Reports suggested beams for beam selection and beam measurements for other beams

Abstract

Various aspects of the present disclosure relate to beam management processes in wireless communication systems. Some implementations of the present disclosure more specifically provide techniques for reporting measurements of suggested beams (such as beams predicted to be optimal beams for communications to and from UEs and network entities) and other beams detected by UEs. For example, these techniques can be used to identify a mismatch between a set of suggested beams and an actual optimal beam for communications to and from UEs and network entities, and allow retraining of a machine learning model for identifying a set of suggested beams for communications to and from UEs and network entities.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Application No. 17 / 238,153, filed on April 22, 2021, entitled “Reporting Beam Measurements for Proposed Beams and Other Beams for Beam Selection,” which claims the benefit and priority of U.S. Provisional Patent Application Serial No. 63 / 015,405, filed on April 24, 2020, entitled “Reporting Beam Measurements for Proposed Beams and Other Beams for Beam Selection,” and is assigned to the assignee of this application, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present disclosure relates generally to wireless communications and, more particularly, to techniques for reporting measurements of proposed beams and other beams for communications between a user equipment (UE) and a network entity. Background Art

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcast. These wireless communication systems may employ multiple access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, time, space, and transmit power). Examples of such multiple access systems include 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) systems, LTE Advanced (LTE-A) systems, Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single Carrier Frequency Division Multiple Access (SC-FDMA) systems, and Time Division Synchronous Code Division Multiple Access (TD-SCDMA) systems, to name a few.

[0005] These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate at a city, country, region, and even global level. New radio (e.g., 5G NR) is an example of an emerging telecommunication standard. NR is an enhancement to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by improving spectrum efficiency, reducing costs, improving services, utilizing new spectrum, and using OFDMA with cyclic prefix (CP) on downlink (DL) and uplink (UL) to better integrate with other open standards. To this end, NR supports beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation.

[0006] However, as the demand for mobile broadband access continues to grow, further improvements to NR and LTE technologies are needed. Preferably, these improvements should be applicable to other multiple access technologies and the telecommunication standards that employ them.

[0007] In 5G NR, beam management procedures are generally used to identify beams for communications to and from UEs and base stations. Beams may be selected using beam scanning, in which a network entity transmits a signal on each of a plurality of beams, and the UE reports information identifying the beams detected by the UE and signal quality metrics for the identified beams. Beam scanning may include transmissions on each of a plurality of beams that may be used for communications to and from the UE and base station, even though the UE may not be able to detect all of the beams. Therefore, communication resources (e.g., time and frequency resources) may be wasted when a network entity performs beam management procedures. Summary of the invention

[0008] The systems, methods, and devices of the present disclosure each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of the present disclosure as expressed by the appended claims, some features will now be briefly discussed. After considering this discussion, and particularly after reading the section entitled "Detailed Description of the Invention," one will understand how the features of the present disclosure provide advantages including efficient selection of beams for communications to and from UEs and network entities, for example, selecting a reduced number of optimal beams, which can allow for reduced monitoring time for the UE and / or freeing up resources for data transmission.

[0009] One innovative aspect of the subject matter described in the present disclosure can be implemented in a method for wireless communication. The method can be performed, for example, by a user equipment (UE). The method generally includes receiving information from a network entity that configures the UE to report measurements of at least one set of one or more suggested beams determined by the network entity; performing measurements on the set of suggested beams and one or more additional beams not identified in the set of suggested beams; and sending one or more reports to the network entity, the one or more reports including measurements of the suggested beams and the one or more additional beams. In some implementations, the method may also include sending location information associated with the positioning of the UE to the network entity, wherein the information that configures the UE to report measurements of the suggested beams and other beams is received based on sending the positioning information.

[0010] In some implementations, the set of suggested beams may include one or more beams predicted by the network entity to have the highest received signal quality based on location information associated with the location of the UE.

[0011] In some implementations, sending may include sending a first report and sending a second report, wherein the first report includes measurements for the set of proposed beams and the second report includes measurements for the additional beams. Typically, the first report and the second report are sent in separate transmissions. Sending may include periodically sending the first report and the second report, wherein the first report is sent less frequently than the second report.

[0012] In some implementations, sending can include sending a single report that includes measurements for the set of proposed beams and measurements for one or more additional beams.

[0013] In some implementations, the information may indicate one or more groups of suggested beams and additional information for each group of suggested beams, and the method may further include determining which group of suggested beams to perform measurements on and send reports on based on the additional information. The additional information includes a cell identifier (cell ID), and the UE may perform measurements on a group of suggested beams associated with the cell ID, if detected. The additional information includes one or more predicted reference signal received power (RSRP) values, and if the RSRP measurement for one or more suggested beams in the group is lower than the predicted RSRP value for the one or more suggested beams, the UE may report the measurement for a group of suggested beams. The additional information may include at least one of reliability or probability for each group. The reliability information may correspond to the predicted likelihood that the UE will successfully receive a transmission on the group of suggested beams, and the probability information may correspond to the predicted probability that the UE will detect the group of suggested beams. The UE may prioritize the measurements of the group of suggested beams based on the reliability or probability of each group.

[0014] Another innovative aspect of the subject matter described in the present disclosure can be implemented in a method for wireless communication. The method can be performed, for example, by a network entity such as a gNodeB. The method generally includes selecting a set of one or more suggested beams from a data set for communicating with a user equipment (UE); sending information to the UE that configures the UE to report measurements of the set of suggested beams selected by the network entity; receiving one or more reports from the UE, the one or more reports including measurements of the set of suggested beams and one or more additional beams that are not in the set of suggested beams; and updating the data set based on the measurements of the set of suggested beams and the one or more additional beams.

[0015] In some implementations, the method may further include receiving, from the UE, location information associated with the location of the UE, wherein information configuring the UE to report measurements of the set of suggested beams and the additional beams is sent based at least on the received location information. In some implementations, the set of suggested beams may include one or more beams predicted to have the highest received signal quality based on the location information associated with the location of the UE.

[0016] In some implementations, receiving may include receiving a first report and receiving a second report, wherein the first report includes measurements for the set of proposed beams and the second report includes measurements for the additional beams. The first report and the second report may be received in separate transmissions from the UE. Receiving may include periodically receiving the first report including measurements for the set of proposed beams and periodically receiving the second report including measurements for the additional beams, wherein the first report is received less frequently than the second report.

[0017] In some implementations, receiving can include receiving a single report that includes measurements for the set of proposed beams and measurements for the additional beams.

[0018] In some implementations, the information indicates one or more groups of suggested beams and additional information for each of the multiple groups of suggested beams. The additional information may include a cell identifier (cell ID), and the report may include measurements of a group of suggested beams associated with the cell ID, if detected. The additional information may include one or more predicted reference signal received power (RSRP) values, and if the RSRP measurement for one or more suggested beams in the group is lower than the predicted RSRP value for the one or more suggested beams, the report may include measurements of a group of suggested beams. The additional information may include at least one of reliability or probability for each group. The reliability information may correspond to the predicted likelihood that the UE will successfully receive a transmission on the group of suggested beams, and the probability information may correspond to the predicted probability that the UE will detect the group of suggested beams. The report may include measurements based on reliability or probability priority for each group.

[0019] Other innovative aspects generally include methods, apparatus, systems, computer-readable media, and processing systems as generally described herein with reference to and as illustrated in the accompanying drawings.

[0020] To achieve the foregoing and related ends, one or more aspects include features fully described below and particularly pointed out in the claims. The following description and the accompanying drawings set forth in detail some illustrative features of the one or more aspects. However, these features are only indicative of a few of the various ways in which the principles of the various innovative aspects can be employed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The details of one or more implementations of the subject matter described in the present disclosure are set forth in the accompanying drawings and the following description. However, the accompanying drawings only illustrate some typical aspects of the present disclosure and should not be considered as limiting its scope. Other features, aspects, and advantages will become apparent from the description, drawings, and claims.

[0022] Figure 1 Shown is a block diagram conceptually illustrating an example wireless communication network in accordance with some aspects of the present disclosure.

[0023] Figure 2 Shown is a block diagram conceptually illustrating an example base station (BS) and an example user equipment (UE) according to some aspects of the present disclosure.

[0024] Figure 3

[0026] Example frame formats for communicating in a wireless communication network are shown in accordance with some aspects of the present disclosure.

[0025] Figure 4 Different synchronization signal blocks (SSBs) transmitted using different beams are shown in accordance with some aspects of the present disclosure.

[0026] Figure 5 An example machine learning (ML) based beam prediction module is shown that supports beam selection based on beam measurements reported by a user equipment (UE) in accordance with some aspects of the present disclosure.

[0027] Figure 6 A flow chart illustrating an example process for wireless communications by a user equipment supporting beam selection based on quantized position information of a user equipment (UE) according to some aspects of the present disclosure is shown.

[0028] Figure 7 A flow chart illustrating an example process for wireless communications by a user equipment supporting beam selection based on quantized position information of a user equipment (UE) according to some aspects of the present disclosure is shown.

[0029] Figure 8 A call flow diagram illustrating an example of UE position information reporting and beam prediction that supports beam selection based on quantized position information of a user equipment (UE) according to some aspects of the present disclosure is shown.

[0030] Fig. 9 An example communications device is shown that includes components configured to perform operations to support beam selection based on quantized position information of a user equipment (UE) in accordance with aspects of the present disclosure.

[0031] Fig.10 An example communications device is shown that includes components configured to perform operations to support beam selection based on quantized position information of a user equipment (UE) in accordance with aspects of the present disclosure.

[0032] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements in the drawings. It is contemplated that elements disclosed in one aspect may be beneficially utilized on other aspects without specific recitation. DETAILED DESCRIPTION

[0033] For the purpose of describing the innovative aspects of the present disclosure, the following description is directed to some specific implementations. The innovative aspects of the present disclosure generally provide example beam selection using quantized azimuth information, and do not limit the scope, applicability, or examples set forth in the claims. Without departing from the scope of the present disclosure, the functions and arrangements of the elements discussed may be changed. Various examples may appropriately omit, replace, or add various processes or components. For example, the method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. In addition, the features described for some examples may be combined in some other examples. For example, any number of the various aspects set forth herein may be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such a device or method, which is practiced using other structures, functions, or structures and functions other than or different from the various aspects of the present disclosure set forth herein. It should be understood that any aspect of the present disclosure disclosed herein may be embodied by one or more elements of the claims. The word "exemplary" is used herein to mean "used as an example, instance, or illustration". Any aspect described as "exemplary" herein is not necessarily interpreted as being preferred or superior to other aspects.

[0034] Various aspects generally relate to beam management processes in wireless communication systems. Some aspects more specifically relate to reporting, by a user equipment (UE), measurements of a set of beams recommended by a network entity, e.g., beams predicted by the network entity as optimal beams for communication between the UE and the network entity. In some aspects, the UE also reports measurements of one or more other beams identified by the UE. In some implementations, the network entity may select beams included in the set of recommended beams based on UE location (e.g., positioning and / or orientation) information and a machine learning model, wherein the machine learning model is configured to predict a set of beams that may be optimal beams for communication to and from the UE and the network entity. The beams in the set of recommended beams may be a subset of beams that the network entity may typically use for communication to and from the UE and the network entity. The network entity may use the measurements of the set of recommended beams and other beams reported by the UE to determine whether a data set used to generate the set of recommended beams is inaccurate and should be updated, the data set may include UE location information mapped to a set of beams used to train the machine learning model.

[0035] Certain aspects of the subject matter described in the present disclosure may be implemented to achieve one or more of the following potential advantages. In some examples, the described techniques may be used to reduce the amount of time spent identifying beams for communications to and from UEs and network entities. Additionally, because fewer time-frequency resources may be used in the beam management process, more time-frequency resources may be used for data transmission to and from UEs and network entities, which may provide increased bandwidth and data carrying capacity in wireless communication systems. Further, by reporting information about other beams measured by the UE, the network entity may detect mismatches between the set of suggested beams, and may modify the data set and retrain the machine learning model to account for the actual best beam identified by the UE for a given location, wherein the set of suggested beams includes a predicted best beam and the actual best beam identified by the UE for the given location.

[0036] In general, any number of wireless networks may be deployed in a given geographic area. Each wireless network may support a specific radio access technology (RAT) and may operate on one or more carriers in one or more frequency bands. RAT may also be referred to as an air interface. Within a carrier, wireless devices such as base stations and UEs may operate on various frequency resources including subcarriers (or "tones"), channels, or subbands. RATs within a given geographic area may operate on different frequencies to avoid mutual interference.

[0037] The techniques described herein can be used for various wireless networks and radio technologies. Although terms commonly associated with 3G, 4G, and / or new radio (e.g., 5G NR) wireless technologies may be used herein to describe various aspects, various aspects of the present disclosure may be applied to other generation-based communication systems.

[0038] NR access can support various wireless communication services, such as enhanced mobile broadband (eMBB) targeting wide bandwidth (e.g., 80MHz or above), millimeter wave (mmW) targeting high carrier frequency (e.g., 25GHz or above), massive machine type communication MTC (mMTC) targeting non-backward compatible MTC technology, and / or mission critical services targeting ultra-reliable low latency communication (URLLC). These services may include latency as well as reliability requirements. These services may also have different transmission time intervals (TTIs) to meet their respective quality of service (QoS) requirements. In addition, these services can coexist in the same subframe. NR supports beamforming and can dynamically configure beam directions. MIMO transmission with precoding can also be supported. DL transmission MIMO configuration in DL can support up to 8 transmit antennas, multi-layer DL transmission up to 8 streams, and up to 2 streams per UE. Multi-layer transmission with up to 2 streams per UE can be supported. Aggregation of multiple cells can be supported with up to 8 serving cells.

[0039] The techniques proposed in this article can be applied in various frequency bands used for NR. For example, for the higher frequency band called FR4 (e.g., 52.6GHz-114.25GHz), an OFDM waveform with a very large subcarrier spacing (960kHz-3.84MHz) is required to combat severe phase noise. Due to the large subcarrier spacing, the slot length tends to be short. In the lower frequency band called FR2 (24.25GHz to 52.6GHz) with 120kHz SCS, the slot length is 125μSec, while in FR4 at 960kHz, the slot length is 15.6μSec.

[0040] Figure 1 An example wireless communication network 100 is shown in which various aspects of the present disclosure may be performed. For example, according to various aspects of the present disclosure, a base station (BS) 110a of the wireless communication network 100 may be configured with a beam configuration module 112 configured to perform (or assist BS 110 in performing) Figure 7The operation 700 is to configure the UE to report the measurement of one or more suggested beams based on the reported UE position information. Similarly, according to various aspects of the present disclosure, the UE 120a of the wireless communication network 100 can be configured with a beam measurement module 122, which is configured to perform (or assist the UE 120 to perform) Figure 6 Operation 600 may be to receive beam configuration information from a network entity and measure at least one or more suggested beams (eg, from BS 110a).

[0041] The wireless communication network 100 may be a NR system (eg, a 5G NR network). Figure 1 As shown, the wireless communication network 100 may communicate with a core network 132. The core network 132 may communicate with one or more base stations (BSs) 110 and / or user equipments (UEs) 120 in the wireless communication network 100 via one or more interfaces.

[0042] like Figure 1 As shown, the wireless communication network 100 may include multiple BSs 110a-z (each also individually referred to herein as BS 110 or collectively referred to as BS 110) and other network entities. BS 110 may provide communication coverage for a specific geographic area, which is sometimes referred to as a "cell", which may be stationary or may move depending on the positioning of the mobile BS 110. In some examples, BS 110 may be interconnected with each other and / or with one or more other BSs or network nodes (not shown) in the wireless communication network 100 through various types of backhaul interfaces (e.g., direct physical connections, wireless connections, virtual networks, etc.) using any suitable transport network. Figure 1 In the example shown, BSs 110a, 110b, and 110c may be macro BSs for macro cells 102a, 102b, and 102c, respectively. BS 110x may be a pico BS for pico cell 102x. BSs 110y and 110z may be femto BSs for femto cells 102y and 102z, respectively. A BS may support one or more cells. A network controller 130 may be coupled to a set of BSs 110 and may provide coordination and control for these BSs 110 (e.g., via a backhaul).

[0043] BS 110 communicates with UEs 120a-y (each also individually referred to herein as UE 120 or collectively referred to as UE 120) in the wireless communication network 100. UEs 120 (e.g., 120x and 120y) may be dispersed throughout the wireless communication network 100, and each UE 120 may be fixed or mobile. The wireless communication network 100 may also include a relay station (e.g., relay station 110r) (also referred to as a relay station, etc.) that receives transmissions of data and / or other information from an upstream station (e.g., BS 110a or UE 120r) and sends transmissions of the data and / or other information to a downstream station (e.g., UE 120 or BS 110), or relays transmissions between UEs 120 to facilitate communication between devices.

[0044] Figure 2 1 and 120a (eg, in the example embodiment of the present invention) that can be used to implement various aspects of the present disclosure. Figure 1 Example components of the wireless communication network 100).

[0045] At BS 110a, transmit processor 220 may receive data from data source 212 and control information from controller / processor 240. The control information may be for a physical broadcast channel (PBCH), a physical control format indicator channel (PCFICH), a physical hybrid ARQ indicator channel (PHICH), a physical downlink control channel (PDCCH), or a group common PDCCH (GC PDCCH), among other examples. The data may be for a physical downlink shared channel (PDSCH), among other examples. A medium access control (MAC)-control element (MAC-CE) is a MAC layer communication structure that may be used for the exchange of control commands between wireless nodes. The MAC-CE may be carried in a shared channel, such as a physical downlink shared channel (PDSCH), a physical uplink shared channel (PUSCH), or a physical sidelink shared channel (PSSCH).

[0046] The processor 220 may process (e.g., encode and symbol map) data and control information to obtain data symbols and control symbols, respectively. The transmit processor 220 may also generate reference symbols, such as for a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a channel state information reference signal (CSI-RS). The transmit (TX) multiple-input multiple-output MIMO processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, and / or reference symbols (if applicable), and may provide an output symbol stream to a modulator (MOD) 232a-232t. Each modulator 232 may process a corresponding output symbol stream (e.g., for OFDM) to obtain an output sample stream. Each modulator may further process (e.g., convert to analog, amplify, filter, and up-convert) the output sample stream to obtain a downlink signal. The downlink signals from modulators 232a-232t may be transmitted via antennas 234a-234t, respectively.

[0047] At UE 120a, antennas 252a-252r can receive downlink signals from BS 110a and can provide received signals to demodulators (DEMODs) in transceivers 254a-254r, respectively. Each demodulator 254 can adjust (e.g., filter, amplify, downconvert, and digitize) a corresponding received signal to obtain input samples. Each demodulator can further process the input samples (e.g., for OFDM) to obtain received symbols. MIMO detector 256 can obtain received symbols from all demodulators 254a-254r, perform MIMO detection on the received symbols (if applicable), and provide detected symbols. Receive processor 258 can process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for UE 120a to data sink 260, and provide decoded control information to controller / processor 280.

[0048] On the uplink, at the UE 120a, a transmit processor 264 may receive and process data from a data source 262 (e.g., data for a physical uplink shared channel (PUSCH)) and control information from a controller / processor 280 (e.g., control information for a physical uplink control channel (PUCCH)). The transmit processor 264 may also generate reference symbols for reference signals (e.g., for a sounding reference signal (SRS)). The symbols from the transmit processor 264 may be precoded by a TX MIMO processor 266 (if applicable), further processed by a modulator in a transceiver 254a-254r (e.g., for SC-FDM), and transmitted to the BS 110a. At the BS 110a, the uplink signal from the UE 120a may be received by the antenna 234, processed by the demodulator 232, detected by the MIMO detector 236 (if applicable), and further processed by the receive processor 238 to obtain decoded data transmitted by the UE 120a and the control information. The receive processor 238 may provide the decoded data to a data sink 239 and the decoded control information to the controller / processor 240 .

[0049] Memories 242 and 282 may store data and program codes for BS 110a and UE 120a, respectively. Scheduler 244 may schedule UEs to transmit data on the downlink and / or uplink.

[0050] The antennas 252, processors 266, 258, 264 and / or controller / processor 280 of the UE 120a, and / or the antennas 234, processors 220, 230, 238 and / or controller / processor 240 of the BS 110a may be used to perform the various techniques and methods described herein. Figure 2 As shown, the controller / processor 240 of the BS 110a has a beam configuration module 241, and the controller / processor 280 of the UE 120a has a beam measurement module 281. The beam configuration module 241 may be configured to perform Figure 7 Operation 700, and / or the beam measurement module 281 may be configured to perform Figure 6 Operations 600 of UE 120a and BS 110a may be used to perform the operations described herein, although shown at the controller / processor.

[0051] NR can use orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) on uplink and downlink. NR can support half-duplex operation using time division duplex (TDD). OFDM and single carrier frequency division multiplexing (SC-FDM) divide the system bandwidth into multiple orthogonal subcarriers, which are also commonly called tone or bin. Each subcarrier can be modulated with data. Modulation symbols can be sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers can be dependent on the system bandwidth. The minimum resource allocation called resource block (RB) can be 12 consecutive subcarriers. The system bandwidth can also be divided into subbands. For example, a subband can cover multiple RBs. NR can support a basic subcarrier spacing (SCS) of 15KHz, and other SCSs can be defined relative to the basic SCS (e.g., 30kHz, 60kHz, 120kHz, or 240kHz). Symbol and slot lengths scale with subcarrier spacing. The CP length also depends on the subcarrier spacing.

[0052] Figure 3 300 is a diagram showing an example of a frame format 300 for NR. The transmission timeline for each of the downlink and uplink can be divided into units of radio frames. Each radio frame can have a predetermined duration (e.g., 10ms) and can be divided into 10 subframes with indices 0 to 9, each subframe being 1ms. Each subframe can include a variable number of time slots (e.g., 1, 2, 4, 8, 16... time slots), depending on the SCS. Each time slot can include a variable number of symbol periods (e.g., 7 or 14 symbols), depending on the SCS. The symbol periods in each time slot can be assigned indexes. A microslot, which may be referred to as a subslot structure, may refer to a transmission time interval having a duration less than a time slot (e.g., 2, 3, or 4 symbols).

[0053] Each symbol within a slot may indicate a link direction (e.g., DL, UL, or flexible link direction) for data transmission, and the link direction of each subframe may be switched dynamically. The link direction may be based on the slot format. Each slot may include DL / UL data and DL / UL control information.

[0054] In NR, a synchronization signal (SS) block (SSB) is transmitted. The SS block includes PSS, SSS, and dual-symbol PBCH. The SS block can be transmitted in a fixed time slot positioning, such as Figure 60-3 shown in . The UE may use PSS and SSS for cell search and acquisition. PSS may provide half-frame timing, and SS may provide CP length and frame timing. PSS and SSS may provide cell identification. PBCH carries some basic system information, such as downlink system bandwidth, timing information within a radio frame, SS burst set periodicity, and system frame number, among other examples.

[0055] Additional system information, such as Remaining Minimum System Information (RMSI), System Information Block (SIB), Other System Information (OSI), may be sent on the Physical Downlink Shared Channel (PDSCH) in certain subframes.

[0056] like Figure 4 As shown, SS blocks can be organized into SS burst sets to support beam scanning performed as part of the beam management process. As shown in the figure, each SSB in the burst set can be sent using a different beam, which can help the UE quickly acquire the transmit (Tx) and receive (Rx) beams (especially for mmW applications). The physical cell identity (PCI) can still be decoded from the PSS and SSS of the SSB. Figure 4 As shown, a synchronization burst set that can be used in a beam management process may include L+1 SSBs. Each of the L+1 SSBs may be sent by a network entity using a different beam sent in a different beam direction. The UE may receive some SSBs sent in the synchronization burst set, and may not receive other SSBs sent by the network entity during the beam management process. In response to detecting at least some of the SSBs in the synchronization burst set, the UE may generate a report and send a report to the network entity, the report indicating which SSB the UE detects corresponding to a specific beam used by the network entity to send the SSB, and indicating a signal strength metric for each of some or all detected beams. The network entity may then select one or more of the beams identified in the report for communication to and from the UE and the network entity. For example, the network entity may select n beams with the highest signal quality metric to communicate with the UE. In some other examples, the UE may identify a single beam, such as the beam with the highest signal quality metric, in a report sent to the network entity, and the network entity may use the identified beam for communication to and from the UE.

[0057] NR deployment options may include non-standalone (NSA) or standalone (SA) options. Some deployment scenarios may include one or both NR deployment options. Standalone cells may need to broadcast SSBs and remaining minimum system information (RMSI), for example, using SIB1 and SIB2. Non-standalone cells may need to broadcast SSBs, but may not need to broadcast RMSI. In a single carrier of NR, multiple SSBs may be sent on different frequencies and may include a variety of different types of SSBs.

[0058] like Figure 4 As shown, SSB bursts can be used for beam management. A typical beam management process may require a beam scanning process, in which a network entity (such as a base station) continuously transmits a beam in each of a plurality of directions. For example, the beam direction may cover 360 degrees around the network entity in a plurality of directions (e.g., 64 different beam directions). In another example, the beam direction may cover a portion of the total coverage area served by the network entity. For example, in a deployment where the network entity includes three antenna panels, each antenna panel may cover a 120-degree segment of the total coverage area, and the beam direction may cover a 120-degree segment in a plurality of directions together. However, for any given UE in such a deployment scenario or other scenarios, only a subset of beams may be detected during the beam management process. For example, a transmission using a beam direction opposite to the direction from the network entity to the UE may not be detected by the UE. Because only a subset of beams may be detected, the network entity may waste network resources when performing beam scanning across each of a plurality of directions, which may reduce the amount of resources available for other transmissions (e.g., data transmission).

[0059] According to some aspects of the present disclosure, side information and machine learning models can be used to enhance beam management processes (e.g., in FR2). The side information can include, for example, UE location information, which can include latitude and longitude information from a satellite positioning system, time difference of arrival (TDOA) information determined for signals sent between a network entity and the UE, and UE position information, among other examples.

[0060] Example ML-based beam prediction

[0061] like Figure 5As shown, a machine learning (ML)-based (beam prediction) model can be trained to learn the relationship between the location of the UE, such as one or more of the location, device orientation, direction, or other information indicating the UE location or location change, and one or more best (or "optimal") beams or other suitable beams that can be used to communicate with the UE. The location determination can be performed by the UE, a network entity such as a gNB, or a positioning server referred to as a positioning management function (LMF). The size of the data set that associates the UE location with one or more best beams may be large because there may be an infinite number of combinations of UE location information and best beams for communication between the network entity and the UE. In addition, the data set size may increase as additional location information is included in the data set. For example, for any given positioning, there may be an infinite number of different orientations of more than six degrees of freedom (vertical, horizontal, and azimuth axes, yaw, roll, and pitch), and may be associated with a beam or a set of beams used for communication between the network entity and the UE. Because the data set size may be very large, and because the UE may not have sufficient processing power to train a machine learning model based on such a large data set, it is not feasible to share the entire data set with the UE. In some cases, instead of sharing the dataset, a more practical approach may be to train a model (such as a neural network (NN)) using the dataset and then share the parameters (e.g., weights, etc.) of the trained model with the UE.

[0062] Various techniques may be used to train such a machine learning model to learn the optimal beam direction given a reported location. Given the input of the reported UE location, and in some examples also the device orientation, a network entity on which the trained machine learning model is deployed may predict a set of beams for performing beam scanning. The set of beams and associated directions may be a subset of beams and associated directions supported by the network entity. The network entity may transmit using the set of predicted beams and need not scan every possible beam direction in order to identify the best beam for communicating with the UE.

[0063] In some implementations, the machine learning model may be trained using supervised learning techniques, wherein an input data set of multiple {position, (multiple) beam directions} pairs is used to train the machine learning model to identify the relationship between different positions and orientations and the optimal beam direction for communicating with the UE at different positions and based on different device orientations. The input data set may include or be derived from, for example, information received about communications using a sub-6 GHz band. The machine learning model may be trained to output information that may be relevant to communications using other frequency bands (e.g., millimeter wave bands), such as information predicting an optimal beam or a set of beams, or information associated with a predicted optimal beam or a set of optimal beams, which may then be used to select and generate beams for communicating with the UE. In some implementations, the machine learning model may include a softmax layer that generates a probability score for each beam direction. The network entity on which the machine learning model is deployed may use the generated probability scores to identify beams for communicating with the UE. Therefore, the (multiple) beams that may be predicted or identified as the best beams for communicating with the UE may be the n beams with the highest probability scores generated by the machine learning model based on the current position of the UE.

[0064] The machine learning model can be trained offline and deployed to a network entity (e.g., a gNodeB) for identifying one or more best beams or possible best beams for communicating with the UE based on the reported positioning of the UE. During wireless communication operations, the UE can independently determine its positioning (and in some examples also determine the orientation), identify the best beam (e.g., based on beam scanning), and report the determined positioning (or positioning and orientation) and the identified best beam to the network entity. If the identified best beam matches the best beam predicted by the machine learning model deployed at the network entity, the network entity can determine that the machine learning model is accurate. However, if the identified best beam mismatches the best beam predicted by the machine learning model, the network entity can determine that there are one or more errors in the machine learning model or in identifying the best beam.

[0065] For example, the best or optimal beam direction determined during the deployment phase may not be accurate, for example, due to poor reference signal received power (RSRP) or signal to interference plus noise (SINR). Another potential source of mismatch stems from the fact that even though the positioning estimate and the best beam estimate may be accurate, the actual environmental conditions at the time of beam selection may be very different from the environmental conditions during the training phase. In other words, there may be some dynamic aspects of the base station and UE surroundings that were not captured during the offline training phase (e.g., such as passing cars, etc.). Due to different environmental conditions, it may be difficult to mitigate mismatches caused by such issues. However, if the network entity consistently observes that for a given UE position, the UE reports a different set of best beams (different from the beams predicted by the ML), the network entity can infer that such mismatch is not due to environmental dynamics and that the dataset needs to be updated.

[0066] The machine learning described herein can utilize any suitable machine learning algorithm. In some non-limiting examples, the machine learning algorithm is a supervised learning algorithm, an unsupervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or an artificial neural network algorithm.

[0067] In some examples, machine learning is performed using neural networks. Neural networks can be designed with a variety of connection patterns. In a feedforward network, information is passed from lower layers to higher layers, and each neuron in a given layer communicates with neurons in higher layers. Layer representations can be built up in successive layers of a feedforward network. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be transmitted to another neuron in the same layer. Recurrent architectures may help recognize patterns that span more than one of the chunks of input data that are passed to the neural network in sequence. The connections from neurons in a given layer to neurons in lower layers are called feedback (or top-down) connections. Networks with many feedback connections may be helpful when recognition of high-level concepts may help distinguish specific low-level features of the input.

[0068] Artificial neural networks can be composed of a group of interconnected artificial neurons (e.g., neuron models). These artificial neural networks can be used in various applications and / or devices, such as Internet Protocol (IP) cameras, Internet of Things (IoT) devices, autonomous vehicles, and / or service robots. Individual nodes in an artificial neural network can simulate biological neurons by obtaining input data and performing simple operations on the data. The results of the operations performed on the input data are selectively passed to other neurons. Weight values ​​are associated with each vector and node in the network, and these values ​​limit the way the input data is associated with the output data. For example, the input data of each node can be multiplied by the corresponding weight value, and then the products are added. The sum of the products can be adjusted by an optional bias, and an activation function can be applied to the result, thereby generating an output signal or "output activation" of the node. The weight values ​​can initially be determined by an iterative flow of training data through the network (e.g., the weight values ​​are established during the training phase in which the network learns how to identify a specific class by its typical input data characteristics).

[0069] Different types of artificial neural networks can be used to implement machine learning, such as recurrent neural networks (RNNs), multilayer perceptron (MLP) neural networks, convolutional neural networks (CNNs), etc. RNNs work by saving the output of a layer and feeding that output back to the input to help predict the outcome of that layer. In an MLP neural network, data can be fed into an input layer, and one or more hidden layers provide a level of abstraction for the data. Predictions can then be made for the output layer based on the abstract data. MLPs can be particularly useful for classification prediction problems that assign categories or labels to inputs. A convolutional neural network (CNN) is a feedforward artificial neural network. A convolutional neural network can include a collection of artificial neurons, each of which has a receptive field (e.g., a spatially local region of the input space) and together tiles the input space. Convolutional neural networks have many applications. In particular, CNNs have been widely used in the fields of pattern recognition and classification. In a layered neural network architecture, the output of the first layer of artificial neurons becomes the input of the second layer of artificial neurons, the output of the second layer of artificial neurons becomes the input of the third layer of artificial neurons, and so on. Convolutional neural networks can be trained to recognize feature hierarchies. Computations in a convolutional neural network architecture can be distributed across a set of processing nodes, which can be configured in one or more computational chains. These multi-layer architectures can be trained one layer at a time and can be fine-tuned using back-propagation.

[0070] In some examples, machine learning is performed using a deep convolutional network (DCN). A DCN is a collection of convolutional networks configured with additional pooling and normalization layers. DCNs have achieved state-of-the-art performance on many tasks. DCNs can be trained using supervised learning, where the inputs and outputs are known for many examples and are used to modify the weights of the network by using a gradient descent method. That is, a training data set may include multiple vectors, each with an input data set, from which the DCN is trained to generate inferences, and the output corresponds to the inferences that the DCN should generate for the input data set. The DCN may be a feedforward network. In addition, as described above, the connections from the neurons in the first layer of the DCN to a group of neurons in the next higher layer are shared between the neurons in the first layer. The feedforward and shared connections of the DCN can be used for fast processing. For example, the computational burden of a DCN may be much smaller than that of a neural network of similar size including loops or feedback connections.

[0071] Example report of suggested beams for beam selection and beam measurements for other beams

[0072] As described above, various aspects of the present disclosure relate to beam management processes in wireless communication systems. Some implementations of the present disclosure more specifically provide techniques for reporting measurements of recommended beams (such as beams predicted to be optimal beams for communications to and from UEs and network entities) and other beams detected by UEs. For example, these techniques can be used to identify a mismatch between a set of recommended beams and an actual optimal beam for communications to and from UEs and network entities, and allow retraining of a machine learning model for identifying a set of recommended beams for communications to and from UEs and network entities. By doing so, these techniques can allow efficient and accurate selection of beams for communications between network entities and UEs, which can allow for reduced monitoring time for UEs and / or freeing up resources (otherwise used for SSB transmissions) for data transmission.

[0073] As described above, a machine learning based algorithm may be trained to predict a set of (optimal / best) beams for a UE given the location of the UE at any particular time. The machine learning based algorithm may be trained prior to regular operation of the network entity and deployed to the network entity immediately after training and validation. Over time, the training data set used to train the machine learning based algorithm may be enhanced with updated information related to the UE location information and the set of (optimal / best) beams for the UE. As the training data set is enhanced, the system may retrain the machine learning based algorithm and the retrained machine learning based algorithm may be deployed to the network entity for subsequent prediction of a set of beams for the UE given the location of the UE at any particular time in the future.

[0074] According to certain aspects, a UE may receive information identifying one or more suggested beams for communication between the UE and a network entity. The UE may be configured to report measurements of the suggested beams and one or more other beams determined by the UE, and to send a report including the measurements to the network entity.

[0075] Accordingly, the network entity may receive the reports and determine whether there is a mismatch between the recommended beams, which represent beams predicted by the network entity as optimal beams for communication between the network entity and the UE, and other beams reported by the UE. For example, a mismatch may exist where a beam identified in a measurement report for one or more other beams is not included in the measurement report for the recommended beam. The network entity may continuously monitor the received reports for differences between the measurement reports for the one or more other beams and the measurement reports for the recommended beam. When the network entity identifies a consistent pattern of differences between the reports, the network entity may determine that the machine learning model did not generate an accurate prediction of the optimal beam for communication with the UE given the specific location information, and may retrain the machine learning model to accurately predict beams for communication to and from the UE and the network entity given the specific location information.

[0076] Figure 6 A flow chart illustrating an example process 600 of wireless communication by a user device that supports reporting of beam measurements for a proposed beam and other beams in accordance with some aspects of the present disclosure is shown. The operations of the process 600 may be implemented by a wireless communication device or components thereof as described herein. For example, in accordance with some aspects of the present disclosure, the process 600 may be performed by Figure 1 or Figure 2 The UE 120a is executed to receive configuration information for reporting measurements of at least one or more suggested beams based on the reported position and / or location information.

[0077] In some implementations, process 600 begins at block 602, where the UE receives information from a network entity that configures the UE to report measurements of at least one set of one or more suggested beams indicated by the network entity. The one or more suggested beams determined by the network entity may be beams in a set of beams that the network entity predicts to be the best beams for communications to and from the network entity and the UE based on location information associated with the location of the UE. The location information may include, for example, a location of the UE determined based on various signals, such as a signal from a satellite positioning system (e.g., NAVSTAR GPS, GALILEO, or other satellite positioning system, whether global or regional in coverage), a signal identifying a room in a building where the UE is located, a time difference of arrival (TDOA) calculation performed based on signals received from multiple network entities, or other signaling that may be used to identify the location of the UE. The UE location may also include, for example, orientation information determined by one or more sensors at the UE. The orientation information may be determined in six degrees of freedom (vertical, horizontal, and azimuth axes, yaw, roll, and pitch), and may identify the direction the UE is facing, the direction the UE's antenna is facing, and other orientation information of the UE.

[0078] At box 604, the UE performs measurements on the set of suggested beams and one or more additional beams that are not in the set of suggested beams. The measurements on the one or more additional beams may include measurements on n beams with the highest measured signal quality metrics. To perform these measurements, the UE may receive information identifying the duration of an SSB burst, wherein the SSBs are sent by a network entity on different beams during the duration of the SSB burst. The UE may detect the SSBs included in the SSB burst and measure a signal quality metric for each SSB in the SSB burst, the signal quality metric corresponding to the signal quality metric of the beam used to send each SSB. The UE may then identify the n highest measured signal quality metrics and the beams associated with the n highest measured signal quality metrics, and report information about the identified beams to the network entity. The signal quality metric may include, for example, a reference signal received power (RSRP) measurement, a received signal strength indicator (RSSI), or other signal quality measurements for each identified beam.

[0079] At box 606, the UE sends one or more reports to the network entity, the one or more reports including measurements of the set of recommended beams and the additional beams. The reports may be sent to the network entity separately or together. In some implementations, the report may include a first report and a second report, the first report including measurements of the set of recommended beams, and the second report including measurements of additional beams (e.g., the n best beams measured in an SSB burst). In some implementations, the measurements of the additional beams may also or alternatively include measurements of beams that deviate from the predicted measurements by more than a threshold amount. The UE may periodically send the first report and the second report to the network entity using the same or different periodicities and in the same or different transmissions. For example, a first report including measurements for the recommended beams may be sent using a first periodicity, and a second report including measurements for other beams may be sent using a second periodicity that results in less frequent reporting than the first periodicity.

[0080] Figure 7 A flow chart illustrating an example process 700 for wireless communication by a network entity that supports beam selection based on quantized position information of a user equipment (UE) is shown. Process 700 may be considered as an example of a process for wireless communication by a network entity that supports beam selection based on quantized position information of a user equipment (UE). Figure 6 The operations of process 700 may be implemented by a wireless communication device or a component thereof as described herein. For example, the operations of process 700 may be implemented by Figure 1 or Figure 2 The base station 120a (e.g., gNB) is configured to configure the UE to measure at least one or more suggested beams identified by the network entity (e.g., based on the quantized position information) and to perform Figure 6 The UE of process 600 receives measurements for at least one or more proposed beams.

[0081] In some implementations, process 700 begins at block 702, where a network entity selects a set of one or more suggested beams from a data set for communicating with a user equipment (UE). In some implementations, the set of suggested beams may be selected based on UE location information previously reported to the network entity and a machine learning model trained to predict beams that are likely to be the best or optimal beams for communications to and from the network entity and the UE.

[0082] At block 704, the network entity sends information to the UE that configures the UE to report measurements of at least the set of recommended beams determined by the network entity. The information may include information indicating the recommended beams selected at block 702 above. In some implementations, the information may also identify one or more additional beams to measure or provide information about various beams that may be measured (e.g., during an SSB burst). For example, the information may include information about the time and duration of the SSB burst that the network entity will perform, and the information may be carried in one or more system information blocks (SIBs).

[0083] At block 706, the network entity receives one or more reports that include measurements for the set of suggested beams and one or more additional beams that are not in the set of suggested beams. The reports may be received at the network entity as separate reports or in a joint report. In some implementations, the reports may include a first report that includes measurements for the suggested beams and a second report that includes measurements for the additional beams (e.g., the n best beams measured in an SSB burst). The network entity may periodically receive the first report and the second report using the same or different periodicities. For example, a first report that includes measurements for the suggested beams may be accepted using a first periodicity, and a second report that includes measurements for the additional beams may be accepted using a second periodicity that results in less frequent reporting than the first periodicity.

[0084] At block 708, the network entity updates the data set based on measurements of the set of suggested beams and the one or more additional beams. In some implementations, to update the data set, the network entity may add an entry associating the best beam identified by the UE with the UE location information. For example, the data set may be updated when the network entity detects a consistent mismatch between the suggested beam and one or more other beams. For example, assume that the network entity reports beams B1, B2, and B3 as suggested beams to the UE based on the UE location information, and the UE indicates that the best beams are beams B3, B4, and B5 for a threshold number of times based on measurements at the UE. Based on the mismatch, the network entity may determine current data in the data set associating the UE location information with beams B1, B2, and B3, and replace the entry in the data set associating the UE location information with beams B1, B2, and B3 with a new entry associating the UE location information with beams B4, B5, and B6. In some implementations, the network entity may retrain a machine learning model for predicting the best beam to use in communications to and from the UE and the network entity based on the updated data set.

[0085] Figure 6 and 7 For operations 600 and 700, please refer to Figure 8In other words, Figure 8 The gNB and UE can perform Figure 6 and Figure 7 Operations 600 and 700.

[0086] Figure 8 A call flow diagram illustrating an example of UE beam measurement reporting in a wireless communication network for devices supporting beam selection based on UE location information according to some aspects of the present disclosure is shown.

[0087] like Figure 8 As shown, the gNB may initially generate a set of suggested beams for communicating with the UE. The set of suggested beams may be generated based on location information reported by the UE to the gNB. The location information may include, for example, the geographic location of the UE determined based on a satellite positioning system, time difference of arrival (TDOA) information derived from signals received from multiple network entities, or other techniques for determining the geographic location of the UE. The location information may also include the UE's orientation information determined based on a compass, a gyroscope, an accelerometer, a barometer, and / or other orientation / position sensors at the UE.

[0088] The gNB may input the received UE location information into a machine learning model that is trained to generate a set of suggested beams for communications to and from the UE and the gNB. The set of suggested beams may be, for example, a reduced set of beams (e.g., a set of "best" beams) for sending communications to or receiving communications from a network entity. The reduced set of beams may be, for example, a set of beams predicted to result in the highest signal strength (such as RSSI or RSRP, among other examples) at the UE given the location information from the UE.

[0089] After generating the set of suggested beams, the gNB may send measurement configuration information to the UE. As discussed, the measurement configuration information may include information configuring the UE to report measurements of one or more suggested beams determined by the network entity in addition to the one or more beams determined by the UE. In some implementations, the configuration information may configure the UE to report measurements of n beams with the highest signal quality metrics (e.g., RSSI or RSRP). The network entity may send the measurement configuration information in one or more system information blocks (SIBs) that identify at least the timing and duration of an SSB burst in which the network entity will send an SSB using a set of suggested beams and one or more other beams.

[0090] The gNB may continue to send SSB bursts to the UE. The UE may receive the SSBs in the SSB burst and may measure signal quality metrics for the proposed beam and one or more other beams used to send the SSBs in the SSB burst. Based on the measured signal quality metrics, the UE may generate and send one or more reports that include measurements for the proposed beam and the one or more other beams. As discussed, the one or more reports may include a single report, or separate reports for the proposed beam and the one or more other beams, where the single report includes measurements for the proposed beam and the one or more other beams. In some implementations, the gNB may perform a full beam scan to send SSBs on each beam that the gNB can transmit. The UE may perform measurements on each beam, and in some implementations, select n beams with the highest measured signal quality metrics, and report the selected n beam reports to the network entity as one or more other beams, and measurements associated with the selected n beams.

[0091] In some implementations, other beams reported to the network entity may include beams for which conditions for reporting measurements have been met. The UE may receive information in configuration information received from the network entity that includes conditions for reporting measurements of other beams. For example, the condition may include a change in the received signal strength of the beam from a value greater than a first threshold to a value less than a second threshold, where the second threshold is less than the first threshold. These beams may correspond to beams that the UE previously received with high signal strength and that the UE now receives with low signal strength. When the UE determines that the condition for the beam has been met, the UE may report the identity of the beam to the network entity, and may indicate to the network entity that the report is triggered based on the condition met for the identified beam.

[0092] In response to receiving a measurement report, the gNB typically updates a data set used to generate recommended beams. The gNB may update the data set, for example, to include a record associating the best beam(s) identified in the measurement report with previously reported location information associated with the UE. By doing so, the gNB may generate an updated set of recommended beams for communicating with the UE when the UE reports the same location information to the gNB.

[0093] In some implementations, a network entity may configure a plurality of best beam sets for a UE. Each beam set in the plurality of best beam sets may include information associated with a cell identifier of a cell from which the beams in each beam set are received, and a predicted signal quality metric (e.g., RSRP or RSSI) for the beams in the beam set. The information associated with each beam set may include an indication of a reliability metric and an indication of a probability metric for each of the plurality of groups of recommended beams. The reliability metric may correspond to a predicted likelihood that the UE will successfully receive a transmission on the group of recommended beams, and the probability metric may correspond to a predicted probability that the UE will detect a beam in the group of recommended beams. In some implementations, the information may include a combination of base stations or transmit receive points (TRPs) that the UE may detect. The UE may use the information included for each beam set, for example, to prioritize measurements of each beam set. For example, the UE may prioritize a measurement beam set with a higher reliability, throughput, or probability metric over a measurement beam set with a lower reliability, throughput, or probability metric. If the UE requires high reliability, the UE may measure (multiple) suggested beam sets with higher reliability metrics; if the UE requires high throughput, the UE may measure (multiple) suggested beam sets with higher throughput metrics; and so on.

[0094] In general, various decisions related to UE positioning and / or SSB beam modification may occur at multiple nodes within a wireless network. For example, a database of each positioned beam (e.g., Figure 5 The gNB scheduler may be located at the gNB or at a central server (e.g., an AI-based server) where training is performed. In some cases, the gNB scheduler may perform beam scheduling / allocation (e.g., for both beam training and data communications).

[0095] In some cases, the location / positioning calculations may be performed at the UE, gNB, or positioning server (e.g., LMF in 5G). A corresponding location message / report may need to be issued to the relevant nodes, depending on where the decision is made. Such messages may be routed through other nodes. For example, such messages may be issued to the gNB, which then passes such messages to a server (e.g., an AI-based server). In other cases, such messages may be issued to the server via the gNB, but in messages that the gNB cannot decode (in messages called "containerized" messages). Such messages may be used to carry location reports in certain protocols such as the LTE Location Protocol (LPP), which utilizes RRC messages between the UE and the LMF. In some cases, the LMF may be located in the gNB. In this case, the LPP protocol may still be used, or faster (lower layer) reports (rather than containerized RRC messages) may be issued to the gNB (e.g., via L1 or MAC-CE signaling).

[0096] Fig. 9 A communication device 900 (eg, user equipment) is illustrated, which may include various components (eg, corresponding to means plus function components) configured to perform operations of the techniques disclosed herein, such as in Figure 6 The communication device 900 includes a processing system 902 coupled to a transceiver 908 (e.g., a transceiver and / or a receiver). The transceiver 908 is configured to transmit and receive signals, such as the various signals described herein, for the communication device 900 via an antenna 910. The processing system 902 can be configured to perform processing functions for the communication device 900, including processing signals received and / or to be transmitted by the communication device 900.

[0097] The processing system 902 includes a processor 904 coupled to a computer readable medium / memory 912 via a bus 906. In some aspects, the computer readable medium / memory 912 is configured to store instructions (e.g., computer executable code) that, when executed by the processor 904, cause the processor 904 to perform Figure 6 The operations shown or other operations for performing various techniques discussed herein. In some aspects, the computer-readable medium / memory 912 stores: code 914 for receiving information from a network entity to configure the UE to report measurements of at least one set of one or more suggested beams indicated by the network entity; code 916 for performing measurements on the set of suggested beams and one or more additional beams not in the set of suggested beams; and code 918 for sending one or more reports to the network entity, the one or more reports including measurements of the set of suggested beams and the one or more additional beams. In some aspects, the processor 904 has circuits configured to implement the code stored in the computer-readable medium / memory 912. The processor 904 includes: circuit 920 for receiving information from a network entity to configure the UE to report measurements of at least one set of one or more suggested beams indicated by the network entity; circuit 922 for performing measurements on the set of suggested beams and one or more additional beams not in the set of suggested beams; and circuit 924 for sending one or more reports to the network entity, the one or more reports including measurements of the set of suggested beams and the one or more additional beams.

[0098] Fig.10 A communication device 1000 (e.g., a network entity such as a gNB) is illustrated, which may include a device configured to perform operations for the techniques disclosed herein (e.g., Figure 71000 includes various components (e.g., corresponding to means plus function components) of the communication device 1000. The communication device 1000 includes a processing system 1002 coupled to a transceiver 1008 (e.g., a transmitter and / or a receiver). The transceiver 1008 is configured to transmit and receive signals, such as the various signals described herein, for the communication device 1000 via an antenna 1010. The processing system 1002 may be configured to perform processing functions of the communication device 1000, including processing signals received and / or transmitted by the communication device 1000.

[0099] The processing system 1002 includes a processor 1004 coupled to a computer readable medium / memory 1012 via a bus 1006. In some aspects, the computer readable medium / memory 1012 is configured to store instructions (e.g., computer executable code) that, when executed by the processor 1004, cause the processor 1004 to perform Figure 7 The operations shown, or other operations for performing various techniques discussed herein. In some aspects, the computer-readable medium / memory 1012 stores: code 1014 for selecting a set of one or more suggested beams from a data set for communicating with a user equipment (UE); code 1016 for sending information to the UE configuring the UE to report measurements of at least the set of suggested beams; code 1018 for receiving one or more reports from the UE, the one or more reports including measurements of the set of suggested beams and one or more additional beams not in the set of suggested beams; and code 1020 for updating the data set based on the measurements of the set of suggested beams and the one or more additional beams. In some aspects, the processor 1004 has circuitry configured to implement the code stored in the computer-readable medium / memory 1012. The processor 1004 includes a circuit 1022 for selecting a set of one or more suggested beams from a data set for communicating with a user equipment (UE); a circuit 1024 for sending information to the UE configuring the UE to report measurements of at least the set of suggested beams; a circuit 1026 for receiving one or more reports from the UE, the one or more reports including measurements of the set of suggested beams and one or more additional beams identified by the UE; and a circuit 1028 for updating the data set based on the measurements of the set of suggested beams and the one or more additional beams.

[0100] Example aspects

[0101] Clause 1: A method for wireless communications by a user equipment (UE), comprising receiving information from a network entity configuring the UE to report measurements of at least one set of one or more suggested beams indicated by the network entity; performing measurements on the set of suggested beams and one or more additional beams not in the set of suggested beams; and sending one or more reports to the network entity, the one or more reports including measurements on the set of suggested beams and the one or more additional beams.

[0102] Clause 2: The method of clause 1 further comprising: sending location information associated with the positioning of the UE to a network entity, wherein information configuring the UE to report measurements of at least one set of suggested beams is received based on sending the location information.

[0103] Clause 3: A method as described in clause 2, wherein the one or more suggested beams include one or more beams predicted by the network entity to have the highest received signal quality based on location information associated with the positioning of the UE.

[0104] Clause 4: A method according to any one of clauses 1 to 3, wherein sending comprises: sending a first report including measurements of the group of recommended beams; and sending a second report including measurements of additional beams not in the group of recommended beams, wherein the first report and the second report are sent in separate transmissions.

[0105] Clause 5: A method according to clause 4, wherein sending includes: periodically sending a first report including measurements of the group of recommended beams; and periodically sending a second report including measurements of additional beams not in the group of recommended beams, wherein the first report is sent less frequently than the second report.

[0106] Clause 6: A method according to any one of clauses 1 to 5, wherein sending comprises: sending a single report, the single report including measurements for the set of proposed beams and measurements for additional beams that are not in the set of proposed beams.

[0107] Clause 7: A method according to any one of clauses 1 to 6, wherein the information indicates one or more groups of recommended beams and additional information for each group of recommended beams; and the method also includes determining which group of recommended beams to perform measurements and send reports on based on the additional information.

[0108] Clause 8: A method according to clause 7, wherein the additional information includes a cell identifier (cell ID); and performing measurements includes: performing measurements on a set of proposed beams associated with the cell ID.

[0109] Clause 9: A method according to any one of clauses 7 or 8, wherein the additional information includes one or more predicted reference signal received power (RSRP) values; and sending one or more reports includes reporting measurements of a group of recommended beams in response to determining that the RSRP measurement of one or more recommended beams in the group is lower than the predicted RSRP value of the one or more recommended beams.

[0110] Clause 10: A method according to any one of clauses 7 to 9, wherein the additional information includes at least one of reliability information or probability information for each group; the reliability information corresponds to the predicted likelihood that the UE will successfully receive the transmission on the group of recommended beams; the probability metric may correspond to the predicted probability that the UE will detect the group of recommended beams; and the method also includes prioritizing measurement of the group of recommended beams, wherein the UE prioritizes measurement of the group of recommended beams based on the reliability or probability of each group.

[0111] Clause 11: A method for wireless communications by a network entity, comprising: selecting a set of one or more recommended beams from a data set for communicating with a user equipment (UE); sending information to the UE configuring the UE to report measurements of at least the set of recommended beams; receiving one or more reports from the UE, the one or more reports including measurements of the set of recommended beams and one or more additional beams not in the set of recommended beams; and updating the data set based on the measurements of the set of recommended beams and the one or more additional beams.

[0112] Clause 12: The method of clause 11, further comprising: receiving location information associated with positioning of the UE from the UE, wherein information configuring the UE to report measurements of at least one or more suggested beams is sent based on receiving the location information.

[0113] Clause 13: A method as described in clause 12, wherein the one or more suggested beams include one or more beams predicted to have the highest received signal quality based on location information associated with the location of the UE.

[0114] Clause 14: A method according to any one of clauses 11 to 13, wherein receiving comprises: receiving a first report comprising measurements of the set of recommended beams; and receiving a second report comprising measurements of one or more additional beams, wherein the first report and the second report are received in different transmissions from the UE.

[0115] Clause 15: A method according to clause 14, wherein receiving comprises: periodically receiving a first report including measurements of the set of recommended beams; and periodically receiving a second report including measurements of one or more additional beams, wherein the first report is received less frequently than the second report.

[0116] Clause 16: A method as described in any of clauses 11 to 15, wherein receiving comprises: receiving a single report including measurements for the set of proposed beams and measurements for one or more additional beams.

[0117] Clause 17: A method according to any one of clauses 11 to 16, wherein the information indicates one or more groups of suggested beams and additional information for each of the groups of suggested beams.

[0118] Clause 18: A method according to clause 17, wherein the additional information includes a cell identifier (cell ID); and receiving one or more reports includes: receiving a report including measurements of a set of proposed beams associated with the cell ID, if detected.

[0119] Clause 19: A method according to any of clauses 17 or 18, wherein the additional information comprises one or more Reference Signal Received Power (RSRP) values.

[0120] Clause 20: A method according to any one of clauses 17 to 18, wherein the additional information includes at least one of a reliability or a probability for each of multiple groups of recommended beams; the reliability information corresponds to the predicted likelihood that the UE will successfully receive transmissions on the group of recommended beams; and the probability metric may correspond to the predicted probability that the UE will detect the group of recommended beams.

[0121] Clause 21: An apparatus comprising: a memory having executable instructions stored thereon; and a processor configured to execute the executable instructions to perform the operations of any one of clauses 1 to 20.

[0122] Clause 22: An apparatus comprising: means for performing the operations of any one of clauses 1 to 20.

[0123] Clause 23: A computer readable medium having instructions stored thereon, which when executed by a processor, perform the operations of any one of clauses 1 to 20.

[0124] Other considerations

[0125] The techniques described herein can be used for various wireless communication technologies, such as NR (e.g., 5G NR), 3GPP Long Term Evolution (LTE), LTE-Advanced (LTE-A), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), Time Division Synchronous Code Division Multiple Access (TD-SCDMA), and other networks. The terms "network" and "system" are often used interchangeably. A CDMA network can implement a radio technology such as Universal Terrestrial Radio Access (UTRA) or cdma2000. UTRA includes Wideband CDMA (WCDMA) and other CDMA variants. cdma2000 covers IS-2000, IS-95, and IS-856 standards. A TDMA network can implement a radio technology such as Global System for Mobile Communications (GSM). OFDMA networks can implement radio technologies such as NR (e.g., 5G RA), Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or Flash-OFDMA. UTRA and E-UTRA are part of the Universal Mobile Telecommunications System (UMTS). LTE and LTE-A are versions of UMTS that use E-UTRA. UTRA, E-UTRA, UMTS, LTE, LTE-A, and GSM are described in documents from an organization named "3rd Generation Partnership Project" (3GPP). cdma2000 and UMB are described in documents from an organization named "3rd Generation Partnership Project 2" (3GPP2). NR is an emerging wireless communication technology under development.

[0126] In 3GPP, the term "cell" may refer to the coverage area of ​​a Node B (NB) and / or a NB subsystem serving the coverage area, depending on the context in which the term is used. In NR systems, the term "cell" and BS, next-generation NodeB (gNB or gNodeB), access point (AP), distributed unit (DU), carrier, or transmission reception point (TRP) may be used interchangeably. A BS may provide communication coverage to a macro cell, a pico cell, a femto cell, and / or other types of cells. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access to UEs with service subscriptions. A pico cell may cover a relatively small geographic area and may allow unrestricted access to UEs with service subscriptions. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access to UEs associated with a femto cell (e.g., UEs in a closed subscriber group (CSG), UEs of users in a home). A BS for a macro cell may be referred to as a macro BS. A BS for a pico cell may be referred to as a pico BS. A BS for a femto cell may be referred to as a femto BS or a home BS.

[0127] UE may also be referred to as a mobile station, terminal, access terminal, subscriber unit, station, customer premises equipment (CPE), cellular phone, smart phone, personal digital assistant (PDA), wireless modem, wireless communication device, handheld device, laptop computer, cordless phone, wireless local loop (WLL) station, tablet computer, camera, gaming device, netbook, smart notebook, super notebook, appliance, medical device or medical equipment, biosensor / device, wearable device (such as smart watch, smart clothing, smart glasses, smart bracelet), smart jewelry (e.g., smart ring or smart bracelet), entertainment device (e.g., music device, video device or satellite radio), vehicle component or sensor, smart meter / sensor, industrial manufacturing equipment, global positioning system device, or any other suitable device configured to communicate via wireless or wired medium. Some UEs may be considered as machine type communication (MTC) devices or evolved MTC (eMTC) devices. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, or location tags, which can communicate with a BS, another device (e.g., a remote device), or some other entity. For example, a wireless node can provide connectivity to or to a network (e.g., a wide area network such as the Internet or a cellular network) via a wired or wireless communication link. Some UEs can be considered Internet of Things (IoT) devices, which can be narrowband IoT (NB-IoT) devices.

[0128] In some examples, access to the air interface can be scheduled. A scheduling entity (e.g., BS) allocates resources for communications between some or all devices and equipment within its service area or cell. A scheduling entity may be responsible for scheduling, allocating, reconfiguring, and releasing resources for one or more subordinate entities. That is, for scheduling communications, subordinate entities utilize resources allocated by the scheduling entity. The base station is not the only entity that can be used as a scheduling entity. In some examples, a UE may be used as a scheduling entity and may schedule resources for one or more subordinate entities (e.g., one or more other UEs), and other UEs may utilize resources scheduled by the UE for wireless communications. In some examples, a UE may be used as a scheduling entity in a point-to-point (P2P) network and / or a mesh network. In a mesh network example, in addition to communicating with a scheduling entity, UEs may also communicate directly with each other.

[0129] The method disclosed herein includes one or more steps or actions for implementing the method. Without departing from the scope of the claims, the method steps and / or actions can be interchangeable with each other. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions can be modified without departing from the scope of the claims.

[0130] As used herein, the term "determining" encompasses a wide variety of actions. For example, "determining" may include calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, a database, or another data structure), ascertaining, etc. In addition, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), etc. In addition, "determining" may include resolving, selecting, choosing, establishing, etc.

[0131] As used herein, "or" is intended to be interpreted in an inclusive sense unless expressly stated otherwise. For example, "a or b" may include only a, only b, or a combination of a and b. As used herein, a phrase referring to "at least one" or "one or more" of a list of items refers to any combination of those items, including single members. For example, "at least one of a, b, or c" is intended to cover the following examples: only a, only b, only c, a combination of a and b, a combination of a and c, a combination of b and c, and a combination of a, b, and c.

[0132] The various illustrative components, logic, logic blocks, modules, circuits, operations, and algorithmic processes described in conjunction with the implementations disclosed herein may be implemented as electronic hardware, firmware, software, or a combination of hardware, firmware, or software, including the structures disclosed in this specification and their structural equivalents. The interchangeability of hardware, firmware, and software has been generally described in terms of functionality and illustrated in the various illustrative components, blocks, modules, circuits, and processes described above. Whether such functionality is implemented in hardware, firmware, or software depends on the specific application and the design constraints imposed on the overall system.

[0133] Various modifications to the implementations described in this disclosure will be apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other implementations without departing from the spirit or scope of the disclosure. Therefore, the claims are not intended to be limited to the implementations shown herein, but should be given the broadest scope consistent with the disclosure, principles, and novel features disclosed herein.

[0134] Additionally, various features described in this specification in the context of separate implementations may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented in multiple implementations, either individually or in any suitable sub-combination. Thus, while these features may be described above as functioning in a particular combination, and even initially claimed as such, one or more features from the claimed combination may be deleted from the combination in some cases, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0135] Similarly, although these operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring these operations to be performed in the particular order shown or in sequence, or requiring the execution of all illustrated operations to obtain the desired result. In addition, the accompanying drawings can schematically depict one or more example processes in the form of a flow chart or a flow chart. However, other operations that are not depicted can be incorporated into the schematically illustrated example process. For example, one or more additional operations can be performed before or after any of the illustrated operations, or simultaneously with any of the illustrated operations, or between any of the illustrated operations. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components in the above-mentioned implementations should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can usually be integrated together or packaged into multiple software products in a single software product.

Claims

1. A method for wireless communication at a user equipment UE, comprising: sending location information associated with the positioning of the UE to a network entity; receiving, from the network entity, information configuring the UE to report measurements of a set of one or more suggested beams indicated by the network entity, wherein the one or more suggested beams include one or more beams predicted by the network entity to have the highest received signal quality based on machine learning beam prediction and the location information associated with the location of the UE; performing measurements on the set of proposed beams and one or more additional beams not in the set of proposed beams; and One or more reports are sent to the network entity, the one or more reports comprising measurements of the set of proposed beams and the one or more additional beams.

2. The method according to claim 1, wherein: The sending of one or more reports comprises: sending a first report including measurements of the set of proposed beams; and sending a second report including measurements for the additional beam not in the set of proposed beams, Wherein the first report and the second report are sent in separate transmissions.

3. The method according to claim 2, wherein: The sending of one or more reports comprises: periodically sending a first report including measurements of the set of proposed beams; and A second report is periodically sent, the second report including measurements for the additional beam not in the set of proposed beams, wherein the first report is sent less frequently than the second report.

4. The method according to claim 1, wherein: The sending of one or more reports comprises: A single report is sent, the single report including measurements for the set of proposed beams and measurements for the additional beams that are not in the set of proposed beams.

5. The method according to claim 1, wherein: The information indicates one or more groups of proposed beams and additional information for each group of proposed beams; and The method also includes determining which set of proposed beams to perform measurements and send reports on based on the additional information.

6. The method according to claim 5, wherein: The additional information includes a cell identifier; and Performing the measurements includes performing measurements on a set of proposed beams associated with the cell identifier.

7. The method according to claim 5, wherein: The additional information includes one or more predicted reference signal received power RSRP values; and Sending the one or more reports includes reporting measurements of the set of suggested beams in response to determining that the RSRP measurement of one or more suggested beams in the set of suggested beams is lower than the predicted RSRP values ​​corresponding to the one or more suggested beams in the one or more predicted RSRP values.

8. The method according to claim 5, wherein: The additional information includes at least one of reliability information or probability information for each group; The reliability information corresponds to a predicted likelihood that the UE will successfully receive a transmission on the set of suggested beams; The probability information corresponds to a predicted probability that the UE will detect the set of suggested beams; as well as The method also includes prioritizing measurement of the group of suggested beams, wherein the UE prioritizes measurement of the group of suggested beams based on the reliability information or probability information of each group.

9. A method for wireless communication at a network entity, comprising: receiving, from a user equipment (UE), location information associated with a positioning of the UE; selecting a set of one or more suggested beams from the data set for communicating with the UE, wherein the one or more suggested beams include one or more beams predicted to have the highest received signal quality based on machine learning beam prediction and the location information associated with the location of the UE; sending information to the UE configuring the UE to report measurements of at least the set of proposed beams; receiving one or more reports from the UE, the one or more reports comprising measurements of the set of suggested beams and one or more additional beams not in the set of suggested beams; and The data set is updated based on the measurements of the set of proposed beams and the one or more additional beams.

10. The method according to claim 9, wherein: The receiving one or more reports comprises: receiving a first report including measurements for the set of proposed beams; and receiving a second report including measurements of the one or more additional beams, The first report and the second report are received in different transmissions from the UE.

11. The method according to claim 10, wherein: The receiving one or more reports comprises: periodically receiving a first report including measurements of the set of proposed beams; and A second report is periodically received, the second report including measurements for the one or more additional beams, wherein the first report is received less frequently than the second report.

12. The method according to claim 9, wherein: The receiving one or more reports comprises: A single report is received, the single report including measurements for the set of proposed beams and measurements for the one or more additional beams.

13. The method according to claim 9, wherein: The information indicates one or more groups of proposed beams and additional information for each of the groups of proposed beams.

14. The method according to claim 13, wherein: The additional information includes a cell identifier; and Receiving the one or more reports includes receiving a report including measurements of a set of proposed beams associated with the cell identifier if detected.

15. The method of claim 13, wherein: The additional information includes one or more reference signal received power RSRP values.

16. The method of claim 13, wherein: The additional information includes at least one of reliability information or probability information for each of the plurality of groups of suggested beams; The reliability information corresponds to a predicted likelihood that the UE will successfully receive transmissions on the set of suggested beams; and The probability information corresponds to the predicted probability that the UE will detect the set of suggested beams.

17. An apparatus for performing wireless communication at a user equipment UE, comprising: A processor is configured to perform operations, the operations comprising: sending location information associated with the positioning of the UE to a network entity; receiving, from the network entity, information configuring the UE to report measurements of a set of one or more suggested beams determined by the network entity, wherein the one or more suggested beams include one or more beams predicted by the network entity to have the highest received signal quality based on machine learning beam prediction and the location information associated with the location of the UE; performing measurements on the set of proposed beams and one or more additional beams not in the set of proposed beams; and sending one or more reports to the network entity, the one or more reports comprising the measurements of the set of proposed beams and the one or more additional beams; and Memory.

18. The device according to claim 17, wherein: The processor is configured to send the one or more reports by: sending a first report including measurements of the set of proposed beams; and sending a second report including measurements of the one or more additional beams, Wherein the first report and the second report are sent in separate transmissions.

19. The device according to claim 17, wherein: The apparatus is configured to send the one or more reports by: A single report is sent, the single report including measurements for the set of proposed beams and measurements for the one or more additional beams.

20. The apparatus of claim 17, wherein: The information indicates one or more groups of proposed beams and additional information for each group of proposed beams; and The operations also include determining which set of proposed beams to perform measurements and send reports on based on the additional information.

21. The apparatus of claim 20, wherein: The additional information includes a cell identifier; and The processor is configured to perform the measuring by: Measurements are performed on a set of proposed beams associated with the cell identifier.

22. The apparatus of claim 20, wherein: The additional information includes one or more predicted reference signal received power RSRP values; and The processor is configured to send the one or more reports in the following manner: in response to determining that the RSRP measurement of one or more suggested beams in a set of suggested beams is lower than the predicted RSRP values ​​corresponding to the one or more suggested beams in the one or more predicted RSRP values, reporting the measurement of the set of suggested beams.

23. The apparatus of claim 20, wherein: The additional information includes at least one of reliability information or probability information for each group; The reliability information corresponds to a predicted likelihood that the UE will successfully receive transmissions on the set of suggested beams; and The probability information corresponds to a predicted probability that the UE will detect the set of suggested beams; as well as The processor is further configured to prioritize measuring the group of suggested beams, wherein the UE prioritizes measuring the group of suggested beams based on the reliability information or probability information of each group.

24. An apparatus for performing wireless communication at a network entity, comprising: The processor is configured as: receiving, from a user equipment (UE), location information associated with a positioning of the UE; selecting a set of one or more suggested beams from the data set for communicating with the UE, wherein the one or more suggested beams include one or more beams predicted to have the highest received signal quality based on machine learning beam prediction and the location information associated with the location of the UE; sending information to the UE configuring the UE to report measurements of at least the set of proposed beams; receiving one or more reports from the UE, the one or more reports comprising measurements of the set of suggested beams and one or more additional beams not in the set of suggested beams; and updating the data set based on measurements of the set of proposed beams and the one or more additional beams; and Memory.

25. An apparatus for wireless communication, comprising means for performing the method according to any one of claims 1-16.

26. A computer readable medium having instructions stored thereon, which when executed by a processor, cause the processor to perform the method according to any one of claims 1-16.

27. A computer program product comprising computer instructions which, when executed by a processor, cause the processor to perform the method according to any one of claims 1-16.

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