USER EQUIPMENT REPORTING FOR UPDATING OF MACHINE LEARNING ALGORITHMS

Wireless communications systems and methods related to user equipment reporting for updating of machine learning algorithms are provided. A user equipment (UE) applies a machine learning-based network to a set of received signal measurements. The UE determines whether an output of the machine learning-based network fails to satisfy one or more criteria. The UE communicates, with a base station (BS), a report when the output of the machine learning-based network fails to satisfy the one or more criteria. The BS communicates, with one or more UEs, a first configuration for a machine learning-based network. The BS receives, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network. The BS communicates, with the first UE, a second configuration for the machine learning-based network based on the received report.

TECHNICAL HELD

This application relates to wireless communication systems, and more particularly to user equipment reporting for updating of machine learning algorithms.

INTRODUCTION

In a wireless communication network, a BS may communicate with a UE in an uplink direction and a downlink direction. The radio frequency channel through which the BS and the UE communicate may have several channel properties that are considered for proper channel performance. The BS and UE may perform channel sounding to better understand these channel properties by measuring and/or estimating various parameters of the channel, such as delay, path loss, absorption, multipath, reflection, fading, doppler effect, among others. These channel measurements can also be used for channel estimation and channel equalization.

BRIEF SUMMARY OF SOME EXAMPLES

For example, in an aspect of the disclosure, a method of wireless communication performed by a UE includes applying a machine learning-based network to a set of received signal measurements; determining whether an output of the machine learning-based network fails to satisfy one or more criteria; and communicating, by the UE with a BS, a report when the output of the machine learning-based network fails to satisfy the one or more criteria.

In an additional aspect of the disclosure, a UE includes a processor configured to apply a machine learning-based network to a set of received signal measurements; and determine whether an output of the machine learning-based network fails to satisfy one or more criteria; and a transceiver configured to communicate, with a BS, a report when the output of the machine learning-based network fails to satisfy the one or more criteria.

For example, in another aspect of the disclosure, a method of wireless communication performed by a BS includes communicating, by the BS with one or more UEs, a first configuration for a machine learning-based network; receiving, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network; and communicating, by the BS with the first UE, a second configuration for the machine learning-based network based on the received report.

In another additional aspect of the disclosure, a BS includes a transceiver configured to communicate, with one or more UEs, a first configuration for a machine learning-based network; receive, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network; and communicate, with the first UE, a second configuration for the machine learning-based network based on the received report.

DETAILED DESCRIPTION

A wireless channel between the network (e.g., a BS) and a UE may vary over time. The BS may configure a set of beams for the UE, which at any point of time may use one or two serving beams to receive DL transmissions from or transmit UL transmissions to the BS. The BS and the UE may keep track of the serving beam(s) as well as candidate beams. For example, the UE may perform one or more measurements of one or more reference signals configured for the UE and may include the one or more measurements in a channel state information (CSI) report. If a serving beam fails, the BS may reconfigure the UE to use of the candidate beams. Candidate beams may be regularly updated because the channel quality between the BS and the UE may change over time. It may be desirable for the UE update the serving beam(s) according to the channel state. The UE may report the link quality of the serving beam(s) and the candidate beams in a CSI report to the BS, and the BS may process the CSI report and determine whether the UE's serving beam(s) or candidate beam(s) should be reconfigured. If the quality of a beam falls below a threshold, the BS may reconfigure a beam the UE's serving beam(s) or candidate beam(s). The BS may configure the threshold. Based on the determination, the BS may transmit a command to reconfigure the UE's serving beam(s) and/or candidate beam(s) in response to the CSI report.

The BS may configure the UE to periodically report the CSI report to the BS. The CSI report may include, for example, channel quality information (CQI) and/or reference signal received power (RSRP). CQI is an indicator carrying information on the quality of a communication channel. The BS may use the CQI to assist in downlink (DL) scheduling. The BS may use the RSRP to manage beams in multi-beam operations. The UE may perform different combinations of measurements for inclusion in the CSI report. Accordingly, the UE may transmit a CSI report including the CQI but not the RSRP, a CSI report including the RSRP but not the CQI, and/or a CSI report including both the CQI and the RSRP.

In 5G NR, machine learning (ML) algorithms are being implemented to assist cellular network performance. These ML algorithms may include neural networks that are implemented at different types of nodes within a wireless communication network. For example, the neural networks may be implemented at a single node (e.g., UE/BS/central cloud server) or may be distributed over multiple nodes. The ML algorithms may be implemented to assist with different functions and/or modules among the nodes of the wireless communication network. In various aspects, the neural network may be implemented as a convolutional neural network (CNN), a recurrent neural network (RNN), a deep convolutional network (DCN), among others.

At each node implemented with one or more ML algorithms, the ML algorithms may interact with different layers within the node. The ML algorithms may interact with one of the physical layer (PHY), the media access control (MAC) layer or upper layers (e.g., application layer) in some instances, or with multiple layers in other instances. For example, a node may include a ML module adapted for low-density parity check (LDPC) decoding at the PHY layer. In another example, a node may include a ML Module for CSI prediction and/or transmission configuration indicator (TCI) selection at the PHY layer and the MAC layer. In another example, a node may include a ML Module for multi-user (MU) scheduling taking account for package latency and/or priority at the PHY layer, the MAC layer and the upper layers. These ML algorithms may involve various ML-related data transfers between different layers of different nodes (e.g., UE, BS, central cloud server). The ML algorithms may be trained with training datasets that are produced through periodic and/or aperiodic data collection at one or more nodes. In various aspects, measurement data collection serves as input to the ML modules. The operation of these ML algorithms at the different nodes may be used for ML model parameter transfer and/or update. The ML model framework within the wireless communication network has the capability to send feedback signals and/or reports between the different nodes. In various aspects, the UE may feed back channel measurements that are indicative of the ML model prediction accuracy. For example, the measurement data collection by the UE that is then sent to the BS and/or central cloud server with a report may indicate that the ML model is producing prediction errors, thus indicative that the ML model requires updating. The ML modules may provide intermediate data transfer between the different nodes (e.g. to facilitate training with stochastic gradient decent and backpropagation for a distributed ML algorithm).

In various aspects, the UE may include different ML algorithms on board to predict channel properties for a future use of that channel. For example, the machine learning-based network may be implemented by a channel property prediction network to predict one or more properties of a channel. In some aspects, the ML algorithms are tasked to predict what transmission beam to use for the BS and/or reception beam to use for the UE. For example, the machine learning-based network may be implemented by a beam selection prediction network to predict the BS transmission beam and/or the UE reception beam. In other aspects, the ML algorithms are tasked to predict what is the delay spread of a channel. For example, the machine learning-based network may be implemented by a delay spread prediction network to predict the delay spread on a channel. In still other aspects, the ML algorithms are tasked to predict when is a best time or condition to hand over channel communication to another BS, and further predict as to which BS to handover. For example, the machine learning-based network may be implemented by a handover prediction network to predict a proper handover condition and/or predict a handover destination. In various scenarios, the BS sends updates of neural networks to the UE to configure, the UE then feeds back sampled data to the BS, and the training and updating of the neural network(s) occurs at the central cloud server. Given the substantial amounts of collected sampled data that is sent back from the UE to the BS, this creates an increasingly burdensome task for the BS to process through the sampled data. Therefore, it is desirable for the UE to decide which sampled data is more useful to feed back to the BS for improving the quality of the ML algorithms.

The present disclosure provides techniques for the UE to feed back sampled data of the channel properties that correspond to an incorrect prediction (or generally referred to as “a prediction error” herein) to the BS to serve as more insightful data that helps improve the ML algorithms. To update the neural network(s), it may be more valuable to feed back the sampled data when a prediction error occurs. In various aspects, the UE is configured to teed back a subset of the sampled data that corresponds to a correct prediction to the BS since this sampled data may not be as indicative as to how to improve the ML algorithms other than to reassure that the ML algorithms are performing as expected.

Aspects of the present disclosure can provide several benefits. For example, by feeding back sampled data that corresponds to a prediction error and/or a subset of the sampled data that corresponds to a correct prediction, the ML-based system can reduce the amount of sampled data that needs to be fed back between the UE and the BS. This helps free up resource elements in the channel and also helps to reduce the burden created at the BS to process and/or determine which of the sampled data is most useful to train the neural network to achieve an increase in performance.

In some aspects, a UE includes a processor configured to apply a machine learning-based network to a set of received signal measurements and determine whether an output of the machine learning-based network fails to satisfy one or more criteria. The UE also may include a transceiver configured to communicate, with a BS, a report when the output of the machine learning-based network fails to satisfy the one or more criteria. In some aspects, the processor configured to determine whether the output of the machine learning-based network fails to satisfy the one or more criteria may be further configured to determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement Obtained by the UE.

In some aspects, the transceiver configured to communicate the report may be further configured to transmit, to the BS in a first subband of a plurality of subbands, sampled data for updating the machine learning-based network. In some aspects, the first subband includes a plurality of physical uplink shared channels (e.g., PUSCHs) multiplexed in at least one of time or frequency in a first portion of a first time period, and the transceiver configured to transmit the sampled data may be further configured to transmit the sampled data in one or more PUSCHs of the plurality of PUSCHs. In some aspects, the sampled data includes a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network associated with the at least one signal measurement.

In some aspects, the transceiver of the UE may be further configured to receive a predetermined threshold from the BS for use with the machine learning-based network. In some instances, the processor configured to determine whether the output of the machine learning-based network fails to satisfy the one or more criteria may be further configured to determine that the output of the machine learning-based network fails to satisfy the one or more criteria based on the predetermined threshold. In some aspects, the processor configured to determine that the output of the machine learning-based network fails to satisfy the one or more criteria may be further configured to determine whether a first signal measurement in the set of received signal measurements is greater than the predetermined threshold, and determine that the output of the machine learning-based network corresponds to a prediction error when the first signal measurement in the set of received signal measurements is not greater than the predetermined threshold. In some aspects, the predetermined threshold corresponds to a target reference signal received power (e.g., RSRP) value for a downlink specific reference signal. In some aspects, the downlink specific reference signal includes a synchronization signal block (e.g., SSB). In other aspects, the downlink specific reference signal comprises a channel state information reference signal (e.g., CSI-RS).

In some aspects, the transceiver of the UE is further configured to receive, in a first subband of a plurality of subbands, a request for the UE to measure sampled ground-truth data. In some instances, the processor is further configured to obtain the sampled ground-truth data in response to the request. In some aspects, the processor configured to determine whether the output of the machine learning-based network fails to satisfy the one or more criteria may be further configured to determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and the sampled ground-truth data. In some aspects, the request includes a request for measurement of the sampled ground-truth data by the UE at a particular time instance during a first time period, in which the first subband includes a plurality of physical downlink control channels (PDCCHs) multiplexed in at least one of time or frequency in a first portion of the first time period. In some instances, the transceiver configured to receive the request may be further configured to receive the request in one or more PDCCHs of the plurality of PDCCHs. In some aspects, the request includes a request for the UE to perform a plurality of periodical signal measurements of the sampled ground-truth data. In some instances, the transceiver configured to receive the request may be further configured to receive the request in a radio resource control (e.g., RRC) signal.

In some aspects, the transceiver may be further configured to receive, in a first subband of a plurality of subbands, a request to communicate sampled data with the BS. In some instances, the transceiver configured to communicate the report may be further configured to communicate, with the BS, the report with the sampled data, in response to the request. In some aspects, the transceiver may be further configured to receive, from the BS, the set of received signal measurements as input data, wherein the set of received signal measurements comprises historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams. In some aspects, the processor may be further configured to measure, a plurality of transmission beams associated with the BS during a first time period, obtain a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams, select one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data, and provide a feedback pairing comprising the input data and the output data as the sampled data. In some aspects, the request includes a request for the UE to perform one or more signal measurements at a particular time instance during the first time period, in which the first subband includes a plurality of PDCCHs multiplexed in at least one of time or frequency in a first portion of the first time period. In some aspects, the transceiver configured to receive the request may be further configured to receive the request in one or more PDCCHs of the plurality of PDCCHs. In other aspects, the request includes a request for the UE to perform a plurality of periodical signal measurements during the first time period. In some instances, the transceiver configured to receive the request may be further configured to receive the request in a RRC signal. In some aspects, the transceiver may be further configured to communicate, with the BS over a plurality of periodic intervals during a second time period greater than the first time period, the sampled data with the plurality of periodical signal measurements, in response to the request.

In some aspects, the request includes a request for the UE to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network. In some instances, the transceiver configured to receive the request may be further configured to receive the request in a RRC signal. In some aspects, the transceiver is further configured to communicate, with the BS, the report comprising the first proportion of the sampled data that corresponds to the prediction error of the machine learning-based network. In some aspects, the request includes a request for the UE to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network. In somec instances, the transceiver may be further configured to communicate, with the BS, the report comprising the second proportion of the sampled data that corresponds to the correct prediction of the machine learning-based network.

In some aspects, the request includes a request for the UE to communicate a subset of sampled data comprising up to a predetermined number of signal measurements that corresponds to a correct prediction of the machine learning-based network when no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data. In some instances, the transceiver configured to receive the request may be further configured to receive the request in a RRC signal. In some aspects, the processor may be further configured to determine that no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data. In some aspects, the transceiver may be further configured to communicate, with the BS, the report comprising the subset of sampled data corresponding to a correct prediction of the machine learning-based network, the subset of sampled data comprising a number of signal measurements up to the predetermined number of signal measurements.

In some aspects, the request includes a request for the UE to measure sampled data for a predetermined number of time instances in a second time period subsequent to the first time period when sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data.

In some aspects, the transceiver may be further configured to encode the sampled data into encoded sampled data during a time period of reporting within the first time period, and, communicate, with the BS during the time period of reporting, the encoded sampled data.

In some aspects, a UE115attempting to access the network100may perform an initial cell search by detecting a PSS from a BS105. The PSS may enable synchronization of period timing and may indicate a physical layer identity value. The UE115may then receive a SSS. The SSS may enable radio frame synchronization, and may provide a cell identity value, which may be combined with the physical layer identity value to identify the cell. The PSS and the SSS may be located in a central portion of a carrier or any suitable frequencies within the carrier.

After receiving the PSS and SSS, the UE115may receive a MIB. The MIB may include system information for initial network access and scheduling information for RMSI and/or OSI. After decoding the MIB, the UE115may receive RMSI and/or OSI. The RMSI and/or OSI may include radio resource control (RRC) information related to random access channel (RACH) procedures, paging, control resource set (CORESET) for physical downlink control channel (e.g., PDCCH) monitoring, physical UL control channel (PUCCH), physical UL shared channel (PUSCH), power control, and SRS.

After obtaining the MIB, the RMSI and/or the OSI, the UE115can perform a random access procedure to establish a connection with the BS105. In some examples, the random access procedure may be a four-step random access procedure. For example, the UE115may transmit a random access preamble and the BS105may respond with a random access response. The random access response (RAR) may include a detected random access preamble identifier (ID) corresponding to the random access preamble, timing advance (TA) information, a UL grant, a temporary cell-radio network temporary identifier (C-RNTI), and/or a backoff indicator. Upon receiving the random access response, the UE115may transmit a connection request to the BS105and the BS105may respond with a connection response. The connection response may indicate a contention resolution. In some examples, the random access preamble, the RAR, the connection request, and the connection response can be referred to as message 1 (MSG1), message 2 (MSG2), message 3 (MSG3), and message 4 (MSG4), respectively. In some examples, the random access procedure may be a two-step random access procedure, where the UE115may transmit a random access preamble and a connection request in a single transmission and the BS105may respond by transmitting a random access response and a connection response in a single transmission.

After establishing a connection, the UE115and the BS105can enter a normal operation stage, where operational data may be exchanged. For example, the BS105may schedule the UE115for UL and/or DL communications. The BS105may transmit UL and/or DL scheduling grants to the UE115via a PDCCH. The scheduling grants may be transmitted in the form of DL control information (DCI). The BS105may transmit a DL communication signal (e.g., carrying data) to the UE115via a PDSCH according to a DL scheduling grant. The UE115may transmit a UL communication signal to the BS105via a PUSCH and/or PUCCH according to a UL scheduling grant.

In some aspects, the BS105may communicate with a UE115using HARQ techniques to improve communication reliability, for example, to provide a URLLC service. The BS105may schedule a UE115for a PDSCH communication by transmitting a DL grant in a PDCCH. The BS105may transmit a DL data packet to the UE115according to the schedule in the PDSCH. The DL data packet may be transmitted in the form of a transport block (TB). If the UE115receives the DL data packet successfully, the UE115may transmit a HARQ ACK to the BS105. Conversely, if the UE115fails to receive the DL transmission successfully, the UE115may transmit a HARQ NACK to the BS105. Upon receiving a HARQ NACK from the UE115, the BS105may retransmit the DL data packet to the UE115. The retransmission may include the same coded version of DL data as the initial transmission. Alternatively, the retransmission may include a different coded version of the DL data than the initial transmission. The UE115may apply soft-combining to combine the encoded data received from the initial transmission and the retransmission for decoding. The BS105and the UE115may also apply HARQ for UL communications using substantially similar mechanisms as the DL HARQ.

In some aspects, the network100may operate over a system BW or a component carrier (CC) BW. The network100may partition the system BW into multiple BWPs (e.g., portions). A BS105may dynamically assign a UE115to operate over a certain BWP (e.g., a certain portion of the system BW). The assigned BWP may be referred to as the active BWP. The UE115may monitor the active BWP for signaling information from the BS105. The BS105may schedule the UE115for UL or DL communications in the active BWP. In some aspects, a BS105may assign a pair of BWPs within the CC to a UE115for UL and DL communications. For example, the BWP pair may include one BWP for UL communications and one BWP for DL communications.

In some aspects, the network100may operate over a shared channel, which may include shared frequency bands and/or unlicensed frequency bands. For example, the network100may be an NR-U network operating over an unlicensed frequency band. In such an aspect, the BSs105and the UEs115may be operated by multiple network operating entities. To avoid collisions, the BSs105and the UEs115may employ a listen-before-talk (LBT) procedure to monitor for transmission opportunities (TXOPs) in the shared channel. A TXOP may also be referred to as COT. For example, a transmitting node (e.g., a BS105or a UE115) may perform an LBT prior to transmitting in the channel. When the LBT passes, the transmitting node may proceed with the transmission. When the LBT fails, the transmitting node may refrain from transmitting in the channel.

An LBT can be based on energy detection (ED) or signal detection. For an energy detection-based LBT, the LBT results in a pass when signal energy measured from the channel is below a threshold. Conversely, the LBT results in a failure when signal energy measured from the channel exceeds the threshold. For a signal detection-based LBT, the LBT results in a pass when a channel reservation signal (e.g., a predetermined preamble signal) is not detected in the channel. Additionally, an LBT may be in a variety of modes. An LBT mode may be, for example, a category 4 (CAT4) LBT, a category 2 (CAT2) LBT, or a category 1 (CAT1) LBT. A CAT1 LBT is referred to a no LBT mode, where no LBT is to be performed prior to a transmission. A CAT2 LBT refers to an LBT without a random backoff period. For instance, a transmitting node may determine a channel measurement in a time interval and determine whether the channel is available or not based on a comparison of the channel measurement against a ED threshold. A CAT4 LBT refers to an LBT with a random backoff and a variable contention window (CW). For instance, a transmitting node may draw a random number and backoff for a duration based on the drawn random number in a certain time unit.

In some aspects, the network100may support sidelink communication among the UEs115over a shared radio frequency band (e.g., in a shared spectrum or an unlicensed spectrum). In some aspects, the UEs115may communicate with each other over a 2.4 GHz unlicensed band, which may be shared by multiple network operating entities using various radio access technologies (RATs) such as NR-U, WiFi, and/or licensed-assisted access (LAA) as shown inFIG. 2.

In some aspects, the network100may be implemented with artificial intelligence to assist cellular network performance by implementing machine learning (ML) algorithms to predict certain properties and/or operations within the network100. These ML algorithms may include neural networks that are implemented at different types of nodes within the network100. For example, the neural networks may be implemented at a single node (e.g., UEs115, BSs115) or may be distributed over multiple nodes. The ML algorithms may be implemented to assist with different functions and/or modules among the nodes of the network100.

FIG. 2illustrates a wireless communication network200that provisions for user equipment reporting according to some aspects of the present disclosure. The network200may correspond to a portion of the network100.FIG. 2illustrates two BSs205(shown as205aand205b) and six UEs215(shown as215a1,215a2,215a3,215a4,215b1, and215b2) for purposes of simplicity of discussion, though it will be recognized that embodiments of the present disclosure may scale to any suitable number of UEs215(e.g., the about 2, 3, 4, 5, 7 or more) and/or BSs205(e.g., the about 1, 3 or more). The BS205and the UEs215may be similar to the BSs105and the UEs115, respectively. The BSs205and the UEs215may share the same radio frequency band for communications. In some instances, the radio frequency band may be a 2.4 GHz unlicensed band, a 5 GHz unlicensed band, or a 6 GHz unlicensed band. In general, the shared radio frequency band may be at any suitable frequency.

The BS205aand the UEs215a1-215a4may be operated by a first network operating entity. The BS205band the UEs215b1-215b2may be operated by a second network operating entity. In some aspects, the first network operating entity may utilize a same RAT as the second network operating entity. For instance, the BS205aand the UEs215a1-215a4of the first network operating entity and the BS205band the UEs215b1-215b2of the second network operating entity are NR-U devices. In some other aspects, the first network operating entity may utilize a different RAT than the second network operating entity. For instance, the BS205aand the UEs215a1-215a4of the first network operating entity may utilize NR-U technology while the BS205band the UEs215b1-215b2of the second network operating entity may utilize WiFi or LAA technology.

The network200also illustrates a cloud server260that may be operated independent of the first network operating entity and the second network operating entity. In some aspects, the cloud server260may be operated in conjunction with one or more of the first or second network operating entities. The cloud server260may be a centralized node in communication with one or more BSs205and/or UEs215via communication links253.

In the network200, some of the UEs215a1-215a4may communicate with each other in peer-to-peer communications. For example, the UE215a1may communicate with the UE215a2over a sidelink252, the UE215a3may communicate with the UE215a4over another sidelink251, and the UE215b1may communicate with the UE215b2over yet another sidelink254. The sidelinks251,252, and254are unicast bidirectional links. Some of the UEs215may also communicate with the BS205aor the BS205bin a UL direction and/or a DL direction via communication links253. For instance, the UE215a1,215a3, and215a4are within a coverage area210of the BS205a,and thus may be in communication with the BS205a.The UE215a2is outside the coverage area210, and thus may not be in direct communication with the BS205a.In some instances, the UE215a1may operate as a relay for the UE215a2to reach the BS205a.Similarly, the UE215b1is within a coverage area212of the BS205b,and thus may be in communication with the BS205band may operate as a relay for the UE215b2to reach the BS205b.In some aspects, some of the UEs215are associated with vehicles (e.g., similar to the UEs115i-k) and the communications over the sidelinks251,252, and254may be C-V2X communications. C-V2X communications may refer to communications between vehicles and any other wireless communication devices in a cellular network.

In various aspects, the network200may be implemented with artificial intelligence to assist cellular network performance by implementing machine learning (ML) algorithms to predict certain properties and/or operations within the network200. These ML algorithms may include neural networks that are implemented at different types of nodes within the network200. For example, the neural networks may be implemented at a single node (e.g., UEs215, BSs215) or may be distributed over multiple nodes. The ML algorithms may be implemented to assist with different functions and/or modules among the nodes of the network200.

In some aspects, each node (e.g., UEs215, BSs205and/or cloud server260) may be implemented with one or more ML algorithms. The ML algorithms may interact with different layers within the node. The ML algorithms may interact with one of the PHY layer, the MAC layer or upper layers (e.g., application layer) in some instances, or with multiple layers in other instances. For example, a node (e.g., the UEs215) may include a ML module adapted for low-density parity check (LDPC) decoding at the PHY layer. In another example, a node (e.g., the BSs205, cloud server260) may include a ML Module for CSI prediction and/or TCI selection at the PHY and MAC layers. In another example, a node (e.g., BS205) may include a ML Module for MU scheduling taking account for package latency and/or priority at the PHY layer, the MAC layer and the upper layers. These ML algorithms may involve various ML-related data transfers between different layers of the different nodes (e.g., the UEs215, the BSs205, the cloud server260). The ML algorithms may be trained with training datasets that are produced through periodic and/or aperiodic data collection at the UEs215. In various aspects, measurement data collection serves as input to the ML modules. The operation of these ML algorithms at the different nodes may be used for ML model parameter transfer and/or update. The ML model framework within the network200has the capability to communicate feedback signals and/or reports between the different nodes (e.g., between UEs215and BSs205). In various aspects, the UE215a1may feed back channel measurements that are indicative of the ML model prediction accuracy. For example, the measurement data collection by the UE215a1that is then sent with a report to the BS205aand/or the cloud server260may indicate that the ML model is producing prediction errors, thus indicative that the ML model requires updating. In some aspects, the ML modules may provide intermediate data transfer between the different nodes (e.g. to facilitate training with stochastic gradient decent and backpropagation for a distributed ML algorithm).

In some aspects, a ML module is distributed in the BSs205, the UEs215and the cloud server260, which is adapted for BS-side beam prediction based on signal measurements obtained by the UEs215. The UEs215may be adapted to collect channel measurements with the UE PHY layer, pack the channel measurement data in the UE application layer, and communicate the channel measurement data with BS-side application layer of the BSs205and the server-side application layer of the cloud server260. The BS-side application layer of the BSs205may be adapted to receive the channel measurement data from the UEs215, pass the channel measurement data as input to one or more neural networks operating at one or more of the BSs205or the cloud server260, forward propagate the neural network, pass the output of the neural network for a beam selection model, change one or more parameters of the beam selection model, as well as receiving neural network parameter updates from the cloud server260. The server-side application layer of the cloud server260may be adapted to receive the channel measurement data from the UEs215, train the neural network with an existing training dataset and/or an updated training dataset, send a neural network update to the BS-side application layer of the BSs205.

In some examples, the BS205atransmits, to UEs215a1,215a3and215a4, a neural network, such as a machine learning-based network, for predicting a best performing transmission beam from the BS205ato monitor a downlink specific reference signal, such as the SSB. The input to the neural network can be the previous 10 signal measurements of the BS205atransmission beam monitored and the corresponding measured signal strength (e.g., RSRP) of the SSB. The output of the neural network may include a prediction of the best performing transmission beam from the BS205a.The neural network may be trained offline from previously collected channel measurement data at the BS205aor at the cloud server260.

From time to time, each UE (e.g., UE215a1, UE215a3, UE215a4) can gather the input data from the BS205aand scan over all transmission beams for detecting the best performing SSB beam to determine the output data from the neural network (e.g., the machine learning-based network), and communicate feedback pair (e.g., input data, output data) as sampled data to the BS205afor updating of the neural network.

In some of the sampled data, the measured best performing SSB beam may not be the same as the predicted best performing SSB beam using the neural network, which indicates a prediction error of the neural network. For other sampled data, the neural network prediction may be correct. When the UEs215determine the neural network prediction is not correct, there may not be sufficient time for the UEs215to obtain additional sampled data. For example, the UEs215may not be able to measure additional SSB beams other than the predicted SSB beam. However, the following time instance may produce similar sampled data. If the UEs215measure the channel during the following time instance and collect the following sampled data, there is a higher likelihood that the UEs215are collecting sampled data with neural network prediction errors.

The present disclosure provides techniques for the UE to feed back sampled data of the channel properties that correspond to an incorrect prediction (or generally referred to as “a prediction error” herein) to the BS to serve as more insightful data that helps improve the ML algorithms. To update the neural network(s), it may be more valuable to feed back the sampled data when a prediction error occurs. In various aspects, the UE is configured to feed back a subset of the sampled data that corresponds to a correct prediction to the BS since this sampled data may not be as indicative as to how to improve the ML algorithms other than to reassure that the ML algorithms are performing as expected.

FIG. 3is a block diagram of an exemplary UE300according to some aspects of the present disclosure. The UE300may be a UE115discussed above inFIG. 1or a UE215discussed above inFIG. 2. As shown, the UE300may include a processor302, a memory304, a reporting communication module308, a transceiver310including a modem subsystem312and a radio frequency (RF) unit314, and one or more antennas316. These elements may be in direct or indirect communication with each other, for example via one or more buses.

The memory304may include a cache memory (e.g., a cache memory of the processor302), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. In an aspect, the memory304includes a non-transitory computer-readable medium. The memory304may store, or have recorded thereon, instructions306. The instructions306may include instructions that, when executed by the processor302, cause the processor302to perform the operations described herein with reference to the UEs115in connection with aspects of the present disclosure, for example, aspects ofFIGS. 1, 2, and 5-9. Instructions306may also be referred to as program code. The program code may be for causing a wireless communication device to perform these operations, for example by causing one or more processors (such as processor302) to control or command the wireless communication device to do so. The terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instructions” and “code” may refer to one or more programs, routines, sub-routines, functions, procedures, etc. “Instructions” and “code” may include a single computer-readable statement or many computer-readable statements.

The reporting communication module308may be implemented via hardware, software, or combinations thereof. For example, the reporting communication module308may be implemented as a processor, circuit, and/or instructions306stored in the memory304and executed by the processor302. In some instances, the reporting communication module308can be integrated within the modem subsystem312. For example, the reporting communication module308can be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the modem subsystem312.

The reporting communication module308may be used for various aspects of the present disclosure, for example, aspects ofFIGS. 1, 2, and 5-9. For instance, the reporting communication module308may coordinate with the processor302to apply a machine learning-based network to a set of received signal measurements and determine whether an output of the machine learning-based network fails to satisfy one or more criteria. In some aspects, the reporting communication module308may determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE.

In some aspects, the reporting communication module308may coordinate with the transceiver310to communicate, with a BS (e.g., BSs105,205and/or400), a report when the output of the machine learning-based network fails to satisfy the one or more criteria. In some aspects, the reporting communication module308, in coordination with the transceiver310, may receive a predetermined threshold from the BS for use with the machine learning-based network. In some aspects, the reporting communication module308, in coordination with the processor302, may be further configured to determine whether the output of the machine learning-based network fails to satisfy the one or more criteria may be further configured to determine that the output of the machine learning-based network fails to satisfy the one or more criteria based on the predetermined threshold. In some aspects, the reporting communication module308may be further configured to determine whether a first signal measurement in the set of received signal measurements is greater than the predetermined threshold, and determine that the output of the machine learning-based network corresponds to a prediction error when the first signal measurement in the set of received signal measurements is not greater than the predetermined threshold. In some aspects, the predetermined threshold corresponds to a target reference signal received power (RSRP) value for a downlink specific reference signal. In some aspects, the downlink specific reference signal includes a synchronization signal block (e.g., SSB). In other aspects, the downlink specific reference signal comprises a channel state information reference signal (e.g., CSI-RS).

In some aspects, the reporting communication module308, in coordination with the transceiver310, may receive, in a first subband of a plurality of subbands, a request for the UE300to measure sampled ground-truth data. In some instances, the reporting communication module308is further configured to obtain the sampled ground-truth data in response to the request. In some aspects, the reporting communication module308may be further configured to determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and the sampled ground-truth data. In some aspects, the request includes a request for measurement of the sampled ground-truth data by the UE300at a particular time instance during a first time period, in which the first subband includes a plurality of physical downlink control channels (e.g., PDCCHs, or enhanced PDCCHs (ePDCCHs)) multiplexed in at least one of time or frequency in a first portion of the first time period. In some instances, the reporting communication module308, in coordination with the transceiver310, may be further configured to receive the request in one or more PDCCHs of the plurality of PDCCHs. In some aspects, the request includes a request for the UE300to perform a plurality of periodical signal measurements of the sampled ground-truth data.

In some aspects, the reporting communication module308, in coordination with the transceiver310, may receive, in a first subband of a plurality of subbands, a request to communicate sampled data with the BS. In some instances, the reporting communication module308, in coordination with the transceiver310, may communicate, with the BS, the report with the sampled data, in response to the request. In some aspects, the reporting communication module308, in coordination with the transceiver310, may receive, from the BS, the set of received signal measurements as input data, wherein the set of received signal measurements comprises historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams. In some aspects, the reporting communication module308may be further configured to measure, a plurality of transmission beams associated with the BS during a first time period, obtain a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams, select one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data, and provide a feedback pairing that includes the input data and the output data as the sampled data. In some aspects, the request includes a request for the UE300to perform one or more signal measurements at a particular time instance during the first time period. In other aspects, the request includes a request for the UE300to perform a plurality of periodical signal measurements during the first time period. In some aspects, the reporting communication module308, in coordination with the transceiver310, may be further configured to communicate, with the BS over a plurality of periodic intervals during a second time period greater than the first time period, the sampled data with the plurality of periodical signal measurements, in response to the request.

In some aspects, the request send from the BS (e.g., BSs105,205and/or400) includes a request for the UE300to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network. In some instances, the reporting communication module308, in coordination with the transceiver310, may communicate, with the BS, the report including the first proportion of the sampled data that corresponds to the prediction error of the machine learning-based network. In some aspects, the request includes a request for the UE300to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network. In some instances, the reporting communication module308, in coordination with the transceiver310, may communicate, with the BS, the report including the second proportion of the sampled data that corresponds to the correct prediction of the machine learning-based network.

In some aspects, the request includes a request for the UE300to communicate a subset of sampled data comprising up to a predetermined number of signal measurements that corresponds to a correct prediction of the machine learning-based network when no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data. In some instances, the reporting communication module308may be further configured to determine that no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data. In some aspects, the reporting communication module308, in coordination with the transceiver310, may communicate, with the BS, the report including the subset of sampled data corresponding to a correct prediction of the machine learning-based network. In some aspects, the subset of sampled data includes a number of signal measurements up to the predetermined number of signal measurements.

In some aspects, the reporting communication module308may be further configured to measure sampled data for a predetermined number of time instances in a second time period subsequent to the first time period when sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data.

As shown, the transceiver310may include the modem subsystem312and the RF unit314. The transceiver310can be configured to communicate bi-directionally with other devices, such as the BSs105. The modem subsystem312may be configured to modulate and/or encode the data from the memory304and/or the reporting communication module308according to a modulation and coding scheme (MCS), e.g., a low-density parity check (LDPC) coding scheme, a turbo coding scheme, a convolutional coding scheme, a polar coding scheme, a digital beamforming scheme, etc. The RF unit314may be configured to process (e.g., perform analog to digital conversion or digital to analog conversion, etc.) modulated/encoded data (e.g., uplink data, synchronization signal, SSBs) from the modem subsystem312(on outbound transmissions) or of transmissions originating from another source such as a UE115or a BS105. The RF unit314may be further configured to perform analog beamforming in conjunction with the digital beamforming. Although shown as integrated together in transceiver310, the modem subsystem312and the RF unit314may be separate devices that are coupled together at the UE115to enable the UE115to communicate with other devices.

The RF unit314may provide the modulated and/or processed data, e.g. data packets (or, more generally, data messages that may contain one or more data packets and other information), to the antennas316for transmission to one or more other devices. The antennas316may further receive data messages transmitted from other devices. The antennas316may provide the received data messages for processing and/or demodulation at the transceiver310. The transceiver310may provide the demodulated and decoded data (e.g., reference signal, synchronization signal, SSBs) to the reporting communication module308for processing. The antennas316may include multiple antennas of similar or different designs in order to sustain multiple transmission links. The RF unit314may configure the antennas316. In some aspects, the RF unit314may include various RF components, such as local oscillator (LO), analog filters, and/or mixers. The LO and the mixers can be configured based on a certain channel center frequency. The analog filters may be configured to have a certain passband depending on a channel BW. The RF components may be configured to operate at various power modes (e.g., a normal power mode, a low-power mode, power-off mode) and may be switched among the different power modes depending on transmission and/or reception requirements at the UE300.

In some aspects, the transceiver310is configured to receive a radio resource control (e.g., RRC) signal containing a request for the UE300to perform channel measurements. In some aspects, the transceiver310is configured to communicate, with the BS, the report when the output of the machine learning-based network fails to satisfy the one or more criteria, for example, by coordinating with the reporting communication module308. In some instances, the transceiver310is configured to communicate the report may be further configured to transmit, to the BS in a first subband of a plurality of subbands, sampled data for updating the machine learning-based network. In some aspects, the first subband includes a plurality of physical uplink shared channels (e.g., PUSCHs) multiplexed in at least one of time or frequency in a first portion of a first time period, and the transceiver configured to transmit the sampled data may be further configured to transmit the sampled data in one or more PUSCHs of the plurality of PUSCHs. In some aspects, the sampled data includes a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network associated with the at least one signal measurement. In some aspects, the transceiver310may be further configured to encode the sampled data into encoded sampled data during a time period of reporting within the first time period, and communicate, with the BS during the time period of reporting, the encoded sampled data.

In an aspect, the UE300can include multiple transceivers310implementing different RATs (e.g., NR and LTE). In an aspect, the UE300can include a single transceiver310implementing multiple RATs (e.g., NR and LTE). In an aspect, the transceiver310can include various components, where different combinations of components can implement different RATs.

FIG. 4is a block diagram of an exemplary BS400according to some aspects of the present disclosure. The BS400may be a BS105in the network100as discussed above inFIG. 1or a BS205in the network200as discussed above inFIG. 2. As shown, the BS400may include a processor402, a memory404, a reporting configuration module408, a transceiver410including a modem subsystem412and a RF unit414, and one or more antennas416. These elements may be in direct or indirect communication with each other, for example via one or more buses.

The memory404may include a cache memory (e.g., a cache memory of the processor402), RAM, MRAM, ROM, PROM, EPROM, EEPROM, flash memory, a solid state memory device, one or more hard disk drives, memristor-based arrays, other forms of volatile and non-volatile memory, or a combination of different types of memory. In some aspects, the memory404may include a non-transitory computer-readable medium. The memory404may store instructions406. The instructions406may include instructions that, when executed by the processor402, cause the processor402to perform operations described herein, for example, aspects ofFIGS. 1, 2, and 5-9. Instructions406may also be referred to as code, which may be interpreted broadly to include any type of computer-readable statement(s) as discussed above with respect toFIG. 3.

The reporting configuration module408may be implemented via hardware, software, or combinations thereof. For example, the reporting configuration module408may be implemented as a processor, circuit, and/or instructions406stored in the memory404and executed by the processor402. In some instances, the reporting configuration module408can be integrated within the modem subsystem412. For example, the reporting configuration module408can be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the modem subsystem412.

The reporting configuration module408may be used for various aspects of the present disclosure, for example, aspects ofFIGS. 1, 2, and 5-9. For instance, the reporting configuration module408, in coordination with the transceiver410, is configured to communicate, with one or more UEs (e.g., the UEs115,215, and/or300), a first configuration for a machine learning-based network, receive, from a first UE, (e.g., the UE300) of the one or more UEs, a report associated with a prediction error in the machine learning-based network, and communicating, with the UE300, a second configuration for the machine learning-based network based on the received report. In some aspects, the second configuration may represent an update to the machine learning-based network. In some aspects, the first configuration sent to the UE300may include a set of signal measurements. The set of signal measurements can include historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

In some aspects, the reporting configuration module408, in coordination with the transceiver410, is configured to transmit, to the UE300in a first subband of a plurality of subbands, a request for the UE300to communicate sampled data with the BS. The transceiver410may be configured to transmit the request in one or more PDCCHs of the plurality of PDCCHs. In some instances, the transceiver is configured to receive the report with the sampled data, in response to the request. The sampled data may be received by the BS400at a particular time instance in some embodiments, or over a plurality of over a plurality of periodic intervals during a period of periodic reporting that is greater than a period designated for obtaining the measurements.

In some aspects, the reporting configuration module408, in coordination with the transceiver410, is configured to transmit a predetermined threshold for use by the UE300with the machine learning-based network in the first configuration. The prediction error in the machine learning-based network may be based at least on a comparison between the predetermined threshold and a signal measurement of a corresponding transmission beam. In other aspects, the reporting configuration module408, in coordination with the transceiver410, is configured to transmit a request for the UE300to communicate a subset of sampled data comprising up to a predetermined number of signal measurements that corresponds to a correct prediction of the machine learning-based network when no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data.

In some aspects, the reporting configuration module408, in coordination with the transceiver410, is configured to transmit a request for the UE300to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network. In other aspects, the reporting configuration module408, in coordination with the transceiver410, is configured to transmit a request for the UE300to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network.

As shown, the transceiver410may include the modem subsystem412and the RF unit414. The transceiver410can be configured to communicate bi-directionally with other devices, such as the UEs115and/or500and/or another core network element. The modem subsystem412may be configured to modulate and/or encode data according to a MCS, e.g., a LDPC coding scheme, a turbo coding scheme, a convolutional coding scheme, a polar coding scheme, a digital beamforming scheme, etc. The RF unit414may be configured to process (e.g., perform analog to digital conversion or digital to analog conversion, etc.) modulated/encoded data (e.g., PDCCH, PDSCH, SSBs, UE reporting configuration, machine learning-based network configuration) from the modem subsystem412(on outbound transmissions) or of transmissions originating from another source such as a UE115and/or UE500. The RF unit414may be further configured to perform analog beamforming in conjunction with the digital beamforming. Although shown as integrated together in transceiver410, the modem subsystem412and/or the RF unit414may be separate devices that are coupled together at the BS105to enable the BS105to communicate with other devices.

The RF unit414may provide the modulated and/or processed data, e.g. data packets (or, more generally, data messages that may contain one or more data packets and other information), to the antennas416for transmission to one or more other devices. This may include, for example, transmission of information to complete attachment to a network and communication with a camped UE115or500according to some aspects of the present disclosure. The antennas416may further receive data messages transmitted from other devices and provide the received data messages for processing and/or demodulation at the transceiver410. The transceiver410may provide the demodulated and decoded data (e.g., CBR reports and/or CR reports) to the reporting configuration module408for processing. The antennas416may include multiple antennas of similar or different designs in order to sustain multiple transmission links.

In an aspect, the BS400can include multiple transceivers410implementing different RATs (e.g., NR and LTE). In an aspect, the BS400can include a single transceiver410implementing multiple RATs (e.g., NR and LTE). In an aspect, the transceiver410can include various components, where different combinations of components can implement different RATs.

FIG. 5is a simplified diagram of an example frame exchange500between a user equipment and a base station for user equipment reporting for updating a machine learning network according to some aspects of the present disclosure. The frame exchange500may be implemented between a BS510and a UE520. The BS510may be similar to the BS105,205,400and the UE520may be similar to the UE115,215,300. Additionally, the BS510and the UE520may operate in a network such as the network100or200. As illustrated, the frame exchange500includes a number of enumerated actions, but embodiments of the frame exchange500may include additional actions before, after, and in between the enumerated actions. In some aspects, one or more of the enumerated actions may be omitted or performed in a different order.

At action512, the BS510transmits a first configuration for a machine learning-based network. The first configuration may include one or more reference signals (e.g., SSB, CSI) configured for the UE520. The UE may receive the first configuration of the machine learning-based network from the BS510. In some aspects, the UE520receives, from the BS510, the set of received signal measurements as input data, in which the set of received signal measurements includes historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

At action514, the BS510transmits a request for the UE520to obtain signal measurements and communicate sampled data back to the BS510. In some aspects, the request is transmitted via a RRC signal. In other aspects, the request from the BS510includes a request for the UE520to perform a plurality of periodical signal measurements during the first time period.

At action522, the UE520may perform one or more measurements of the one or more reference signals. The UE520may, for example, measure RSRP and/or CQI of one or more transmission beams of the BS510. In some aspects, the UE520may measure, a plurality of transmission beams associated with the BS510during a first time period, obtain a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams, select one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data, and provide a feedback pairing that includes the input data and the output data as the sampled data.

At action524, the UE520determines that the sampled data corresponds to a prediction error of the machine learning-based network. The UE520may apply the machine learning-based network with the first configuration to a set of received signal measurements and determine whether an output of the machine learning-based network fails to satisfy one or more criteria. In some aspects, the UE520may determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE520.

At action526, the UE520may transmit a report in response to the request (e.g.,514) from the BS510. The UE520may transmit the report to include one or more of the signal measurements that correspond to both a prediction error and a correct prediction. In some aspects, the BS510may request to receive sampled data that corresponds exclusively to the prediction error. In some examples, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap. In some aspects, the BS520transmits a CSI report. In some aspects, the sampled data includes a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network associated with the at least one signal measurement.

At action516, the BS510updates and/or retrains the machine learning-based network based on the report with the sampled data. In some aspects, the BS510obtains a second configuration for the machine learning-based network.

At action518, the BS510communicates with the UE520an updated machine learning-based network by transmitting the second configuration for the machine learning-based network.

FIG. 6is a simplified diagram of another example frame exchange600between a user equipment and a base station for user equipment reporting for updating a machine learning network according to some aspects of the present disclosure. The frame exchange600may be implemented between a BS610and a UE620. The BS610may be similar to the BS105,205,400and the UE620may be similar to the UE115,215,300. Additionally, the BS610and the UE620may operate in a network such as the network100or200. As illustrated, the frame exchange600includes a number of enumerated actions, but embodiments of the frame exchange600may include additional actions before, after, and in between the enumerated actions. In some aspects, one or more of the enumerated actions may be omitted or performed in a different order.

At action612, the BS610transmits a first configuration for a machine learning-based network. The first configuration may include one or more reference signals (e.g., SSB, CSI) configured for the UE620. The UE may receive the first configuration of the machine learning-based network from the BS610. In some aspects, the UE620receives, from the BS610, the set of received signal measurements as input data, in which the set of received signal measurements includes historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

At action614, the BS610transmits a request for the UE620to obtain signal measurements and communicate sampled data back to the BS610. In some aspects, the BS610transmits a predetermined threshold with the request for use with the machine learning-based network. In some aspects, the request is transmitted via a RRC signal.

At action622, the UE620may perform one or more measurements of the one or more reference signals. The UE620may, for example, measure RSRP and/or CQI of one or more transmission beams of the BS610. In some aspects, the UE620may measure, a plurality of transmission beams associated with the BS610during a first time period, obtain a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams, select one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data, and provide a feedback pairing that includes the input data and the output data as the sampled data.

At action624, the UE620compares one or more of the obtained signal measurements to the predetermined threshold. For example, the UE620may determine whether a first signal measurement in the set of received signal measurements is greater than the predetermined threshold, and determine that the output of the machine learning-based network corresponds to a prediction error when the first signal measurement in the set of received signal measurements is not greater than the predetermined threshold. In some aspects, the predetermined threshold corresponds to a target reference signal received power (RSRP) value for a downlink specific reference signal.

At action626, the UE620determines that the output of the machine learning-based network fails to satisfy the one or more criteria by determining that the sampled data corresponds to a prediction error of the machine learning-based network based on the predetermined threshold the UE620. The UE620may apply the machine learning-based network with the first configuration to a set of received signal measurements and determine whether an output of the machine learning-based network fails to satisfy one or more criteria. In some aspects, the UE620may determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE620.

At action628, the UE620may transmit a report in response to the request (e.g.,614) from the BS610. The UE620may transmit the report to include one or more of the signal measurements that correspond to both a prediction error and a correct prediction. In some aspects, the BS610may request to receive sampled data that corresponds exclusively to the prediction error. In some examples, the report may include at least one of RSRP or CQI. In some aspects, the report is multiplexed between at least one of RSRP or CQI or other UL control information (UCI) when configured PUCCH resources of the RSRP, or CQI, or the other UCI overlap. In some aspects, the BS620transmits a CSI report. In some aspects, the sampled data includes a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network associated with the at least one signal measurement.

At action616, the BS610updates and/or retrains the machine learning-based network based on the report with the sampled data. In some aspects, the BS610obtains a second configuration for the machine learning-based network.

At action618, the BS610communicates with the UE620an updated machine learning-based network by transmitting the second configuration for the machine learning-based network.

FIG. 7is a simplified diagram of another example frame exchange700between a user equipment and a base station for user equipment reporting for updating a machine learning network according to some aspects of the present disclosure. The frame exchange700may be implemented between a BS710and a UE720. The BS710may be similar to the BS105,205,400and the UE720may be similar to the UE115,215,300. Additionally, the BS710and the UE720may operate in a network such as the network100or200. As illustrated, the frame exchange700includes a number of enumerated actions, but embodiments of the frame exchange700may include additional actions before, after, and in between the enumerated actions. In some aspects, one or more of the enumerated actions may be omitted or performed in a different order.

At action712, the BS710transmits a first configuration for a machine learning-based network. The first configuration may include one or more reference signals (e.g., SSB, CSI) configured for the UE720. The UE may receive the first configuration of the machine learning-based network from the BS710. In some aspects, the UE720receives, from the BS710, the set of received signal measurements as input data, in which the set of received signal measurements includes historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

At action714, the BS710transmits a request for the UE720to obtain signal measurements and communicate sampled data back to the BS710. In some aspects, the request includes a request for the UE720to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network. In other aspects, the request includes a request for the UE720to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network. In some aspects, the request is transmitted via a RRC signal. In other aspects, the request from the BS710includes a request for the UE720to perform a plurality of periodical signal measurements during the first time period.

At action722, the UE720may perform one or more measurements of the one or more reference signals. The UE720may, for example, measure RSRP and/or CQI of one or more transmission beams of the BS710. In some aspects, the UE720may measure, a plurality of transmission beams associated with the BS710during a first time period, obtain a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams, select one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data, and provide a feedback pairing that includes the input data and the output data as the sampled data.

At action724, the UE720determines that the sampled data corresponds to a prediction error of the machine learning-based network. The UE720may apply the machine learning-based network with the first configuration to a set of received signal measurements and determine whether an output of the machine learning-based network fails to satisfy one or more criteria. In some aspects, the UE720may determine that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE720.

At action726, the UE720prepares the first proportion of the sampled data to include signal measurements that correspond exclusively to the prediction error of the machine learning-based network. This allows for a reduced size of the sampled data that needs to be communicated with the BS710, thus allowing the BS710to focus more efficiently on how to improve the machine learning-based network through one or more iterations of training with the proportioned sampled data.

At action728, the UE720may transmit a report in response to the request (e.g.,714) from the BS710. In some aspects, the UE720communicates, with the BS710, the report including the first proportion of the sampled data that corresponds to the prediction error of the machine learning-based network. In some aspects, the proportioned sampled data includes a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network corresponding to the prediction error. In other instances, the UE720communicates, with the BS710, the report including the first proportion along with the second proportion of the sampled data that corresponds to the correct prediction of the machine learning-based network.

At action716, the BS710updates and/or retrains the machine learning-based network based on the report with the sampled data. In some aspects, the BS710obtains a second configuration for the machine learning-based network.

At action718, the BS710communicates with the UE720an updated machine learning-based network by transmitting the second configuration for the machine learning-based network.

FIG. 8is a flow diagram of an example process800of reporting performed by a user equipment for updating a machine learning network according to some aspects of the present disclosure. Aspects of the process800can be executed by a computing device (e.g., a processor, processing circuit, and/or other suitable component) of a wireless communication device or other suitable means for performing the steps. For example, a wireless communication device, such as the UEs85,215, and/or300, may utilize one or more components, such as the processor302, the memory304, the reporting communication module308, the transceiver310, the modem312, and the one or more antennas316, to execute the steps of process800. As illustrated, the process800includes a number of enumerated steps, but aspects of the process800may include additional steps before, after, and in between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted or performed in a different order.

At block810, the UE applies a machine learning-based network to a set of received signal measurements. In some instances, the UE may be a narrowband communication device and may utilize one or more components, such as the processor302, the communication module308, and the transceiver310, to apply the machine learning-based network to the set of received signal measurements. The machine learning-based network may be provided with a first configuration to the UE, by a BS. The machine learning-based network may have been trained by the BS and/or a cloud server (e.g., the cloud server260) using prior signal measurements of the downlink channel. In some aspects, the set of received signal measurements may include historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

At block820, the UE determines whether an output of the machine learning-based network fails to satisfy one or more criteria. For instance, the UE may utilize one or more components, such as the processor302, the communication module308, the transceiver310, the modem312, and the one or more antennas316, to determine that the machine learning-based network output produces a prediction error.

At block830, the UE communicates, with the BS, a report when the output of the machine learning-based network fails to satisfy the one or more criteria. For instance, the UE may utilize one or more components, such as the processor302, the reporting communication module308, the transceiver310, the modem312, and the one or more antennas316, to communicate the report with the BS. In some aspects, the report may be represented as a CSI report. In some aspects, the UE may receive an updated machine learning-based network with a second configuration from the BS in response to the report communicated with the BS.

FIG. 9is a flow diagram of an example process900of a machine learning network update performed by a base station using user equipment reporting according to some aspects of the present disclosure. Aspects of the process900can be executed by a computing device (e.g., a processor, processing circuit, and/or other suitable component) of a base station or other suitable means for performing the steps. For example, a base station, such as the BSs95,205, and/or400, may utilize one or more components, such as the processor402, the memory404, the reporting configuration module408, the transceiver410, the modem412, and the one or more antennas416, to execute the steps of process900. As illustrated, the process900includes a number of enumerated steps, but aspects of the process900may include additional steps before, after, and in between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted or performed in a different order.

At block910, the BS communicates, with one or more UEs, a first configuration for a machine learning-based network. For instance, the BS may utilize one or more components, such as the processor402, the reporting configuration module408, the transceiver410, the modem412, and the one or more antennas416, to communicate the first configuration for the machine learning-based network. In some aspects, the machine learning-based network includes one or more neural networks.

At block920, the BS receives, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network. For instance, the BS may utilize one or more components, such as the processor402, the reporting configuration module408, the transceiver410, the modem412, and the one or more antennas416, to receive the report from the first UE.

At block930, the BS communicates, with one or more UEs, a second configuration for the machine learning-based network. For instance, the BS may utilize one or more components, such as the processor402, the reporting configuration module408, the transceiver410, the modem412, and the one or more antennas416, to communicate the second configuration for the machine learning-based network. In some aspects, the BS may update the machine learning-based network by generating the second configuration based on feedback supplied by the first UE within the report. The second configuration of the machine learning-based network may be intended to produce a more accurate prediction such that the prediction can be determined by the UE as a correct prediction.

Recitations of Some Aspects of the Disclosure

Aspect 1: A method of wireless communication performed by a user equipment (UE), comprising: applying a machine learning-based network to a set of received signal measurements; determining whether an output of the machine learning-based network fails to satisfy one or more criteria; and communicating, by the UE with a base station (BS), a report when the output of the machine learning-based network fails to satisfy the one or more criteria.

Aspect 2: The method of aspect 1, wherein the determining whether the output of the machine learning-based network fails to satisfy the one or more criteria comprises determining that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and a signal measurement obtained by the UE.

Aspect 3: The method of aspect 1 or 2, wherein the communicating the report comprises transmitting, by the UE to the BS in a first subband of a plurality of subbands, sampled data for updating the machine learning-based network.

Aspect 4: The method of any of aspects 1-3, wherein: the first subband includes a plurality of physical uplink shared channels (PUSCHs) multiplexed in at least one of time or frequency in a first portion of a first time period, and the communicating the report comprises transmitting, by the UE, the sampled data in one or more PUSCHs of the plurality of PUSCHs.

Aspect 5: The method of any of aspects 1-3, wherein the sampled data comprises a feedback pairing of at least one signal measurement in the set of received signal measurements and the output of the machine learning-based network associated with the at least one signal measurement.

Aspect 6: The method of any of aspects 1-5, further comprising: receiving, by the UE, a predetermined threshold from the BS for use with the machine learning-based network, wherein the determining whether the output of the machine learning-based network fails to satisfy the one or more criteria comprises: determining that the output of the machine learning-based network fails to satisfy the one or more criteria based on the predetermined threshold.

Aspect 7: The method of any of aspects 1-6, wherein the determining that the output of the machine learning-based network fails to satisfy the one or more criteria comprises: determining whether a first signal measurement in the set of received signal measurements is greater than the predetermined threshold, and determining that the output of the machine learning-based network corresponds to a prediction error when the first signal measurement in the set of received signal measurements is not greater than the predetermined threshold.

Aspect 8: The method of aspect 7, wherein the predetermined threshold corresponds to a target reference signal received power (RSRP) value for a downlink specific reference signal.

Aspect 9: The method of aspect 8, wherein the downlink specific reference signal comprises a synchronization signal block (SSB).

Aspect 10: The method of aspect 7 or 8, wherein the downlink specific reference signal comprises a channel state information reference signal (CSI-RS).

Aspect 11: The method of any of aspects 1-10, further comprising: receiving, by the UE in a first subband of a plurality of subbands, a request for the UE to measure sampled ground-truth data; and obtaining, by the UE, the sampled ground-truth data in response to the request, wherein the determining whether the output of the machine learning-based network fails to satisfy the one or more criteria comprises: determining that the output of the machine learning-based network corresponds to a prediction error based on a comparison between at least one signal measurement in the set of received signal measurements and the sampled ground-truth data.

Aspect 12: The method of aspect 11, wherein: the request comprises a request for measurement of the sampled ground-truth data by the UE at a particular time instance during a first time period, the first subband includes a plurality of physical downlink control channels (PDCCHs) multiplexed in at least one of time or frequency in a first portion of the first time period, and the receiving the request comprises receiving, by the UE, the request in one or more PDCCHs of the plurality of PDCCHs.

Aspect 13: The method of aspect 10 or 11, wherein: the request comprises a request for the UE to perform a plurality of periodical signal measurements of the sampled ground-truth data, the receiving the request comprises receiving, by the UE, the request in a radio resource control (RRC) signal.

Aspect 14: The method of any of aspects 1-13, further comprising: receiving, by the UE in a first subband of a plurality of subbands, a request to communicate sampled data with the BS, wherein the communicating the report comprises communicating, by the UE with the BS, the report with the sampled data, in response to the request.

Aspect 15: The method of any of aspects 1-14, further comprising: receiving, by the UE from the BS, the set of received signal measurements as input data, wherein the set of received signal measurements comprises historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams; measuring, by the UE, a plurality of transmission beams associated with the BS during a first time period; obtaining, by the UE, a RSRP measurement of a downlink specific reference signal carried in each of the plurality of transmission beams; selecting, by the UE, one of the plurality of transmission beams carrying a downlink specific reference signal with a highest RSRP measurement as output data; and providing a feedback pairing comprising the input data and the output data as the sampled data.

Aspect 16: The method of aspect 15, wherein: the request comprises a request for the UE to perform one or more signal measurements at a particular time instance during the first time period, the first subband includes a plurality of physical downlink control channels (PDCCHs) multiplexed in at least one of time or frequency in a first portion of the first time period, the receiving the request comprises receiving, by the UE, the request in one or more PDCCHs of the plurality of PDCCHs.

Aspect 17: The method of aspect 15 or 16, wherein: the request comprises a request for the UE to perform a plurality of periodical signal measurements during the first time period, the receiving the request comprises receiving, by the UE, the request in a radio resource control (RRC) signal.

Aspect 18: The method of any of aspects 1-17, further comprising: communicating, by the UE with the BS over a plurality of periodic intervals during a second time period greater than the first time period, the sampled data with the plurality of periodical signal measurements, in response to the request.

Aspect 19: The method of aspect 15, wherein: the request comprises a request for the UE to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network, the receiving the request comprises receiving, by the UE, the request in a radio resource control (RRC) signal, further comprising: communicating, by the UE with the BS, the report comprising the first proportion of the sampled data that corresponds to the prediction error of the machine learning-based network.

Aspect 20: The method of aspect 15 or 19, wherein: the request comprises a request for the UE to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network, further comprising: communicating, by the UE with the BS, the report comprising the second proportion of the sampled data that corresponds to the correct prediction of the machine learning-based network.

Aspect 21: The method of aspect 15, wherein: the request comprises a request for the UE to communicate a subset of sampled data comprising up to a predetermined number of signal measurements that corresponds to a correct prediction of the machine learning-based network when no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data, the receiving the request comprises receiving, by the UE, the request in a radio resource control (RRC) signal, further comprising: determining, by the UE, that no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data; and communicating, by the UE with the BS, the report comprising the subset of sampled data corresponding to a correct prediction of the machine learning-based network, the subset of sampled data comprising a number of signal measurements up to the predetermined number of signal measurements.

Aspect 22: The method of aspect 15, wherein: the request comprises a request for the UE to measure sampled data for a predetermined number of time instances in a second time period subsequent to the first time period when sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data.

Aspect 23: The method of any of aspects 1-15, further comprising: encoding, by the UE, the sampled data into encoded sampled data during a time period of reporting within the first time period; and communicating, by the UE with the BS during the time period of reporting, the encoded sampled data.

Aspect 24: A user equipment (UE), comprising: a memory; a processor coupled to the memory and configured to, when executing instructions stored on the memory, to cause the UE to perform the methods of aspects 1-23.

Aspect 25: A non-transitory computer-readable medium (CRM) having program code recorded thereon, the program code comprises code for causing a UE to perform the methods of aspects 1-23.

Aspect 26: A user equipment (UE) comprising means for performing the methods of aspects 1-23.

Aspect 27: A method of wireless communication performed by a base station (BS), comprising: communicating, by the BS with one or more UEs, a first configuration for a machine learning-based network; receiving, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network; and communicating, by the BS with the first UE, a second configuration for the machine learning-based network based on the received report.

Aspect 28: The method of aspect 27, wherein the communicating the first configuration for the machine learning-based network comprises: transmitting, by the BS in a first subband of a plurality of subbands, a request for the first UE to communicate sampled data with the BS, wherein the receiving the report comprises receiving, by the BS, the report with the sampled data, in response to the request.

Aspect 29: The method of aspect 27 or 28, the request comprises a request for the UE to perform one or more signal measurements at a particular time instance during a first time period, the first subband includes a plurality of physical downlink control channels (PDCCHs) multiplexed in at least one of time or frequency in a first portion of the first time period, the transmitting the request comprises transmitting, by the BS, the request in one or more PDCCHs of the plurality of PDCCHs.

Aspect 30: The method of any of aspects 27-29, wherein the communicating the first configuration for the machine learning-based network comprises: transmitting, by the BS, a predetermined threshold for use by the first UE with the machine learning-based network in the first configuration, wherein the prediction error in the machine learning-based network is based at least on a comparison between the predetermined threshold and a signal measurement of a corresponding transmission beam.

Aspect 31: The method of aspect 30, wherein the predetermined threshold corresponds to a target reference signal received power (RSRP) value for a downlink specific reference signal.

Aspect 32: The method of aspect 31, wherein the downlink specific reference signal comprises a synchronization signal block (SSB).

Aspect 33: The method of aspect 31 or 32, wherein the downlink specific reference signal comprises a channel state information reference signal (CSI-RS).

Aspect 34: The method of any of aspects 27-33, wherein the communicating the first configuration for the machine learning-based network comprises transmitting, by the BS to the first UE, a set of signal measurements, wherein the set of signal measurements comprises historical measurements of a plurality of transmission beams associated with the BS and historical signal strength measurements of downlink specific reference signals carried in the plurality of transmission beams.

Aspect 35: The method of any of aspects 27-34, wherein the receiving the report comprises receiving, by the BS from the first UE, sampled data obtained by the first UE during a first time period for updating the machine learning-based network.

Aspect 36: The method of any of aspects 27-35, wherein the sampled data comprises a feedback pairing of at least one historical measurement associated with the first configuration and an output of the machine learning-based network associated with the at least one historical measurement.

Aspect 37: The method of any of aspects 27-36, wherein the communicating the first configuration for the machine learning-based network comprises: transmitting, by the BS in a first subband of a plurality of subbands, a request for the first UE to measure sampled ground-truth data, wherein the prediction error in the machine learning-based network is based at least on a comparison between at least one signal measurement in the set of signal measurements and the sampled ground-truth data.

Aspect 38: The method of aspect 37, wherein: the request comprises a request for measurement of the sampled ground-truth data by the first UE at a particular time instance during a first time period, the first subband includes a plurality of physical downlink control channels (PDCCHs) multiplexed in at least one of time or frequency in a first portion of the first time period, and the transmitting the request comprises transmitting, by the BS, the request in one or more PDCCHs of the plurality of PDCCHs.

Aspect 39: The method of aspect 37 or 38, wherein: the request comprises a request for the first UE to perform a plurality of periodical signal measurements of the sampled ground-truth data, the transmitting the request comprises transmitting, by the BS, the request in a radio resource control (RRC) signal.

Aspect 40: The method of aspect 38 or 39, wherein the communicating the first configuration for the machine learning-based network comprises: transmitting, by the BS in a radio resource control (RRC) signal, a request for the first UE to perform a plurality of periodical signal measurements during the first time period.

Aspect 41: The method of any of aspects 38-40, further comprising: receiving, by the BS from the first UE over a plurality of periodic intervals during a second time period greater than the first time period, the sampled data with the plurality of periodical signal measurements, in response to the request.

Aspect 42: The method of any of aspects 35-41, wherein the communicating the first configuration for the machine learning-based network comprises transmitting, by the BS in a radio resource control (RRC) signal, a request for the first UE to communicate a first proportion of the sampled data that corresponds to a prediction error of the machine learning-based network, further comprising: receiving, by the BS from the first UE, the report comprising the first proportion of the sampled data that corresponds to the prediction error of the machine learning-based network.

Aspect 43: The method of any of aspects 35-42, wherein: the request comprises a request for the first UE to communicate a second proportion of the sampled data that corresponds to a correct prediction of the machine learning-based network, further comprising: receiving, by the BS from the first UE, the report comprising the second proportion of the sampled data that corresponds to the correct prediction of the machine learning-based network.

Aspect 44: The method of any of aspects 35-43, wherein the communicating the first configuration for the machine learning-based network comprises transmitting, by the BS in a radio resource control (RRC) signal, a request for the first UE to communicate a subset of sampled data comprising up to a predetermined number of signal measurements that corresponds to a correct prediction of the machine learning-based network when no sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data, further comprising: receiving, by the BS from the first UE, the report comprising the subset of sampled data corresponding to a correct prediction of the machine learning-based network, the subset of sampled data comprising a number of signal measurements up to the predetermined number of signal measurements.

Aspect 45: The method of any of aspects 35-44, wherein the communicating the first configuration for the machine learning-based network comprises transmitting, by the BS, a request for the first UE to measure sampled data for a predetermined number of time instances in a second time period subsequent to the first time period when sampled data corresponding to a prediction error of the machine learning-based network is present in the sampled data.

Aspect 46: The method of any of aspects 35-45, further comprising: receiving, by the BS from the first UE during a time period of reporting within the first time period, encoded sampled data, wherein the sampled data is encoded into the encoded sampled data during the time period of reporting.

Aspect 47: A base station (BS), comprising: a memory; a processor coupled to the memory and configured to, when executing instructions stored on the memory, to cause the BS to perform the methods of aspects 27-46.

Aspect 48: A non-transitory computer-readable medium (CRM) having program code recorded thereon, the program code comprises code for causing a BS to perform the methods of aspects 27-46.

Aspect 49: A base station (BS) comprising means for performing the methods of aspects 27-46.