Method and apparatus for beam selection refinement for wireless communication in stationary conditions

Abstract

The present disclosure relates to beam selection refinement for wireless communications in stationary conditions. Systems and processes are described for beam selection performance improvement in stationary conditions for a wireless device (e.g., a user equipment, UE). A data processing system of the UE is configured to perform a beam sweep to test a plurality of high-gain candidate beams on a physical downlink shared channel (PDSCH) and a link adaptation (LA) channel state information reference signal (LA-CSI-RS) when the UE is not moving and the data processing system of the UE determines that the channel is stable. The data processing system is configured to refine a given beam selection based on determined beam capacity (e.g., from PDSCH signaling and / or LA-CSI-RS signals) to optimize data throughput.

Description

TECHNICAL FIELD

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

[0002] A wireless device can include a phased array antenna (e.g., in a wireless network) for transmitting signals to and receiving signals from a remote device. A phased array includes a computer-controlled array of antennas that produces one or more radio wave beams that can be electronically pointed in different directions without moving the antennas.

[0003] Beamforming or spatial filtering is a signal processing technique that uses an array of antennas to shape the beam of signals. This is achieved by combining elements in the antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends (e.g., by a phased array antenna) to achieve spatial selectivity. SUMMARY

[0004] This application describes systems and processes for beam selection performance improvement in stationary conditions of a wireless device (e.g., user equipment, UE). A data processing system of the UE is configured to perform a beam sweep to test a plurality of high-gain candidate beams on a physical downlink shared channel (PDSCH) and a link adaptation (LA) channel state information reference signal (LA-CSI-RS) when the UE is not moving and the data processing system of the UE determines that the channel is stable. The data processing system is configured to refine a given beam selection based on determined beam capacity (e.g., from PDSCH signaling and / or LA-CSI-RS signals) to optimize data throughput.

[0005] As described in greater detail subsequently, a UE using a millimeter wave (mmWave) network typically performs a beamforming operation. Beamforming includes functionality where the UE configures a phased array of transmitters to focus transmission energy in a particular direction (beam), thereby overcoming millimeter wave propagation losses. Beams are typically fixed and designed a priori in a codebook, such as phase-amplitude combinations of antenna elements. In a typical communication system, the UE performs multiple beam measurements on reference symbols, which are signals known to the UE.

[0006] Cellular communication technologies, such as Third Generation Partnership Project (3GPP) Fifth Generation New Radio (5G NR) millimeter wave communication systems, enable a UE to perform beam management measurements using synchronization symbol block (SSB) signals. These signals are single-port signals, while data signals (e.g., physical downlink shared channel, PDSCH) are dual-port signals. Single-port signals enable the UE to estimate only a general received power level (reference signal received power or RSRP) or signal-to-noise or signal-to-interference-plus-noise ratio (SNR or SINR) per beam based on the SSB signals. In this case, the UE’s beamforming process is a limited evaluation of the beams based on these link metrics.

[0007] The systems and processes described herein for beam selection performance improvement in stationary conditions of a wireless device enable the UE to use PDSCH signals for beamforming. PDSCH supports multiple-input multiple-output (MIMO) communication with more than one layer (e.g., two or more layers). Channel capacity depends on the signal level / SINR, but also on the general conditioning of the channel matrix. Here, channel capacity refers to the total throughput that the channel can accommodate.

[0008] Generally, to estimate the capacity of a MIMO channel on PDSCH, the UE uses link-adaptive CSI-RS (or LA-CSI-RS). LA-CSI-RS is a 2-port reference signal. The UE estimates the PDSCH channel capacity through the LA-CSI-RS and reports to the base station (e.g., next generation nodeB or gNB) using a channel quality indicator (CQI), a rank indicator (RI), and a precoding matrix indicator (PMI). The systems and processes described herein enable the UE to evaluate the UE’s beams using LA-CSI-RS.

[0009] The systems and methods described in this document can enable one or more of the following advantages. The systems and processes described herein enable the UE to estimate PDSCH capacity (e.g., throughput) before selecting a final beam during a beam acquisition process. Generally, the UE first selects a beam (e.g., during a beam acquisition process) based on information included in an SSB (and, in some cases, a single-port signal). This is because LA-CSI does not have multiple repetitions. The CSI reference signal is also available in a CQI report that corresponds to the channel capacity on PDSCH. The UE is then able to estimate PDSCH capacity. However, in this case, the UE does not select a beam based on the channel capacity estimate. As a result, the UE does not necessarily select the optimal beam, where the optimal beam has a higher throughput or capacity than other available beams.

[0010] The processes described herein enable a UE to select beams using an estimate of channel capacity. The UE is configured to analyze a plurality of high-gain candidate beams on a PDSCH or with LA-CSI-RS signals. The UE is configured to refine beam selection based on a capacity estimate so that the UE can fully optimize throughput. When the UE detects that it is stationary, the UE can refine beam selection.

[0011] The UE includes a classifier that classifies channel stability metrics (e.g., based on RSRP / SNR or any other link metric). The classifier is configured to perform the classification when motion data from a motion sensor indicates that the UE is stationary rather than mobile. A stationary scenario refers to a situation in which the UE is not moving, turning, or otherwise changing position or direction relative to a base station over a given time period. A mobile scenario is a situation in which the UE is currently moving or turning or has changed position or direction relative to the base station over a recent time period. When the UE is stationary, the UE is eligible to enter a state of further beam refinement. The UE includes a beam scheduler module and a capacity estimator module to perform beam selection based on channel capacity when operating in the stationary state. In this way, the UE is able to select a beam with the best overall capacity rather than simply selecting a beam with the highest detected power or similar link metric.

[0012] One or more of the advantages described previously can be realized by one or more implementations described in the following sections.

[0013] In a general aspect, a method includes determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from a set of available beams; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); determining a modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; estimating a channel capacity of the PDSCH based at least on the MCS threshold; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0014] In some implementations, the MCS threshold represents a modulation order of the MCS, the modulation order specifying a number of symbols and a coding rate of a PDSCH signal, and wherein estimating the channel capacity includes determining a data throughput based on the number of symbols and the coding rate.

[0015] In some implementations, selecting the candidate beam group includes selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0016] In some implementations, selecting the candidate beam group includes selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0017] In some implementations, determining that the channel is stable includes: obtaining motion data from one or more motion sensors coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beam, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold. In some implementations, 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 in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0018] In some implementations, the stability threshold is based at least on a standard deviation of the value of one or more of the link metrics.

[0019] In some implementations, wherein the one or more motion sensors include one or more of an accelerometer or a gyroscope.

[0020] In some implementations, the wireless device and the remote device are configured for millimeter wave (mmWave) communication using frequency range 2 (FR2).

[0021] In some implementations, the method further includes periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0022] In some implementations, the wireless device includes an antenna array including at least 10 beam configurations, and wherein selecting the particular beam includes selecting one of the at least 10 beam configurations.

[0023] In a general aspect, a method includes determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from a group of available beams; establishing signaling of a physical downlink shared channel (PDSCH) for candidate beams in the candidate beam group; receiving a link adaptation (LA) channel state information reference signal (LA-CSI-RS); estimating a channel capacity of the PDSCH based at least on the LA-CSI-RS; and selecting a particular beam from the candidate beam group for further PDSCH communication based at least on the estimated channel capacity.

[0024] In some implementations, selecting the candidate beam group includes selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0025] In some implementations, selecting the candidate beam group includes selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0026] In some implementations, determining that the channel is stable includes: obtaining motion data from one or more motion sensors coupled to the wireless device; determining from the motion data that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beam, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0027] In some implementations, 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 in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0028] In some implementations, the stability threshold is based at least on a standard deviation of the value of one or more of the link metrics.

[0029] In some implementations, the one or more motion sensors include one or more of an accelerometer or a gyroscope.

[0030] In some implementations, the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2).

[0031] In some implementations, the method further includes periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0032] 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.

[0033] The details of one or more implementations are set forth in the accompanying drawings and the description below. The technology described herein can be implemented by one or more wireless communication systems, components of wireless communication systems (e.g., stations, access points, user devices, base stations, etc.), or other systems, devices, methods, or non-transitory computer-readable media, etc. Other features and advantages will be apparent from the following detailed description, the drawings, and the claims. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 An example wireless communication system is shown in accordance with various embodiments herein.

[0035] Figure 2 An example of a platform or device configured for sensor-assisted antenna and beam selection is shown in accordance with some implementations of the present disclosure.

[0036] Figure 3A An example system including a device or platform configured for sensor-assisted antenna and beam selection is shown in accordance with some implementations of the present disclosure. Figure 2

[0037] An example process for sensor-assisted antenna and beam selection performed by portions of a system is shown in accordance with some implementations of the present disclosure. Figure 3B Figure 3A An example device for sensor-assisted antenna motion and rotation detection and beam selection is shown in accordance with some implementations of the present disclosure.

[0038] Figure 4 An example beam set for beam collection is shown.

[0039] Figure 5 An example process of sensor-assisted antenna and beam selection by a platform or device is shown.

[0040] Figure 6 Figures 2 to 4 An example process of sensor-assisted antenna and beam selection by a platform or device is shown.

[0041] Figure 7 A process for selecting a beam based on capacity estimation is shown in accordance with some implementations of the present disclosure.

[0042] Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION

[0043] The technology described herein enables a wireless device to perform beam selection in response to changes in a channel of a communication link. One 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.​​

[0044] Generally, wireless networks include transmissions using the millimeter wave (mmWave) spectrum. For example, the mmWave spectrum can be used for New Radio (NR) Fifth Generation (5G) and / or Long Term Evolution (LTE) networks for mmWave frequency range (e.g., frequency range 2 (FR2), frequency range 3 (FR3), etc.) transmissions from and to a base station (e.g., gNB) or a client device (e.g., mobile device described throughout this specification). Generally, FR2 transmissions are between 24.25 GHz and 52.6 GHz. Generally, cellular mmWave transmissions have higher propagation loss compared to traditional microwave in the sub-3 GHz range. For example, mmWave transmissions can have an additional 20 dB of loss relative to sub-6 GHz bands, such as bands used for frequency range 1 (FR1) transmissions.

[0045] To overcome this additional loss, the mmWave-enabled devices described herein are configured for beamforming, beam management, and antenna selection based on sensor feedback of one or more sensors of the mmWave-enabled devices. Beamforming enables a device to steer radio frequency (RF) energy in a particular direction. A transmitting device forms a beam by changing the amplitude and / or phase of one or more elements of a phased array antenna. Generally, a transmitting device generates a beam based on a predefined phase-amplitude combination for each antenna of the array to ensure a narrow beam that transmits a relatively high power in a desired direction relative to the phased array antenna. Beam management enables a device to identify a beam for transmission in each of an uplink and a downlink direction. Beam selection enables a mmWave-enabled device (e.g., UE) to ensure high-speed connectivity by improving wireless coverage for a given uplink or downlink transmission.

[0046] Beam management includes a process in which a UE modifies the settings of phase shifters of a phased antenna array. Beam management includes receiving reference signals from a remote device, such as a base station, that the UE knows. Generally, the base station transmits multiple signals using the same transmission (Tx) power, Tx antenna pattern, and Tx precoding, which includes the same transmission configuration. The UE can measure link metrics using several different phase shifter settings, referred to as a beam sweep. The UE measures those reference signals. Generally, the UE makes multiple measurements of the reference signals with multiple phase shifter settings using the same transmission configuration. Based on these measurements, the UE attempts to optimize the phase shifter settings for ongoing communications and the particular transmission configuration used by the base station to obtain a beam with the best overall link metrics.

[0047] Generally, beam selection is performed to improve communication bandwidth in the context of millimeter wave systems (e.g., using FR2 frequencies, FR3 frequencies, or other millimeter wave frequencies). Millimeter wave communication links have relatively high propagation losses over long distances (e.g., over tens or hundreds of meters) relative to FR1 links. To mitigate propagation losses and improve performance of the communication link, mm-wave enabled devices are configured for beamforming, beam management, and antenna selection based on sensor feedback of one or more sensors of the mm-wave enabled device.

[0048] The UE periodically (e.g., almost continuously) receives reference signals from the base station or other remote device. The UE periodically (e.g., almost continuously) optimizes phase shifter settings for beamforming. For example, a synchronization signal block (SSB) is a block of 4 symbols, each including reference symbols. The UE uses demodulation reference signals (DMRS) including these symbols for beam management.

[0049] In some implementations, the UE uses a codebook to perform beam management. The codebook includes a set of phase shifter settings, each corresponding to a respective beam. The codebook enables the UE to perform beam management as follows. The UE tries beams on the reference symbols and then uses the best corresponding measurement with respective link metrics. The codebook has a number of possible beams that is not too large compared to the number of available measurement occasions. The number of measurement occasions corresponds to the number of available measurement symbols with the same transmission configuration in a given time.

[0050] To comply with radio frequency (RF) requirements, the UE can include multiple phased arrays (e.g., with 4 elements per phased array, 8 elements per phased array, or 16 elements per phased array, etc.). The codebook size can be greater than 30 beams. Generally, the base station provides SSB signals for UE beam management such that up to 4 measurements can be made every 20 milliseconds. Thus, the UE performs an initial acquisition phase in which the UE acquires the best beam in multiple steps if no additional information is available. Then, the UE performs a tracking phase in which the UE tracks the best UE beam by measuring a limited number of beams based on the best current beam.

[0051] Beam management enables a device to identify beams for transmission in each of the uplink and downlink directions. In one example, for 5G NR mm-wave transmissions, a node (e.g., gNB) periodically (e.g., between 5 milliseconds to 160 milliseconds (monitoring system) periods) transmits SSBs to identify the best transmission beam and the best reception beam. In an example of beam management, a UE can use multiple beams to perform an initial beam training step. In this first step, a wider beam width is used to cover a wide scan range. A second step includes a beam refinement step. In this step, the UE scans a narrower beam in a narrower range than in the first step. This enables the UE to make adjustments in the desired beam direction. In a third step, the device is configured for beam refinement. In the beam refinement step, a user equipment (UE) tunes the reception angle of the beam and the node uses a fixed beam transmission. The UE measures different signal strengths until the best configuration of the beam is found. However, other methods for beam management can be performed. In one example, for 802.11 ad / ay mm-wave transmissions, an access point (AP) and a wireless device (e.g., UE) train their respective beams during a Sector Level Sweep (SLS) and a Beam Refinement Procedure (BRP) as defined in the 802.11 standard.

[0052] Antenna selection enables a device (e.g., UE) to ensure high-speed connectivity by improving the wireless coverage of a given uplink or downlink transmission. In one example, blockage of a first antenna or misalignment of an antenna can result in a reduced level of throughput relative to an ideal transmission environment. In this case, the UE is configured to select from a plurality of phased antenna arrays (also referred to as antenna panels).

[0053] Devices that support mm-waves include one or more sensors configured to provide motion data. The motion data indicates how the device is moving in the environment. The motion data from the sensors enables the device to estimate beamforming parameters for optimal connectivity based on prior data indicating strong signals.

[0054] The data processing system of the UE is configured to determine whether the UE is in a stationary scenario or in a mobile scenario. A stationary scenario refers to a situation in which the UE does not move, turn, or otherwise change position or direction relative to a base station over a given period of time. A mobile scenario refers to a situation in which the UE is currently moving or turning or has changed position or direction relative to the base station over a recent period of time (e.g., within 5 seconds or less, although the threshold can be shorter or longer as desired). Based on the determination of whether the UE is stationary or mobile, the UE performs beam selection.

[0055] Additional examples of these processes are described later with respect to the accompanying figures. The described systems and processes are compatible with any millimeter wave technology (e.g., 802.11ad / ay, 5G, etc.). The system is lightweight and configured to select a beam, antenna, or both independently of any antenna or beam sweep.

[0056] Figure 1 An example wireless communication system 100 is shown. For purposes of convenience, without limitation, the example system 100 is described in the context of LTE and 5G NR communication standards defined by the Third Generation Partnership Project (3GPP) Technical Specifications. More specifically, the wireless communication system 100 is described in the context of Non-Standalone (NSA) networks that incorporate both LTE and NR, such as E-UTRA (Evolved Universal Terrestrial Radio Access)-NR Dual Connectivity (EN-DC) networks and NE-DC networks. However, the wireless communication system 100 can also be a Standalone (SA) network that incorporates only NR. Moreover, other types of communication standards are possible, including future 3GPP systems (e.g., Sixth Generation (6G)) systems, IEEE 802.16 protocols (e.g., WMAN, WiMAX, etc.), and the like.

[0057] The system 100 includes UEs 101a and 101b (collectively referred to as “UEs 101”) in this example. The UEs 101 are illustrated as smartphones (e.g., handheld touchscreen mobile computing devices connectable to one or more cellular networks). In other examples, any of the UEs 101 can include other mobile computing devices or non-mobile computing devices such as consumer electronics devices, cellular phones, smartphones, 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 cluster (IC), head-up display (HUD) devices, onboard diagnostic (OBD) devices, DME, MDT, EEMS, ECUs, ECMs, embedded systems, microcontrollers, control modules, EMS, networked or “smart” appliances, MTC devices, M2M devices, IoT devices, or combinations of them, among others.

[0058] In some examples, any of the UEs 101 in the plurality of UEs 101 can be an IoT UE, which can include a network access layer designed for low-power IoT applications utilizing short-lived UE connections. An IoT UE can utilize technologies such as M2M or MTC for exchanging data with an MTC server or device using, for example, public land mobile network (PLMN), Proximity-Based Service (ProSe) for device-to-device (D2D) communication, sensor networks, IoT networks, or combinations of the foregoing, among other examples. M2M or MTC data exchanges can be machine-initiated exchanges of data between machines. An IoT network describes interconnecting IoT UEs, which can include uniquely identifiable embedded computing devices (within the Internet infrastructure), with short-lived connections to the internet. The IoT UEs can execute background applications (e.g., keep-alive messages or status updates) to facilitate connections to an IoT network.

[0059] The UEs 101 are configured to connect (e.g., communicatively couple) with an access network (AN) or radio access network (RAN) 110. In some examples, the 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 a RAN 110 operating in a 5G NR system 100, while the term “E-UTRAN” can refer to a RAN 110 operating in an LTE or 4G system 100.

[0060] To connect to the RAN 110, the plurality of UEs 101 utilize connections (or channels) 103 and 104, respectively, each of which can comprise a physical communications interface or layer, as described below. In this example, the connections 103 and 104 are illustrated as air interfaces to implement communications

[0061] The UE 101b is shown configured to access an Access Point (AP) 106 (also referred to as“WLAN node 106,”“WLAN 106,”“WLAN Termination 106,”“WT 106,” etc.) using connection 107. Connection 107 can comprise a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, Bluetooth connection, etc. In these examples, the AP 106 will include a router. In this example, the AP 106 is shown as connected to the Internet without a connection to the core network of the wireless system, as described in further detail below. In various examples, the UE 101b, RAN 110 and AP 106 can be configured to use LTE-WLAN aggregation (LWA) operation or LTW / WLAN Radio Level Intimization with IPsec tunnel (LWIP) operation. The LWA operation can involve the UE 101b in RRC CONNECTED state being configured by a RAN node 111a, 111b to utilize radio resources of LTE and WLAN. The LWIP operation can involve the UE 101b using IPsec protocol tunnel to authenticate and encrypt packets (e.g., IP packets) sent over the connection 107 using WLAN radio resources (e.g., connection 107). IPsec tunneling can include encapsulating the entire original IP packet and adding a new packet header, thus protecting the original header of the IP packet.

[0062] The RAN 110 includes 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” or the like can describe equipment that provides the radio baseband functions for data or voice connectivity between a network and one or more users, also referred to as user equipment (UE). These access nodes can be referred to as base stations, gNode Bs (gNBs), eNode Bs (eNBs), Node Bs, RAN nodes, road side units (RSUs), transmission reception points (TRxPs or TRPs), and the like, and can include ground stations (e.g., terrestrial access points) or satellite stations providing coverage over a geographic area (e.g., a cell) or the like. As used herein, the term“NG RAN node” or the like can refer to a RAN node 111 operating in the 5G NR system 100 (for example, a gNB), while the term“E-UTRAN node” can refer to a RAN node 111 operating in the LTE or 4G system 100 (for example, an eNB). In some examples, multiple RAN nodes 111 can be implemented as one of a dedicated physical device such as a macrocell base station or a low power (LP) base station for providing coverage over a small geographic area, such as for femtocell, picocell, or other like areas.

[0063] In some examples, some or all of the RAN nodes 111 can be implemented as one or more software entities running on server computers, as part of a virtual network, which can be referred to as a Cloud RAN (CRAN) or a virtual Base Band Unit Pool (vBBUP). The CRAN or vBBUP can enable RAN functions split, such as a Packet Data Convergence Protocol (PDCP) split, where 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) split, where 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” split, where RRC, PDCP, RLC, and MAC layers and upper parts of the PHY layer are operated by the CRAN / vBBUP and lower parts of the PHY layer are operated by individual RAN nodes 111. This virtualized framework allows the freed-up processors cores of the RAN nodes 111 to perform, for example, other virtualized applications. In some examples, an individual RAN node 111 can represent a virtual RAN node (e.g., runs on one or more of the server computers), where the operations of the individual RAN nodes 111 are run as a virtualized application. Figure 1 Not shown are individual gNB Distributed Units (DUs) connected to a gNB Central

[0064] In vehicle-to-everything (V2X) scenarios, one or more of the RAN nodes 111 can be a RSU or function as a RSU. The term “Road Side Unit” or “RSU” refers to any transportation infrastructure entity used for V2X communication. A RSU can be implemented in or by a suitable RAN node or a stationary (or relatively stationary) UE, where a RSU implemented in or by a UE can be referred to as“UE-type RSU,” a RSU implemented in or by an eNB can be referred to as“eNB-type RSU,” a RSU implemented in or by a gNB can be referred to as“gNB-type RSU,” and the like. In some examples, a RSU is a computing device coupled with radio frequency circuitry located on a roadside that provides connectivity support to passing vehicle UEs 101 (UEs 101). The RSU can also include internal data storage circuitry to store intersection map geometry, traffic statistics, media, and applications or other software for sensing and controlling ongoing vehicle and pedestrian traffic. The RSU can operate on the 5.9 GHz Direct Short Range Communications (DSRC) band to provide extremely low latency communications required for high-speed events such as crash avoidance, traffic warnings, and the like. Additionally or alternatively, the RSU can operate on the cellular V2X frequency band to provide the aforementioned low latency communications as well as other cellular communications services. Additionally or alternatively, the RSU can 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 radio frequency circuitry of the RSU can be encased in a weatherproof casing suitable for outdoor installation and can include a network interface controller to provide a wired connection (e.g., Ethernet) to a traffic signal controller or backhaul network, or both.

[0065] Any of the RAN nodes 111 can terminate the air interface protocol and can be the first point of contact for a UE 101. In some examples, any of the RAN nodes 111 can perform

[0066] In some examples, multiple UEs 101 can be configured to communicate with each other or with any of the RAN nodes 111 using orthogonal frequency division multiplexing (OFDM) communication signals in a multicarrier communication channel, according to various communication techniques, such as, but not limited to, OFDMA communication techniques (e.g., for downlink communications) or SC-FDMA communication techniques (e.g., for uplink and ProSe or sidelink communications), although the scope of the techniques described herein is not limited in scope in this respect. OFDM signals can comprise a plurality of orthogonal subcarriers.

[0067] In some examples, a downlink resource grid can be used for downlink transmissions from any of the RAN nodes 111 to the UEs 101, while uplink transmissions can utilize a similar approach. The grid can be a time-frequency grid, called a resource grid or time-frequency resource grid, which is the physical resource in the downlink in each slot. For an OFDM system, such a time-frequency plane representation graphically illustrates mapping between time and frequency resources, denoted as x-axis and y-axis in the grid, to convey information. The resource grid is only one of several such grids, with different representations being useful for different functions (such as scheduling, bit loading, and

[0068] In some examples, the UEs 101 and the RAN nodes 111 communicate using a licensed frequency spectrum (also referred to as the “licensed spectrum” or “licensed band”) and an unlicensed shared frequency spectrum (also referred to as the “unlicensed spectrum” and / or “unlicensed band”). The licensed spectrum can include channels that are licensed by a government agency, such as the Federal Communications Commission (FCC). The channels in the licensed spectrum can be purchased by network operators on a first-come-first-served basis. The licensed spectrum can be used for transmission of both primary (or “mission critical”) and best effort data. The unlicensed shared spectrum can include channels that are available for use on an unlicensed basis, such as the 5 GHz band. While the primary use of the unlicensed shared spectrum can be Wi-Fi, the unlicensed shared spectrum can also be used by LTE, such as LTE-U, LAA, or MulteFire. The unlicensed shared spectrum can be used for transmission of best effort data.

[0069] Figure 2 An example of a platform or device 200 configured for sensor-assisted antenna and beam selection is shown in accordance with some implementations of the present disclosure. Figure 2 The wireless device 200 includes a sensor module 204, a motion detection module 206 configured to receive data from the sensor module 204, a beam selection module 208 configured to select a particular beam, and at least one antenna panel 212 for performing beamforming to transmit data from the wireless device 200. The wireless device 200 receives wireless data through baseband circuitry 210, as described subsequently.

[0070] 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 LDOs, interrupt controllers, serial interfaces (such as SPI), I2C or general-purpose programmable serial interface modules, RTCs, timer-counters (including interval timers and watchdog timers), general-purpose I / O, memory card controllers (such as SD MMC or similar controllers), USB interfaces, MIPI interfaces, and JTAG test access ports. The processor (or core) of data processing system 202 may be coupled to or may include memory / storage elements, and may be configured to execute instructions stored in memory or storage devices to enable various applications or operating systems to run on system 200. In some examples, the memory or storage element may be on-chip memory circuitry, which may include any suitable volatile or non-volatile memory, such as DRAM, SRAM, EPROM, EEPROM, flash memory, solid-state memory, or combinations thereof.

[0071] The processor of the 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, multi-threaded processors, ultra-low voltage processors, embedded processors, some other known processing elements, or any suitable combination thereof. In some examples, the data processing system 202 may include or may be a dedicated processor / controller for performing the techniques described herein.

[0072] As an 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: based on... Architecture Core TM processors, such as Quark TM Atom TM i3, i5, i7 or MCU-level processors, or available from Santa Clara, California. company( Another processor of this type from [Company Name], Santa Clara, CA; and Advanced Micro Devices (AMD). Processor or Accelerated Processing Unit (APU); from Snapdragon by Technologies, Inc.TM processors, Texas Instruments, Open Multimedia Applications Platform (OMAP) TM Processors; MIPS-based designs from MIPS Technologies, Inc. such as 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 family of processors; and the like. In some implementations, the data processing system 202 can be part of a system on a chip (SoC), in which the data processing system 202 and other components are formed into a single integrated circuit.

[0073] Additionally or alternatively, the data processing system 202 can 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 a combination thereof, and the like. In some examples, the data processing system 202 can include logic blocks or logic fabric, as well as other interconnected resources that can be programmed to perform various functions such as the procedures, methods, and functions described herein. In some examples, the data processing system 202 can include a memory unit (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), etc.) for storing logic blocks, logic fabric, other data in look-up tables (LUTs), and the like.

[0074] The baseband circuitry 210 can be implemented, for example, as a solder-down 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.

[0075] The antenna beam panel 212 (also referred to as a radio front end module (RFEM)) can include a millimeter wave (mmWave) RFEM and one or more sub-millimeter wave radio frequency integrated circuits (RFICs). In some examples, the one or more sub-millimeter wave RFICs can be physically separated from the millimeter wave antenna beam panel 212. The RFICs can include connections to one or more antennas or antenna arrays, and the antenna beam panel 212 can be connected to multiple antennas. In some examples, both millimeter wave and sub-millimeter wave radio functionality can be implemented in the same physical antenna beam panel 212 that incorporates both millimeter wave and sub-millimeter wave antennas. In some embodiments, the mmWave functionality implements IEEE 802.11 ad and 802.11 ay standards.

[0076] The platform 200 can also include interface circuitry (not shown) for connecting external devices with the platform 200. The external devices connected to the platform 200 using the interface circuitry include sensor circuitry and electro-mechanical components (EMCs), as well as removable memory devices coupled to removable memory circuitry.

[0077] The sensor circuitry 204 includes devices, modules, or subsystems that detect events or changes in the environment of the device, 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, a gyroscope, or a magnetometer; a micro-electromechanical system (MEMS) or nano-electromechanical system (NEMS) including a three-axis accelerometer, a three-axis gyroscope, or a magnetometer; a level sensor; a flow sensor; a temperature sensor (e.g., a thermistor); a pressure sensor; a barometric pressure sensor; a gravimeter; 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, and the like), a depth sensor, an ambient light sensor, an ultrasonic transceiver; a microphone or other audio capture device, or combinations of them, and the like. Relative to Figure 4 The sensors are further described. As the pose of the system 200 changes in the system’s environment, the sensors 204 capture the motion of the system and send motion data to the motion detection module 206.

[0078] The data processing system 202 is configured to host the motion detection module 206 and the beam selection module 208. The motion detection module 206 is configured to determine how the device moves in the environment from the motion data of the sensors 204. The motion detection module 206 can determine a new position and orientation (e.g., pose) of the device relative to a previous pose of the device for which an optimal beam and antenna was selected. The updated pose can be provided to the beam selection module 208.

[0079] In short, turn Figure 4 Devices 400 and 410 show the results of... Figure 2 Examples of data collected by sensor 204. For example, device 400 includes a gyroscope. The gyroscope is configured to measure the rate of rotation of device 400 about spatial axes, including the rotational pitch, roll, and yaw of device 400, and movement (e.g., in degrees or radians). Device 400 enables a data processing system that receives data from the gyroscope to determine the directional changes of one or more antennas (such as antenna 402), such as relative to a base station. Similarly, device 410 includes an accelerometer configured to measure the velocity changes (e.g., translational motion) of device 410 along the x, y, and z axes. Device 410 enables a data processing system that receives data from the accelerometer to determine the directional changes of one or more antennas (such as antenna 402), such as relative to a base station. Devices 400 and 410 can be combined into a single device that includes both a gyroscope and an accelerometer.

[0080] return Figure 2 The motion detection module 206 receives accelerometer motion data and identifies translational movements (e.g., in centimeters) along each of the x, y, and z axes. The motion detection module 206 also receives 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 system 200 relative to the system's previous pose. The 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 BB panel 210). In some implementations, if AoA data is unavailable, a one-time beam scan is performed to determine the AoA, as described later. The motion detection module 206 sends the motion data to a beam selection module 208 configured for beam selection.

[0081] Beam selection module 208 performs beam selection based on data from BB panel 210 and updated pose provided by motion detection module 206. Because beam radiation patterns and antenna positions are predefined for a given device (e.g., devices in system 200), beam selection module 208 includes coverage data. The coverage data includes the highest gain antenna panel and beam identifier for each available device orientation and location.

[0082] Figure 3A Some specific embodiments according to this disclosure are shown, including Figure 2 An exemplary system 300 of the device or platform is configured for sensor-assisted antenna and beam selection. In some specific implementations, Figure 3A The functions of any module or system can be combined into one or more modules or systems. For example, data processing system 202 can perform... Figure 3AOne or more modules, or one or more functions. Beam selection system 302 can be similar to previously mentioned... Figure 2 The beam selection module 208 is described. The beam selection system 302 includes a beam measurement module 304 and a classification and beam scheduling module 306. System 300 includes a capacity estimation system 310, which includes a capacity estimation module 312, a PDSCH transmission module 314, and a capacity estimation + beam management module 324. Systems 302 and 310 may be part of data processing system 202, as previously described. Modules of capacity estimation system 310 are configured to communicate with base station 320 (e.g., gNB), such as transmitting CQI signals and data transmission 218. Typically, system 302 is configured to perform a classifier (e.g., the classifier of classification module 306) using a given channel stability metric. Typically, system 310 is configured to perform beam scheduling and capacity estimation for further beam refinement when the UE is in a stable state.

[0083] Typically, beam measurement module 304 is configured to obtain link metric values. Link metrics may include signal-to-noise ratio (SNR or SINR), RSRP, delay spread, angle of arrival (AoA) (if available), channel frequency response, or similar link metrics of the radio signal. Typically, system 302 is configured to perform a classifier (e.g., the classifier of classification module 306) using a given channel stability metric from module 304. Classification and beam scheduling module 306 is configured to classify signals as stable or unstable. An unstable signal is a signal in which one or more link stability metrics do not meet a stability threshold. Link stability metrics may include a threshold for the link metric (e.g., the value of RSRP) or a function of the link metric value (e.g., the standard deviation of the RSRP of the values ​​measured over a given time period). If a link stability metric does not meet a threshold, classification module 306 indicates that the measured signal is classified as unstable. If a link stability metric meets a threshold, the signal is classified as stable. For example, classification module 306 may determine that a metric such as RSRP or SNR meets a threshold and that the UE is stationary. The classification module 306 indicates that the UE is in a stable state that allows for further beam refinement.

[0084] When the UE is in a stable state, it is considered to have a sufficiently stable connection to allow it to evaluate the performance of backup beams with high Rx power on the PDSCH / LA-CSI-RS, as those channels require a long time to estimate performance or are infrequently scheduled. Therefore, when the UE is in a channel stable state, further beam refinement can be performed on the PDSCH / LA-CSI-RS. Channel stability provides sufficient time to evaluate the capacity of each beam in the candidate beamgroups based on LA-CSI-RS and / or PDSCH.

[0085] In general, the classification module 306 can use specific channel stability metrics, such as RSRP stability per beam, channel variability per subcarrier, SNR stability, etc., to perform the evaluation of whether the UE is in a stable state. Alternatively or additionally, the UE can refer to motion data from a motion sensor (e.g., a gyroscope or accelerometer) that explicitly indicates whether the UE is fully mobile. In general, the lack of movement complements the measured link metrics to guarantee channel stability.

[0086] Once the UE is in a stable state for further beam refinement, the channel is stable and the UE is generally not moving. In one example, the UE is on a table or desk without being adjusted, such as when a user is watching a video. In some implementations, the UE can also have a reasonable line of sight to the base station 320. If the UE moves, the UE is no longer in a stable state and ends the performance of further beam refinement. In unstable scenarios, the UE operates using the beam decision based only on the SSB and / or single-port signal operating mode.

[0087] The classification module 306 includes a classifier. The classifier can be a result of a machine learning model trained using motion data. In some implementations, the thresholds for link stability metrics (e.g., SNR deviation, delay spread, or any other channel metric) are determined by training the classifier (e.g., using machine learning (ML) or similar models) prior to runtime. For example, a machine learning model can be trained with data including various metric values to classify a measured beam as stable or unstable for each of various combinations of link metric values. The machine learning model can be used to determine appropriate thresholds for each of the one or more metrics to ensure that the signal of the current beam as a stable or unstable characterization represents the correct scenario. The thresholds can be set based on the output of the trained machine learning model.

[0088] In addition to using link stability metrics, the data processing system 202 of the UE classifies the UE as stationary or in motion based on motion data from a sensor, as previously described. If the UE is in motion, the signal is considered unstable regardless of the stability link metric values.

[0089] The beam selection system 302 selects a group of one or more candidate beams 222 based on determining that the signal is stable. The transmission and capacity estimation system 310 performs a channel capacity / throughput estimation for each candidate beam in the candidate beam group. As described subsequently, the candidate beams 222 can include all beams within some threshold of the beam with the largest received power, as measured by beam measurements of the SSB / 1-port signal. The classification module 306 is configured to determine whether a beam qualifies as a candidate beam based on comparing a link metric to a threshold. The threshold can be set by a machine learning model in a similar manner to the stability classifier. For example, the qualifying beams can include all beams with at least 90% power relative to the beam with the largest power. Other thresholds and metrics are possible for developing the classifier.

[0090] The capacity estimation system 310 includes a capacity estimation module 312, a PDSCH reception module 314, and a capacity estimation and beam management module 324. The capacity estimation module 312 is configured to estimate the potential throughput of each candidate beam by post-processing the LA-CSI-RS. For example, the capacity estimation module 312 can obtain a LA-CSI-RS based channel capacity estimate for each beam (e.g., related to CQI / RI reports 316 that indicate channel quality). The PDSCH reception module 314 is configured to perform PDSCH reception of data 318 using PDSCH. The PSDCH reception module 314 and the capacity estimation module 312 enable the UE to evaluate the capacity of various beams (e.g., high gain beams) when selecting the various beams for evaluation. The testing of each high gain beam is managed by the beam management 324 module.

[0091] The beam management module 324 is configured to determine a throughput estimate for each test beam and select a particular beam based on the testing. The beam management module 324 performs beam scheduling in order to test each candidate beam when a stable link is available.

[0092] When in a stable state, the system 310 of the UE utilizes the channel stability by including feedback from the channel capacity estimates of the module 312 for the beam selection process. When the UE is stable and performs further beam refinement, the capacity estimation module 324 sends feedback data to the beam scheduler including capacity estimates for the current test beams. The beam scheduling module 306 determines which beam is optimal in terms of capacity for the UE’s current location relative to the base station.

[0093] Figure 3B FIG. 6 illustrates a method 600 for beam selection according to some implementations of the present disclosure. The method 600 is performed by a UE, such as the UE 104 of FIG. 1, the UE 350 of FIG. 3, or the UE 400 of FIG. 4. In some implementations, the method 600 is performed by a processor executing software instructions or by one or more hardware components. Figure 3Aof the system 300. The beam selection module 302 determines 330 a set of candidate beams based on single port or SSB measurements. The candidate beams satisfy one or more metric thresholds (e.g., power values above a minimum absolute or relative value). For example, the power threshold can be -1 dB or -2 dB. In some implementations, the candidate beams can be the next adjacent beams to the highest power beam, such as in the same group (as shown in FIG. 3B). The beam selection module 302 schedules 332 one or more beams that satisfy the thresholds established as candidate beams. Figure 5

[0094] When the UE is in a stable state for beam refinement, the set of candidate beams is scheduled and each is tested for beam capacity or throughput. The capacity of each beam is determined or estimated 336 by the capacity estimation module 324 as follows.

[0095] In the first case, the capacity estimation module 312 determines channel capacity from LA-CSI-RS. LA-CSI-RS is generally used to estimate channel capacity and report channel quality information back to the base station. LA-CSI-RS is a downlink only signal and is used for downlink (DL) CSI acquisition. LA-CSI-RS can be a CSI-RS configured for reporting CSI information (i.e., CQI, RI, and PMI). LA-CSI-RS is used for precoding based on UL reciprocity. Generally, the CSI-RS can start at any OFDM symbol of a slot and occupy 1 / 2 / 4 OFDM symbols depending on the configured number of ports. In some implementations, the CSI-RS can be periodic, semi-persistent, or aperiodic (due to DCI triggering). In some implementations, for time / frequency tracking, the CSI-RS can be periodic or aperiodic and transmitted in bursts of two or four symbols spanning one or two slots.

[0096] Generally, LA-CSI-RS is available periodically (e.g., every 100 milliseconds), but LA-CSI-RS can also be aperiodic and sent on an approximately periodic basis (e.g., without strict scheduling). LA-CSI-RS enables the UE to compute channel capacity based on which the UE can derive CQI / RI / PMI. This is also referred to as direct estimation. The module 310 can test beams and determine channel estimates when making CQI / RI reports 316.

[0097] ​In the second case, channel capacity estimation is performed by module 324 using PDSCH transmissions 318 from module 314. Using PDSCH can bypass the need for test timing based on periodic LA-CSI-RS measurements. When PDSCH is transmitted to base station 320, the base station uses a modulation coding scheme (MCS) that includes a modulation order and a coding order. If the MCS modulation order is too high for the current beam, the UE cannot decode the modulation. Higher order MCSs mean that the modulation alphabet is expanded to include additional signaling alternatives, allowing each modulation symbol to convey more bits of information (e.g., higher data throughput or data rate in a limited bandwidth). Lower order MCSs include fewer symbols and thus can encode fewer bits. For example, the MCS can set the type of modulation, including phase and amplitude modulation for bit encoding. Higher order modulation (e.g., 256 quadrature amplitude modulation) transmits more information. The MCS can support QPSK, 16QAM, 64QAM, and 256QAM modulation.

[0098] The MCS defines the coding rate. The code rate is the ratio between data bits and total transmitted bits (e.g., data + redundancy bits). Redundancy bits are added for forward error correction (FEC). Thus, the ratio is between the number of information bits at the top of the physical layer and the number of bits mapped to the PDSCH at the bottom of the physical layer. A low coding rate corresponds to increased redundancy.

[0099] In some implementations, the MCS specifies the coding rate, or how many bits transmit information and how many bits are used for error correction. A higher value (e.g., 5 / 6) transmits more information. The MCS specifies the spatial streams, such as how many independent data streams are used. A higher value (e.g., 4 streams) increases the data rate. The MCS can specify a guard interval, which is a pause between each packet transmission. A short pause allows more packets to be transmitted.

[0100] The base station 320 tests different MCS levels in the transmission. Some packets fail and others succeed due to the channel, so the base station is able to determine the best MCS for the channel. The UE can determine the MCS threshold established in the channel from the PDSCH transmission. The capacity estimation module 324 is then configured to estimate (336) the channel capacity based on the determined MCS threshold for the PDSCH transmission 318.

[0101] Once the capacity estimation (336) is complete (e.g., using LA-CSI-RS or PDSCH data transmission 318 of data 316), the beam management module 310 is configured to select (338) the beam with the highest capacity value. In some implementations, any beam with a capacity value that exceeds a threshold value can be selected by the management module 310. In this example, only beam capacity is estimated until a “good enough” beam is found, rather than the beam with the absolute highest capacity. However, beam management can alternatively test each candidate beam to find the highest capacity beam in the candidate beam set 222.

[0102] Figure 5 An illustration of an exemplary beam group 450 is shown for selection by a beam selection module (e.g., module 208 of Figure 2 Although the initial beam in the example is the center beam 452, any of the beams 452-460 can be the initial beam for the scan. The scan starts with a first beam (e.g., beam 452). For the first beam, a link metric (e.g., power) is compared to a predefined threshold. As previously mentioned, the predefined threshold can be determined based on a machine learning approach or through a heuristic model. Figure 5

[0103] The beam selection module 208 classifies each beam of the set 450 into groups. In the beam group 450, five groups are shown as an illustrative example. The groups can be classified based on distance from the current beam 452. Each group can include one or more beams.

[0104] The beam selection module 208 is configured to scan successive groups to determine the candidate beam set 222. The beam selection module 208 can start with group 1, then scan group 1-2, then scan group 1-3, and so on. In a first case, a limited number of beam groups are scanned (e.g., group 1 or group 1-2). In some second cases, all of the beams are scanned by the beam selection module 208.

[0105] The beam selection module terminates the scan when a particular link metric (e.g., link power) is above a predetermined threshold. In some implementations, the metric can be a metric related to layer 1 (physical layer). For example, the metric can include throughput, received power, SNR, SRQ, SSI, and so on. The beam selection module 208 can transmit the selected beam as a candidate beam for capacity estimation.

[0106] Figure 6 An illustration of an exemplary beam group 450 is shown for selection by a beam selection module (e.g., module 208 of Figure 1 The UE 101 of Figure 2 The device 200 of Figures 3A to 3B ​Exemplary process 600 for beam capacity estimation by a UE (e.g., of system 300). The data processing system of the UE is configured to determine (602) that the UE is stationary. As previously described, the UE is stationary when the motion data indicates that the rotation and translation values of the UE are below a threshold (e.g., ~0) for a given time period. For example, the UE can obtain motion data from one or both of an accelerometer and a gyroscope. The state of the wireless device can be determined based on a given time period (e.g., last few seconds). The motion data indicates a change (or lack of change) in the position and / or orientation of the wireless device.

[0107] The data processing system of the UE receives (604) link metrics for the wireless connection. In one example, the link metrics can include SNR, delay spread, RSRQ, AoA, RSRP, received signal strength indicator (RSSI), or any similar link metric that indicates link stability. The wireless device is configured to determine a value that represents link stability from the link metric values. For example, the standard deviation of one or more of RSRP, SNR, or delay spread. The measured values of the individual link metrics can be measured over a time period.

[0108] The UE compares the link metrics to a threshold to determine (606) whether the link is stable. In some implementations, the threshold can be set by a machine learning model based on training data, such as labeled link metrics labeled as representing a stable or unstable connection. For example, if the standard deviation value of the link metrics exceeds a threshold (e.g., 1 dB for SNR), the link is considered unstable, even if the wireless device is not moving. If the UE is stationary and the link metrics satisfy the stability threshold, the UE initiates or enables (610) a beam refinement mode in which the capacity of one or more beams is tested to select a high-throughput beam.

[0109] Figure 7 Exemplary process 700 for selecting a beam in a beam refinement mode of a UE is shown, in accordance with some implementations of the present disclosure. In some examples, Figures 1 to 6 An electronic device, network, system, chip or component, or portions or implementations thereof, of the present disclosure can be configured to perform process 700. Process 700 includes receiving (708) a set of candidate beams for a wireless device (e.g., UE) to communicate with a remote device (e.g., base station). The candidate beams can be associated with link metrics above a threshold. In some implementations, each of the candidate beams is selected based on a power level associated with the beam or an adjacent beam. Process 700 includes selecting (704) a beam, such as the beam associated with the highest power. In some implementations, any of the candidate beams can be selected for testing capacity in any order.

[0110] For the selected beam, the process 700 includes establishing (706) LA-CSI-RS or PDSCH signaling. In some implementations, the LA-CSI-RS is used to directly estimate (708) the capacity of the selected beam. In this example, the LA-CSI-RS is received periodically. The selected beam is maintained until the LA-CSI-RS for capacity estimation is received. In some implementations, the PDSCH signaling is used to indirectly estimate the capacity. The base station uses a higher order MCS or a lower order MCS during the PDSCH signaling. The base station attempts to maximize the throughput by adjusting the modulation order to test the upper order limit. The order of the MCS selected by the base station (e.g., the MCS threshold) indicates the throughput of the channel to the UE.

[0111] The process 700 includes comparing (710) the estimated capacity to a threshold channel value. For example, the threshold can include the highest capacity estimate of the candidate beams so far. In some implementations, the threshold capacity is set based on a machine learning model. For example, the UE can measure the capacity of various beams over time, and the machine learning model outputs a threshold value indicating a capacity target in a given scenario based on the measurements. In some implementations, the capacity threshold is set to a particular value, and the first candidate beam that exceeds the threshold is selected for further communication. If no satisfactory beam is found, the data processing system extends (712) the search to the next candidate beam or a new set of beams.

[0112] The process 700 includes selecting (714) a particular beam of the candidate beams for further communication. In some implementations, the data processing system tests all of the candidate beams and selects the particular beam with the highest capacity estimate. In some implementations, the data processing system selects the first particular beam that satisfies a threshold capacity value.

[0113] The process 700 includes causing, by the data processing system, transmission (716) of data by a wireless device (e.g., a UE) to a remote device (e.g., a base station) using the particular beam. In some embodiments, the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2). In some embodiments, the wireless device includes at least three antenna arrays, and wherein each antenna array includes at least 10 beam configurations.

[0114] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled in a way to minimize risk of unintentional or unauthorized access or use, and the nature of authorization should be clearly expressed to the users.

[0115] Specific implementations of the subject matter and functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible non-transitory computer-readable computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions can be encoded in / on an artificially generated propagated signal, for example, a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer storage mediums.

[0116] The terms“data processing apparatus,”“computer,” and“computing device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can include a variety of apparatuses, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A device can also include special purpose logic circuitry, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, a data processing apparatus or special purpose logic circuitry (or a combination of data processing apparatus or special purpose logic circuitry) can be based on hardware, for example, formed to perform specific operations using hardware components, or a combination of hardware and software, for example, using hardware components to accelerate the processing tasks of a particular software program. The apparatus can optionally include a code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of operating environment. The present disclosure contemplates the use of data processing apparatuses with or without commonly known operating systems (such as LINUX, UNIX, WINDOWS, MAC OS, ANTROL, or IOS).

[0117] A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. The programming language can include, for example, a compiled language, an interpreted language, declarative language, or a procedural language. The program can be deployed in any form, including as a stand-alone program, a module, a component, a subroutine, or a

[0118] Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data includes 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 tape, cartridges, cassettes, and disks that are permanently, removably, or removably fixed. The memory can store various 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. Types of objects and data that can be stored in memory include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, memory can include log files, policy files, security or access files, and reporting files. Processors and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0119] While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what can be claimed, but rather as descriptions of features that can be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented in combinations with each other. Conversely, various features that are described in the context of a single implementation can also be implemented on other systems not expressly described herein. Although not required, certain embodiments can be presented through a method claim, either directly or with respect to a specific apparatus, with the different acts performing the operation being either of the apparatus or a method. Such claims, to the extent feasible, will perform equally well if the order or combination of acts are changed.

[0120] Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described specific implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations can be depicted in the drawings or claims in a particular, serial order, this should not be understood as a requirement that such operations be performed in the illustrated order, or serially, or in any order, but rather these operations can be performed in any order, and in parallel, or in any combination, depending on the circumstances. It will also be apparent that some operations can be implemented rather than others.

[0121] Moreover, the division or integration of system modules and / or components in the previously described embodiments should not be understood as requiring such division or integration in all embodiments and should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0122] Accordingly, the previously described example implementations do not limit or restrict the present disclosure. Other changes, substitutions, and alterations are also possible.

[0123] Example

[0124] In the following sections, additional example implementations are provided.

[0125] Example 1 includes a method of operating a UE, the method comprising: determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from a set of available beams; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); determining a modulation coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; estimating a channel capacity of the PDSCH based on the MCS threshold; and selecting, based on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0126] Example 2 includes the method of example 1 or some other example herein, wherein the MCS threshold represents a modulation order of the MCS, the modulation order specifying a number of symbols and a coding rate of a PDSCH signal, and wherein estimating the channel capacity comprises determining a data throughput based on the number of symbols and the coding rate.

[0127] Example 3 includes the method of example 1 or some other example herein, wherein selecting the candidate beam group is based on a relative power level of each candidate beam relative to one or more other available beams.

[0128] Example 4 includes the method of example 3 or some other example herein, wherein selecting the candidate beam group is based on a location of each candidate beam relative to one or more other available beams.

[0129] Example 5 includes the method of example 1 or some other example herein, wherein determining that the channel is stable comprises: obtaining motion data from the motion sensor coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to a remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beam, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0130] Example 6 includes the method of example 5 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 the remote device, a delay spread value of the signal, a change in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0131] Example 7 includes the method of example 1 or some other example herein, wherein the stability threshold is based on a standard deviation of the value of one or more of the link metrics.

[0132] Example 8 includes the method of example 1 or some other example herein, wherein the motion sensor comprises one or more of: an accelerometer, a gyroscope, or both the accelerometer and the gyroscope.

[0133] Example 9 includes the method of example 1 or some other example herein, wherein the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2).

[0134] Example 10 includes the method of example 1 or some other example herein, the method further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0135] Example 11 includes the method of example 1 or some other example herein, wherein the wireless device comprises an antenna array comprising at least 10 beam configurations, and wherein selecting the particular beam comprises selecting one of the at least 10 beam configurations.

[0136] Example 12 includes a method of operating a UE, the method comprising: determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communications between the wireless device and a remote device; selecting a candidate beam group from a set of available beams; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); receiving a local area (LA) channel state information reference signal (LA-CSI-RS); estimating a channel capacity of the PDSCH based on the LA-CSI-RS; and selecting, based on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communications.

[0137] Example 13 includes the method of example 12 or some other example herein, wherein selecting the candidate beam group is based on a relative power level of each candidate beam relative to one or more other available beams.

[0138] Example 14 includes the method of example 12 or some other example herein, wherein selecting the candidate beam group is based on a location of each candidate beam relative to one or more other available beams.

[0139] Example 15 includes the method of example 12 or some other example herein, wherein determining that the channel is stable comprises: obtaining motion data from the motion sensor coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to a remote device in communication with the wireless device; obtaining data representing one or more link metrics for the beams, the link metrics associated with a time period in which the wireless device was stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0140] Example 16 includes the method of example 15 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 the remote device, a delay spread value of the signal, a change in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0141] Example 17 includes the method of example 12 or some other example herein, wherein the stability threshold is based on a standard deviation of the values of one or more of the link metrics.

[0142] Example 18 includes the method of example 12 or some other example herein, wherein the motion sensor includes one or more of: an accelerometer, a gyroscope, or both the accelerometer and the gyroscope.

[0143] Example 19 includes the method of example 12 or some other example herein, wherein the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2).

[0144] Example 20 includes the method of example 12 or some other example herein, wherein the operations further include periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0145] Example 21 can include an apparatus comprising means for performing one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein.

[0146] Example 22 can include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein.

[0147] Example 23 can include an apparatus comprising logic, modules, or circuitry to perform one or more elements of a method described in or related to any of examples 1-20, or any other method or process described herein.

[0148] Example 24 can include methods, techniques, or processes as described in or related to any of examples 1-20, or portions or parts thereof.

[0149] Example 25 can include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform the method, techniques, or process as described in or related to any of Examples 1-20, or portions thereof.

[0150] Example 26 can include a signal as described in or that can be related to any of Examples 1-20, or portions or components thereof.

[0151] Example 27 can include a datagram, information element, packet, frame, segment, PDU, or message as described in or that can be related to any of Examples 1-20, or portions or components thereof, or otherwise described in the present disclosure.

[0152] Example 28 can include a signal encoded with data as described in or that can be related to any of Examples 1-20, or portions or components thereof, or otherwise described in the present disclosure.

[0153] Example 29 can include a signal encoded with a datagram, IE, packet, frame, segment, PDU, or message as described in or that can be related to any of Examples 1-20, or portions or components thereof, or otherwise described in the present disclosure.

[0154] Example 30 can include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors is to cause the one or more processors to perform a method, technique, or process as described in or related to any of Examples 1-20, or portions thereof.

[0155] Example 31 can include a computer program comprising instructions, wherein execution of the program by a processing element is to cause the processing element to perform a method, technique, or process as described in or related to any of Examples 1-20, or portions thereof.

[0156] Example 32 can include a signal in a wireless network as shown and described herein.

[0157] Example 33 can include a method of communicating in a wireless network as shown and described herein.

[0158] Example 34 can include a system for providing wireless communication as shown and described herein.

[0159] Example 35 can include an apparatus for providing wireless communication as shown and described herein.

[0160] Example 36 includes a method comprising: determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from a set of available beams; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); determining a modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; estimating a channel capacity of the PDSCH based at least on the MCS threshold; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0161] Example 37 includes the method of Example 36, wherein the MCS threshold represents a modulation order of the MCS, the modulation order specifying a number of symbols and a coding rate of a PDSCH signal, and wherein estimating the channel capacity comprises determining a data throughput based on the number of symbols and the coding rate.

[0162] Example 38 includes the method of Example 36, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0163] Example 39 includes the method of Example 36, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0164] Example 40 includes the method of Example 36, wherein determining that the channel is stable comprises: obtaining motion data from one or more motion sensors coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beams, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0165] Example 41 includes the method of Example 40, wherein the one or more link metrics comprise 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 in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0166] Example 42 includes the method of Example 40, wherein the stability threshold is based at least on a standard deviation of the value of one or more of the link metrics.

[0167] Example 43 includes the method of example 36, wherein the one or more motion sensors include one or more of an accelerometer or a gyroscope.

[0168] Example 44 includes the method of example 36, wherein the wireless device and the remote device are configured for millimeter wave (mmWave) communications using frequency range 2 (FR2).

[0169] 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.

[0170] Example 46 includes the method of example 36, wherein the wireless device includes an antenna array, the antenna array including at least 10 beam configurations, and wherein selecting the particular beam includes selecting one of the at least 10 beam configurations.

[0171] Example 47 includes a method comprising: determining, based on motion data from motion sensors coupled to a wireless device, that a channel is stable for communications between the wireless device and a remote device; selecting a candidate beam group from a set of available beams; establishing signaling of a physical downlink shared channel (PDSCH) for candidate beams in the candidate beam group; receiving a link adaptation (LA) channel state information reference signal (LA-CSI-RS); estimating a channel capacity of the PDSCH based at least on the LA-CSI-RS; and selecting a particular beam from the candidate beam group for further PDSCH communications based at least on the estimated channel capacity.

[0172] Example 48 includes the method of example 47, wherein selecting the candidate beam group includes selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0173] Example 49 includes the method of example 47, wherein selecting the candidate beam group includes selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0174] Example 50 includes the method of example 47, wherein determining that the channel is stable includes: obtaining motion data from one or more motion sensors coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beams, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0175] Example 51 includes the method of Example 50, 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 in angle-of-arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0176] Example 52 includes the method of Example 50, wherein the stability threshold is based at least on a standard deviation of the values of one or more of the link metrics.

[0177] Example 53 includes the method of Example 47, wherein the one or more motion sensors include one or more of an accelerometer or a gyroscope.

[0178] Example 54 includes the method of Example 47, wherein the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2).

[0179] Example 55 includes the method of Example 47, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0180] Example 56 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: determining, based on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communications between the wireless device and a remote device; selecting a candidate beam group from among available beam groups; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); determining a modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; estimating a channel capacity of the PDSCH based at least on the MCS threshold; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communications.

[0181] Example 57 includes the UE of Example 56, wherein the MCS threshold represents a modulation order of the MCS, the modulation order specifying a number of symbols and a coding rate of a PDSCH signal, and wherein estimating the channel capacity includes determining a data throughput based at least on the number of symbols and the coding rate.

[0182] Example 58 includes the UE of Example 56, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0183] Example 59 includes the UE of Example 56, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0184] Example 60 includes the UE of Example 56, wherein determining that the channel is stable comprises: obtaining motion data from the motion sensor coupled to the wireless device; determining from the motion data that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beams, the link metrics being associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0185] Example 61 includes the UE of Example 60, wherein the one or more link metrics comprise 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 in angle-of-arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0186] Example 62 includes the UE of Example 60, wherein the stability threshold is based at least on a standard deviation of the value of one or more of the link metrics.

[0187] Example 63 includes the UE of Example 56, wherein the motion sensor comprises one or more of an accelerometer or a gyroscope.

[0188] Example 64 includes the UE of Example 56, wherein the wireless device and the remote device are configured for millimeter wave (mmWave) communications using a frequency range 2 (FR2).

[0189] Example 65 includes the UE of Example 56, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0190] Example 66 includes the UE of Example 56, wherein the wireless device comprises an antenna array comprising at least 10 beam configurations, and wherein selecting the particular beam comprises selecting one of the at least 10 beam configurations.

[0191] Example 67 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: determining, based at least on motion data from the at least one motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from among available beam groups; establishing, for a candidate beam in the candidate beam group, signaling of a physical downlink shared channel (PDSCH); receiving a link adaptation (LA) channel state information reference signal (LA-CSI-RS); estimating, based at least on the LA-CSI-RS, a channel capacity of the PDSCH; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0192] Example 68 includes the UE of Example 67, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a relative power level of each candidate beam relative to one or more other available beams.

[0193] Example 69 includes the UE of Example 67, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on a location of each candidate beam relative to one or more other available beams.

[0194] Example 70 includes the UE of Example 67, wherein determining that the channel is stable comprises: obtaining motion data from the at least one motion sensor coupled to the wireless device; determining, from the motion data, that the wireless device is stationary relative to the remote device in communication with the wireless device; obtaining data representing one or more link metrics of the beams, the link metrics associated with a time period in which the wireless device is stationary; and determining that a value of one or more of the link metrics satisfies a stability threshold.

[0195] Example 71 includes the UE of Example 70, wherein the one or more link metrics comprise 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 in angle of arrival (AoA) of the signal, or a reference signal received power (RSRP) of the signal.

[0196] Example 72 includes the UE of Example 70, wherein the stability threshold is based at least on a standard deviation of the value of one or more of the link metrics.

[0197] Example 73 includes the UE of example 67, wherein the at least one motion sensor comprises one or more of: an accelerometer, a gyroscope, or both the accelerometer and the gyroscope.

[0198] Example 74 includes the UE of example 67, wherein the wireless device and the remote device are configured for millimeter wave communications using frequency range 2 (FR2).

[0199] Example 75 includes the UE of example 67, the operations further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

[0200] Example 76 includes a processor for a user equipment (UE), the processor comprising: circuitry configured for communication with a remote device; and circuitry for executing one or more instructions that, when executed, cause the processor to perform operations comprising: determining, based at least on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from among available beam groups; establishing, for a candidate beam of the candidate beam group, signaling of a physical downlink shared channel (PDSCH); determining a modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; estimating a channel capacity of the PDSCH based at least on the MCS threshold; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0201] Example 77 includes a processor for a user equipment (UE), the processor comprising: circuitry configured for communication with a remote device; and circuitry for executing one or more instructions that, when executed, cause the processor to perform operations comprising: determining, based at least on motion data from a motion sensor coupled to a wireless device, that a channel is stable for communication between the wireless device and a remote device; selecting a candidate beam group from among available beam groups; establishing, for a candidate beam of the candidate beam group, signaling of a physical downlink shared channel (PDSCH); receiving a link adaptation (LA) channel state information reference signal (LA-CSI-RS); estimating a channel capacity of the PDSCH based at least on the LA-CSI-RS; and selecting, based at least on the estimated channel capacity, a particular beam from the candidate beam group for further PDSCH communication.

[0202] Example 78 includes a method substantially as shown and described herein.

[0203] Example 79 includes a system substantially as shown and described herein.

[0204] Example 80 includes a mobile device substantially as shown and described herein.

Claims

1. A method for wireless communication, the method comprising: The stability of the channel for communication between the wireless device and the remote device is determined based on motion data from motion sensors coupled to the wireless device. Select candidate beam groups from the available beam groups; For the candidate beams in the candidate beam group, establish signaling for the Physical Downlink Shared Channel (PDSCH); Determine the modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; The channel capacity of the PDSCH is estimated based at least on the MCS threshold; as well as Based at least on the estimated channel capacity, a specific beam is selected from the candidate beam group for further PDSCH communication.

2. The method of claim 1, wherein the MCS threshold represents the modulation order of the MCS, the modulation order specifying the number of symbols and coding rate of the PDSCH signal, and wherein estimating the channel capacity includes determining the data throughput based on the number of symbols and the coding rate.

3. The method of claim 1, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on the relative power level of each candidate beam relative to one or more other available beams.

4. The method of claim 1, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on the position of each candidate beam relative to one or more other available beams.

5. The method of claim 1, wherein determining that the channel is stable comprises: Motion data is obtained from one or more motion sensors coupled to the wireless device; Based on the motion data, it is determined that the wireless device is stationary relative to the remote device communicating with the wireless device; Obtain data representing one or more link metrics of the available beams, the link metrics being associated with a period of time during which the wireless device is stationary; as well as Determine that the value of one or more of the link metrics satisfies the stability threshold.

6. The method of claim 5, wherein the one or more link metrics include at least one of the following: signal-to-noise ratio (SNR) of a signal received from the remote device, delay spread of the signal, change in angle of arrival (AoA) of the signal, or reference signal received power (RSRP) of the signal.

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 10 beam configurations, and wherein selecting the particular beam comprises selecting one of the at least 10 beam configurations.

9. A method for wireless communication, the method comprising: The stability of the channel for communication between the wireless device and the remote device is determined based on motion data from motion sensors coupled to the wireless device. Select candidate beam groups from the available beam groups; For the candidate beams in the candidate beam group, establish signaling for the Physical Downlink Shared Channel (PDSCH); Receive link adaptive LA channel state information reference signal LA-CSI-RS; The channel capacity of the PDSCH is estimated at least based on the LA-CSI-RS; as well as Based at least on the estimated channel capacity, a specific beam is selected from the candidate beam group for further PDSCH communication.

10. The method of claim 9, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on the relative power level of each candidate beam relative to one or more other available beams.

11. The method of claim 9, wherein selecting the candidate beam group comprises selecting the candidate beam group based at least on the position of each candidate beam relative to one or more other available beams.

12. The method of claim 9, wherein determining that the channel is stable comprises: Motion data is obtained from one or more motion sensors coupled to the wireless device; Based on the motion data, it is determined that the wireless device is stationary relative to the remote device communicating with the wireless device; Obtain data representing one or more link metrics of the available beams, the link metrics being associated with a period of time during which the wireless device is stationary; as well as Determine that the value of one or more of the link metrics satisfies the stability threshold.

13. The method of claim 12, wherein the one or more link metrics include at least one of the following: signal-to-noise ratio (SNR) of a signal received from the remote device, delay spread of the signal, change in angle of arrival (AoA) of the signal, or reference signal received power (RSRP) of the signal.

14. The method of claim 13, wherein the stability threshold is based at least on the standard deviation of the values ​​of one or more of the link metrics.

15. The method of claim 9, further comprising periodically retrieving the motion data to determine whether the wireless device is moving or stationary.

16. An apparatus for wireless communication, comprising: At least one motion sensor; One or more antenna arrays, each antenna array being 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 the following operations: The stability of the channel for communication between the wireless device and the remote device is determined based on motion data from motion sensors coupled to the wireless device. Select candidate beam groups from the available beam groups; For the candidate beams in the candidate beam group, establish signaling for the Physical Downlink Shared Channel (PDSCH); Determine the modulation and coding scheme (MCS) threshold set by the remote device for the PDSCH signaling; The channel capacity of the PDSCH is estimated at least based on the MCS threshold; and Based at least on the estimated channel capacity, a specific beam is selected from the candidate beam group for further PDSCH communication.

17. The apparatus of claim 16, wherein the MCS threshold represents the modulation order of the MCS, the modulation order specifying the number of symbols and coding rate of the PDSCH signal, and wherein estimating the channel capacity includes determining the data throughput based at least on the number of symbols and the coding rate.

18. The apparatus of claim 16, wherein determining that the channel is stable comprises: Motion data is obtained from the motion sensor coupled to the wireless device; Based on the motion data, it is determined that the wireless device is stationary relative to the remote device communicating with the wireless device; Obtain data representing one or more link metrics of the available beams, the link metrics being associated with a period of time during which the wireless device is stationary; as well as Determine that the value of one or more of the link metrics satisfies the stability threshold.

19. An apparatus for wireless communication, comprising: At least one motion sensor; One or more antenna arrays, each antenna array being 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 the following operations: The stability of the channel for communication between the wireless device and the remote device is determined based at least on motion data from the at least one motion sensor coupled to the wireless device. Select candidate beam groups from the available beam groups; For the candidate beams in the candidate beam group, establish signaling for the Physical Downlink Shared Channel (PDSCH); Receive link adaptive LA channel state information reference signal LA-CSI-RS; The channel capacity of the PDSCH is estimated at least based on the LA-CSI-RS; and Based at least on the estimated channel capacity, a specific beam is selected from the candidate beam group for further PDSCH communication.

20. The apparatus of claim 19, wherein determining that the channel is stable comprises: Motion data is obtained from the at least one motion sensor coupled to the wireless device; Based on the motion data, it is determined that the wireless device is stationary relative to the remote device communicating with the wireless device; Obtain data representing one or more link metrics of the available beams, the link metrics being associated with a period of time during which the wireless device is stationary; as well as Determine that the value of one or more of the link metrics satisfies the stability threshold.

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