing apparatus," "computer," and "computing device" (or equivalents as understood by those of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus may encompass various apparatuses, devices, and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. The apparatus may also include a dedicated logic circuit, including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some specific implementations, the data processing apparatus or dedicated logic circuit (or a combination of data processing apparatus or dedicated logic circuit) may be based on hardware or software (or a combination of hardware and software). The apparatus may optionally include code that creates an execution environment for a computer program, such as code that constitutes a processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of a data processing apparatus with or without a conventional operating system (e.g., LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS).

[0137] A computer program, which may also be referred to or described as a program, software, software application, module, software module, script, or code, may be written in any form of programming language. Programming languages may include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. The program may be deployed in any form, including as a standalone program, module, component, subroutine, or unit for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. The program may be stored in a portion of a file that stores other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files that store one or more modules, subroutines, or code portions. The computer program may be deployed to execute on a single computer or on multiple computers located at, for example, a single site or distributed across multiple sites interconnected by a communication network. Although the portions of the program shown in the various figures may be shown as separate modules that implement various features and functions through various objects, methods, or processes, the program may alternatively include multiple submodules, third-party services, components, and libraries. Conversely, the features and functions of the various components may be combined into a single component as appropriate. The threshold value used to perform the computational determination may be determined statically, dynamically, or both statically and dynamically.

[0138] Computer-readable media (transitory or non-transitory, as the case may be) suitable for storing computer program instructions and data can include all forms of permanent / non-permanent and volatile / non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as magnetic tape, magnetic tape cartridges, cassettes, and internal / removable disks. Memory can store a variety of objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. The types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. In addition, memory can include logs, policies, security or access data, and report files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0139] Although this specification contains many specific implementation details, these details should not be construed as limitations on the scope of what is claimed, but rather as descriptions of features that may be unique to a particular implementation. Certain features described in this specification in the context of different implementations may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented in multiple implementations, either individually or in any suitable subcombination. Furthermore, although previously described features may be described as functioning in certain combinations and even initially claimed as such, one or more features in a claimed combination may, in some cases, be removed from that combination, and a claimed combination may involve subcombinations or variations of subcombinations.

[0140] Specific implementations of the subject matter have been described. Other implementations, modifications, and permutations of the described implementations are within the scope of the following claims and will be apparent to those skilled in the art. Although operations are shown in a particular order in the drawings or claims, this should not be construed as requiring that such operations be performed in the particular order shown or in a sequential order, or that all shown operations (some operations may be considered optional) be performed to achieve the desired result. In some cases, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and may be performed as appropriate.

[0141] In addition, the division or integration of various system modules and components in the previously described specific implementations should not be understood as requiring such division or integration in all specific implementations, and it should be understood that the program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0142] Therefore, the exemplary embodiments described above do not limit or restrict the present disclosure. Other changes, substitutions and alterations are also possible without departing from the spirit and scope of the present disclosure.

[0143] Example

[0144] In the following sections, additional exemplary embodiments are provided.

[0145] Example 1 includes a method of operating a UE, the method comprising: obtaining motion data from one or more motion sensors coupled to a wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device, the remote device communicating with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics of the beam, the link metric being associated with a time period during which the wireless device is stationary; determining, during the time period during which the wireless device is stationary, a link stability value associated with the beam based on the one or more link metrics; determining, based on the link stability value associated with the beam, that the wireless device is out of coverage (OoC) relative to the remote device; in response to determining that the wireless device is OoC relative to the remote device, selecting a first beam group comprising one or more first beams generated by the wireless device; determining, for each beam of the first beam group, a corresponding link stability value for the beam; selecting, based on the determination, a specific beam from the one or more first beams; and reestablishing communication with the remote device using the specific beam.

[0146] Example 2 includes a method according to Example 1 or some other example herein, wherein determining that the wireless device is out of coverage (OoC) relative to the remote device based on a link stability value associated with the beam includes: comparing the link stability value with a predetermined threshold; and determining that the link stability value fails to satisfy the threshold based on the comparison.

[0147] Example 3 includes a method according to Example 1 or some other example herein, wherein the threshold is determined using a machine learning model trained with link metric data, the link metric data being labeled as an unstable link representing an OoC scenario or a stable link representing a line of sight (LOS) scenario.

[0148] Example 4 includes the method of Example 3 or some other example herein, wherein the link stability value represents a link metric standard deviation of the one or more link metrics.

[0149] Example 5 includes the method of Example 1 or some other example herein, wherein the link stability value represents a link metric minimum value of the one or more link metrics.

[0150] Example 6 includes a method according to Example 1 or some other example herein, wherein the one or more link metrics include at least one of a signal-to-noise ratio (SNR) of a signal received from a remote device, a delay spread value of the signal, a variation value of an angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0151] Example 7 includes the method of Example 1 or some other example herein, wherein selecting a particular beam comprises selecting a first beam of a first beam group associated with a respective link stability value that satisfies a threshold link stability value.

[0152] Example 8 includes a method according to Example 1 or some other example herein, wherein selecting a particular beam includes: determining that a first beam group does not include any beam associated with a link stability value that satisfies a link stability threshold; in response to the determination, selecting a second beam group, wherein a second beam of the second beam group is farther away from the beam than the first beam of the first beam group; and selecting a second beam from the second beam group.

[0153] Example 9 includes a method according to Example 1 or some other example herein, wherein the one or more motion sensors include at least an accelerometer or a gyroscope.

[0154] Example 10 includes the method of Example 1 or some other example herein, wherein the wireless device and the remote device are configured for mm-wave communication using Frequency Range 2 (FR2).

[0155] Example 11 includes the method of Example 1 or some other example herein, further comprising periodically retrieving motion data to determine whether the wireless device is moving or stationary.

[0156] Example 12 includes the method of Example 1 or any other example herein, wherein the wireless device includes an antenna array, the antenna array including a specified number of configurations, and wherein selecting a particular beam includes selecting one of the specified number of configurations. The specified number of beam configurations may include between 1-30 beam configurations per panel. In some implementations, there are more than 30 beam configurations per panel.

[0157] Example 13 includes a method of operating a UE, the method comprising: receiving motion data from one or more motion sensors of a wireless device, the motion data indicating motion of the wireless device during an interruption of a baseband (BB) communication link between the wireless device and a remote device; retrieving a mapping of an amount of motion of the wireless device to a corresponding beam group of the wireless device; classifying the motion data as representing an amount of motion; based on the classification, identifying at least one mapped beam group to perform beam acquisition; selecting a specific beam for the wireless device from the at least one beam group; and reestablishing communication with the remote device using the specific beam.

[0158] Example 14 includes a method according to Example 13 or some other example herein, further comprising detecting a BB communication link interruption; and in response to the detection, initiating measurement of movement of the wireless device during the BB communication link interruption, wherein the movement data represents a total movement of the wireless device during the BB communication link interruption.

[0159] Example 15 includes a method according to Example 13 or some other example herein, wherein classifying the motion data as representing an amount of motion includes: determining that the wireless device has not moved during the BB communication link interruption; based on the determination, identifying an initial beam included in at least one beam group used when the wireless device detects the BB communication link interruption; and wherein the specific beam includes the initial beam.

[0160] Example 16 includes the method of Example 15 or some other example herein, wherein the initial beam is selected independent of a beam acquisition process of the wireless device.

[0161] Example 17 includes the method of Example 13 or some other example herein, wherein selecting a particular beam is based on one or more link metrics associated with each beam in at least one beam group in a beam acquisition process.

[0162] Example 18 includes the method of Example 13 or some other example herein, wherein the mapping of the amount of motion of the wireless device to the corresponding beam group includes a relationship of the specific amount of motion to a beam configuration entry in a beamforming codebook.

[0163] Example 19 includes a method according to Example 13 or some other example herein, wherein the classification comprises executing a machine learning model trained using labeled motion data.

[0164] Example 20 may include an apparatus comprising means for performing one or more elements of a method as described in or related to any of Examples 1 to 19, or any other method or process described herein.

[0165] Example 21 may include one or more non-transitory computer-readable media, which include instructions that, when executed by one or more processors of an electronic device, cause the electronic device to perform one or more elements of a method described in or related to any of Examples 1 to 19 or any other method or process described herein.

[0166] Example 22 may include a device comprising a logic component, module, or circuit for performing one or more elements of a method described in or related to any of Examples 1 to 19 or any other method or process described herein.

[0167] Example 23 may include a method, technique, or process as described in or related to any of Examples 1 to 19, or a portion or part thereof.

[0168] Example 24 may include a device comprising: one or more processors and one or more computer-readable media, wherein the one or more computer-readable media include instructions that, when executed by the one or more processors, cause the one or more processors to perform a method, technique, or process, or portion thereof, as described in or related to any of Examples 1 to 19.

[0169] Example 25 may include a signal as described in or related to any of Examples 1 to 19, or a portion or part thereof.

[0170] Example 26 may include a datagram, information element, packet, frame, fragment, PDU, or message, or a portion or component thereof, as described or related to any of Examples 1 to 19 or otherwise described in this disclosure.

[0171] Example 27 may include a signal encoded with data as described in or related to any of Examples 1 to 19 or otherwise described in this disclosure, or a portion or component thereof.

[0172] Example 28 may include a signal encoded with a datagram, IE, packet, frame, segment, PDU, or message as described or related to any of Examples 1 to 19, or a portion or component thereof, or otherwise described in this disclosure.

[0173] Example 29 may include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors causes the one or more processors to perform a method, technique, or process, or portion thereof, according to or related to any one of Examples 1 to 19.

[0174] Example 30 may include a computer program including instructions, wherein execution of the program by a processing element causes the processing element to perform a method, technique, or process according to or related to any one of Examples 1 to 19, or a portion thereof.

[0175] Example 31 may include signals in a wireless network as shown and described herein.

[0176] Example 32 may include a method of communicating in a wireless network as shown and described herein.

[0177] Example 33 may include a system for providing wireless communications as shown and described herein.

[0178] Example 34 may include an apparatus for providing wireless communications as shown and described herein.

[0179] Example 35 includes a method comprising: obtaining motion data from one or more motion sensors coupled to a wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device, the remote device communicating with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics for the beam, the link metrics being associated with a time period during which the wireless device is stationary; determining, based on the one or more link metrics, a link stability value associated with the beam during the time period during which the wireless device is stationary; determining, based on the link stability value associated with the beam, that the wireless device is out of coverage (OoC) relative to the remote device; in response to determining that the wireless device is OoC relative to the remote device, selecting a first beam group comprising one or more first beams generated by the wireless device; determining, for each beam of the first beam group, a corresponding link stability value for the beam; selecting, based on the determination, a particular beam from the one or more first beams; and reestablishing communication with the remote device using the particular beam.

[0180] Example 36 includes a method according to Example 35, wherein based on the link stability value associated with the beam, determining that the wireless device is out of coverage (OoC) relative to the remote device includes: comparing the link stability value with a predetermined threshold; and determining that the link stability value fails to meet the threshold based on the comparison.

[0181] Example 37 includes a method according to Example 36, wherein the threshold is determined using a machine learning model trained with link metric data, the link metric data being labeled as an unstable link representing an OoC scenario or a stable link representing a line of sight (LOS) scenario.

[0182] Example 38 includes the method of Example 36, wherein the link stability value represents a link metric standard deviation of the one or more link metrics.

[0183] Example 39 includes the method of Example 36, wherein the link stability value represents a link metric minimum value of the one or more link metrics.

[0184] Example 40 includes a method according to Example 35, wherein the one or more link metrics include at least one of a signal-to-noise ratio (SNR) of a signal received from the remote device, a delay spread value of the signal, a change value of an angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0185] Example 41 includes the method of Example 35, wherein selecting a particular beam comprises selecting a first beam of the first beam group associated with a corresponding link stability value that satisfies a threshold link stability value.

[0186] Example 42 includes a method according to Example 35, wherein selecting a particular beam includes: determining that the first beam group does not include any beam associated with a link stability value that satisfies a link stability threshold; in response to the determination, selecting a second beam group, wherein a second beam of the second beam group is farther away from the beam than the first beam of the first beam group; and selecting a second beam from the second beam group.

[0187] Example 43 includes the method of Example 35, wherein the one or more motion sensors include at least an accelerometer or a gyroscope.

[0188] Example 44 includes the method of Example 35, wherein the wireless device and the remote device are configured for mm-wave communication using Frequency Range 2 (FR2).

[0189] Example 45 includes the method of Example 36, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0190] Example 46 includes a method according to Example 35, wherein the wireless device includes an antenna array, the antenna array includes at least a specified number of beam configurations, and wherein selecting the particular beam includes selecting one beam configuration of the at least specified number of beam configurations.

[0191] Example 47 includes a method comprising: receiving motion data from one or more motion sensors of a wireless device, the motion data indicating motion of the wireless device during an interruption of a baseband (BB) communication link between the wireless device and a remote device; retrieving a mapping of an amount of motion of the wireless device to a corresponding beam group of the wireless device; classifying the motion data as representing an amount of motion; based on the classification, identifying at least one beam group of the mapping to perform beam acquisition; selecting a specific beam for the wireless device from the at least one beam group; and reestablishing communication with the remote device using the specific beam.

[0192] Example 48 includes the method according to Example 47, further comprising: detecting the BB communication link interruption; and in response to the detection, initiating measurement of the movement of the wireless device during the BB communication link interruption, wherein the movement data represents the total movement of the wireless device during the BB communication link interruption.

[0193] Example 49 includes a method according to Example 47, wherein classifying the motion data as representing the amount of motion includes: determining that the wireless device has not moved during the interruption of the BB communication link; based on the determination, identifying an initial beam included in the at least one beam group used when the wireless device detects the interruption of the BB communication link; and wherein the specific beam includes the initial beam.

[0194] Example 50 includes the method of Example 49, wherein the initial beam is selected independent of a beam acquisition process of the wireless device.

[0195] Example 51 includes the method of Example 47, wherein selecting the particular beam is based on one or more link metrics associated with each beam in the at least one beam group in a beam acquisition process.

[0196] Example 52 includes the method of Example 47, wherein the mapping of the amount of motion of the wireless device to the corresponding beam group comprises a relationship of a specific amount of motion to a beam configuration entry in a beamforming codebook.

[0197] Example 53 includes a method according to Example 47, wherein the classification comprises executing a machine learning model trained using labeled motion data.

[0198] Example 54 includes a user equipment (UE) comprising: at least one motion sensor; one or more antenna arrays, each antenna array configured for at least two beam configurations; one or more processors; and a non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining motion data from one or more motion sensors coupled to a wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device that communicates with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics for the beam, the link metrics representing one or more link metrics for the beam; a metric associated with a time period during which the wireless device is stationary; determining, during the time period during which the wireless device is stationary, a link stability value associated with the beam based on the one or more link metrics; determining, based on the link stability value associated with the beam, that the wireless device is out of coverage (OoC) relative to the remote device; in response to determining that the wireless device is OoC relative to the remote device, selecting a first beam group comprising one or more first beams generated by the wireless device; determining, for each beam of the first beam group, a corresponding link stability value for the beam; selecting, based on the determination, a specific beam from the one or more first beams; and reestablishing communication with the remote device using the specific beam.

[0199] Example 55 includes a UE according to Example 54, wherein based on the link stability value associated with the beam, determining that the wireless device is out of coverage (OoC) relative to the remote device includes: comparing the link stability value with a predetermined threshold; and determining that the link stability value fails to meet the threshold based on the comparison.

[0200] Example 56 includes a UE according to Example 55, wherein the threshold is determined using a machine learning model trained with link metric data, the link metric data being labeled as an unstable link representing an OoC scenario or a stable link representing a line of sight (LOS) scenario.

[0201] Example 57 includes a UE according to Example 55, wherein the link stability value represents a link metric standard deviation of the one or more link metrics.

[0202] Example 58 includes a UE according to Example 55, wherein the link stability value represents a link metric minimum value of the one or more link metrics.

[0203] Example 59 includes a UE according to Example 54, wherein the one or more link metrics include at least one of a signal-to-noise ratio (SNR) of a signal received from the remote device, a delay spread value of the signal, a change value of an angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0204] Example 60 includes a UE according to Example 54, wherein selecting a particular beam comprises selecting a first beam of the first beam group associated with a corresponding link stability value that satisfies a threshold link stability value.

[0205] Example 61 includes a UE according to Example 54, wherein selecting a particular beam includes: determining that the first beam group does not include any beam associated with a link stability value that satisfies a link stability threshold; in response to the determination, selecting a second beam group, wherein a second beam of the second beam group is farther away from the beam than the first beam of the first beam group; and selecting a second beam from the second beam group.

[0206] Example 62 includes a UE according to Example 54, wherein the one or more motion sensors include at least an accelerometer or a gyroscope.

[0207] Example 63 includes the UE of Example 54, wherein the wireless device and the remote device are configured for mm-wave communication using frequency range 2 (FR2).

[0208] Example 64 includes the UE of Example 54, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0209] Example 65 includes a UE according to Example 54, wherein the wireless device includes an antenna array, the antenna array includes at least a specified number of beam configurations, and wherein selecting the particular beam includes selecting one beam configuration of the at least specified number of beam configurations.

[0210] Example 66 includes a user equipment (UE) comprising: at least one motion sensor; one or more antenna arrays, each antenna array configured for at least two beam configurations; one or more processors; and a non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: receiving motion data from one or more motion sensors of a wireless device, the motion data indicating motion of the wireless device during an interruption of a baseband (BB) communication link between the wireless device and a remote device; retrieving a mapping of an amount of motion of the wireless device to a corresponding beam group of the wireless device; classifying the motion data as representing an amount of motion; based on the classification, identifying at least one beam group of the mapping to perform beam acquisition; selecting a specific beam for the wireless device from the at least one beam group; and reestablishing communication with the remote device using the specific beam.

[0211] Example 67 includes a UE according to Example 66, wherein the operation further includes: detecting the interruption of the BB communication link; and in response to the detection, initiating measurement of the movement of the wireless device during the interruption of the BB communication link, wherein the movement data represents the total movement of the wireless device during the interruption of the BB communication link.

[0212] Example 68 includes a UE according to Example 66, wherein classifying the motion data as representing the amount of motion includes: determining that the wireless device has not moved during the interruption of the BB communication link; based on the determination, identifying an initial beam included in the at least one beam group used when the wireless device detects the interruption of the BB communication link; and wherein the specific beam includes the initial beam.

[0213] Example 69 includes a UE according to Example 68, wherein the initial beam is selected independent of a beam acquisition process of the wireless device.

[0214] Example 70 includes a UE according to Example 66, wherein selecting the particular beam is based on one or more link metrics associated with each beam in the at least one beam group in a beam acquisition process.

[0215] Example 71 includes the UE of Example 66, wherein the mapping of the amount of motion of the wireless device to the corresponding beam group comprises a relationship of a specific amount of motion to a beam configuration entry in a beamforming codebook.

[0216] Example 72 includes a UE according to Example 66, wherein the classification comprises executing a machine learning model trained using labeled motion data.

[0217] Example 73 includes a processor for a user equipment (UE), the processor comprising: circuitry configured to communicate with a remote device; and circuitry for executing one or more instructions that, when executed, cause the processor to perform operations comprising: obtaining motion data from one or more motion sensors coupled to a wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device that communicates with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics for the beam, the link metrics being associated with a time period during which the wireless device is stationary; and determining, based at least on the motion data, that the wireless device is stationary relative to a remote device that communicates with the wireless device using a beam generated by the wireless device. During the period of inactivity, determine a link stability value associated with the beam based on the one or more link metrics; determine that the wireless device is out of coverage (OoC) relative to the remote device based on the link stability value associated with the beam; in response to determining that the wireless device is OoC relative to the remote device, select a first beam group, the first beam group including one or more first beams generated by the wireless device; determine a corresponding link stability value of the beam for each beam of the first beam group; based on the determination, select a specific beam from the one or more first beams; and re-establish communication with the remote device using the specific beam.

[0218] Example 74 includes a processor for a user equipment (UE), the processor comprising: a circuit configured to communicate with a remote device; and a circuit for executing one or more instructions, which, when executed, cause the processor to perform operations including: receiving motion data from one or more motion sensors of a wireless device, the motion data indicating motion of the wireless device during an interruption of a baseband (BB) communication link between the wireless device and a remote device; retrieving a mapping of an amount of motion of the wireless device to a corresponding beam group of the wireless device; classifying the motion data as representing an amount of motion; based on the classification, identifying at least one beam group of the mapping to perform beam acquisition; selecting a specific beam for the wireless device from the at least one beam group; and reestablishing communication with the remote device using the specific beam.

[0219] Example 75 comprises a method substantially as shown and described herein.

[0220] Example 76 comprises a system substantially as shown and described herein.

[0221] Example 77 comprises a mobile device substantially as shown and described herein.

Claims

1. A method for wireless communication, comprising: obtaining motion data from one or more motion sensors coupled to the wireless device; determining, based at least on the motion data, that the wireless device is stationary relative to a remote device that communicates with the wireless device using a beam generated by the wireless device; obtaining data representing one or more link metrics for the beam, the link metrics associated with a time period during which the wireless device is stationary; determining, for the time period when the wireless device is stationary, a link stability value associated with the beam based on the one or more link metrics; determining, based on the link stability value associated with the beam, that the wireless device is out of coverage (OoC) relative to the remote device; In response to determining that the wireless device is out of range with respect to the remote device, selecting a first beam set comprising one or more first beams generated by the wireless device; determining, for each beam of the first beam group, a respective link stability value for the beam; selecting a particular beam of the one or more first beams based on the determination; as well as Communication with the remote device is re-established using the specific beam.

2. The method of claim 1 , wherein determining that the wireless device is out of coverage (OoC) relative to the remote device based on the link stability value associated with the beam comprises: comparing the link stability value with a predetermined threshold; as well as A determination is made based on the comparison that the link stability value does not satisfy the threshold.

3. The method of claim 2, wherein the threshold is determined using a machine learning model trained with link metric data, the link metric data being labeled as an unstable link representing an OoC scenario or a stable link representing a line-of-sight (LOS) scenario.

4. The method according to claim 1, wherein the one or more link metrics include at least one of the following: a signal-to-noise ratio (SNR) of a signal received from the remote device, a delay spread value of the signal, a variation value of an angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

5. The method of claim 1 , wherein selecting a specific beam comprises: A first beam in the first beam group associated with a corresponding link stability value that satisfies a threshold link stability value is selected.

6. The method of claim 1 , wherein selecting a specific beam comprises: determining that the first beam set does not include any beam associated with a link stability value that satisfies a link stability threshold; In response to the determining, selecting a second beam group, wherein a second beam of the second beam group is further away from the beam than the first beam of the first beam group; as well as A second beam is selected from the second beam group.

7. The method of claim 1, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

8. The method of claim 1, wherein the wireless device comprises an antenna array comprising at least a specified number of beam configurations, and wherein selecting the particular beam comprises selecting one of the at least specified number of beam configurations.

9. An apparatus for wireless communication, comprising: one or more processors; as well as A non-transitory computer-readable storage medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the following operations, including: Obtaining motion data; determining, based at least on the motion data, that the device is stationary relative to a remote device that communicates with the device using a beam generated by the device; obtaining data representing one or more link metrics for the beam, the link metrics associated with a time period during which the device is stationary; determining, for the time period when the device is stationary, a link stability value associated with the beam based on the one or more link metrics; determining, based on the link stability value associated with the beam, that the device is out of coverage (OoC) relative to the remote device; In response to determining that the device is out of range with respect to the remote device, selecting a first beam set comprising one or more first beams generated by the device; determining, for each beam of the first beam group, a respective link stability value for the beam; Based on the determination, selecting a particular beam of the one or more first beams; and Communication with the remote device is re-established using the specific beam.

10. The apparatus of claim 9, wherein determining that the device is out of coverage (OoC) relative to the remote device based on the link stability value associated with the beam comprises: comparing the link stability value with a predetermined threshold; as well as A determination is made based on the comparison that the link stability value does not satisfy the threshold.

11. The apparatus of claim 10, wherein the threshold is determined using a machine learning model trained with link metric data, the link metric data being labeled as an unstable link representing an Out-of-Case scenario or a stable link representing a Line-of-Sight (LOS) scenario.

12. The apparatus according to claim 9, wherein the one or more link metrics include at least one of the following: a signal-to-noise ratio (SNR) of a signal received from the remote device, a delay spread value of the signal, a variation value of an angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

13. The apparatus of claim 9, wherein selecting a particular beam comprises: A first beam in the first beam group associated with a corresponding link stability value that satisfies a threshold link stability value is selected.

14. The apparatus of claim 9, wherein selecting a particular beam comprises: determining that the first beam set does not include any beam associated with a link stability value that satisfies a link stability threshold; In response to the determining, selecting a second beam group, wherein a second beam of the second beam group is further away from the beam than the first beam of the first beam group; as well as A second beam is selected from the second beam group.

15. The apparatus of claim 9, wherein the operations further comprise periodically retrieving the motion data to determine whether the device is moving or stationary.

16. The apparatus of claim 9, wherein the device comprises an antenna array comprising at least a specified number of beam configurations, and wherein selecting the particular beam comprises selecting one of the at least specified number of beam configurations.

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