Methods, apparatuses and systems for sounding reference signal-based precoder for downlink data multiple-input multiple-output transmissions

Abstract

Methods, apparatuses and systems for sounding reference signal-based precoder for downlink data Multiple-Input Multiple-Output (MIMO) transmissions. In one embodiment, a method includes: receiving, at a wireless communication device, a first signal from a wireless communication node, wherein the first signal includes a request for the wireless communication device to transmit a second signal to the wireless communication node; transmitting, at the wireless communication device, the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and receiving, at the wireless communication device, a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

Description

METHODS, APPARATUSES AND SYSTEMS FOR SOUNDING REFERENCE SIGNAE-BASED PRECODER FOR DOWNEINK DATA MULTIPLE-INPUT MULTIPLE-OUTPUT TRANSMISSIONSTECHNICAL FIELD

[0001] The disclosure relates generally to wireless communications and, more particularly, to methods, apparatuses and systems for sounding reference signal-based precoder for downlink data Multiple-Input Multiple- Output (MIMO) transmissions.BACKGROUND

[0002] Massive MIMO is used in 5G networks and future communication systems to improve data throughput and spectrum efficiency by utilizing numerous antennas at a base station. Following the standardization of the initial 5G New Radio (NR) Release 15 in 2018, the evolution of 5G NR has progressed swiftly, laying the groundwork for the global commercial development of 5G technology. The utilization of millimeter Wave (mmWave) spectrum has been pivotal in delivering performance enhancements due to its provision of large bandwidth. However, mmWave signals are susceptible to significant free space path loss and other atmospheric perturbations.

[0003] Consequently, both the 5G Node Base Station (BS) and the User Equipment (UE) must establish highly directional links to maintain acceptable communication quality. This necessitates the process of aligning transmit beams at the BS with receive beams at the UE, particularly in Downlink (DL) transmission scenarios, which is termed as Beam Management (BM). As transmit and receive beams are selected from finite-sized codebooks, identifying the optimal beam pair primarily relies on an exhaustive search process involving sweepingthrough all beams in the codebook. However, this approach incurs considerable training overhead. Therefore, there is a need to streamline the beam selection process and reduce the above training overhead for the beam search in a wireless communication network.SUMMARY

[0004] The exemplary embodiments disclosed herein are directed to solving the issues relating to one or more of the problems presented in the prior art, as well as providing additional features that will become readily apparent by reference to the following detailed description when taken in conjunction with the accompany drawings. In accordance with various embodiments, exemplary systems, methods, devices and computer program products are disclosed herein. It is understood, however, that these embodiments are presented by way of example and not limitation, and it will be apparent to those of ordinary skill in the art who read the present disclosure that various modifications to the disclosed embodiments can be made while remaining within the scope of the present disclosure.

[0005] In some embodiments, a method includes receiving, at a wireless communication device, a first signal from a wireless communication node, wherein the first signal includes a request for the wireless communication device to transmit a second signal to the wireless communication node; transmitting, by the wireless communication device, the second signal to the wireless communication node, wherein the second signal contains information to determine a first precoder beam; and receiving, at the wireless communication device, a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

[0006] In some embodiments, the request for the wireless communication device to transmit the second signal to the wireless communication node is transmitted: via a configuration message, wherein the configuration message is transmitted through: Radio Resource Control (RRC), Medium Access Control - Control Element (MAC-CE), or Downlink Control Information (DCI).

[0007] In some embodiments, the second signal is transmitted on uplink (UL) resources, wherein the UL resources are on an UL carrier frequency, wherein the DL carrier frequency and the UL carrier frequency are different.

[0008] In some embodiments, the request for the wireless communication device to transmit the second signal is transmitted by an UL grant that assigns specific uplink resources to the wireless communication device to transmit the second signal on a Physical Uplink Shared Channel (PUSCH). In some embodiments, the second signal includes at least one Sounding Reference Signal (SRS).

[0009] In some embodiments, the method further includes: receiving, at the wireless communication device, at least one precoded Channel State Information-Reference Signal (CSLRS) from the wireless communication node, wherein the at least one precoded CSLRS is transmitted based on a first plurality of precoded beams, wherein the first plurality of precoded beams is predicted from the second signal, reporting, at the wireless communication device, a second plurality of precoded beams to the wireless communication node, wherein the second plurality of precoded beams is selected from the first plurality of precoded beams, and wherein the second plurality of precoded beams is used to transmit a fourth signal from the wireless communication node to the wireless communication device.

[0010] In some embodiments, the first precoder beam is determined from the second signal using a deep neural network (DNN) model implemented in the wireless communication node.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various exemplary embodiments of the present disclosure are described in detail below with reference to the following Figures. The drawings are provided for purposes of illustration only and merely depict exemplary embodiments of the present disclosure to facilitate the reader's understanding of the present disclosure. Therefore, the drawings should not be considered limiting of the breadth, scope, or applicability of the present disclosure. It should be noted that for clarity and ease of illustration these drawings are not necessarily drawn to scale.

[0012] FIG. 1A illustrates an exemplary wireless communication network, in accordance with some embodiments of the present disclosure.

[0013] FIG. IB illustrates a block diagram of an exemplary wireless communication system, in accordance with some embodiments of the present disclosure.

[0014] FIG. 2A illustrates a signaling diagram between a BS and a UE for spatial domain beam prediction with a machine learning model implemented on the BS side, in accordance with some embodiments.

[0015] FIG. 2B illustrates another signaling diagram between a BS and a UE for spatial domain beam prediction with a machine learning model implemented on the UE side, in accordance with some embodiments.

[0016] FIG. 2C illustrates yet another signaling diagram between a BS and a UE for temporal domain beam prediction with a machine learning model implemented on the BS side, in accordance with some embodiments.

[0017] FIG. 2D illustrates still another signaling diagram between a BS and a UE for temporal domain beam prediction with machine model implemented on the UE side, in accordance with some embodiments.

[0018] FIG. 3 illustrates an exemplary design framework for machine learning modelbased beam prediction, in accordance with some embodiments of the present disclosure.

[0019] FIG. 4 illustrates a deep neural network (DNN) model used to implement the machine learning model illustrated in FIG. 3, in accordance with some embodiments of the present disclosure.

[0020] FIG. 5A illustrates a channel sounding diagram, in accordance with some embodiments of the present disclosure.

[0021] FIG. 5B illustrates another channel sounding diagram, in accordance with some embodiments of the present disclosure.

[0022] FIG. 6 illustrates an example method for performing optimal beam prediction in a wireless communication system, in accordance with some embodiments.DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0023] Various exemplary embodiments of the present disclosure are described below with reference to the accompanying figures to enable a person of ordinary skill in the art to make and use the present disclosure. As would be apparent to those of ordinary skill in the art, after reading the present disclosure, various changes or modifications to the examples described herein can be made without departing from the scope of the present disclosure.Thus, the present disclosure is not limited to the exemplary embodiments and applications described and illustrated herein. Additionally, the specific order and / or hierarchy of steps in the methods disclosed herein are merely exemplary approaches. Based upon design preferences, the specific order or hierarchy of steps of the disclosed methods or processes can be re-arranged while remaining within the scope of the present disclosure. Thus, those of ordinary skill in the art will understand that the methods and techniques disclosed herein present various steps or acts in a sample order, and the present disclosure is not limited to the specific order or hierarchy presented unless expressly stated otherwise.

[0024] Figure 1A illustrates an exemplary wireless communication network 100, in accordance with some embodiments of the present disclosure. In a wireless communication system, a network side communication node or a base station (BS) 102 can be a node B, an E-UTRA Node B (also known as Evolved Node B, eNodeB or eNB), a New Generation eNB (ng-eNB), a gNodeB (also known as gNB) in new radio (NR) technology, a pico station, a femto station, or the like. A terminal side communication device or a user equipment (UE) 104 can be a long range communication system like a mobile phone, a smart phone, a personal digital assistant (PDA), tablet, laptop computer, or a short range communication system such as, for example a wearable device, a vehicle with a vehicular communication system and the like. A network communication node and a terminal side communication device are represented by a BS 102 and a UE 104, respectively, and in all the embodiments in this disclosure hereafter, and are generally referred to as “communication nodes” and “communication device” herein. Such communication nodes and communication devices may be capable of wireless and / or wired communications, in accordance with various embodiments of the invention. It is noted that all the embodiments are merely preferred examples, and are not intended to limit the present disclosure. Accordingly, it is understoodthat the system may include any desired combination of BSs 102 and UEs 104, while remaining within the scope of the present disclosure.

[0025] Referring to Figure 1A, the wireless communication network 100 includes a first BS 102-1, a second BS 102-2, a first UE 104-1, a second UE 104-2, a third UE 104-3, and a fourth UE 104-4. In some embodiments, the first BS 102-1 and the second BS 102-2 comprise a first plurality of antennas 106-1 to 106-n and a second plurality of antennas 116-1 to 116-n’, respectively. The first plurality of antennas 106-1 to 106-n may communicate with a plurality of UEs 104 to form a first multiple-input multiple-output (MIMO) system, and the second plurality of antennas 116-1 to 116-n’ may communicate with the plurality of UEs 104 to form a second MIMO system.

[0026] In some embodiments, a plurality of UEs 104 may form direct communication (e.g., uplink) channels 103-1, 103-2, 103-3, and 103-4 with the first BS 102-1 and the second BS 102-2. In some embodiments, the plurality of UEs 104 may also form direct communication (e.g., downlink) channels 105-1, 105-2, 105-3, and 105-4 with the first BS 102-1 and the second BS 102-2. The direct communication channels between the plurality of UEs 104 and a distributed unit of the BS 102 can be through interfaces such as an Uu interface, which is also known as E-UTRAN air interface. In some embodiments, the UE 104 comprises a plurality of transceivers which enables the UE 104 to support multi connectivity so as to receive data simultaneously from the first BS 102-1 and the second BS 102-2. The first BS 102-1 and the second BS 102-2 each is connected to a core network (CN) 108 on a user plane (UP) through an external interface 107, e.g., an lu interface, an NG-U interface, or an Sl-U interface. In some embodiments, the CN 108 is one of the following: an EvolvedPacket Core (EPC) and a 5G Core Network (5GC). In some embodiments, the CN 108further comprises at least one of the following: Access and Mobility Management Function(AMF), User Plane Function (UPF), and System Management Function (SMF).

[0027] A direct communication channel 111 between the first BS 102-1 and the second 102-2 is through an X2 interface, in accordance with some embodiments. In some embodiments, a BS (gNB) is split into a Distributed Unit (DU) and a Central Unit (CU) on the UP, between which the direct communication is through a Fl-U interface. In some embodiments, a CU of the second BS 102-2 can be further split into a Control Plane (CP) and a User Plane (UP), between which the direct communication is through an El interface.Hereinafter in the present disclosure, an Xx interface is used to describe one of the following interfaces, the NG interface, the SI interface, the X2 interface, the Xn interface, the Fl interface, and the El interface. When an Xx interface is established between two nodes, the two nodes can transmit control signaling on the CP and / or data on the UP.

[0028] Figure IB illustrates a block diagram of an exemplary wireless communication system 150, in accordance with some embodiments of the present disclosure. The system 150 may include components and elements configured to support known or conventional operating features that need not be described in detail herein. In some embodiments, the system 150 can be used to transmit and receive data symbols in a wireless communication environment such as the wireless communication network 100 of Figure 1 A, as described above.

[0029] The system 150 generally includes a first BS 102-1, a second BS 102-2, and a UE 104, collectively referred to as BS 102 and UE 104 below for ease of discussion. The first BS 102-1 and the second BS 102-2 each comprises a BS transceiver module 152, a BS antenna array 154, a BS memory module 156, a BS processor module 158, and a network interface 160. In the illustrated embodiment, each module of the BS 102 is coupled andinterconnected with one another as necessary via a data communication bus 180. The UE 104 comprises a UE transceiver module 162, a UE antenna 164, a UE memory module 166, a UE processor module 168, and an VO interface 169. In the illustrated embodiment, each module of the UE 104 is coupled and interconnected with one another as necessary via a date communication bus 190. The BS 102 communicates with the UE 104 via a communication channel 192, which can be any wireless channel or other medium known in the art suitable for transmission of data as described herein.

[0030] As would be understood by persons of ordinary skill in the art, the system 150 may further include any number of modules other than the modules shown in Figure IB. Those skilled in the art will understand that the various illustrative blocks, modules, circuits, and processing logic described in connection with the embodiments disclosed herein may be implemented in hardware, computer-readable software, firmware, or any practical combination thereof. To clearly illustrate this interchangeability and compatibility of hardware, firmware, and software, various illustrative components, blocks, modules, circuits, and steps are described generally in terms of their functionality. Whether such functionality is implemented as hardware, firmware, or software depends upon the particular application and design constraints imposed on the overall system. Those familiar with the concepts described herein may implement such functionality in a suitable manner for each particular application, but such implementation decisions should not be interpreted as limiting the scope of the present invention.

[0031] A wireless transmission from a transmitting antenna of the UE 104 to a receiving antenna of the BS 102 is known as an uplink (UL) transmission, and a wireless transmission from a transmitting antenna of the BS 102 to a receiving antenna of the UE 104 is known as a downlink (DL) transmission. In accordance with some embodiments, the UE transceiver 162may be referred to herein as an “uplink” transceiver 162 that includes a radio frequency (RF) transmitter and receiver circuitry that is each coupled to the UE antenna 164. A duplex switch (not shown) may alternatively couple the uplink transmitter or receiver to the uplink antenna in time duplex fashion. Similarly, in accordance with some embodiments, the BS transceiver 152 may be referred to herein as a “downlink” transceiver 152 that includes RF transmitter and receiver circuitry that are each coupled to the antenna array 154. A downlink duplex switch may alternatively couple the downlink transmitter or receiver to the downlink antenna array 154 in time duplex fashion. The operations of the two transceivers 152 and 162 are coordinated in time such that the uplink receiver is coupled to the uplink UE antenna 164 for reception of transmissions over the wireless communication channel 192 at the same time that the downlink transmitter is coupled to the downlink antenna array 154. Preferably, there is close synchronization timing with only a minimal guard time between changes in duplex direction. The UE transceiver 162 communicates through the UE antenna 164 with the BS 102 via the wireless communication channel 192. The BS transceiver 152 communications through the BS antenna 154 of a BS (e.g., the first BS 102-1) with the other BS (e.g., the second BS 102-2) via a wireless communication channel 196. The wireless communication channel 196 can be any wireless channel or other medium known in the art suitable for direct communication between BSs.

[0032] The UE transceiver 162 and the BS transceiver 152 are configured to communicate via the wireless data communication channel 192, and cooperate with a suitably configured RF antenna arrangement 154 / 164 that can support a particular wireless communication protocol and modulation scheme. In some exemplary embodiments, the UE transceiver 162 and the BS transceiver 152 are configured to support industry standards such as the Long Term Evolution (LTE) and emerging 5G standards (e.g., NR), and the like. It is understood, however, that the invention is not necessarily limited in application to aparticular standard and associated protocols. Rather, the UE transceiver 162 and the BS transceiver 152 may be configured to support alternate, or additional, wireless data communication protocols, including future standards or variations thereof.

[0033] The processor modules 158 and 168 may be implemented, or realized, with a general purpose processor, a content addressable memory, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. In this manner, a processor module may be realized as a microprocessor, a controller, a microcontroller, a state machine, or the like. A processor module may also be implemented as a combination of computing devices, e.g., a combination of a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration.

[0034] Furthermore, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in firmware, in a software module executed by processor modules 158 and 168, respectively, or in any practical combination thereof. The memory modules 156 and 166 may be realized as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In this regard, the memory modules 156 and 166 may be coupled to the processor modules 158 and 168, respectively, such that the processors modules 158 and 168 can read information from, and write information to, memory modules 156 and 166, respectively. The memory modules 156 and 166 may also be integrated into their respective processor modules 158 and 168. In some embodiments, the memory modules 156 and 166 may each include acache memory for storing temporary variables or other intermediate information during execution of instructions to be executed by processor modules 158 and 168, respectively. The memory modules 156 and 166 may also each include non-volatile memory for storing instructions to be executed by the processor modules 158 and 168, respectively.

[0035] The network interface 160 generally represents the hardware, software, firmware, processing logic, and / or other components of the base station 102 that enable bi-directional communication between BS transceiver 152 and other network components and communication nodes configured to communication with the BS 102. For example, network interface 160 may be configured to support internet or WiMAX traffic. In a typical deployment, without limitation, network interface 160 provides an 802.3 Ethernet interface such that BS transceiver 152 can communicate with a conventional Ethernet based computer network. In this manner, the network interface 160 may include a physical interface for connection to the computer network (e.g., Mobile Switching Center (MSC)). The terms “configured for” or “configured to” as used herein with respect to a specified operation or function refers to a device, component, circuit, structure, machine, signal, etc. that is physically constructed, programmed, formatted and / or arranged to perform the specified operation or function. The network interface 160 could allow the BS 102 to communicate with other BSs or a CN over a wired or wireless connection.

[0036] Referring again to Figure 1A, as mentioned above, the BS 102 repeatedly broadcasts system information associated with the BS 102 to one or more UEs 104 so as to allow the UEs 104 to access the network within the cells where the BS 102 is located, and in general, to operate properly within the cell. Plural information such as, for example, downlink and uplink cell bandwidths, downlink and uplink configuration, cell information, configuration for random access, etc., can be included in the system information. Typically,the BS 102 broadcasts a first signal carrying some major system information, for example, configuration of the cell where the BS 102 is located through a Physical Broadcast Channel (PBCH). For purposes of clarity of illustration, such a broadcasted first signal is herein referred to as “first broadcast signal.” It is noted that the BS 102 may subsequently broadcast one or more signals carrying some other system information through respective channels (e.g., a Physical Downlink Shared Channel (PDSCH)).

[0037] Referring again to Figure IB, in some embodiments, the major system information carried by the first broadcast signal may be transmitted by the BS 102 in a symbol format via the communication channel 192 (e.g., a PBCH). In accordance with some embodiments, an original form of the major system information may be presented as one or more sequences of digital bits and the one or more sequences of digital bits may be processed through plural steps (e.g., coding, scrambling, modulation, mapping steps, etc.), all of which can be processed by the BS processor module 158, to become the first broadcast signal. Similarly, when the UE 104 receives the first broadcast signal (in the symbol format) using the UE transceiver 162, in accordance with some embodiments, the UE processor module 168 may perform plural steps (de-mapping, demodulation, decoding steps, etc.) to estimate the major system information such as, for example, bit locations, bit numbers, etc., of the bits of the major system information. The UE processor module 168 is also coupled to the RO interface 169, which provides the UE 104 with the ability to connect to other devices such as computers. The RO interface 169 is the communication path between these accessories and the UE processor module 168.

[0038] Referring again to Figure 1A, during the transmission of signals between the BS 102 and the UE 104, the established wireless transmission channels between the BS 102 and the UE 104 may introduce various impairments and distortions to the transmitted signals dueto factors such as fading, interference, and noise. Channel estimation can be performed to estimate the characteristics of the communication channel between the BS 102 and the UE 104 to optimize wireless communication system performance and to improve the reliability of communication. A conventional way to perform channel estimation is to use channel reciprocity for MIMO precoding in the downlink by estimating the UL channel based on the symmetry properties between the UL and DL channels. That is, the UE 104 can periodically transmit pilot signals or Sounding Reference Signals (SRSs) during specific time slots allocated for UL channel sounding, and the corresponding BS 102 can measure the received SRSs to estimate the UL channel characteristics such as channel gains and phases. In case of a time-division duplexing (TDD) transmission, there is channel reciprocity between the UL and DL channels. This means that the UL and DL channel responses are related, allowing information obtained from UL measurements to be used for DL transmission. Lor example, the BS 102 can perform DL MIMO precoding based on the extracted UL channel state information (CSI) from the received pilot signals or SRSs. Once the DL MIMO precoding matrix is determined, the BS 102 can use it to precode the DL data transmission, which helps in mitigating the effects of channel fading and interference and improving the quality of the received signal at the UEs.

[0039] In some embodiments, a massive MIMO system is used with a number of antennas at the BS 102 to enhance data throughput and spectrum efficiency. However, the implementation of MIMO systems necessitates accurate CSI acquisition at the BS and UE transmitters, which can be achieved through codebook-based feedback in Erequency Division Duplexing (EDD) networks or reciprocity-based sounding in Time Division Duplexing (TDD) networks. Despite these methods, the exhaustive beam search required for selecting optimal transmit-receive (Tx-Rx) beam pairs in 5G New Radio (NR) technology results in significant signaling overhead and delays, thus necessitating more efficient approaches.

[0040] A significant milestone in the progression of 5G NR technology is the introduction of 5G-Advanced, initially delineated in the 3rd Generation Partnership Project (3GPP) release 18. Within the framework of 5G-Advanced, one prominent aspect involves the integration of Artificial Intelligence (Al) leveraging Machine Learning (ML) techniques to provide solutions across various use cases, including enhancements in Channel State Information (CSI), Beam Management (BM), and positioning accuracy. In contrast to conventional methods, Al-based approaches harness ML techniques, particularly Neural Networks (NNs), to extract features from training data. Consequently, AFML-based algorithms can be effectively employed in BM to mitigate overhead and enhance beam prediction accuracy, marking a departure from traditional approaches.

[0041] In line with the 3GPP design for 5G- Advanced, two representative BM sub-use cases are considered, namely Spatial Domain Beam Prediction (BM Case-1) and Temporal Domain Beam Prediction (BM Case-2). In the Spatial Domain Beam Prediction (BM Case-1) sub-use case, the prediction of the spatial domain DL beam is conducted by providing an element of the prediction Set c / Z, relying on measurements provided by the measurement Set B. Within this context, 3GPP has defined two alternatives for the relationship between sets Jl and B:

[0042] Alternative 1: Set B being a subset of Set <A. In this scenario, both sets <A and B comprise narrow beams. For example, the prediction Set <A and the measurement Set B could be the beams in Channel State Information-Reference Signal (CSI- RS) resources. In one embodiment, Set <A comprises 100 narrow beams covering the entire spatial area around the UE, and Set B is a smaller subset of 10 beams from Set <A. Then the UE may measure signal quality such as Layer 1 Reference Signal Received Power (Ll-RSRP) on each beam in Set B,then based on these measurements, an ML model may be configured to predict which of the100 beams in Set c / Z (including those not directly measured) would be optimal for the UE.

[0043] Alternative 2: Set B being different from Set c / Z. Alternatively, Set B could differ from Set A while there is no overlap between Set B and Set A. In this case, Set Jl may comprise Narrow Beams (NBs) accompanying CSI-RS resources while Set B may comprise Wide Beams (WBs) on Synchronization Signal Block (SSB) resources. In one embodiment, Set c / Z comprises 100 narrow DL beams for precise communication, while Set B comprises fewer, wider beams (e.g., 10 wide beams from the SSB that provide coarse location data). The UE may then measure the signal quality (e.g. Ll-RSRP) on each of the wider beams in Set B. Next, the ML model may use the measurements from these 10 wide beams in Set B to predict the best narrow beam(s) in Set <A.

[0044] In the Temporal Domain Beam Prediction (BM Case-2) sub-use case, the temporal prediction of the DL beam for A future time instances involves predicting beams from Set <A based on inputs from the measurement Set B. This scenario parallels BM Case-1, where Set B could either be a subset of Set <A or different from Set <A. Furthermore, in BM Case-2, an additional option exists where Set B could be identical to Set <A but obtained at a different time (e.g. an earlier time) from Set <A. In one embodiment, a BS communicates with a moving UE (such as a vehicle) in a dynamic environment. The BS can then use an ML model to predict which beams in Set <A will be optimal over the next few seconds (i.e., A future time instances) based on current or past measurements from Set B. In another embodiment, Set <A comprises 100 narrow beams covering the entire area, while Set B comprises 10 beams selected from within Set <A that cover the region around the UE’s current position. At the current time (time 0), the UE may measure Ll-RSRP values for the 10 beams in Set B and reports them to the BS, and the ML model may use the reportedmeasurements from Set B to predict which beam(s) in Set c / Z will be optimal for the UE at N future time instances (e.g., time 1, time 2, etc.). In yet another embodiment, Set c / Z comprises narrow beams (e.g., 100 narrow DL beams for precise coverage), while Set B comprises a smaller set of wider beams (e.g., 5 wide beams from SSB signals) that provide broader, coarse-grained measurements. At the current time (time 0), the UE may measure Ll-RSRP values for the 10 wider beams in Set B and reports them to the BS, and the ML model at the BS may use the reported measurements from Set B to predict which narrow beam(s) in Set Jl will be optimal for the UE at N future time instances. In still another embodiment, both Set Jl and Set B comprise the same set of 100 beams, but the measurements in Set B are taken at an earlier time. For example, at time point T — 1, the UE may measure the Ll-RSRP values on all the 100 beams in Set B (which is the same as Set c / Z). Then using these past measurements from time point T — 1, the ML model can predict which of the beams in Set Jl will be optimal at N future time points: T + 1,..., T + IV.

[0045] In both BM Case-1 and BM Case-2, 3GPP delineated two options for the placement of the ML model. The first option involves the placement of the model on the BS (e.g. gNB) side, while the second option entails the placement of the ML model on the UE side. In some embodiments, when the ML model is placed / implemented on the BS side, the ML model can be a software program that is executed by the BS based on data stored and / or received at the BS. In some other embodiments, the ML model can be a hardware component (e.g. Field-Programmable Gate Array (FPGA), Application-Specific Integrated Circuit (ASIC), or Graphics Processing Unit (GPU)) that is embedded in the BS hardware. In yet some other embodiments, when the ML model is placed / implemented on the UE side, the ML model can be a software program that is executed by the UE based on data stored and / or received at the UE. In still some other embodiments, the ML model can be a hardware component (e.g. FPGA, ASIC, or GPU) that is embedded in the UE hardware.

[0046] PIG. 2A illustrates a signaling diagram between a BS 202 and a UE 204 for spatial domain beam prediction with ML model placed on the BS side, in accordance with some embodiments. In some embodiments, the BS 202 may transmit one or more reference signals such as CSLRSs or Synchronization Signal Burst (SSB) to the UE 204. The one or more reference signals may be used by the UE 204 to measure the received signal quality and strength across different beams. In some embodiments, the one or more reference signals are transmitted across multiple antenna arrays or beams, wherein each of the multiple antenna arrays or beams corresponds to a different spatial direction, allowing the UE 204 to assess the quality of each beam.

[0047] Upon receiving the one or more reference signals, the UE 204 may be configured to measure the Layer 1 Reference Signal Received Power (Ll-RSRP) for each received beam or group of beams, wherein the measured Ll-RSRP reflects the power and quality of the signals received from the BS 202 across different beams. In some embodiments, the UE 204 compiles different Ll-RSRP values into a Ll-RSRP report, which is a low-level report directly related to signal strength. Then the UE 204 may send the Ll-RSRP report back to the BS 202.

[0048] In some embodiments, upon receiving the Ll-RSRP report, the BS 202 may use an ML model 206 placed within the BS 202 to perform beam prediction from input Set B to output Set c / Z, wherein Set Jl represents the predicted optimal beams used for future communication, and Set B may be a specific subset of narrow beams that the UE 204 has measured and reported in the Ll-RSRP report. The specific subset may represent the beams with the best signal strength in the Ll-RSRP report, or the beams that are expected to provide reliable communication based on historical performances. In some embodiments, Set B is a subset of Set Jl. In this case, the prediction of Set Jl and the measurements in Set B may bethe beams in the CSI-RS resources used in the communication. The ML model 206 may be configured to analyze the Ll-RSRP report from Set B (e.g. the subset of narrow beams the UE 204 evaluated). The BS 202 may then use the ML model 206 to perform model inference and predict which beams in Set Jl (e.g. the broader set of beams) will be optimal for the upcoming data transmissions. Based on these predictions, the BS 202 can dynamically select the best beams from the full set (Set c / Z) for future transmissions. This improves efficiency by narrowing the choices down to the most relevant beams.

[0049] In some embodiments, the ML model 206 may be a separate entity that is connected to the BS 202 via wireless or wired connection. In some other embodiments, Set B comprises only Ll-RSRP measurements used as inputs of the ML model 206 for performing beam predictions, wherein the Ll-RSRP measurements may be stored in a memory of the UE and used as inputs for the ML model implemented on the UE side. In case the ML model is implemented on the BS side, the Ll-RSRP measurements may be reported from the UE to the BS, and then the Ll-RSRP measurements may serve as inputs of the ML model at the BS. In some embodiments, Set B may be obtained on the UE side, wherein the UE is configured to take Ll-RSRP measurements on a subset of (e.g. selected based on spatial beam patterns, previous history, or channel conditions) or all available beams from the BS, wherein the Ll- RSRP measurements form Set B. In yet some other embodiments, Set B comprises Ll-RSRP measurements and assistance information (such as UE location, mobility patterns, historical data, and environment- specific parameters) used as inputs of the ML model 206 to improve beam prediction accuracy. In still some other embodiments, Set B comprises Channel Impulse Response (CIR) used as inputs of the ML model 206. In still some other embodiments, the inputs of the ML model 206 comprise Ll-RSRP measurements from Set B combined with the corresponding downlink transmit (Tx) and / or receive (Rx) beam IDs.

[0050] In some embodiments, Set B is different from Set c / Z, and Set c / Z may be used forDL beam prediction. The codebook construction of Set c / Z and Set 'B may be clarified by users or companies. In this case, Set Jl may comprise Narrow Beams (NBs) accompanying CSI-RS resources while Set B comprises Wide Beams (WBs) on Synchronization Signal Burst (SSB) resources. That is, the ML model 206 may use the Ll-RSRP report based on wide beam measurements to predict which narrow beams (Set c / Z, CSLRS beams) will provide optimal performance for the UE 204. Therefore, the wide beams may serve as a precursor for determining the more granular, directional narrow beams that the BS 202 will use for data transmission. The ML model 206 may then link the wide beam performance (from Set B) to predict the best narrow beams (Set c / Z), reducing the need for an exhaustive beam sweep of all narrow beams.

[0051] FIG. 2B illustrates another signaling diagram between a BS 212 and a UE 214 for spatial domain beam prediction with ML model placed on the UE side, in accordance with some embodiments. In some embodiments, the BS 212 may transmit one or more reference signals such as CSLRSs or SSBs to the UE 214. Upon receiving the one or more reference signals, the UE 214 may be configured to generate an Ll-RSRP report as discussed above with reference to FIG. 2A. In some embodiments, instead of transmitting the Ll-RSRP report back to the BS 212, the UE 214 may use the Ll-RSRP report along with assistance information to perform beam prediction based on an ML model 216 placed in the UE 214. Examples of assistance information that can be used for beam prediction with the ML model 216 include: Channel State Information (CSI), Channel Quality Indicators (CQI), Signal-to- Noise Ratio (SNR), beam indices and measurement history, UE location and mobility information, time-domain information of the network. The ML model 216 may be a component placed within the UE 214, or implemented in a different entity that is connected to the UE 214 via wireless or wired connection. In some embodiments, the ML model 216may use the Ll-RSRP report along with the assistance information to perform model training and beam prediction, as described above with reference to FIG. 2A.

[0052] FIG. 2C illustrates yet another signaling diagram between a BS 222 and a UE 224 for temporal domain beam prediction with ML model placed on the BS side, in accordance with some embodiments. In some embodiments, the BS 222 may transmit one or more reference signals such as CSI-RSs or SSBs to the UE 224. Upon receiving the one or more reference signals, the UE 224 may be configured to generate a historical Ll-RSRP report and send the historical Ll-RSRP report back to the BS 222. In some embodiments, the historical Ll-RSRP report may include historical Ll-RSRP measurements, UE mobility patterns including UE location information and UE speed, beam performance history including beam ID and beam switching events, and historical channel condition data including signal to noise and CQI.

[0053] In some embodiments, upon receiving the historical Ll-RSRP report, the BS 222 may use an ML model 226 placed within the BS 222 to perform beam prediction from input Set B to output Set <A. In some embodiments, Set <A and Set B are different and Set B is not a subset of Set <A. In this case, Set <A may comprise a distinct set of beams used for downlink beam prediction, while Set B may comprise a separate set of beams used for measurements. The ML model 226 may then use measurements from Set B to predict the optimal beams in Set c / Z. In some other embodiments, Set B is a subset of Set Jl. In this case, Set Jl may comprise all the beams for downlink prediction, while Set B is a smaller subset of Set Jl. The ML model 226 may use measurements from the narrower Set B to make predictions for beams in Set <A. In yet some other embodiments, Set <A and Set B comprise the same measurements obtained at different times. In this case, Set B nv^ comprise historical beammeasurements, and Set c / Z may comprise predicted future values for the same beam measurements from Set B.

[0054] In some embodiments, the ML model 226 may use measurement data from the latest K measurement instances (where K > 1) with the following input alternatives: Alternative 1: The ML model 226 uses only Ll-RSRP values measured from Set B as inputs to make predictions on Set Jl. Alternative 2: along with Ll-RSRP values, the ML model 226 incorporates additional assistance information (such as UE location, mobility data, or environmental conditions) to enhance prediction accuracy. Alternative 3: The Ll-RSRP measurements from Set B are combined with information on the corresponding downlink Tx and / or Rx beam IDs as model inputs to improve the prediction accuracy of the ML model 226. In some embodiments, the ML model 226 is configured to generate predictions for F future time instances (F > 1). For each future time instance, the ML model 226 may provide a prediction for the optimal beam configuration, which can be used to assist the UE 224 for anticipating changes in beam performance based on the input measurements. This approach provides dynamic prediction of beam performance over time, and the beamforming strategies can be adjusted accordingly to optimize connectivity.

[0055] FIG. 2D illustrates still another signaling diagram between a BS 232 and a UE 234 for temporal domain beam prediction with ML model placed on the UE side, in accordance with some embodiments. In some embodiments, the BS 232 may transmit one or more reference signals such as CSLRSs or SSBs to the UE 234. Upon receiving the one or more reference signals, the UE 234 may be configured to generate a historical Ll-RSRP report. In some embodiments, the historical Ll-RSRP report may be generated along with assistance information as discussed above with reference to FIG. 2C. In some embodiments, instead of transmitting the historical Ll-RSRP report back to the BS 232, the UE 234 mayuse the historical Ll-RSRP report along with the assistance information to perform beam prediction based on an ML model 236 placed in the UE 234. The ML model 236 may be a component placed within the UE 234, or implemented in a different entity that is connected to the UE 234 via wireless or wired connection. The ML model 236 may use the historical Ll-RSRP report along with the assistance information to perform model training and beam prediction, as described above with reference to FIG. 2C.

[0056] In some embodiments, the Ll-RSRP measurements from Set B are used as the primary inputs of the ML model used for beam prediction, wherein the Ll-RSRP measurements are discrete digital values converted from continuous Ll-RSRP values in a process named RSRP quantization. In some embodiments, 7 bits are utilized to report the absolute Ll-RSRP of each the strongest, or all, beams in Set B as measured by the UE. Alternatively, other embodiments may employ differential Ll-RSRP reporting using only 4 bits for each beam to reduce the uplink reporting overhead. This RSRP quantization strategy can be used to balance precision with the need to minimize overhead in the wireless communication system. A more precise quantization provides better information for the ML model but also increases the amount of data that needs to be processed. That is, the precision of the RSRP quantization can impact the performance of the trained ML model. Higher precision allows the ML model to make more accurate predictions but comes at the cost of increased data and computational overhead. In some embodiments, an optimal trade-off between precision and efficiency can be obtained to maximize model performance while minimizing system burden. In some embodiments, one can set a maximum acceptable computational overhead (e.g. 1ms) for the ML model. Then the number of bits in the RSRP quantization can be incremented starting from 2 bits while the computational overhead of the ML model is measured each time the number of bits in the RSRP quantization is increased. The final number of bits in the RSRP quantization may be determined to be the maximumnumber of bits in this process while the computational overhead is still within the acceptable value (e.g. 1ms).

[0057] FIG. 3 illustrates an exemplary design framework 300 for ML model-based beam prediction, in accordance with some embodiments of the present disclosure. In some embodiments, a BS 302 may transmit an SRS request to a UE 304 through SRS transmissions, wherein the SRS transmissions are used as Set B described above with reference to FIG. 2A and 2C to determine the optimal precoder for DL data transmission from the predefined Set <A. In some embodiments, the optimal precoder for DL data transmission is predicted by an ML model placed in the UE 304. In some other embodiments, the optimal precoder for DL data transmission is predicted by an ML model placed in the BS 302, wherein the ML model operates in EDD mode and employs inputs from either Alternative 2 or 3 of BM Case-1 as defined above, or solely from Alternative 2 of BM Case-2 defined above, wherein Set B is different from Set <A. That is, for Alternative 2 or 3 of BM Case-1, the ML model may use Ll-RSRP measurements from Set B combined with assistance information as inputs for performing spatial domain beam predictions, or the ML model may use Ll-RSRP measurements from Set B and the corresponding DL Tx / Rx beam IDs for performing spatial domain beam predictions. Lor Alternative 2 of BM Case-2, the ML model may use historical Ll-RSRP measurements from Set B with assistance information for performing temporal beam predictions. In some yet other embodiments, the ML model is implemented within the BS 302, wherein the ML model employs inputs from Alternative 2 of BM Case-1 defined above, wherein the inputs of the ML model are based on SRS beam measurements, as will be illustrated in detail below with reference to EIG. 3.

[0058] In some embodiments, the interaction between the BS 302 and UE 304 signaling is predicated on gathering Uplink (UL) channel information via the transmission of SRSsfrom different UEs and treated as Set 'B. This UL channel information may encompass a variety of parameters, including UL Channel Impulse Response (CIR) or other channel characteristics such as the UL channel receive response vector, Doppler, and delay spreads. This UL channel information, in conjunction with various Ll-RSRP measurements, may serve as inputs to the ML model that uses an ML-based algorithm for beam prediction. The primary objective of the ML-based algorithm may be to translate the provided UL channel properties into DL equivalents, ultimately generating predictions for DL data precoders.

[0059] In some embodiments, by sending the SRS request, the BS 302 may request the UE 304 to transmit UE-specific SRSs on a set of UL resources (e.g., sub-bands), wherein the UL resources may be on the UL carrier- frequency for EDD deployments or on the UL-DL carrier-frequency for TDD deployments. The BS 302 may configure the UE 304 to transmit the SRS transmissions on the SRS-specific UL resources. In some embodiments, to mitigate the channel aging issue, the BS 302 may schedule the UE 304 for the SRS transmissions on the Physical Uplink Shared Channel (PUSCH) resources such that the SRS transmissions occur close enough to the scheduled DL data transmissions to the UE 304. Lor example, the wireless communication system including the BS 302 and the UE 304 may operate with a 10- ms frame duration comprising 10 subframes of 1 ms each. The BS 302 may schedule an SRS transmission in subframe 7, and the BS transmits DL data to the UE 304 in subframe 8.Therefore, the scheduling is done in such a way that the time gap between the SRS transmission and the DL data transmission is minimized (e.g., 1 ms gap in this case). Upon receiving the UE-specific SRSs, the BS 302 may perform UL channel estimate based on the received SRSs, as illustrated by the UL Channel Estimate block 306 in EIG. 3. The outputs of the UL Channel Estimate block 306 may be used as inputs to an ML model 308 to determine the best precoder beam from Set c / Z. The BS 302 may then apply the best precoder beampredicted by the ML model 308 to precode the DL data transmitted to the UE 304, as illustrated by the DL Precoder block 310 in FIG. 3.

[0060] In some embodiments, the ML model 308 may use UL channel measurements as inputs, wherein the UL channel measurements may take the form of Ll-RSRP along with additional assistance information or CIR. In some other embodiments, the inputs for the ML model 308 may be derived from the received SRS transmissions from the UE 304. In contrast to the ML-based BM setups depicted in FIG. 2A the ML model 308 at the BS 302 side does not necessitate the UE 304 to measure Ll-RSRP and report it back in a quantized format. Since SRS signals can provide raw channel quality information directly to the BS 302, the ML model 308 at the BS 302 can perform beam predictions using more precise, unquantized data. Therefore, while the technique depicted in FIG. 3 still mandates UE SRS transmissions, akin to Ll-RSRP described in FIG 2A and 2C it mitigates potential performance degradation that may arise in the ML algorithm implemented in the ML model 308 due to significant quantization errors. This approach also eliminates the necessity to devise compression techniques for the Uplink Control Information (UCI) payload, thereby reducing computational complexity at both the BS and UE sides.

[0061] In some embodiments, the design framework 300 disclosed herein does not necessitate any modifications in the Life Cycle Management (LCM) operations as defined in TR 38.843, which provides a description of the LCM operations encompassing data acquisition, training, inference, and performance monitoring. In addition, the design framework 300 depicted in FIG. 3 can also be adaptable to any potential modifications in signaling aspects introduced by 3GPP in the future. This flexibility ensures that the design framework 300 disclosed herein can seamlessly accommodate any forthcoming changes insignaling procedures mandated by 3GPP and allows for the continued compatibility of the design framework 300 disclosed herein in different environments.

[0062] In some embodiments, data collection and training for the ML model 308 are performed for BM Case-1 (e.g. Spatial Domain Beam Prediction). For BM Case-1, training data is gathered to facilitate the training of the ML model 308. In this phase, the BS 302 may transmit Set Jl on CSLRS resources to the UE 304. Subsequently, the UE 304 may evaluate the received signals and determine the optimal beam based on predetermined criteria, such as Ll-RSRP. Upon identifying the best beam, the UE 304 may send SRS signals to the BS 302 on the allocated UL resources, which may be on PUSCH as discussed above, along with information regarding the best beam from Set <A. The BS 302 may then utilize the received SRS transmissions to perform UL channel estimation, and the outputs of the UL channel estimation may be used as inputs to the ML model 308. Additionally, the information regarding the best beam can be utilized as the output label for the corresponding input data sample of the ML model 308, thereby facilitating supervised learning during the training process. In some embodiments, the ML model 308 output label generation process is mathematically formulated as described below.

[0063] Assume that the BS 302 and the UE 304 form a MIMO system, wherein the BS 302 comprises Mttransmit antennas associated with a transmit codebook F, and the UE 304 comprises Mrreceive antennas associated with a receive beam codebook W, where Mtand Mrare positive integers. In some embodiment, |F| = Ntand | W | = Mrsuch that |set| is the cardinality operator of set. That is, the codebook F may comprise Ntpossible beams, where Ntis a positive integer, and the codebook IT may comprise only one beam with | W | = 1. Assuming a Discrete Fourier Transform (DFT) based codebooks, the mthtransmit beam fmG F can be defined as:

[0065] where ymdenotes the direction of the mthcandidate beam at the BS 302 side.Similarly, the receive beam w E W at the UE 304 can be defined as:

[0067] where 0 denotes the direction of the receive beam at the UE 304 side, T denotes the transpose operation of a matrix. The channel matrix can be defined as H E (,MrXMtwhich represents the channel response between the transmit and receive antennas, where (C is the set of complex numbers. In some embodiments, the channel matrix H is estimated from CSI-RS transmissions, and the output label of the ML model 308 can be identified by finding the optimal transmit beamforming vector index m* that maximizes the beamforming performance in terms of the channel gain:

[0068] m* = arg max \ wHHfm\2mEF

[0069] Here, wHis the Hermitian (complex conjugate transpose) of the receive beam w, H E (FMrxMt js(he channel matrix that represents the channel response between the transmit and receive antennas as defined above, and | x | denotes the absolute value of x. The above formula computes the power of the beamformed signal for each transmit beam fm, and the beam index m* corresponding to the maximum power is selected as the optimal beam used as the output label of the ML model 308 corresponding to the input data sample in the supervised learning process. In some embodiments, the ML model 308 is configured to learn to predict this optimal beam index m* based on input channel conditions.

[0070] In some other embodiments, data collection and training for the ML model 308 are performed for BM Case-2 (e.g. temporal downlink beam prediction for Set c / Z of beamsbased on the historic measurement results of Set B of beams). In BM-Case 2, the process of training the ML model 308 involves collecting temporally correlated data samples from various beams. Each data sample may consist of a matrix of M X T input measurements, such as Ll-RSRPs, for M beams included in Set Jl (or potentially all beams in Set c / Z), collected over T time instances. For a network-sided AI / ML model, as illustrated in FIG. 3, the sample input can be gathered by UE 304 transmitting Set B (or a subset thereof) of beams over T time instances. The corresponding label is an / V-dimcnsional vector (or a K X N matrix if K beams are predicted for each time instance) that represents the beam prediction result of the AI / ML model for the N future time instances. Methods to standardize the time stamping for data samples and model predictions are described in various 3GPP documents, ensuring consistency and coherence across the system.

[0071] In some embodiments, after performing the data collection and training, the ME model 308 may be configured to perform inference and performance monitoring. In some embodiments, during the inference and performance monitoring, the BS 302 may collect UL channel measurements from the UE 304 and use the trained ML model 308 to predict optimal downlink beams for data transmission. This process can be applied to either spatial beam prediction (for BM Case-1) or temporal beam prediction (for BM Case-2). Periodically, the BS 302 may monitor the performance of the trained ML model 308 to determine if the BS 302 should continue using the current trained ML model 308, switch to an alternative model, or revert to a legacy method.

[0072] In some embodiments, during the performance monitoring for the trained ML model 308, the BS 302 may first collect input data samples and labels from the UE 304 using a similar method used in the data collection and training of the ML model 308 as described above. Then, the BS 302 may be configured to apply the collected input data samples to thetrained ML model 308, which produces a predicted label (e.g., the beam the trained ML model 308 predicts as optimal). Next, the BS 302 may compare the collected label (actual beam used) to the predicted label (beam predicted by the trained ML model 308). If there is a consistent match, the BS 302 may decide to maintain the current trained ML model 308. If there are discrepancies, the BS 302 may switch models or revert to a more traditional, non- ML method (e.g. legacy fallback).

[0073] In some embodiments, the ML model 308 uses of raw, unquantized UL channel measurements (such as Ll-RSRP) as inputs, which contrasts with approaches in 3GPP where quantized or pre-processed data is used. By using unquantized data, the ML model 308 may benefit from more accurate and precise channel information, which can enhance the prediction accuracy of DL beams. This approach reduces performance degradation that typically arises from quantization errors, enabling the ML model 308 to make more reliable beam predictions. The method disclosed herein ensures that the design framework 300 continues to operate efficiently by dynamically adjusting or retraining the ML model 308 as necessary based on real- world performance data.

[0074] FIG. 4 illustrates a deep neural network (DNN) model 400 used to implement the ML model 308 illustrated in FIG. 3, in accordance with some embodiments of the present disclosure. Although a DNN example is illustrated in FIG. 4, the ML model in the present disclosure is not limited to DNN implementation, and can take any other forms of ML model, such as multilayer perceptron, feedforward neural networks, convolutional neural networks, recurrent neural networks, autoencoder, generative adversarial networks, and long short-term memory and transformers. In some embodiments, in the ML model 308 described in FIG. 3, layer 410-k output can be arranged in the vector o(fc) = [o1(... , onfc] with layer k consisting of n neurons and the input layer corresponding to k = 0. The relationship between theoutputs of layers k — 1 and k can be represented as o(fc) = s o(k —where s(. ) is a non-linear activation function applied elementwise to the input argument andis a weight matrix that connects layers 410-(k) and 410-(k-l).

[0075] In some embodiments, the DNN model 400 is trained using a plurality of training samples, wherein each training sample comprises an input vector and a corresponding output vector. In one embodiment, to find the optimal values of the weight matrices W to Wk+1during the training of the DNN model 400, a back propagation algorithm is used by taking an error rate of a forward propagation and feeding this loss backward through the layers of the DNN model 400 to fine-tune the weights. In another embodiment, to find the optimal values of the weight matrices W to Wk+1during the training of the ANN model 400, a weight perturbation technique can be used. The weight perturbation technique may be applied in an iterative manner for a plurality of iterations, wherein in each of the plurality of iterations, a weight variation of random sign is added to each of the elements in the weight matrices IV1to Wk+1and a corresponding training error is observed. If the training error is increased in a given iteration, then the elements in the weight matrices IV1to Wk+1will be changed to the opposite directions of the weight variations; if the training error is decreased in a given iteration, then the elements in the weight matrices IV1to Wk+1will be changed to the same directions of the weight variations. This iterative training can be stopped if at least one of the following conditions is met: the training error becomes smaller than a predetermined error threshold value, a maximum number of iterations is reached, and the training error does not decrease for a predetermined number of iterations. In some embodiments, a dynamic weight perturbation technique can be applied to train the DNN model 400 by decreasing the amount of weight variations in each iteration, such that the DNN model 400 is fine-tuned towards the end of the training process. In one embodiment, the weight variation in the t-th iteration vtcan be calculated as: vtwhere v0is an initial weight variation amount, and is a user-defined parameter which controls the decrease rate of vt.

[0076] Referring back to FIG. 3, in some embodiments, the number of receive radio chains n at the UE 304 side exceeds the number of transmit chains m. In such instances, antenna switching mechanisms, as defined in Release 15, may be supported at the UE 304. For instance, if the UE 304 is equipped with n = 4 receive chains and m = 2 transmit chains, the UE 304 may need to perform two rounds of channel sounding (e.g., receive the signal twice, using two different chains each time).

[0077] FIG. 5A illustrates a channel sounding diagram, in accordance with some embodiments of the present disclosure. In some embodiments, a BS 502 may transmit one or more reference signals such as CSI-RSs or SSBs to a UE 504. The UE 504 may be equipped with more receive chains than transmit chains. For example, the UE 504 may be equipped with n = 4 receive chains and m = 2 transmit chains. In such a case, the BS 502 may 71 conduct — = 2 different transmit cycles. In the A-th transmission cycle, the BS 502 may transmit a corresponding CSI-RS named CSI-RS-U In response to CSI-RS-fc transmitted by the BS 502 in the A-th transmission cycle, the UE 504 may compute the index of the optimal beam (e.g. the label) and transmits an UL signal including an SRS transmission along with information about the computed optimal beam. The UL signal (including the SRS transmission along with information about the optimal beam) transmitted by the UE 504 in response to CSI-RS-fc is named SRS-fc + L-k in FIG. 5A. Upon receiving signals from SRS- J + L-7 to SRS-Z. + L-k, the BS 502 may process the aggregated information about the labels 71 over the — transmissions and receptions to generate the final label for training an ML model comprising an ML algorithm for beam prediction, as indicated by block 506 in FIG. 5A.Once successfully trained, the ML model may be used to produce precoded data, which maybe transmitted from the BS 502 to the UE 504. While this process may entail a high processing time in the training phase due to the transmission of multiple CSI-RS signals and their prior configuration, the latency involved is not a significant concern if the process is performed offline.

[0078] FIG. 5B illustrates another channel sounding diagram, in accordance with some embodiments of the present disclosure. In some embodiments, a BS 512 may transmit a single CSI-RS to a UE 514. In some embodiments, the BS 512 may configure only one CSI- RS transmission (shown by CSI-RS in FIG. 5B) and receive the corresponding single label along with different SRS measurements from the UE 514 in a way similar to the SRS transmissions depicted in FIG. 5A. Based on the initial single label and information on the channel characteristics correlation from various UL (e.g. SRS) measurements, the BS 512 may derive the intended label for training an ML model for beam prediction, as indicated by the block 516 “ML algorithm for beam prediction” in FIG. 5B. That is, based on the correlation of the SRS measurements with the initial single label, the BS 512 may derive a more accurate final label for ML model training.

[0079] In one embodiment, the BS 512 might conclude that initially selected beam performs well across other antenna setups associated with other SRS measurements, validating the initial label. In another embodiment, the BS 512 may identify a second beam different from the initially selected beam, wherein the second beam performs better than the initially selected beam when considering the full SRS data. Therefore, the final label for training the ML model would be the beam that offers the best overall performance based on the analysis of both the initial label and the correlated SRS measurements. After the ML model is trained, the ML model may be used to produce precoded data, which may be then transmitted from the BS 512 to the UE 514. Compared to the channel sounding methodillustrated in FIG. 5A, the method illustrated in FIG. 5B entails reduced training latency, rendering it more suitable for online training and deployment scenarios.

[0080] FIG. 6 illustrates an example method 600 for performing optimal beam prediction in a wireless communication system, in accordance with some embodiments. The operations of method 600 presented below are intended to be illustrative. In some embodiments, method 600 may be accomplished with one or more additional operations not described and / or without one or more of the operations discussed. Additionally, the order in which the operations of method 600 are illustrated in FIG. 6 and described below is not intended to be limiting.

[0081] At step 602, a BS sends a first signal to a UE. In some embodiments, the first signal comprises a request for the UE to transmit SRSs to the BS. In some embodiments, the request for the UE to transmit SRSs to the BS is transmitted via a configuration message, wherein the configuration message is transmitted through Radio Resource Control (RRC), Medium Access Control - Control Element (MAC-CE), or Downlink Control Information (DO). In some other embodiments, the first signal comprises a resource allocation message that allows the UE to transmit data on specific uplink resources. That is, the first signal may comprise an UL grant that assigns specific uplink resources to the UE to transmit SRS on the PUSCH. In this case, the request for the UE to transmit SRSs to the BS is transmitted by the UL grant.

[0082] At step 604, upon receiving the first signal, the UE may be configured to send a second signal back to the BS. In some embodiments, the second signal comprises at least one SRS, wherein the at least one SRS is used as inputs (e.g. Set B as described above with reference to FIG. 2A-2D) for an ML model implemented at the BS side to optimize downlink beamforming. In some embodiments, the at least one SRS is transmitted on UL resources,wherein the UL resources can be on the UL carrier frequency in FDD deployments or on theUL-DL carrier frequency in TDD deployments.

[0083] At step 606, upon receiving the second signal comprising the at least one SRS, the BS may use the second signal as inputs of an ML model implemented in the BS to perform beam prediction. In some embodiments, the ML is used to predict the best downlink beam(s) from Set Jl as described above with reference to FIG. 2A-2D. That is, the ML model may predict optimal precoded beam from Set Jl as outputs, which will be used to transmit data to the UE. The BS may apply the predicted optimal beam as a precoder to shape the downlink signal that will be transmitted to the UE.

[0084] In some other embodiments, the ML model processes the second signal and generates a set of candidate precoded beams from Set c / Z, wherein the set of candidate precoded beams comprises potential beams that could be used for downlink data transmission. That is, the set of candidate precoded beams may be predicted from the second signal using the ML model. Then the BS may apply the set of candidate precoded beams to transmit precoded CSLRS to the UE. The purpose of sending these precoded CSLRS signals is for the UE to evaluate the different precoded beams. Upon receiving the precoded CSLRS, the UE may measure the quality of the candidate precoded beams (e.g. Ll-RSRP values), select best precoded beam(s) among the candidate precoded beams based on the quality of the different candidate precoded beams, and report back the selected best precoded beam(s) to the BS. This feedback allows the BS to identify which precoded beam(s) performed the best (e.g. the beam(s) with the highest Ll-RSRP) from the UE’s perspective.

[0085] At step 608, the BS may transmit downlink data to the UE based on the beam prediction results provided by the ML model in step 606. In some embodiments, when the outputs produced by the ML model are optimal precoded beam from Set Jl as depicted instep 606, the BS may transmit the downlink data using the precoded beam on the DL resources based on the precoder obtained from the predicted optimal beam at step 606. The DL resources on which the downlink data are transmitted may be on the DL carrier frequency for FDD deployments or the UL-DL carrier frequency for TDD deployments.

[0086] In some other embodiments, when the outputs produced by the ML model are the set of candidate precoded beams from Set Jl as depicted in step 606, the BS may apply the best precoded beam(s) as reported by the UE for the downlink data transmission, wherein the BS uses the best precoded beam(s) to optimize the downlink data transmission to the UE.

[0087] While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example only, and not by way of limitation. Likewise, the various diagrams may depict an example architectural or configuration, which are provided to enable persons of ordinary skill in the art to understand exemplary features and functions of the present disclosure. Such persons would understand, however, that the present disclosure is not restricted to the illustrated example architectures or configurations, but can be implemented using a variety of alternative architectures and configurations. Additionally, as would be understood by persons of ordinary skill in the art, one or more features of one embodiment can be combined with one or more features of another embodiment described herein. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments.

[0088] It is also understood that any reference to an element herein using a designation such as "first," "second," and so forth does not generally limit the quantity or order of those elements. Rather, these designations can be used herein as a convenient means of distinguishing between two or more elements or instances of an element. Thus, a reference tofirst and second elements does not mean that only two elements can be employed, or that the first element must precede the second element in some manner.

[0089] Additionally, a person having ordinary skill in the art would understand that information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits and symbols, for example, which may be referenced in the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0090] A person of ordinary skill in the art would further appreciate that any of the various illustrative logical blocks, modules, processors, means, circuits, methods and functions described in connection with the aspects disclosed herein can be implemented by electronic hardware (e.g., a digital implementation, an analog implementation, or a combination of the two), firmware, various forms of program or design code incorporating instructions (which can be referred to herein, for convenience, as "software" or a "software module), or any combination of these techniques.

[0091] To clearly illustrate this interchangeability of hardware, firmware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware, firmware or software, or a combination of these techniques, depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in various ways for each particular application, but such implementation decisions do not cause a departure from the scope of the present disclosure. In accordance with various embodiments, a processor, device, component, circuit, structure, machine, module, etc. can be configured to perform one or more of thefunctions described herein. The term “configured to” or “configured for” as used herein with respect to a specified operation or function refers to a processor, device, component, circuit, structure, machine, module, etc. that is physically constructed, programmed and / or arranged to perform the specified operation or function.

[0092] Furthermore, a person of ordinary skill in the art would understand that various illustrative logical blocks, modules, devices, components and functions described herein can be implemented within or performed by one or more circuits or circuitry. As used herein, the term “circuitry” refers to and includes any one or more of the following: discrete circuit components or devices coupled to each other to form circuit, logic circuitry, integrated circuits, application specific integrated circuits, state machines, general purpose processors, special purpose processors, digital signal processors (DSP), microprocessors, field programmable gate arrays (FPGA) or other programmable logic devices, or any combination thereof. Circuitry can further include antennas, reflectors, transmitters, receivers and / or transceivers to communicate with various components, devices or nodes within a communication network. As used herein, the term “processor” refers to a combination of structures including processing circuitry, a memory coupled to the processing circuitry, and executable code stored in the memory that when executed by the processing circuitry perform the functions or operations instructed by the executable code.

[0093] If implemented in software, the functions can be stored as one or more instructions or code on a computer-readable medium. Thus, the steps of a method or algorithm disclosed herein can be implemented as software stored on a computer-readable medium. Computer- readable media includes both computer storage media and communication media including any medium that can be enabled to transfer a computer program or code from one place to another. A storage media can be any available media that can be accessed by a computer. Byway of example, and not limitation, such non-transitory computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0094] In this document, the term "module" as used herein, refers to software, firmware, hardware, and any combination of these elements for performing the associated functions described herein. Additionally, for purpose of discussion, the various modules are described as discrete modules; however, as would be apparent to one of ordinary skill in the art, two or more modules may be combined to form a single module that performs the associated functions according to embodiments of the present disclosure.

[0095] Additionally, memory or other storage, as well as communication components, may be employed in embodiments of the present disclosure. It will be appreciated that, for clarity purposes, the above description has described embodiments of the present disclosure with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processing logic elements or domains may be used without detracting from the present disclosure. For example, functionality illustrated to be performed by separate processing logic elements, or controllers, may be performed by the same processing logic element, or controller. Hence, references to specific functional units are only references to a suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization.

[0096] Various modifications to the implementations described in this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein can beapplied to other implementations without departing from the scope of this disclosure. Thus, the disclosure is not intended to be limited to the implementations shown herein, but is to be accorded the widest scope consistent with the novel features and principles disclosed herein, as recited in the claims below.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: receiving, at a wireless communication device, a first signal from a wireless communication node, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; transmitting, at the wireless communication device, the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and receiving, at the wireless communication device, a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

2. The method of claim 1, wherein the request for the wireless communication device to transmit the second signal to the wireless communication node is transmitted via a configuration message, wherein the configuration message is transmitted through:Radio Resource Control (RRC),Medium Access Control - Control Element (MAC-CE), orDownlink Control Information (DCI).

3. The method of claim 1, wherein the second signal comprises at least one Sounding Reference Signal (SRS).

4. The method of claim 1, wherein the second signal is transmitted on uplink (UL) resources, wherein the UL resources are on an UL carrier frequency, wherein the DL carrier frequency and the UL carrier frequency are different.

5. The method of claim 1, wherein the request for the wireless communication device to transmit the second signal is transmitted by an uplink (UL) grant that assigns specific uplink resources to the wireless communication device to transmit the second signal on a Physical Uplink Shared Channel (PUSCH).

6. The method of claim 1, further comprising: receiving, at the wireless communication device, at least one precoded Channel State Information-Reference Signal (CSLRS) from the wireless communication node, wherein the at least one precoded CSLRS is transmitted based on a first plurality of precoded beams, wherein the first plurality of precoded beams is determined from the second signal; reporting, at the wireless communication device, a second plurality of precoded beams to the wireless communication node, wherein the second plurality of precoded beams is selected from the first plurality of precoded beams, and wherein the second plurality of precoded beams is used to transmit a fourth signal from the wireless communication node to the wireless communication device.

7. The method of claim 1, wherein the first precoder beam is determined from the second signal using a deep neural network (DNN) model implemented in the wireless communication node.

8. A wireless communication device comprising: a transceiver configured to: receive a first signal from a wireless communication node, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; transmit the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and receive a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

9. A non-transitory computer readable medium storing computer-executable instructions which when executed perform a method comprising: receiving, at a wireless communication device, a first signal from a wireless communication node, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; transmitting, at the wireless communication device, the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and receiving, at the wireless communication device, a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the thirdsignal is transmitted on downlink (DL) resources at a DL carrier frequency.

10. Circuitry configured to perform a method, the method comprising: receiving, at a wireless communication device, a first signal from a wireless communication node, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; transmitting, at the wireless communication device, the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and receiving, at the wireless communication device, a third signal from the wireless communication node based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

11. A method comprising: transmitting, at a wireless communication node, a first signal to a wireless communication device, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; receiving, at the wireless communication node, the second signal from the wireless communication device, wherein the second signal is used to determine a first precoder beam; and transmitting, at the wireless communication node, a third signal to the wireless communication device based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein thecommunication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

12. A method of claim 11, wherein the request for the wireless communication device to transmit the second signal to the wireless communication node is transmitted via a configuration message, wherein the configuration message is transmitted through:Radio Resource Control (RRC),Medium Access Control - Control Element (MAC-CE), or Downlink Control Information (DCI).

13. The method of claim 11, wherein the second signal comprises at least one Sounding Reference Signal (SRS).

14. The method of claim 11, wherein the second signal is transmitted on uplink (UL) resources, wherein the UL resources are on an UL carrier frequency, wherein the DL carrier frequency and the UL carrier frequency are different.

15. The method of claim 11, wherein the request for the wireless communication device to transmit the second signal is transmitted by an uplink (UL) grant that assigns specific uplink resources to the wireless communication device to transmit the second signal on a Physical Uplink Shared Channel (PUSCH).

16. The method of claim 11, further comprising: transmitting, at the wireless communication node, at least one precoded Channel StateInformation-Reference Signal (CSLRS) to the wireless communication device, wherein the atleast one precoded CSI-RS is transmitted based on a first plurality of precoded beams, wherein the first plurality of precoded beams is predicted from the second signal, receiving, at the wireless communication node, a second plurality of precoded beams reported from the wireless communication device, wherein the second plurality of precoded beams is selected from the first plurality of precoded beams, and wherein the second plurality of precoded beams is used to transmit a fourth signal from the wireless communication node to the wireless communication device.

17. A wireless communication node comprising: a transceiver configured to: transmit a first signal to a wireless communication device, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; receive the second signal from the wireless communication device, wherein the second signal is used to determine a first precoder beam; and transmit a third signal to the wireless communication device based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

18. A non-transitory computer readable medium storing computer-executable instructions which when executed perform a method comprising: transmitting, at a wireless communication node, a first signal to a wireless communication device, wherein the first signal comprises a request for the wirelesscommunication device to transmit a second signal to the wireless communication node; receiving, at the wireless communication node, the second signal from the wireless communication device, wherein the second signal is used to determine a first precoder beam; and transmitting, at the wireless communication node, a third signal to the wireless communication device based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

19. Circuitry configured to perform a method, the method comprising: transmitting, at a wireless communication node, a first signal to a wireless communication device, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; receiving, at the wireless communication node, the second signal from the wireless communication device, wherein the second signal is used to determine a first precoder beam; and transmitting, at the wireless communication node, a third signal to the wireless communication device based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

20. A communication system comprising a wireless communication device and a wireless communication node, wherein:the wireless communication node comprises a first transceiver configured to transmit a first signal to a wireless communication device, wherein the first signal comprises a request for the wireless communication device to transmit a second signal to the wireless communication node; the wireless communication device comprises a second transceiver configured to transmit the second signal to the wireless communication node, wherein the second signal is used to determine a first precoder beam; and the first transceiver in the wireless communication node is further configured to transmit a third signal to the wireless communication device based on the first precoder beam for communication between the wireless communication node and the wireless communication device, wherein the communication is based on a Frequency Division Duplexing (FDD) mode, wherein the third signal is transmitted on downlink (DL) resources at a DL carrier frequency.

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