reporting weight updates to a neural network to generate channel state information feedback

Abstract

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device can receive a request to report an update to one or more weights of a neural network configured to encode a channel state information feedback message. The first device can transmit a report indicating the update to the one or more weights. Numerous other aspects are provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to Greek Patent Application No. 20200100485, filed on August 18, 2020, entitled “REPORTING WEIGHT UPDATES TO ANEURAL NETWORK FOR GENERATING CHANNEL STATE INFORMATION FEEDBACK” and assigned to the assignee of the present application. The disclosure of that prior application is considered a part of and incorporated by reference into the present patent application.

[0003] public domain

[0004] Aspects of the present disclosure generally relate to wireless communications and techniques and apparatus for reporting weight updates to a neural network. Background Art

[0005] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0006] A wireless network may include several base stations (BSs) capable of supporting communications for several user equipment (UEs). The UEs may communicate with the BSs via downlinks and uplinks. A "downlink" (or "forward link") refers to the communication link from the BS to the UE, while an "uplink" (or "reverse link") refers to the communication link from the UE to the BS. As will be described in greater detail herein, a BS may be referred to as a Node B, gNB, access point (AP), radio head, transmit / receive point (TRP), new radio (NR) BS, 5G Node B, and so on.

[0007] The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different user equipment to communicate at a city, country, region, and even global level. NR (which may also be referred to as 5G) is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to better support mobile broadband Internet access by improving spectral efficiency, reducing costs, improving services, utilizing new spectrum, and better integrating with orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink (DL), CP-OFDM and / or SC-FDM (e.g., also known as discrete Fourier transform spread OFDM (DFT-s-OFDM) on the uplink (UL), and other open standards that support beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation. As the demand for mobile broadband access continues to grow, further improvements to LTE, NR, and other radio access technologies remain valuable.

[0008] Overview

[0009] In some aspects, a wireless communication method performed by a first device includes receiving a request to report an update to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message. The method may also include transmitting a report indicating the update to the one or more weights.

[0010] In some aspects, a wireless communication method performed by a second device includes transmitting a request to a first device to report an update to one or more weights of a neural network configured to encode a CSF message. The method may also include receiving a report indicating the update to the one or more weights.

[0011] In some aspects, a first device for wireless communication includes: a memory; and one or more processors coupled to the memory. The memory and the one or more processors are configured to receive a request to report an update to one or more weights of a neural network configured to encode a CSF message. The memory and the one or more processors are further configured to transmit a report indicating the update to the one or more weights.

[0012] In some aspects, a second device for wireless communication includes: a memory; and one or more processors coupled to the memory. The memory and the one or more processors are configured to transmit a request to a first device for reporting an update to one or more weights of a neural network configured to encode a CSF message. The memory and the one or more processors are further configured to receive a report indicating the update to the one or more weights.

[0013] In some aspects, a non-transitory computer-readable medium storing an instruction set for wireless communication includes one or more instructions that, when executed by one or more processors of a first device, cause the first device to: receive a request to report an update to one or more weights of a neural network configured to encode a CSF message. The one or more instructions further cause the first device to transmit a report indicating the update to the one or more weights.

[0014] In some aspects, a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a second device, cause the second device to: transmit a request to a first device for a report of an update to one or more weights of a neural network configured to encode a CSF message. The one or more instructions further cause the first device to receive a report indicating the update to the one or more weights.

[0015] In some aspects, an apparatus for wireless communication includes receiving a request to report an update to one or more weights of a neural network configured to encode a CSF message. The apparatus further includes transmitting a report indicating the update to the one or more weights.

[0016] In some aspects, an apparatus for wireless communication includes: transmitting to a first device a request for a report of an update to one or more weights of a neural network configured to encode a CSF message. The apparatus further includes receiving a report indicating the update to the one or more weights.

[0017] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, base stations, wireless communication devices, and / or processing systems substantially as described herein with reference to and as illustrated in the accompanying figures and description.

[0018] The foregoing has broadly outlined the features and technical advantages of examples according to the present disclosure in an effort to make the following detailed description better understood. Additional features and advantages will be described hereinafter. The concepts and specific examples disclosed can be readily used as a basis for modifying or designing other structures for implementing the same purposes as the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. The characteristics of the concepts disclosed herein, both in terms of their organization and method of operation, as well as the associated advantages, will be better understood by considering the following description in conjunction with the accompanying drawings. Each of the figures is provided for illustration and description purposes and is not intended to define limitations on the claims.

[0019] Although various aspects are described in the present disclosure by explaining some examples, it will be understood by those skilled in the art that such aspects can be implemented in many different arrangements and scenarios. The technology described herein can be implemented using different platform types, devices, systems, shapes, sizes and / or packaging arrangements. For example, some aspects can be implemented via integrated chip embodiments or other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / shopping equipment, medical equipment, or devices that enable artificial intelligence). Various aspects can be implemented in chip-level components, module components, non-module components, non-chip-level components, device-level components, or system-level components. The equipment incorporated into the various aspects and features described may include additional components and features for implementing and practicing the various aspects claimed and described. For example, the transmission and reception of wireless signals may include several components (e.g., hardware components, including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, (all) processors, interleavers, adders, or summers) for analog and digital purposes. The various aspects described herein are intended to be practiced in various devices, components, systems, distributed arrangements, or end-user devices of various sizes, shapes, and configurations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to understand in detail the features of the present disclosure set forth above, a more particular description of the content briefly summarized above may be obtained with reference to various aspects, some of which are illustrated in the accompanying drawings. It should be noted, however, that the accompanying drawings illustrate only certain typical aspects of the present disclosure and are not to be considered limiting of its scope, as the description may admit to other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0022] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0023] Figure 2 is a diagram illustrating an example of a base station and a user equipment (UE) in communication in a wireless network according to the present disclosure.

[0024] Figure 3 is a diagram illustrating an example of an encoding apparatus and a decoding apparatus using previously stored channel state information according to the present disclosure.

[0025] Figure 4 is a diagram illustrating an example associated with an encoding device and a decoding device according to the present disclosure.

[0026] Figure 5-8 is a diagram illustrating an example associated with using a neural network to encode and decode a data set for uplink communication according to the present disclosure.

[0027] Figure 9 and10 is a diagram illustrating an example process associated with using a neural network to encode a data set for uplink communication in accordance with the present disclosure.

[0028] Figure 11 is a diagram illustrating an example associated with reporting weight updates to a neural network for generating channel state information feedback according to the present disclosure.

[0029] Figure 12 and 13 is a diagram illustrating an example process associated with reporting weight updates to a neural network for generating channel state information feedback in accordance with the present disclosure.

[0030] Figure 14 and 15 is an example of an apparatus for wireless communication according to the present disclosure.

[0031] Figure 16 and 17 is a diagram illustrating an example of a hardware implementation for a device employing a processing system.

[0032] Figure 18 and 19 is a diagram illustrating an example of an implementation of code and circuitry for a device.

[0033] Detailed description

[0034] A coding device operating in a network may measure reference signals, etc., to report to a network entity. For example, the coding device may measure reference signals during a beam management process to implement channel state feedback (CSF), may measure the received power of reference signals from a serving cell and / or neighboring cells, may measure signal strength across an inter-radio access technology (e.g., WiFi) network, may measure sensor signals used to detect the location of one or more objects within an environment, etc. However, reporting this information to a base station may consume communication and / or network resources.

[0035] In some aspects described herein, a coding device (e.g., a UE, a base station, a transmit reception point (TRP), a network device, a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a highly elliptical orbit (HEO) satellite, etc.) may train one or more neural networks to learn the dependencies of measured qualities on individual parameters, isolate these measured qualities through various layers (also referred to as "operations") of the one or more neural networks, and compress the measurements in a manner that limits compression losses. In some aspects, the coding device may use the properties of the number of bits being compressed to construct a process for extracting and compressing each feature (also referred to as a dimension) that affects the number of bits. In some aspects, the number of bits may be associated with samples of one or more reference signals and / or may indicate channel state information. For example, the coding device may encode the measurements using one or more extraction operations and compression operations associated with the neural network to produce compressed measurements, where the one or more extraction operations and compression operations are based at least in part on a set of features of the measurements.

[0036] The encoding device may transmit the compressed measurements to a network entity such as a server, a TRP, another UE, a base station, etc. Although the examples described herein cite a base station as a decoding device, the decoding device may be any network entity. The network entity may be referred to as a "decoding device."

[0037] The decoding device may decode the compressed measurements using one or more decompression operations and reconstruction operations associated with the neural network. The one or more decompression and reconstruction operations may be based at least in part on a feature set of the compressed data set to produce a reconstructed measurement. The decoding device may use the reconstructed measurement as channel state information feedback.

[0038] Compressing measurements using CSFs encoded using a neural network can save network resources. However, as the channel and / or environment changes, the weights of the neural network should also change. For example, if the Doppler parameters change (e.g., the encoding device is carried by a vehicle), the Doppler-related layers may need to change. If a pedestrian holding the encoding device turns a corner, the non-Doppler-related weights may need to change. If the encoding device switches from a first decoding device (e.g., a base station) with 128 ports to a second decoding device with 32 or fewer ports, the non-Doppler-related weights of the layers that take into account decoder-side information may need to change. However, if the encoding device changes the weights of the neural network, the decoding device may not be able to decode the CSF, which may consume network resources to detect and correct.

[0039] In some aspects described herein, an encoding device may receive a request to report updates to one or more weights of a neural network configured for encoding a CSF. In some aspects, a decoding device (e.g., a base station) may transmit a request for updates by the encoding device and may identify one or more layers (e.g., with one or more layer identifiers) for which the encoding device will report weights. In some aspects, the request may indicate a subset of weights within the one or more layers for which the encoding device will report weights.

[0040] Based at least in part on the decoding device requesting and receiving a report indicating updates to the weights of the neural network, the decoding device can decode the CSF based at least in part on the updates to the weights. In this manner, computational, communication, and / or network resources that could otherwise be used to detect and recover from errors based at least in part on the decoding device's failure to decode the CSF can be conserved.

[0041] The various aspects of the present disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be implemented in many different forms and should not be interpreted as being limited to any specific structure or function given throughout the present disclosure. On the contrary, these aspects are provided to make the present disclosure thorough and complete, and they will fully convey the scope of the present disclosure to those skilled in the art. Based on the teachings of this article, those skilled in the art will appreciate that the scope of the present disclosure is intended to cover any aspect of the present disclosure disclosed herein, whether it is implemented independently of any other aspect of the present disclosure or implemented in combination. For example, any number of aspects set forth herein can be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such devices or methods that are practiced using a supplement to the various aspects of the present disclosure set forth herein or other other structures, functionality, or structure and functionality. It should be understood that any aspect of the present disclosure disclosed herein can be implemented by one or more elements of the claims.

[0042] Several aspects of telecommunications systems will now be presented with reference to various devices and techniques. These devices and techniques are described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements can be implemented using hardware, software, or a combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0043] It should be noted that while various aspects may be described herein using terminology generally associated with 5G or New Radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applied to other RATs, such as 3G RAT, 4G RAT, and / or RATs beyond 5G (e.g., 6G).

[0044] Figure 11 is a diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be a 5G (NR) network and / or an LTE network, etc., or may include elements thereof. The wireless network 100 may include several base stations 110 (shown as BS 110a, BS 110b, BS 110c, and BS 110d) and other network entities. A base station (BS) is an entity that communicates with user equipment (UE) and may also be referred to as an NR BS, B node, gNB, 5G B node (NB), access point, transmit reception point (TRP), etc. Each BS may provide communication coverage for a specific geographic area. In 3GPP, the term "cell" may refer to the coverage area of ​​a BS and / or a BS subsystem serving that coverage area, depending on the context in which the term is used.

[0045] A BS may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by UEs with service subscriptions. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs with service subscriptions. A femto cell may cover a relatively small geographic area (e.g., a residence) and may allow restricted access by UEs associated with the femto cell (e.g., UEs in a closed subscriber group (CSG)). A BS for a macro cell may be referred to as a macro BS. A BS for a pico cell may be referred to as a pico BS. A BS for a femto cell may be referred to as a femto BS or a home BS. In Figure 1 In the example shown in FIG, BS 110a may be a macro BS for macro cell 102a, BS 110b may be a pico BS for pico cell 102b, and BS 110c may be a femto BS for femto cell 102c. A BS may support one or more (e.g., three) cells. The terms "eNB," "base station," "NR BS," "gNB," "TRP," "AP," "Node B," "5G NB," and "cell" may be used interchangeably herein.

[0046] In some aspects, the cells may not necessarily be stationary, and the geographic area of ​​the cells may move depending on the location of the mobile BS. In some aspects, the BSs may be interconnected to each other and / or to one or more other BSs or network nodes (not shown) in the wireless network 100 via various types of backhaul interfaces, such as direct physical connections or virtual networks, using any suitable transport network.

[0047] The wireless network 100 may also include a relay station. A relay station is an entity that can receive transmissions of data from an upstream station (e.g., a BS or a UE) and send transmissions of the data to a downstream station (e.g., a UE or a BS). A relay station may also be a UE that can relay transmissions for other UEs. Figure 1 In the example shown in , relay BS 110d may communicate with macro BS 110a and UE 120d to facilitate communication between BS 110a and UE 120d. A relay BS may also be referred to as a relay station, relay base station, relay, etc.

[0048] The wireless network 100 may be a heterogeneous network including different types of BSs, such as macro BSs, pico BSs, femto BSs, relay BSs, etc. These different types of BSs may have different transmit power levels, different coverage areas, and different impacts on interference in the wireless network 100. For example, a macro BS may have a high transmit power level (e.g., 5 to 40 watts), while a pico BS, a femto BS, and a relay BS may have a lower transmit power level (e.g., 0.1 to 2 watts).

[0049] The network controller 130 may be coupled to the set of BSs and may provide coordination and control of these BSs. The network controller 130 may communicate with each BS via a backhaul. These BSs may also communicate with each other directly or indirectly via a wireless or wired backhaul.

[0050] UEs 120 (e.g., 120a, 120b, 120c) may be dispersed throughout the wireless network 100, and each UE may be stationary or mobile. A UE may also be referred to as an access terminal, terminal, mobile station, subscriber unit, station, etc. A UE may be a cellular phone (e.g., a smartphone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, a biometric sensor / device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring, a smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicle component or sensor, a smart meter / sensor, industrial manufacturing equipment, a global positioning system device, or any other suitable device configured to communicate via a wireless or wired medium.

[0051] Some UEs may be considered machine type communication (MTC) devices, or evolved or enhanced machine type communication (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags that can communicate with a base station, another device (e.g., a remote device), or some other entity. A wireless node may provide connectivity to or to a network (e.g., a wide area network such as the Internet or a cellular network), for example, via a wired or wireless communication link. Some UEs may be considered Internet of Things (IoT) devices and / or may be implemented as NB-IoT (Narrowband Internet of Things) devices. Some UEs may be considered customer premises equipment (CPE). UE 120 may be included within a housing that houses components of UE 120, such as a processor component and / or a memory component. In some aspects, the processor component and the memory component may be coupled together. For example, the processor component (e.g., one or more processors) and the memory component (e.g., memory) may be operatively coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0052] In general, any number of wireless networks may be deployed in a given geographic area. Each wireless network may support a specific RAT and may operate on one or more frequencies. A RAT may also be referred to as a radio technology, air interface, etc. A frequency may also be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0053] In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly (e.g., without using base station 110 as an intermediary) using one or more sidelink channels. For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols or vehicle-to-infrastructure (V2I) protocols), and / or mesh networks. In this scenario, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by base station 110.

[0054] The devices of the wireless network 100 can communicate using an electromagnetic spectrum that can be subdivided into various categories, frequency bands, channels, etc. based on frequency or wavelength. For example, the devices of the wireless network 100 can communicate using an operating band having a first frequency range (FR1) that can span 410 MHz to 7.125 GHz and / or can communicate using an operating band having a second frequency range (FR2), the first frequency range (FR1) can span 410 MHz to 7.125 GHz, and the second frequency range (FR2) can span 24.25 GHz to 52.6 GHz. Frequencies between FR1 and FR2 are sometimes referred to as mid-band frequencies. Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to as a "sub-6 GHz" band. Similarly, although different from the extremely high frequency (EHF) band (30 GHz–300 GHz) identified as a "millimeter wave" band by the International Telecommunication Union (ITU), FR2 is often referred to as a "millimeter wave" band. Therefore, unless otherwise specifically stated, it should be understood that the terms "sub-6 GHz," etc., if used herein, may broadly refer to frequencies less than 6 GHz, frequencies within FR1, and / or mid-band frequencies (e.g., greater than 7.125 GHz). Similarly, unless otherwise specifically stated, it should be understood that the terms "millimeter wave," etc., if used herein, may broadly refer to frequencies within the EHF band, frequencies within FR2, and / or mid-band frequencies (e.g., less than 24.25 GHz). It is contemplated that the frequencies included in FR1 and FR2 may be modified, and that the techniques described herein are applicable to those modified frequency ranges.

[0055] like Figure 1 As shown in FIG, UE 120 may include a communication manager 140. As described in detail elsewhere herein, communication manager 140 may receive a request to report an update to one or more weights of a neural network configured to encode a CSF message. Communication manager 140 may also transmit a report indicating the update to the one or more weights. Additionally or alternatively, communication manager 140 may perform one or more other operations described herein.

[0056] In some aspects, base station 110 may include a communication manager 150. As described in greater detail elsewhere herein, communication manager 150 may transmit a request to the first device to report an update to one or more weights of a neural network configured to encode a CSF message. Communication manager 150 may also receive a report indicating the update to the one or more weights. Additionally or alternatively, communication manager 150 may perform one or more other operations described herein.

[0057] As indicated above, Figure 1 are provided as examples. Other examples may differ from those described in Figure 1 Examples described.

[0058] Figure 2 is a diagram illustrating an example 200 of a base station 110 and a UE 120 in communication in a wireless network 100 according to the present disclosure. The base station 110 may be equipped with T antennas 234a through 234t, and the UE 120 may be equipped with R antennas 252a through 252r, where in general T≥1 and R≥1.

[0059] At base station 110, transmit processor 220 may receive data for one or more UEs from data source 212, select one or more modulation and coding schemes (MCS) for each UE based at least in part on a channel quality indicator (CQI) received from the UE, process (e.g., encode and modulate) the data for each UE based at least in part on the MCS selected for the UE, and provide data symbols for all UEs. Transmit processor 220 may also process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling) and provide overhead symbols and control symbols. Transmit processor 220 may also generate reference symbols for reference signals (e.g., cell-specific reference signal (CRS) or demodulation reference signal (DMRS)) and synchronization signals (e.g., primary synchronization signal (PSS) or secondary synchronization signal (SSS)). A transmit (TX) multiple-input, multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, as applicable, and may provide T output symbol streams to T modulators (MODs) 232a through 232t. Each modulator 232 may process a respective output symbol stream (e.g., for OFDM) to obtain an output sample stream. Each modulator 232 may further process (e.g., convert to analog, amplify, filter, and frequency upconvert) the output sample stream to obtain a downlink signal. The T downlink signals from modulators 232a through 232t may be transmitted via T antennas 234a through 234t, respectively.

[0060] At UE 120, antennas 252a through 252r may receive downlink signals from base station 110 and / or other base stations and may provide received signals to demodulators (DEMODs) 254a through 254r, respectively. Each demodulator 254 may condition (e.g., filter, amplify, downconvert, and digitize) the received signal to obtain input samples. Each demodulator 254 may further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from all R demodulators 254a through 254r, perform MIMO detection on the received symbols where applicable, and provide detected symbols. A receive processor 258 may process (e.g., demodulate and decode) the detected symbols, provide decoded data for UE 120 to a data sink 260, and provide decoded control information and system information to a controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, etc. In some aspects, one or more components of the UE 120 may be included in the housing 284 .

[0061] The network controller 130 may include a communication unit 294, a controller / processor 290, and a memory 292. The network controller 130 may include, for example, one or more devices in a core network. The network controller 130 may communicate with the base station 110 via the communication unit 294.

[0062] The antennas (e.g., antennas 234a through 234t and / or antennas 252a through 252r) may include, or may be included within, one or more antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays, etc. The antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements. The antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include coplanar sets of antenna elements and / or non-coplanar sets of antenna elements. The antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include antenna elements within a single housing and / or antenna elements within multiple housings. The antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include antenna elements coupled to one or more transmit and / or receive components, such as Figure 2 One or more antenna elements of one or more components).

[0063] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI) from a controller / processor 280. The transmit processor 264 may also generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by a TX MIMO processor 266, if applicable, further processed by modulators 254a through 254r (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to the base station 110. In some aspects, the modulator and demodulator (e.g., MOD / DEMOD 254) of the UE 120 may be included in a modem of the UE 120. In some aspects, the UE 120 comprises a transceiver. The transceiver may include any combination of antenna(s) 252, modulator and / or demodulator 254, MIMO detector 256, receive processor 258, transmit processor 264, and / or TX MIMO processor 266. The transceiver may be used by a processor (e.g., controller / processor 280) and memory 282 to perform aspects of any method described herein (e.g., as described with reference to FIG. Figure 3-19 described).

[0064] At base station 110, uplink signals from UE 120 and other UEs may be received by antenna 234, processed by demodulator 232, detected by MIMO detector 236 where applicable, and further processed by receive processor 238 to obtain decoded data and control information sent by UE 120. Receive processor 238 may provide the decoded data to data sink 239 and the decoded control information to controller / processor 240. Base station 110 may include a communication unit 244 and communicate with network controller 130 via communication unit 244. Base station 110 may include a scheduler 246 to schedule UE 120 for downlink and / or uplink communications. In some aspects, the modulator and demodulator (e.g., MOD / DEMOD 232) of base station 110 may be included in a modem of base station 110. In some aspects, base station 110 includes a transceiver. The transceiver may include any combination of antenna(s) 234, modulator and / or demodulator 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform aspects of any method described herein (e.g., as described with reference to Figure 3-19 described).

[0065] The controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component(s) of the UE 120 may perform one or more techniques associated with reporting weight updates to the neural network to generate channel state information feedback (CSF), as described in more detail elsewhere herein. For example, the controller / processor 240 of the base station 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component of the may perform or direct e.g. Figure 8 The process of 800 Figure 9 The process of 900 Figure 12 The process of 1200 Figure 13 1300, and / or operations of other processes as described herein. Memories 242 and 282 may store data and program codes for base station 110 and UE 120, respectively. In some aspects, memory 242 and / or memory 282 may include: a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed (e.g., directly or after compilation, conversion, and / or interpretation) by one or more processors of base station 110 and / or UE 120, may cause the one or more processors, UE 120, and / or base station 110 to perform or direct, for example Figure 8 The process of 800 Figure 9 The process of 900 Figure 12 The process of 1200 Figure 13 In some aspects, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, among other things.

[0066] In some aspects, an encoding device (e.g., UE 120) may include: means for receiving a request to report an update to one or more weights of a neural network configured for encoding a CSF message; means for transmitting a report indicating the update to the one or more weights; etc. Additionally or alternatively, UE 120 may include means for performing one or more other operations described herein. In some aspects, such means may include the communication manager 140. Additionally or alternatively, such means may include a communication manager 140 in conjunction with Figure 2 One or more other components of the UE 120 are depicted, such as a controller / processor 280, a transmit processor 264, a TX MIMO processor 266, a MOD 254, antennas 252, a DEMOD 254, a MIMO detector 256, a receive processor 258, and the like.

[0067] In some aspects, a decoding device (e.g., UE 120, base station 110, etc.) may include: means for transmitting a request to a first device for reporting an update to one or more weights of a neural network configured to encode a CSF message; and means for receiving a report indicating an update to the one or more weights, etc. Additionally or alternatively, base station 110 may include means for performing one or more other operations described herein. In some aspects, such means may include a communications manager 150. In some aspects, such means may include in conjunction with Figure 2 One or more other components of base station 110 are depicted, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller / processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and so forth.

[0068] although Figure 2 The blocks in FIG. 2 are illustrated as distinct components, but the functionality described above with respect to these blocks may be implemented using a single hardware, software, or combined component or a combination of various components. For example, the functionality described with respect to the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0069] As indicated above, Figure 2 are provided as examples. Other examples may differ from those described in Figure 2 Examples described.

[0070] Figure 3 Examples of encoding and decoding devices 300 and 350 using previously stored channel state information (CSI) according to various aspects of the present disclosure are illustrated. Figure 3 An encoding device 300 (eg, UE 120 ) is shown having a CSI instance encoder 310 , a CSI sequence encoder 320 , and a memory 330 . Figure 3 Also shown is a decoding device 350 (eg, BS 110 ) having a CSI sequence decoder 360 , a memory 370 , and a CSI instance decoder 380 .

[0071] In some aspects, the encoding device 300 and the decoding device 350 may utilize the correlation of CSI instances over time (temporal aspect) or a sequence of CSI instances to perform a series of channel estimates. The encoding device 300 and the decoding device 350 may save and use previously stored CSI and only encode and decode CSI changes from the previous instance. This may provide less CSI feedback overhead and improve performance. The encoding device 300 may also be able to encode more accurate CSI, and the neural network may be trained with more accurate CSI.

[0072] like Figure 3 As shown in , the CSI instance encoder 310 may encode the CSI instance into intermediate coded CSI for each DL channel estimate in the DL channel estimate sequence. The CSI instance encoder 310 (e.g., a feed-forward network) may use neural network encoder weights θ. The intermediate coded CSI may be represented as The CSI sequence encoder 320 (e.g., a long short-term memory (LSTM) network) may determine a previously encoded CSI instance h(t-1) from the memory 330 and compare the intermediate encoded CSI m(t) with the previously encoded CSI instance h(t-1) to determine a change n(t) in the encoded CSI. The change n(t) may be a portion of the channel estimate that is new and may not have been predicted by the decoding device 350. The encoded CSI at this point may be represented by The CSI sequence encoder 320 may provide the change n(t) on a physical uplink shared channel (PUSCH) or a physical uplink control channel (PUCCH), and the encoding device 300 may transmit the change (e.g., information indicating the change) n(t) as encoded CSI to the decoding device 350 on a UL channel. Because the change is smaller than the entire CSI instance, the encoding device 300 may send a smaller payload for the encoded CSI on the UL channel while including more detailed information about the change in the encoded CSI. The CSI sequence encoder 320 may generate the encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t-1). The CSI sequence encoder 320 may store the encoded CSI h(t) in the memory 330.

[0073] The CSI sequence decoder 360 may receive the encoded CSI on the PUSCH or PUCCH. The CSI sequence decoder 360 may determine that only the change n(t) of the CSI is received as the encoded CSI. The CSI sequence decoder 360 may determine the intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of the previous intermediate decoded CSI instance h(t-1) from the memory 370 and the change. The CSI instance decoder 380 may decode the intermediate decoded CSI m(t) into decoded CSI. The CSI sequence decoder 360 and the CSI instance decoder 380 may use the neural network decoder weights φ. The intermediate decoded CSI may be determined by The CSI sequence decoder 360 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t-1). The decoding device 350 may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as The CSI sequence decoder 360 may store the decoded CSI h(t) in the memory 370 .

[0074] Because the change n(t) is smaller than the entire CSI instance, the encoding device 300 can send a smaller payload on the UL channel. For example, if the DL channel has barely changed from the previous feedback due to low Doppler or minimal movement of the encoding device 300, the output of the CSI sequence encoder can be quite compact. In this way, the encoding device 300 can exploit the correlation of the channel estimate over time. In some aspects, because the output is smaller, the encoding device 300 can include more detailed information about the change in the encoded CSI. In some aspects, the encoding device 300 can transmit an indication (e.g., a flag) to the decoding device 350 that the encoded CSI was encoded in time (CSI change). Alternatively, the encoding device 300 can transmit an indication that the encoded CSI was encoded independently of any previously encoded CSI feedback. The decoding device 350 can decode the encoded CSI without using the previously decoded CSI instance. In some aspects, a device (which may include the encoding device 300 or the decoding device 350) can use the CSI sequence encoder and the CSI sequence decoder to train a neural network model.

[0075] In some aspects, CSI may be a function of a channel estimate (referred to as a channel response) H and interference N. There may be a variety of ways to convey H and N. For example, the encoding device 300 may encode the CSI as N -1 / 2 H. The encoding device 300 may encode H and N separately. The encoding device 300 may partially encode H and N separately and then jointly encode the two partially encoded outputs. It may be advantageous to encode H and N separately. Interference and channel variations may occur on different time scales. In low Doppler scenarios, the channel may be stable, but interference may still change rapidly due to traffic or scheduler algorithms. In high Doppler scenarios, the channel may change faster than the UE's scheduler grouping. In some aspects, a device (which may include the encoding device 300 or the decoding device 350) may use the separately encoded H and N to train a neural network model.

[0076] In some aspects, the reconstructed DL channel The DL channel H may be reflected truthfully, and this may be referred to as explicit feedback. In some aspects, Only the information required for the decoding device 350 to derive rank and precoding can be captured. CQI can be fed back separately. In the time-coded scenario, CSI feedback can be expressed as m(t) or n(t). Similar to Type II CSI feedback, m(t) can be structured as a concatenation of a rank index (RI), a beam index, and coefficients representing amplitude or phase. In some aspects, m(t) can be a quantized version of a real-valued vector. The beam can be predefined (not obtained through training) or can be part of the training (e.g., part of θ and φ and communicated to the encoding device 300 or decoding device 350).

[0077] In some aspects, the decoding device 350 and the encoding device 300 may maintain multiple encoder and decoder networks, each targeting a different payload size (to achieve a different accuracy-to-UL overhead tradeoff). For each CSI feedback, depending on the reconstruction quality and the uplink budget (e.g., PUSCH payload size), the encoding device 300 may select, or the decoding device 350 may instruct the encoding device 300 to select, one of the encoders to construct the encoded CSI. The encoding device 300 may send an encoder index along with the CSI based at least in part on the encoder selected by the encoding device 300. Similarly, the decoding device 350 and the encoding device 300 may maintain multiple encoder and decoder networks to account for different antenna geometries and channel conditions. Note that although some operations are described with respect to the decoding device 350 and the encoding device 300, these operations may also be performed by another device as part of preconfiguration of the encoder and decoder weights and / or structure.

[0078] As indicated above, Figure 3 Other examples may differ from those provided for Figure 3 Examples described.

[0079] Figure 4 4 is a diagram illustrating an example 400 associated with an encoding device and a decoding device according to various aspects of the present disclosure. An encoding device (e.g., UE 120, encoding device 300, etc.) can be configured to perform one or more operations on data to compress the data. A decoding device (e.g., base station 110, decoding device 350, etc.) can be configured to decode the compressed data to determine information.

[0080] As used herein, a "layer" of a neural network is used to represent an operation on input data. For example, a convolutional layer, a fully connected layer, etc., represent associated operations on the data input to the layer. A convolution AxB operation is an operation that converts several input features A into several output features B. "Kernel size" refers to the number of adjacent coefficients that are combined in a dimension.

[0081] As used herein, "weights" are used to refer to one or more coefficients used in operations in various layers to combine rows and / or columns of input data. For example, a fully connected layer operation may have an output y that is determined at least in part based on the product of the input matrix x and the weights A (which may be a matrix) and the sum of the bias values ​​B (which may be a matrix). The term "weights" may be used herein to generally refer to both weights and bias values.

[0082] As shown in example 400, the encoding device may perform a convolution operation on the samples. For example, the encoding device may receive a set of bits structured as a 2x64x32 data set, which indicates IQ samples for tap features (e.g., associated with multipath timing offset) and spatial features (e.g., associated with different antennas of the encoding device). The convolution operation may be a 2x2 operation with kernel sizes of 3 and 3 on the data structure. The output of the convolution operation may be input to a batch normalization (BN) layer, followed by a LeakyReLU activation, thereby giving an output data set with size 2x64x32. The encoding device may perform a flattening operation to flatten the bits into a 4096-bit vector. The encoding device may apply a fully connected operation with size 4096xM to the 4096-bit vector to output an M-bit payload. The encoding device may transmit the M-bit payload to the decoding device.

[0083] The decoding device may apply a fully connected operation of size Mx4096 to the M-bit payload to output a 4096-bit vector. The decoding device may reshape the 4096-bit vector to have a size of 2x64x32. The decoding device may apply one or more RefineNet operations to the reshaped bit vector. For example, the RefineNet operation may include: applying a 2x8 convolution operation (e.g., with kernel sizes of 3 and 3), whose output is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 8x64x32; applying an 8x16 convolution operation (e.g., with kernel sizes of 3 and 3), whose output is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 16x64x32; and / or applying a 16x2 convolution operation (e.g., with kernel size of 3x3), whose output is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 2x64x32. The decoding device may also apply 2x2 convolution operations with kernel sizes of 3 and 3 to generate decoded and / or reconstructed output.

[0084] As indicated above, Figure 4 This is provided as an example only. Other examples may differ from those described in relation to Figure 4 Examples described.

[0085] As described herein, an encoding device operating in a network may measure reference signals, etc., to report to a decoding device. For example, a UE may measure reference signals during a beam management process to report a CSF, may measure the received power of reference signals from a serving cell and / or neighboring cells, may measure signal strength across an inter-radio access technology (e.g., WiFi) network, may measure sensor signals for detecting the location of one or more objects within an environment, etc. However, reporting such information to a network entity may consume communication and / or network resources.

[0086] In some aspects described herein, a coding device (e.g., a UE) may train one or more neural networks to learn the dependencies of measured qualities on individual parameters, isolate these measured qualities through various layers (also referred to as "operations") of the one or more neural networks, and compress the measurements in a manner that limits compression losses.

[0087] In some aspects, the encoding device may use the properties of the number of bits being compressed to construct a process for extracting and compressing each feature (also referred to as a dimension) that affects the number of bits. In some aspects, the number of bits may be associated with samples of one or more reference signals and / or may indicate channel state information.

[0088] Based at least in part on using a neural network to encode and decode a data set for uplink communication, the encoding device can transmit the CSF with a reduced payload. This can save network resources that might otherwise have been used to transmit the full data set as sampled by the encoding device.

[0089] Figure 5 is a diagram illustrating an example 500 associated with using a neural network to encode and decode a data set for uplink communication according to various aspects of the present disclosure. An encoding device (e.g., UE 120, encoding device 300, etc.) can be configured to perform one or more operations on samples (e.g., data) received via one or more antennas of the encoding device to compress the samples. A decoding device (e.g., base station 110, decoding device 350, etc.) can be configured to decode the compressed samples to determine information, such as a CSF.

[0090] In some aspects, the encoding device may identify features to be compressed. In some aspects, the encoding device may perform a first type of operation in a first dimension associated with the features to be compressed. The encoding device may perform a second type of operation in other dimensions (e.g., in all other dimensions). For example, the encoding device may perform a fully connected operation in the first dimension and perform convolution (e.g., point-by-point convolution) in all other dimensions.

[0091] In some aspects, reference numerals identify operations that include multiple neural network layers and / or operations. The neural networks of the encoding device and the decoding device can be formed by a cascade of one or more of the recited operations.

[0092] As indicated by reference numeral 505, the encoding device may perform spatial feature extraction on the data. As indicated by reference numeral 510, the encoding device may perform tap domain feature extraction on the data. In some aspects, the encoding device may perform tap domain feature extraction before performing spatial feature extraction. In some aspects, the extraction operation may include multiple operations. For example, the multiple operations may include one or more convolution operations that may or may not be activated, one or more fully connected operations, etc. In some aspects, the extraction operation may include a residual neural network (ResNet) operation.

[0093] As shown by reference numeral 515, the encoding device may compress the one or more features that have been extracted. In some aspects, the compression operation may include one or more operations, such as one or more convolution operations, one or more fully connected operations, etc. After compression, the bit count of the output may be less than the bit count of the input.

[0094] The encoding device may perform a quantization operation, as indicated by reference numeral 520. In some aspects, the encoding device may perform the quantization operation after flattening the output of the compression operation and / or performing a fully connected operation after flattening the output.

[0095] As indicated by reference numeral 525, the decoding device may perform feature decompression. As indicated by reference numeral 530, the decoding device may perform tap-domain feature reconstruction. As indicated by reference numeral 535, the decoding device may perform spatial feature reconstruction. In some aspects, the decoding device may perform spatial feature reconstruction before performing tap-domain feature reconstruction. After the reconstruction operation, the decoding device may output a reconstructed version of the input of the encoding device.

[0096] In some aspects, the decoding device may perform operations in the reverse order of the operations performed by the encoding device. For example, if the encoding device follows operations (a, b, c, d), the decoding device may follow the reverse operations (D, C, B, A). In some aspects, the decoding device may perform operations that are completely symmetrical to the operations of the encoding device. This may reduce the number of bits required for the neural network configuration at the UE. In some aspects, the decoding device may perform additional operations in addition to the operations of the encoding device (e.g., convolution operations, fully connected operations, ResNet operations, etc.). In some aspects, the decoding device may perform operations that are asymmetrical to the operations of the encoding device.

[0097] Based at least in part on the coding device using a neural network to encode a data set for uplink communication, the coding device (e.g., a UE) can transmit the CSF with a reduced payload. This can save network resources that might otherwise have been used to transmit the full data set as sampled by the coding device.

[0098] As indicated above, Figure 5 This is provided as an example only. Other examples may differ from those described in relation to Figure 5 Examples described.

[0099] Figure 6 6 is a diagram illustrating an example 600 associated with using a neural network to encode and decode a data set for uplink communication according to various aspects of the present disclosure. An encoding device (e.g., UE 120, encoding device 300, etc.) can be configured to perform one or more operations on samples (e.g., data) received via one or more antennas of the encoding device to compress the samples. A decoding device (e.g., base station 110, decoding device 350, etc.) can be configured to decode the compressed samples to determine information, such as a CSF.

[0100] As shown by example 600, the encoding device may receive samples from the antennas. For example, the encoding device may receive a data set of size 64x64 based at least in part on the number of antennas, the number of samples per antenna, and the tap characteristics.

[0101] The encoding device may perform spatial feature extraction, short-term (tap) feature extraction, and the like. In some aspects, this may be achieved by using a 1-dimensional convolution operation that is fully connected in the spatial dimension (to extract spatial features) and a simple convolution with a small kernel size (e.g., 3) in the tap dimension (to extract short-tap features). The output from such a 64xW 1-dimensional convolution operation may be a Wx64 matrix.

[0102] The encoding device may perform one or more ResNet operations. The one or more ResNet operations may further refine the spatial features and / or temporal features. In some aspects, the ResNet operation may include multiple operations associated with the features. For example, the ResNet operation may include multiple (e.g., 3) 1-dimensional convolution operations, skip connections (e.g., between the input of the ResNet and the output of the ResNet to avoid applying a 1-dimensional convolution operation), a summation operation of a path through multiple 1-dimensional convolution operations and a path through a skip connection, etc. In some aspects, the plurality of 1-dimensional convolution operations may include: a Wx256 convolution operation with a kernel size of 3, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 256x64; a 256x512 convolution operation with a kernel size of 3, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 512x64; and a 512xW convolution operation with a kernel size of 3, the output of which has a BN dataset of size Wx64. The output from the one or more ResNet operations may be a Wx64 matrix.

[0103] The encoding device may perform a WxV convolution operation on the output from one or more ResNet operations. The WxV convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The WxV convolution operation may compress spatial features into a reduced dimension for each tap. The WxV convolution operation has an input of W features and an output of V features. The output from the WxV convolution operation may be a Vx64 matrix.

[0104] The encoding device may perform a flattening operation to flatten the Vx64 matrix into a 64V element vector. The encoding device may perform a 64VxM fully connected operation to further compress the spatial-temporal feature dataset into a low-dimensional vector of size M for transmission over the air to the decoding device. The encoding device may perform quantization before transmitting the low-dimensional vector of size M over the air to map the transmitted samples to discrete values ​​for the low-dimensional vector of size M.

[0105] The decoding device may perform an Mx64V fully connected operation to decompress a low-dimensional vector of size M into a space-time feature dataset. The decoding device may perform a reshape operation to reshape the 64V element vector into a 2-dimensional Vx64 matrix. The decoding device may perform a VxW (with a kernel of 1) convolution operation on the output from the reshape operation. The VxW convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The VxW convolution operation may decompress the spatial features from the reduced dimension for each tap. The VxW convolution operation has an input of V features and an output of W features. The output from the VxW convolution operation may be a Wx64 matrix.

[0106] The decoding device may perform one or more ResNet operations. The one or more ResNet operations may further decompress spatial features and / or temporal features. In some aspects, the ResNet operation may include multiple (e.g., 3) 1-dimensional convolution operations, skip connections (e.g., to avoid applying 1-dimensional convolution operations), summation operations of paths through multiple convolution operations and paths through skip connections, etc. The output from the one or more ResNet operations may be a Wx64 matrix.

[0107] The decoding device may perform spatial and temporal feature reconstruction. In some aspects, this may be achieved using a 1-dimensional convolution operation that is fully connected in the spatial dimension (to reconstruct spatial features) and a simple convolution with a small kernel size (e.g., 3) in the tap dimension (to reconstruct short tap features). The output from the 64xW convolution operation may be a 64x64 matrix.

[0108] In some aspects, the values ​​of M, W, and / or V may be configurable to adjust the weight of a feature, payload size, etc.

[0109] As indicated above, Figure 6 This is provided as an example only. Other examples may differ from those described in relation to Figure 6 Examples described.

[0110] Figure 7 is a diagram illustrating an example 700 associated with using a neural network to encode and decode a data set for uplink communication in accordance with various aspects of the present disclosure. An encoding device (e.g., UE 120, encoding device 300, etc.) may be configured to perform one or more operations on samples (e.g., data) received via one or more antennas of the encoding device to compress the samples. A decoding device (e.g., base station 110, decoding device 350, etc.) may be configured to decode the compressed samples to determine information, such as a CSF. As shown by example 700, features may be compressed and decompressed in sequence. For example, the encoding device may extract and compress features associated with the input to produce a payload, and then the decoding device may extract and compress features associated with the payload to reconstruct the input. The encoding and decoding operations may be symmetric (as shown) or asymmetric.

[0111] As shown by example 700, the encoding device may receive samples from the antennas. For example, the encoding device may receive a data set of size 256x64 based at least in part on the number of antennas, the number of samples per antenna, and the tap characteristics. The encoding device may reshape the data into a (64x64x4) data set.

[0112] The encoding device may perform a 2-dimensional 64x128 convolution operation (with kernel sizes of 3 and 1). In some aspects, the 64x128 convolution operation may perform spatial feature extraction associated with the decoding device antenna dimension, short-term (tap) feature extraction associated with the decoding device (e.g., base station) antenna dimension, etc. In some aspects, this may be achieved by using a 2D convolution layer that is fully connected in the decoding device antenna dimension, has a small kernel size (e.g., 3) in the tap dimension, and has a simple convolution operation with a small kernel size (e.g., 1) in the encoding device antenna dimension. The output from the 64xW convolution operation may be a matrix of size (128x64x4).

[0113] The encoding device may perform one or more ResNet operations. The one or more ResNet operations may further refine the spatial features associated with the decoding device and / or the temporal features associated with the decoding device. In some aspects, the ResNet operation may include multiple operations associated with the features. For example, the ResNet operation may include multiple (e.g., 3) 2D convolution operations, skip connections (e.g., between the input of the ResNet and the output of the ResNet to avoid applying a 2D convolution operation), a summation operation of a path through multiple 2D convolution operations and a path through a skip connection, etc. In some aspects, the plurality of 2-dimensional convolution operations may include: a Wx2W convolution operation with kernel sizes of 3 and 1, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 2Wx64xV; a 2Wx4W convolution operation with kernel sizes of 3 and 1, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 4Wx64xV; and a 4WxW convolution operation with kernel sizes of 3 and 1, the output of which has a BN dataset of size (128x64x4). The output from the one or more ResNet operations may be a matrix of size (128x64x4).

[0114] The encoding device may perform a 2-dimensional 128xV convolution operation (with kernel sizes of 1 and 1) on the output from the one or more ResNet operations. The 128xV convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The WxV convolution operation may compress the spatial features associated with the decoding device into a reduced dimension for each tap. The output from the 128xV convolution operation may be a matrix of size (4x64xV).

[0115] The encoding device may perform a 2-dimensional 4x8 convolution operation (with kernel sizes of 3 and 1). In some aspects, the 4x8 convolution operation may perform spatial feature extraction associated with the encoding device antenna dimensions, short-term (tap) feature extraction associated with the encoding device antenna dimensions, etc. The output from the 4x8 convolution operation may be a matrix of size (8x64xV).

[0116] The encoding device may perform one or more ResNet operations. The one or more ResNet operations may further refine the spatial features associated with the encoding device and / or the temporal features associated with the encoding device. In some aspects, the ResNet operation may include multiple operations associated with the features. For example, the ResNet operation may include multiple (e.g., 3) 2D convolution operations, skip connections (e.g., to avoid applying 2D convolution operations), summation operations of paths through multiple 2D convolution operations and paths through skip connections, etc. The output from the one or more ResNet operations may be a matrix of size (8x64xV).

[0117] The encoding device may perform a 2-dimensional 8xU convolution operation (with kernel sizes of 1 and 1) on the output from the one or more ResNet operations. The 8xU convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The 8xU convolution operation may compress the spatial features associated with the decoding device into a reduced dimension for each tap. The output from the 128xV convolution operation may be a matrix of size (Ux64xV).

[0118] The encoding device may perform a flattening operation to flatten the matrix of size (Ux64xV) into a 64UV element vector. The encoding device may perform a 64UVxM fully connected operation to further compress the 2D space-time feature dataset into a low-dimensional vector of size M for transmission over the air to the decoding device. The encoding device may perform quantization before transmitting the low-dimensional vector of size M over the air to map the transmitted samples to discrete values ​​for the low-dimensional vector of size M.

[0119] The decoding device may perform an Mx64UV fully connected operation to decompress the low-dimensional vector of size M into a spatial-temporal feature dataset. The decoding device may perform a reshape operation to reshape the 64UV element vector into a matrix of size (Ux64xV). The decoding device may perform a 2-dimensional Ux8 (with a kernel of 1,1) convolution operation on the output from the reshape operation. The Ux8 convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The Ux8 convolution operation may decompress the spatial features from the reduced dimension for each tap. The output from the Ux8 convolution operation may be a dataset of size (8x64xV).

[0120] The decoding device may perform one or more ResNet operations. The one or more ResNet operations may further decompress the spatial features and / or temporal features associated with the encoding device. In some aspects, the ResNet operation may include multiple (e.g., 3) 2D convolution operations, skip connections (e.g., to avoid applying 2D convolution operations), summation operations of paths through multiple 2D convolution operations and paths through skip connections, etc. The output from the one or more ResNet operations may be a dataset of size (8x64xV).

[0121] The decoding device may perform a 2-dimensional 8x4 convolution operation (with kernel sizes of 3 and 1). In some aspects, the 8x4 convolution operation may perform spatial feature reconstruction in the antenna dimension of the encoding device, as well as short-term feature reconstruction. The output from the 8x4 convolution operation may be a dataset of size (Vx64x4).

[0122] The decoding device may perform a 2-dimensional Vx128 (with a kernel of 1) convolution operation on the output from the 2-dimensional 8x4 convolution operation to reconstruct the tap features and spatial features associated with the decoding device. The Vx128 convolution operation may include a point-by-point (e.g., tap-by-tap) convolution operation. The Vx128 convolution operation may decompress the spatial features associated with the decoding device antenna from a reduced dimension for each tap. The output from the Ux8 convolution operation may be a matrix of size (128x64x4).

[0123] The decoding device may perform one or more ResNet operations. The one or more ResNet operations may further decompress spatial features and / or temporal features associated with the decoding device. In some aspects, the ResNet operation may include multiple (e.g., 3) 2D convolution operations, skip connections (e.g., to avoid applying 2D convolution operations), summation operations of paths through multiple 2D convolution operations and paths through skip connections, etc. The output from the one or more ResNet operations may be a matrix of size (128x64x4).

[0124] The decoding device may perform a 2-dimensional 128x64 convolution operation (with kernel sizes of 3 and 1). In some aspects, the 128x64 convolution operation may perform spatial feature reconstruction, short-term feature reconstruction, etc. associated with the antenna dimensions of the decoding device. The output from the 128x64 convolution operation may be a dataset of size (64x64x4).

[0125] In some aspects, the values ​​of M, V, and / or U may be configurable to adjust feature weights, payload sizes, etc. For example, the value of M may be 32, 64, 128, 256, or 512, the value of V may be 16, and / or the value of U may be 1.

[0126] As indicated above, Figure 7 This is provided as an example only. Other examples may differ from those described in relation to Figure 7 Examples described.

[0127] Figure 8 is a diagram illustrating an example 800 associated with using a neural network to encode and decode a data set for uplink communication according to various aspects of the present disclosure. An encoding device (e.g., UE 120, encoding device 300, etc.) may be configured to perform one or more operations on samples (e.g., data) received via one or more antennas of the encoding device to compress the samples. A decoding device (e.g., base station 110, decoding device 350, etc.) may be configured to decode the compressed samples to determine information, such as a CSF. The encoding device and decoding device operations may be asymmetric. In other words, the decoding device may have a greater number of layers than the decoding device.

[0128] As shown by example 800, the encoding device may receive samples from the antennas. For example, the encoding device may receive a data set of size 64x64 based at least in part on the number of antennas, the number of samples per antenna, and the tap characteristics.

[0129] The encoding device may perform a 64xW convolution operation (with a kernel size of 1). In some aspects, the 64xW convolution operation may be fully connected in the antenna, may be a convolution in the tap, and so on. The output from the 64xW convolution operation may be a Wx64 matrix. The encoding device may perform one or more WxW convolution operations (with a kernel size of 1 or 3). The output from the one or more WxW convolution operations may be a Wx64 matrix. The encoding device may perform a convolution operation (with a kernel size of 1). In some aspects, one or more WxW convolution operations may perform spatial feature extraction, short-term (tap) feature extraction, and the like. In some aspects, the WxW convolution operation may be a series of 1-dimensional convolution operations.

[0130] The encoding device may perform a flattening operation to flatten the Wx64 matrix into a 64W element vector. The encoding device may perform a 4096xM fully connected operation to further compress the spatial-temporal feature dataset into a low-dimensional vector of size M for transmission over the air to the decoding device. The encoding device may perform quantization before transmitting the low-dimensional vector of size M over the air to map the transmitted samples to discrete values ​​for the low-dimensional vector of size M.

[0131] The decoding device may perform a 4096xM fully connected operation to decompress the low-dimensional vector of size M into a spatial-temporal feature dataset. The decoding device may perform a reshape operation to reshape the 6W element vector into a Wx64 matrix.

[0132] The decoding device may perform one or more ResNet operations. The one or more ResNet operations may decompress spatial features and / or temporal features. In some aspects, the ResNet operation may include multiple (e.g., 3) 1-dimensional convolution operations, skip connections (e.g., between the input of the ResNet and the output of the ResNet to avoid applying the 1-dimensional convolution operation), summation operations of paths through multiple 1-dimensional convolution operations and paths through the skip connections, etc. In some aspects, the multiple 1-dimensional convolution operations may include: a Wx256 convolution operation with a kernel size of 3, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 256x64; a 256x512 convolution operation with a kernel size of 3, the output of which is input to a BN layer, followed by a LeakyReLU activation, which produces an output dataset of size 512x64; and a 512xW convolution operation with a kernel size of 3, the output of which has a BN dataset of size Wx64. The output from the one or more ResNet operations may be a Wx64 matrix.

[0133] The decoding device may perform one or more WxW convolution operations (with a kernel size of 1 or 3). The output from the one or more WxW convolution operations may be a Wx64 matrix. The encoding device may perform a convolution operation (with a kernel size of 1). In some aspects, the WxW convolution operation may perform spatial feature reconstruction, short-term (tap) feature reconstruction, etc. In some aspects, the WxW convolution operation may be a series of 1-dimensional convolution operations.

[0134] The encoding device may perform a Wx64 convolution operation (with a kernel size of 1). In some aspects, the Wx64 convolution operation may be a 1-dimensional convolution operation. The output from the 64xW convolution operation may be a 64x64 matrix.

[0135] In some aspects, the values ​​of M and / or W may be configurable to adjust feature weights, payload sizes, etc.

[0136] As indicated above, Figure 8 This is provided as an example only. Other examples may differ from those described in relation to Figure 8 Examples described.

[0137] Figure 9 1 is a diagram illustrating an example process 900 performed, for example, by a first device, according to various aspects of the present disclosure. The example process 900 is a diagram in which the first device (eg, encoding device, UE 120, Figure 14 An example of a device 1400, etc.) performing operations associated with using a neural network to encode a data set.

[0138] like Figure 9As shown in , in some aspects, process 900 may include encoding a data set using one or more extraction operations and compression operations associated with a neural network to produce a compressed data set, the one or more extraction operations and compression operations based at least in part on a feature set of the data set (block 910). For example, a first device (e.g., using encoding component 1408) may encode a data set using one or more extraction operations and compression operations associated with a neural network to produce a compressed data set, the one or more extraction operations and compression operations based at least in part on a feature set of the data set, as described above.

[0139] like Figure 9 As further shown in , in some aspects, process 900 can include transmitting the compressed data set to the second device (block 920). For example, the first device (eg, using transmission component 1404) can transmit the compressed data set to the second device, as described above.

[0140] Process 900 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0141] In a first aspect, the data set is based at least in part on sampling of one or more reference signals.

[0142] In a second aspect, alone or in combination with the first aspect, transmitting the compressed data set to the second device includes transmitting channel state information feedback to the second device.

[0143] In a third aspect, alone or in combination with one or more of the first and second aspects, process 900 includes identifying a feature set of a data set, wherein the one or more extraction operations and compression operations include: a first type of operation performed in a dimension associated with a feature in the feature set of the data set, and a second type of operation performed in the remaining dimensions associated with other features in the feature set of the data set, the second type of operation being different from the first type of operation.

[0144] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first type of operation comprises a one-dimensional fully connected layer operation, and the second type of operation comprises a convolution operation.

[0145] In a fifth aspect, either alone or in combination with one or more of the first to fourth aspects, the one or more extraction operations and compression operations include multiple operations comprising one or more of: convolution operations, fully connected layer operations, or residual neural network operations.

[0146] In the sixth aspect, alone or in combination with one or more of the first to fifth aspects, the one or more extraction operations and compression operations include a first extraction operation and a first compression operation performed on a first feature in a feature set of a data set, and a second extraction operation and a second compression operation performed on a second feature in the feature set of the data set.

[0147] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, process 900 includes performing one or more additional operations on the intermediate data set output after performing the one or more extraction operations and compression operations.

[0148] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the one or more additional operations include one or more of the following: a quantization operation, a flattening operation, or a fully connected operation.

[0149] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the feature set of the data set includes one or more of the following: spatial features, or tap-domain features.

[0150] In the tenth aspect, alone or in combination with one or more of the first to ninth aspects, the one or more extraction operations and compression operations include one or more of the following: spatial feature extraction using a one-dimensional convolution operation, temporal feature extraction using a one-dimensional convolution operation, a residual neural network operation for refining the extracted spatial features, a residual neural network operation for refining the extracted temporal features, a point-by-point convolution operation for compressing the extracted spatial features, a point-by-point convolution operation for compressing the extracted temporal features, a flattening operation for flattening the extracted spatial features, a flattening operation for flattening the extracted temporal features, or a compression operation for compressing one or more of the extracted temporal features or the extracted spatial features into a low-dimensional vector for transmission.

[0151] In the eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the one or more extraction operations and compression operations include: a first feature extraction operation associated with one or more features associated with the second device, a first compression operation for compressing one or more features associated with the second device, a second feature extraction operation associated with one or more features associated with the first device, and a second compression operation for compressing one or more features associated with the first device.

[0152] although Figure 9 Example blocks of process 900 are shown, but in some aspects, process 900 may include Figure 9900. Additionally or alternatively, two or more blocks of process 900 may be executed in parallel.

[0153] Figure 10 is a diagram illustrating an example process 1000 performed, for example, by a second device, according to various aspects of the present disclosure. The example process 1000 is a diagram in which the second device (eg, a decoding device, a base station 110, Figure 15 An example of a device 1500, etc.) performing operations associated with using a neural network to decode a data set.

[0154] like Figure 10 As shown in , in some aspects, process 1000 may include receiving a compressed data set from a first device (block 1010). For example, a second device (e.g., using Figure 15 The receiving component 1502) can receive the compressed data set from the first device, as described above.

[0155] like Figure 10 As further shown in FIG15 , in some aspects, process 1000 may include decoding the compressed data set using one or more decompression operations and reconstruction operations associated with a neural network to produce a reconstructed data set, the one or more decompression operations and reconstruction operations based at least in part on a feature set of the compressed data set (block 1020). For example, the second device (e.g., using decoding component 1508) may decode the compressed data set using one or more decompression operations and reconstruction operations associated with a neural network to produce a reconstructed data set, the one or more decompression operations and reconstruction operations based at least in part on a feature set of the compressed data set, as described above.

[0156] Process 1000 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0157] In a first aspect, decoding a compressed data set using the one or more decompression operations and reconstruction operations includes performing the one or more decompression operations and reconstruction operations based at least in part on an assumption that the first device generated the compressed data set using a set of operations that are symmetric to the one or more decompression operations and reconstruction operations, or performing the one or more decompression operations and reconstruction operations based at least in part on an assumption that the first device generated the compressed data set using a set of operations that are asymmetric to the one or more decompression operations and reconstruction operations.

[0158] In a second aspect, alone or in combination with the first aspect, the compressed data set is based at least in part on sampling of one or more reference signals by the first device.

[0159] In a third aspect, alone or in combination with one or more of the first and second aspects, receiving the compressed data set includes receiving channel state information feedback from the first device.

[0160] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the one or more decompression operations and reconstruction operations include: a first type of operation performed in a dimension associated with a feature in a feature set of the compressed data set, and a second type of operation performed in the remaining dimensions associated with other features in the feature set of the compressed data set, the second type of operation being different from the first type of operation.

[0161] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the first type of operation comprises a one-dimensional fully connected layer operation, and wherein the second type of operation comprises a convolution operation.

[0162] In a sixth aspect, either alone or in combination with one or more of the first to fifth aspects, the one or more decompression operations and reconstruction operations include multiple operations comprising one or more of the following: convolution operations, fully connected layer operations, or residual neural network operations.

[0163] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the one or more decompression operations and reconstruction operations include a first operation performed on a first feature in a feature set of the compressed data set, and a second operation performed on a second feature in the feature set of the compressed data set.

[0164] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, process 1000 includes performing a reshape operation on the compressed data set.

[0165] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the feature set of the compressed data set includes one or more of the following: spatial features, or tap-domain features.

[0166] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the one or more decompression operations and reconstruction operations include one or more of the following: a feature decompression operation, a temporal feature reconstruction operation, or a spatial feature reconstruction operation.

[0167] In the eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the one or more decompression operations and reconstruction operations include a first feature reconstruction operation performed on one or more features associated with the first device, and a second feature reconstruction operation performed on one or more features associated with the second device.

[0168] although Figure 10 Example blocks of process 1000 are shown, but in some aspects, process 1000 may include Figure 10 1000. Additionally or alternatively, two or more blocks of process 1000 may be executed in parallel.

[0169] Compressing measurements using a CSF encoded using a neural network can save network resources. However, as the channel and / or environment changes, the weights of the neural network may also change. For example, if the Doppler metric changes (e.g., the encoding device is carried by a vehicle), the layers related to the Doppler metric may need to change. If a pedestrian holding the encoding device turns a corner, the non-Doppler related weights may need to change. If the encoding device switches from a first decoding device (e.g., a base station) with 128 ports to a second decoding device with 32 or fewer ports, the non-Doppler related weights of the layers that take into account decoder-side information may need to change. However, if the encoding device changes the weights of the neural network, the decoding device may not be able to decode the CSF, which may consume network resources to detect and correct.

[0170] In some aspects described herein, an encoding device may receive a request to report updates to one or more weights of a neural network configured for encoding a CSF. In some aspects, a decoding device (e.g., a base station) may transmit a request for updates by the encoding device and may identify one or more layers (e.g., with one or more layer identifiers) for which the encoding device will report weights. In some aspects, the request may indicate a subset of weights within the one or more layers for which the encoding device will report weights.

[0171] Based at least in part on the decoding device requesting and receiving a report indicating updates to the weights of the neural network, the decoding device can decode the CSF based at least in part on the updates to the weights. In this manner, computational, communication, and / or network resources that could otherwise be used to detect and recover from errors based at least in part on the decoding device's failure to decode the CSF can be conserved.

[0172] Figure 11 is a diagram illustrating an example 1100 of reporting weight updates to a neural network for generating channel state information feedback according to various aspects of the present disclosure. Figure 11As shown in , an encoding device (e.g., UE 120, base station, transmit reception point (TRP), network device, low earth orbit (LEO) satellite, medium earth orbit (MEO) satellite, geostationary earth orbit (GEO) satellite, highly elliptical orbit (HEO) satellite, etc.) can communicate (e.g., transmit uplink transmissions and / or receive downlink transmissions) with a decoding device (e.g., base station 110, UE 120, server, TRP, network entity, etc.). The encoding device and the decoding device can be part of a wireless network (e.g., wireless network 100).

[0173] As indicated by reference numeral 1105, the decoding device may transmit configuration information, and the encoding device may receive the configuration information. In some aspects, the encoding device may receive the configuration information from another device (e.g., from a base station, a UE, etc.), a communication standard, etc. In some aspects, the encoding device may receive the configuration information via one or more of radio resource control (RRC) signaling, media access control (MAC) signaling (e.g., a MAC control element (MAC CE)), etc. In some aspects, the configuration information may include an indication of one or more configuration parameters selected by the encoding device (e.g., already known to the encoding device), explicit configuration information for the encoding device to configure the encoding device, etc.

[0174] In some aspects, the configuration information may indicate that the encoding device is to transmit a report indicating updates to one or more weights of a neural network configured for encoding a CSF message. In some aspects, the configuration information may indicate that the encoding device is to generate a report indicating updates to less than all weights of the neural network (e.g., based at least in part on the configuration information, dynamic signaling, etc.).

[0175] In some aspects, the configuration information may indicate that the encoding device is to train a neural network to operate based at least in part on joint learning with additional devices. The configuration information may indicate that the encoding device is to transmit reports indicating updates to one or more weights of the neural network to multiple devices (e.g., decoding devices, UEs, etc.).

[0176] In some aspects, the configuration information may indicate that the encoding device is to report updates to one or more weights at a configured periodicity. In some aspects, the configuration information may indicate that the encoding device is to report a first subset of updates to the one or more weights at a first configured periodicity and a second subset of updates associated with a second layer of the neural network at a second periodicity. In some aspects, the configuration information may indicate that the encoding device is to report updates to the one or more weights based at least in part on a Doppler metric of the encoding device (e.g., a speed or change in speed of the encoding device).

[0177] As shown by reference numeral 1110, the encoding device can configure the encoding device to communicate with the decoding device. In some aspects, the encoding device can configure the encoding device based at least in part on the configuration information. In some aspects, the encoding device can be configured to perform one or more operations described herein.

[0178] As indicated by reference numeral 1115, the encoding device may transmit an indication that one or more weights have been updated. In some aspects, the encoding device may notify the decoding device that weights in various layers of the neural network have changed. The indication may identify the weights and / or layers (e.g., using a layer identifier). In some aspects, the encoding device may transmit the indication via uplink control information (e.g., mapped to PUCCH, PUSCH, etc.), one or more MAC CEs, etc.

[0179] As indicated by reference numeral 1120, the encoding device may transmit an indication of the ability to use a neural network to determine differential updates. For example, the encoding device may indicate that the encoding device supports differential weight increment calculation based on a neural network. In some aspects, the encoding device may indicate this capability in uplink control information, one or more MAC CEs, etc.

[0180] As indicated by reference numeral 1125, the encoding device may receive a request to report an update to one or more weights of a neural network configured to encode a CSF message. In some aspects, the encoding device may receive the request via aperiodic signaling, semi-persistent signaling, downlink control information, one or more MAC CEs, etc.

[0181] In some aspects, the request includes an indication of one or more layers of the neural network for which the first device is to report updates. In some aspects, the request includes an indication of a subset of weights within one or more layers of the neural network for which the first device is to report updates.

[0182] As indicated by reference numeral 1130, the encoding device may receive an indication to use a neural network to determine a differential update. In some aspects, the indication to use a neural network to determine a differential update may be included in a request to report an update to one or more weights of a neural network configured to encode a CSF message. In some aspects, the indication may include an indication to report the update as a differential update to the one or more weights, an indication of a differential time period to be used to determine the differential update to the one or more weights, and the like.

[0183] As indicated by reference numeral 1135, the encoding device may transmit a report indicating the update to the one or more weights. In some aspects, the encoding device may transmit the report via one or more MAC CEs, PUSCH, etc. In some aspects, the encoding device may transmit the report to multiple devices (e.g., decoding devices, UEs, etc.).

[0184] In some aspects, the encoding device may report updates to the one or more weights at a configured periodicity. In some aspects, the encoding device may report a first subset of updates to the one or more weights at a first configured periodicity and a second subset of updates associated with a second layer of the neural network at a second periodicity. In some aspects, the encoding device may report updates to the one or more weights based at least in part on a Doppler metric of the encoding device (e.g., a speed or change in speed of the encoding device).

[0185] As indicated by reference numeral 1140, the encoding device may transmit an indication of an environmental change and / or a request to reset weights of the neural network. For example, the encoding device may transmit an indication of an environmental change at the first device, a request to reset all weights of the neural network, etc. In some aspects, the encoding device may transmit the indication via one or more MAC CEs, uplink control information, etc.

[0186] As indicated by reference numeral 1145, the encoding device may receive an indication to reset weights of the neural network. In some aspects, the encoding device may receive an indication to reset all weights of the neural network based at least in part on a dynamic radio access network mode update. For example, the encoding device may change from an indoor environment to an outdoor environment, from a line-of-sight connection to a non-line-of-sight connection, etc. In some aspects, the dynamic radio access network mode update may allow the encoding device to modify one or more transmission parameters (e.g., MCS), which may allow the encoding device to modify the payload size of the CSF report. This may cause the encoding device to update the one or more weights.

[0187] Based at least in part on the decoding device requesting and receiving a report indicating updates to the weights of the neural network, the decoding device can decode the CSF based at least in part on the updates to the weights. In this manner, computational, communication, and / or network resources that could otherwise be used to detect and recover from errors based at least in part on the decoding device's failure to decode the CSF can be conserved.

[0188] Figure 12 1 is a diagram illustrating an example process 1200, for example, performed by a first device, according to various aspects of the present disclosure. The example process 1200 is a diagram in which the first device (eg, encoding device, UE 120, Figure 14An example of an apparatus 1400, etc.) performing operations associated with reporting weight updates to a neural network to generate channel state information feedback.

[0189] like Figure 12 As shown, in some aspects, process 1200 may include receiving a request to report an update to one or more weights of a neural network configured to encode a CSF message (block 1210). For example, a first device (e.g., using receiving component 1402) may receive a request to report an update to one or more weights of a neural network configured to encode a CSF message, as described above.

[0190] like Figure 12 As further shown in , in some aspects, process 1200 may include transmitting a report indicating an update to the one or more weights (block 1220). For example, the first device (e.g., using transmission component 1404) may transmit a report indicating an update to the one or more weights, as described above.

[0191] Process 1200 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0192] In a first aspect, the request includes an indication to the first device of the neural network of one or more layers to which the updates are to be reported.

[0193] In a second aspect, alone or in combination with the first aspect, the request includes an indication to the first device of the neural network that a subset of weights including the one or more weights within one or more layers for which updates are to be reported.

[0194] In a third aspect, alone or in combination with the first and second aspects, receiving the request comprises receiving the request via non-periodic signaling, receiving the request via semi-persistent signaling, receiving the request via downlink control information, receiving the request via one or more MAC CEs, or a combination thereof.

[0195] In a fourth aspect, alone or in combination with one or more of the first to third aspects, transmitting the report includes transmitting the report via one or more MAC CEs, or transmitting the report via a PUSCH.

[0196] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, process 1200 includes transmitting an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0197] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, transmitting the indication comprises transmitting the indication via one or more of uplink control information or one or more MAC CEs.

[0198] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the neural network is based at least in part on federated learning.

[0199] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, transmitting the report includes transmitting the report to the second device, transmitting the report to the UE, or transmitting the report to the second device and the UE.

[0200] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the request indicates reporting updates to the one or more weights at a configured periodicity.

[0201] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the request indicates reporting of a first subset of updates associated with a first layer of the neural network at a first periodicity, and the request indicates reporting of a second subset of updates associated with a second layer of the neural network at a second periodicity.

[0202] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the request indicates reporting updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

[0203] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, process 1200 includes receiving an indication to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

[0204] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, process 1200 includes transmitting an indication of an environmental change at the first device, a request to reset all weights of the neural network, or an indication of an environmental change at the first device and a request to reset all weights of the neural network.

[0205] In a fourteenth aspect, alone or in combination with one or more of the first to thirteenth aspects, transmitting the indication comprises transmitting the indication via one or more MAC CEs or uplink control information.

[0206] In the fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, the request includes one or more of the following: an indication that the update is to be reported as a differential update for the one or more weights, or an indication of a differential time period to be used to determine the differential update for the one or more weights.

[0207] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, process 1200 includes receiving an indication to use an additional neural network to determine a differential update for the one or more weights.

[0208] In a seventeenth aspect, alone or in combination with one or more of aspects one to sixteen, process 1200 includes transmitting an indication of a capability of the first device to use a neural network to determine a differential update for the one or more weights, wherein receiving an indication to use an additional neural network to determine a differential update for the one or more weights is based at least in part on transmitting the indication of the capability of the first device.

[0209] although Figure 12 Example blocks of process 1200 are shown, but in some aspects, process 1200 may include Figure 12 12. In some embodiments, the process 1200 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. Additionally or alternatively, two or more blocks of process 1200 may be executed in parallel.

[0210] Figure 13 is a diagram illustrating an example process 1300 performed, for example, by a second device, according to various aspects of the present disclosure. The example process 1300 is a diagram in which the second device (e.g., decoding device, base station 110, Figure 15 An example of an apparatus 1500, etc.) performing operations associated with reporting weight updates to a neural network to generate channel state information feedback.

[0211] like Figure 13 As shown, in some aspects, process 1300 may include transmitting to the first device a request to report updates to one or more weights of a neural network configured to encode a CSF message (block 1310). For example, the second device (e.g., using transmission component 1504) may transmit to the first device a request to report updates to one or more weights of a neural network configured to encode a CSF message, as described above.

[0212] like Figure 13As further shown in , in some aspects, process 1300 may include receiving a report indicating an update to the one or more weights (block 1320). For example, the second device (e.g., using receiving component 1502) may receive a report indicating an update to the one or more weights, as described above.

[0213] Process 1300 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0214] In a first aspect, the request includes an indication to the first device of the neural network of one or more layers to which the updates are to be reported.

[0215] In a second aspect, alone or in combination with the first aspect, the request includes an indication to the first device of the neural network that a subset of weights including the one or more weights within one or more layers for which updates are to be reported.

[0216] In a third aspect, alone or in combination with the first and second aspects, transmitting the request comprises transmitting the request via aperiodic signaling, transmitting the request via semi-persistent signaling, transmitting the request via downlink control information, transmitting the request via one or more MAC CEs, or a combination thereof.

[0217] In a fourth aspect, alone or in combination with one or more of the first to third aspects, receiving the report includes receiving the report via one or more MAC CEs, or receiving the report via a PUSCH.

[0218] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, process 1300 includes receiving an indication that the one or more weights have been updated, wherein transmitting the request is based at least in part on receiving the indication.

[0219] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, receiving the indication comprises receiving the indication via one or more of uplink control information or one or more MAC CEs.

[0220] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the neural network is based at least in part on federated learning.

[0221] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the request indicates reporting updates to the one or more weights at a configured periodicity.

[0222] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the request indicates reporting of a first subset of updates associated with a first layer of the neural network at a first periodicity, and the request indicates reporting of a second subset of updates associated with a second layer of the neural network at a second periodicity.

[0223] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the request indicates reporting updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

[0224] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, process 1300 includes transmitting an indication to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

[0225] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, process 1300 includes receiving an indication of an environmental change at a first device, a request to reset all weights of a neural network, or an indication of an environmental change at the first device and a request to reset all weights of a neural network.

[0226] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, receiving the indication comprises receiving the indication via one or more MAC CEs or uplink control information.

[0227] In a fourteenth aspect, either alone or in combination with one or more of aspects one to thirteen, the request comprises one or more of: an indication that the update is to be reported as a differential update to the one or more weights, or an indication of a differential time period to be used to determine the differential update to the one or more weights.

[0228] In a fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, process 1300 includes transmitting an indication to use an additional neural network to determine a differential update for the one or more weights.

[0229] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, process 1300 includes receiving an indication of a capability of the first device to use a neural network to determine a differential update for the one or more weights, wherein transmitting an indication to use an additional neural network to determine a differential update for the one or more weights is based at least in part on receiving the indication of the capability of the first device.

[0230] although Figure 13 Example blocks of process 1300 are shown, but in some aspects, process 1300 may include Figure 13 13. In some embodiments, the process 1300 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. Additionally or alternatively, two or more blocks of process 1300 may be executed in parallel.

[0231] Figure 14 1 is a block diagram of an example apparatus 1400 for wireless communication. Apparatus 1400 may be an encoding device, or an encoding device may include apparatus 1400. In some aspects, apparatus 1400 includes a receiving component 1402 and a transmitting component 1404, which may be in communication with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1400 may use receiving component 1402 and transmitting component 1404 to communicate with another apparatus 1406 (such as a UE, a base station, or another wireless communication device). As further shown, apparatus 1400 may include an encoding component 1408.

[0232] In some aspects, the apparatus 1400 may be configured to perform the Figure 3-8 Additionally or alternatively, the apparatus 1400 may be configured to perform one or more of the processes described herein (such as Figure 9 The process of 900 Figure 12 In some aspects, Figure 14 The apparatus 1400 and / or one or more components shown in FIG. 1 may include a combination of the above Figure 2 Additionally or alternatively, one or more components of the encoding device described. Figure 14 One or more components shown above may be combined Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and may be executed by a controller or processor to perform the function or operation of the component.

[0233] The receiving component 1402 may receive communications (such as reference signals, control information, data communications, or a combination thereof) from the apparatus 1406. The receiving component 1402 may provide the received communications to one or more other components of the apparatus 1400. In some aspects, the receiving component 1402 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, among other examples) on the received communications and may provide the processed signals to one or more other components of the apparatus 1406. In some aspects, the receiving component 1402 may include a combination of the above. Figure 2One or more antennas, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described encoding devices.

[0234] The transmission component 1404 may transmit communications (such as reference signals, control information, data communications, or a combination thereof) to the device 1406. In some aspects, one or more other components of the device 1406 may generate communications and may provide the generated communications to the transmission component 1404 for transmission to the device 1406. In some aspects, the transmission component 1404 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 1406. In some aspects, the transmission component 1404 may include a combination of the above. Figure 2 One or more antennas, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described encoding devices. In some aspects, the transmitting component 1404 can be co-located with the receiving component 1402 in a transceiver.

[0235] Receiving component 1402 can receive a request to report an update to one or more weights of a neural network configured to encode a CSF message. Receiving component 1402 can receive an indication to reset all weights of the neural network based at least in part on a dynamic radio access network mode update. Receiving component 1402 can receive an indication to use an additional neural network to determine a differential update to the one or more weights.

[0236] Transmitting component 1404 may transmit a report indicating an update to the one or more weights. Transmitting component 1404 may transmit an indication that the one or more weights have been updated. Transmitting component 1404 may transmit an indication of an environmental change at the first device and a request to reset all weights of the neural network, or an indication of an environmental change at the first device and a request to reset all weights of the neural network. Transmitting component 1404 may transmit an indication of an ability of the first device to use the neural network to determine a differential update to the one or more weights.

[0237] The encoding component 1408 may perform differential encoding of the weights used to generate the CSF message. In some aspects, the encoding component 1408 may include the above combined Figure 2 A controller / processor, memory, or combination thereof of the described encoding device.

[0238] Figure 14 The number and arrangement of components shown in the FIG are provided as examples. In practice, there may be Figure 14 Components may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. Figure 14 Two or more components shown in may be implemented in a single component, or Figure 14 The single component shown in may be implemented as multiple distributed components. Additionally or alternatively, Figure 14 The set of components (e.g., one or more components) shown in FIG can perform the operations described as being performed by Figure 14 One or more functions performed by another set of components shown in .

[0239] Figure 15 1 is a block diagram of an example apparatus 1500 for wireless communication. Apparatus 1500 may be a decoding device, or a decoding device may include apparatus 1500. In some aspects, apparatus 1500 includes a receiving component 1502 and a transmitting component 1504, which may be in communication with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 1500 may use receiving component 1502 and transmitting component 1504 to communicate with another apparatus 1506 (such as a UE, a base station, or another wireless communication device). As further shown, apparatus 1500 may include a decoding component 1508.

[0240] In some aspects, the apparatus 1500 may be configured to perform the Figure 3-8 Additionally or alternatively, the apparatus 1500 may be configured to perform one or more of the processes described herein (such as Figure 10 The process of 1000 Figure 13 In some aspects, Figure 15 The apparatus 1500 and / or one or more components shown in FIG. 1 may include a combination of the above Figure 2 Additionally or alternatively, one or more components of the decoding device described. Figure 15 One or more components shown above may be combined Figure 2 Additionally or alternatively, one or more components in the component set may be implemented at least in part as software stored in a memory. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and may be executed by a controller or processor to perform the function or operation of the component.

[0241] Receive component 1502 may receive communications (such as reference signals, control information, data communications, or a combination thereof) from apparatus 1506. Receive component 1502 may provide the received communications to one or more other components of apparatus 1500. In some aspects, receive component 1502 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding, among other examples) on the received communications and may provide the processed signals to one or more other components of apparatus 1506. In some aspects, receive component 1502 may include a combination of the above. Figure 2 One or more antennas, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof of the described decoding devices.

[0242] Transmission component 1504 may transmit communications (such as reference signals, control information, data communications, or a combination thereof) to device 1506. In some aspects, one or more other components of device 1506 may generate communications and may provide the generated communications to transmission component 1504 for transmission to device 1506. In some aspects, transmission component 1504 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to device 1506. In some aspects, transmission component 1504 may include a combination of the above. Figure 2 One or more antennas, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described decoding devices. In some aspects, the transmitting component 1504 can be co-located with the receiving component 1502 in a transceiver.

[0243] Transmitting component 1504 can transmit, to the first device, a request to report updates to one or more weights of a neural network configured to encode a CSF message. Transmitting component 1504 can transmit an indication to reset all weights of the neural network based at least in part on a dynamic radio access network pattern update. Transmitting component 1504 can transmit an indication to use an additional neural network to determine a differential update to the one or more weights.

[0244] Receiving component 1502 may receive a report indicating an update to the one or more weights. Receiving component 1502 may receive an indication that the one or more weights have been updated. Receiving component 1502 may receive an indication of an environmental change at the first device and a request to reset all weights of the neural network, or an indication of an environmental change at the first device and a request to reset all weights of the neural network. Receiving component 1502 may receive an indication of an ability of the first device to use the neural network to determine a differential update to the one or more weights.

[0245] The decoding component 1508 can decode the CSF based on the multi-part neural network. In some aspects, the decoding component 1508 can include the above combined Figure 2 A controller / processor, memory, or combination thereof of the described encoding device.

[0246] Figure 15 The number and arrangement of components shown in the FIG are provided as examples. In practice, there may be Figure 15 Components may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. Figure 15 Two or more components shown in may be implemented in a single component, or Figure 15 The single component shown in may be implemented as multiple distributed components. Additionally or alternatively, Figure 15 The set of components (e.g., one or more components) shown in FIG can perform the operations described as being performed by Figure 15 One or more functions performed by another set of components shown in .

[0247] Figure 16 is a diagram illustrating an example 1600 of a hardware implementation for a device 1605 employing a processing system 1610. The device 1605 may be an encoding device.

[0248] The processing system 1610 can be implemented with a bus architecture generally represented by bus 1615. Depending on the specific application and overall design constraints of the processing system 1610, the bus 1615 may include any number of interconnecting buses and bridges. The bus 1615 links together various circuits including one or more processors and / or hardware components (represented by the processor 1620, the illustrated components, and the computer-readable medium / memory 1625). The bus 1615 may also link various other circuits, such as timing sources, peripherals, voltage regulators, power management circuits, and the like.

[0249] Processing system 1610 may be coupled to transceiver 1630. Transceiver 1630 is coupled to one or more antennas 1635. Transceiver 1630 provides a means for communicating with various other devices over a transmission medium. Transceiver 1630 receives signals from one or more antennas 1635, extracts information from the received signals, and provides the extracted information to processing system 1610 (specifically, receiving component 1402). In addition, transceiver 1630 receives information from processing system 1610 (specifically, transmitting component 1404) and generates signals to be applied to one or more antennas 1635 based at least in part on the received information.

[0250] The processing system 1610 includes a processor 1620 coupled to a computer-readable medium / memory 1625. The processor 1620 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory 1625. The software, when executed by the processor 1620, causes the processing system 1610 to perform the various functions described herein for any particular device. The computer-readable medium / memory 1625 may also be used to store data manipulated by the processor 1620 when executing the software. The processing system further includes at least one of the illustrated components. Each component may be a software module running on the processor 1620, a software module resident / stored in the computer-readable medium / memory 1625, one or more hardware modules coupled to the processor 1620, or some combination thereof.

[0251] In some aspects, the processing system 1610 may be a component of the UE 120 and may include the memory 282 and / or at least one of the TX MIMO processor 266, the RX processor 258, and / or the controller / processor 280. In some aspects, the apparatus 1605 for wireless communication includes: means for receiving a request to report an update to one or more weights of a neural network configured to encode a CSF message; and means for transmitting a report indicating the update to the one or more weights. The aforementioned means may be one or more components of the aforementioned components of the apparatus 1400 and / or the processing system 1610 of the apparatus 1605 configured to perform the functions recited by the aforementioned means. As described elsewhere herein, the processing system 1610 may include the TX MIMO processor 266, the RX processor 258, and / or the controller / processor 280. In one configuration, the aforementioned means may be the TX MIMO processor 266, the RX processor 258, and / or the controller / processor 280 configured to perform the functions and / or operations recited herein.

[0252] Figure 16 are provided as examples. Other examples may differ from those incorporating Figure 16 Examples described.

[0253] Figure 17 is a diagram illustrating an example 1700 of a hardware implementation for a device 1705 employing a processing system 1710. The device 1705 may be a decoding device.

[0254] The processing system 1710 can be implemented with a bus architecture generally represented by bus 1715. Depending on the specific application and overall design constraints of the processing system 1710, the bus 1715 may include any number of interconnecting buses and bridges. The bus 1715 links together various circuits including one or more processors and / or hardware components (represented by the processor 1720, the illustrated components, and the computer-readable medium / memory 1725). The bus 1715 may also link various other circuits, such as timing sources, peripherals, voltage regulators, power management circuits, and the like.

[0255] The processing system 1710 may be coupled to a transceiver 1730. The transceiver 1730 is coupled to one or more antennas 1735. The transceiver 1730 provides a means for communicating with various other devices via a transmission medium. The transceiver 1730 receives signals from the one or more antennas 1735, extracts information from the received signals, and provides the extracted information to the processing system 1710 (specifically, the receiving component 1502). In addition, the transceiver 1730 receives information from the processing system 1710 (specifically, the transmitting component 1504) and generates signals to be applied to the one or more antennas 1735 based at least in part on the received information.

[0256] The processing system 1710 includes a processor 1720 coupled to a computer-readable medium / memory 1725. The processor 1720 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory 1725. The software, when executed by the processor 1720, causes the processing system 1710 to perform the various functions described herein for any particular device. The computer-readable medium / memory 1725 may also be used to store data manipulated by the processor 1720 when executing the software. The processing system further includes at least one of the illustrated components. Each component may be a software module running on the processor 1720, a software module resident / stored in the computer-readable medium / memory 1725, one or more hardware modules coupled to the processor 1720, or some combination thereof.

[0257] In some aspects, the processing system 1710 may be a component of the base station 110 and may include the memory 242 and / or at least one of the TX MIMO processor 230, the RX processor 238, and / or the controller / processor 240. In some aspects, the apparatus 1705 for wireless communication includes: means for transmitting a request to a first device for a report of an update to one or more weights of a neural network configured for encoding a CSF message; and means for receiving a report indicating the update to the one or more weights. The aforementioned means may be the aforementioned components of the apparatus 1500 and / or one or more components of the processing system 1710 of the apparatus 1705 configured to perform the functions recited by the aforementioned means. As described elsewhere herein, the processing system 1710 may include the TX MIMO processor 266, the RX processor 258, and / or the controller / processor 280. In one configuration, the aforementioned means may be the TX MIMO processor 266, the RX processor 258, and / or the controller / processor 280 configured to perform the functions and / or operations recited herein.

[0258] Figure 17 are provided as examples. Other examples may differ from those incorporating Figure 17 Examples described.

[0259] Figure 18 is a diagram illustrating an example 1800 of an implementation of code and circuitry for a device 1805. The device 1805 may be a coding device (eg, a UE).

[0260] like Figure 18 As shown, device 1805 may include circuitry for receiving a request to report an update to one or more weights (circuitry 1820). For example, circuitry 1820 may provide means for receiving a request to report an update to one or more weights of a neural network configured to encode a CSF message.

[0261] like Figure 18 As shown, the device 1805 may include circuitry for transmitting a report indicating the update (circuitry 1825). For example, the circuitry 1825 may provide means for transmitting a report indicating the update to the one or more weights.

[0262] like Figure 18 As shown in , device 1805 may include circuitry (circuitry 1830) for transmitting an indication that the one or more weights have been updated. For example, circuitry 1830 may provide means for transmitting an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0263] Circuit systems 1820, 1825 and / or 1830 may include a combination of the above Figure 2 One or more components of the UE are depicted, such as transmit processor 264, TX MIMO processor 266, MOD 254, DEMOD 254, MIMO detector 256, receive processor 258, antenna 252, controller / processor 280, and / or memory 282.

[0264] like Figure 18 As shown in FIG, device 1805 may include code (code 1840) stored in computer-readable medium 1625 for receiving a request to report an update to one or more weights. For example, code 1840, when executed by processor 1620, may cause device 1805 to receive a request to report an update to one or more weights of a neural network configured to encode a CSF message.

[0265] like Figure 18 As shown, device 1805 may include code (code 1845) stored in computer-readable medium 1625 for transmitting a report indicating an update. For example, code 1845, when executed by processor 1620, may cause device 1805 to transmit a report indicating an update to the one or more weights.

[0266] like Figure 18 As shown in , device 1805 may include code (code 1850) stored in computer-readable medium 1625 for transmitting an indication that the one or more weights have been updated. For example, code 1850, when executed by processor 1620, may cause device 1805 to transmit, to a second device, an indication of one or more weights for transmitting an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0267] Figure 18 are provided as examples. Other examples may differ from those incorporating Figure 18 Examples described.

[0268] Figure 19 is a diagram illustrating an example 1900 of an implementation of code and circuitry for a device 1905. The device 1905 can be a coding device (e.g., a network device, a base station, another UE, a TRP, etc.).

[0269] like Figure 19 As shown, device 1905 may include circuitry for transmitting a request to report an update to one or more weights (circuitry 1920). For example, circuitry 1920 may provide means for transmitting a request to the first device to report an update to one or more weights of a neural network configured to encode a CSF message.

[0270] like Figure 19 As shown in , device 1905 may include circuitry for receiving a report of weight updates (circuitry 1925). For example, circuitry 1925 may provide means for receiving a report indicating an update to the one or more weights.

[0271] like Figure 19 As shown in , device 1905 may include circuitry (circuitry 1930) for receiving an indication that the one or more weights have been updated. For example, circuitry 1930 may provide means for receiving an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0272] Circuit systems 1920, 1925 and / or 1930 may include a combination of the above Figure 2 One or more components of a base station are depicted, such as antenna 234, DEMOD 232, MIMO detector 236, receive processor 238, controller / processor 240, transmit processor 220, TX MIMO processor 230, MOD 232, antenna 234, and so forth.

[0273] like Figure 19 As shown in FIG, device 1905 may include code (code 1940) stored in computer-readable medium 1725 for transmitting a request to report an update to one or more weights. For example, code 1940, when executed by processor 1720, may cause device 1905 to transmit a request to a first device to report an update to one or more weights of a neural network configured to encode a CSF message.

[0274] like Figure 19 As shown in FIG, device 1905 may include code (code 1945) stored in computer-readable medium 1725 for receiving a report of weight updates. For example, code 1945, when executed by processor 1720, may cause device 1905 to receive a report indicating an update to the one or more weights.

[0275] like Figure 19 As shown in FIG, device 1905 may include code (code 1950) stored in computer-readable medium 1725 for receiving an indication that the one or more weights have been updated. For example, code 1950, when executed by processor 1720, may cause device 1905 to receive an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0276] Figure 19 are provided as examples. Other examples may differ from those incorporating Figure 19 Examples described.

[0277] The following provides an overview of some aspects of the disclosure:

[0278] Aspect 1: A wireless communication method performed by a first device, comprising: receiving a request to report an update to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message; and transmitting a report indicating the update to the one or more weights.

[0279] Aspect 2: The method of aspect 1, wherein the request includes: an indication to the first device of the neural network of one or more layers to which the update is to be reported.

[0280] Aspect 3: The method of aspect 2, wherein the request comprises: an indication to the first device of the neural network that the subset of weights including the one or more weights within one or more layers for which the first device is to report updates.

[0281] Aspect 4: A method as in any one of Aspects 1-3, wherein receiving the request includes: receiving the request via non-periodic signaling, receiving the request via semi-persistent signaling, receiving the request via downlink control information, receiving the request via one or more media access control elements (MAC CEs), or a combination thereof.

[0282] Aspect 5: The method of any one of aspects 1-4, wherein transmitting the report comprises transmitting the report via one or more medium access control elements (MAC CEs), or transmitting the report via a physical uplink shared channel.

[0283] Aspect 6: The method of any one of aspects 1-5, further comprising: transmitting an indication that the one or more weights have been updated, wherein receiving the request is based at least in part on transmitting the indication.

[0284] Aspect 7: The method of aspect 6, wherein transmitting the indication comprises transmitting the indication via one or more of: uplink control information, or one or more medium access control elements (MAC CEs).

[0285] Aspect 8: The method of any one of aspects 1-7, wherein the neural network is based at least in part on federated learning.

[0286] Aspect 9: The method of aspect 8, wherein transmitting the report comprises: transmitting the report to the second device, transmitting the report to a user equipment (UE), or transmitting the report to the second device and the UE.

[0287] Aspect 10: The method of any one of aspects 8-9, wherein the request indicates that updates to the one or more weights are reported at a configured periodicity.

[0288] Aspect 11: The method of any of Aspects 8-10, wherein the request indicates reporting a first subset of updates associated with a first layer of the neural network at a first periodicity, and wherein the request indicates reporting a second subset of updates associated with a second layer of the neural network at a second periodicity.

[0289] Aspect 12: The method of any of aspects 8-11, wherein the request indicates reporting updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

[0290] Aspect 13: The method of any of aspects 1-12, further comprising: receiving an indication to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

[0291] Aspect 14: The method of Aspect 13, further comprising: transmitting an indication of: an environmental change at the first device, a request to reset all weights of the neural network, or an environmental change at the first device and a request to reset all weights of the neural network.

[0292] Aspect 15: The method of aspect 14, wherein transmitting the indication comprises transmitting the indication via one or more medium access control elements (MAC CEs) or uplink control information.

[0293] Aspect 16: A method as in any of Aspects 1-15, wherein the request includes one or more of: an indication that the update is to be reported as a differential update for the one or more weights, or an indication of a differential time period to be used to determine the differential update for the one or more weights.

[0294] Aspect 17: The method of aspect 16, further comprising: receiving an indication to use an additional neural network to determine a differential update for the one or more weights.

[0295] Aspect 18: The method of Aspect 17, further comprising: transmitting an indication of the capability of the first device to use a neural network to determine differential updates for the one or more weights, wherein receiving an indication of using an additional neural network to determine differential updates for the one or more weights is based at least in part on transmitting the indication of the capability of the first device.

[0296] Aspect 19: A wireless communication method performed by a second device, comprising: transmitting to a first device a request to report an update to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message; and receiving a report indicating an update to the one or more weights.

[0297] Aspect 20: The method of aspect 19, wherein the request comprises: an indication to the first device of the neural network of one or more layers to which the update is to be reported.

[0298] Aspect 21: The method of aspect 20, wherein the request comprises: an indication to the first device of the neural network that the subset of weights including the one or more weights within one or more layers for which the first device is to report updates.

[0299] Aspect 22: A method as in any one of Aspects 19-21, wherein transmitting the request comprises: transmitting the request via non-periodic signaling, transmitting the request via semi-persistent signaling, transmitting the request via downlink control information, transmitting the request via one or more media access control elements (MAC CEs), or a combination thereof.

[0300] Aspect 23: The method of any one of aspects 19-22, wherein receiving the report comprises receiving the report via one or more medium access control elements (MAC CEs), or receiving the report via a physical uplink shared channel.

[0301] Aspect 24: The method of any of Aspects 19-23, further comprising: receiving an indication that the one or more weights have been updated, wherein transmitting the request is based at least in part on receiving the indication.

[0302] Aspect 25: The method of aspect 24, wherein receiving the indication comprises receiving the indication via one or more of: uplink control information, or one or more medium access control elements (MAC CEs).

[0303] Aspect 26: The method of any one of Aspects 19-25, wherein the neural network is based at least in part on federated learning.

[0304] Aspect 27: The method of aspect 26, wherein the request indicates that updates to the one or more weights are reported at a configured periodicity.

[0305] Aspect 28: The method of any of Aspects 26-27, wherein the request indicates reporting a first subset of updates associated with a first layer of the neural network at a first periodicity, and wherein the request indicates reporting a second subset of updates associated with a second layer of the neural network at a second periodicity.

[0306] Aspect 29: The method of any of Aspects 26-28, wherein the request indicates reporting updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

[0307] Aspect 30: The method of any of Aspects 19-29, further comprising: transmitting an indication to reset all weights of the neural network based at least in part on the dynamic radio access network mode update.

[0308] Aspect 31: The method of Aspect 30, further comprising: receiving an indication of: an environmental change at the first device, a request to reset all weights of the neural network, or an environmental change at the first device and a request to reset all weights of the neural network.

[0309] Aspect 32: The method of aspect 31, wherein receiving the indication comprises receiving the indication via one or more medium access control elements (MAC CEs) or uplink control information.

[0310] Aspect 33: A method as in any of Aspects 19-32, wherein the request includes one or more of: an indication that the update is to be reported as a differential update for the one or more weights, or an indication of a differential time period to be used to determine the differential update for the one or more weights.

[0311] Aspect 34: The method of Aspect 33, further comprising: transmitting an indication to use an additional neural network to determine a differential update for the one or more weights.

[0312] Aspect 35: The method of Aspect 34 further comprises: receiving an indication of the capability of the first device to use a neural network to determine differential updates for the one or more weights, wherein transmitting the indication of using an additional neural network to determine differential updates for the one or more weights is based at least in part on receiving the indication of the capability of the first device.

[0313] Aspect 36: An apparatus for wireless communication at a device, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform a method as in one or more aspects of Aspects 1-35.

[0314] Aspect 37: A device for wireless communication, comprising a memory and one or more processors coupled to the memory, the one or more processors configured to perform the method of one or more aspects of aspects 1-35.

[0315] Aspect 38: An apparatus for wireless communication, comprising at least one means for performing the method as recited in one or more of Aspects 1-35.

[0316] Aspect 39: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method of one or more of aspects 1-35.

[0317] Aspect 40: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform the method of one or more of aspects 1-35.

[0318] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the aspects to the precise form disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the aspects.

[0319] As used herein, the terms "first" and "second" may be used to distinguish one device from another. The terms "first" and "second" are intended to be broadly interpreted and do not indicate an order of devices, relative positions of devices, or an order of operational performance of communications between devices.

[0320] As used herein, the term "component" is intended to be broadly interpreted as a combination of hardware and / or hardware and software. "Software" should be broadly interpreted as meaning an instruction, an instruction set, a code, a code segment, a program code, a program, a subroutine, a software module, an application, a software application, a software package, a routine, a subroutine, an object, an executable, a thread of execution, a procedure, and / or a function, etc., whether it is described in software, firmware, middleware, microcode, hardware description language or other terms. As used herein, a processor is implemented with hardware and / or a combination of hardware and software. It will be apparent that the systems and / or methods described herein can be implemented in different forms of hardware and / or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit various aspects. Thus, the operation and behavior of these systems and / or methods are described herein without reference to specific software code - it is understood that software and hardware can be designed to implement these systems and / or methods based at least in part on the description herein.

[0321] As used herein, satisfying a threshold may refer to a value being greater than a threshold, greater than or equal to a threshold, less than a threshold, less than or equal to a threshold, equal to a threshold, not equal to a threshold, etc., depending on the context.

[0322] Although specific feature combinations are described in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. In fact, many of these features can be combined in a manner not specifically described in the claims and / or not disclosed in the specification. Although each dependent claim listed below can be directly subordinate to only one claim, the disclosure of various aspects includes that each dependent claim is combined with each other claim in this group of claims. As used herein, the phrase quoting "at least one of" a column item refers to any combination of these items, including single members. As an example, "at least one of a, b or c" is intended to encompass: a, b, c, ab, ac, bc, and abc, and any combination with multiple identical elements (for example, aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other sorting of a, b and c).

[0323] The elements, actions or instructions used herein should not be interpreted as key or necessary unless explicitly described as such. Moreover, as used herein, the articles "one" and "a" are intended to include one or more projects and can be used interchangeably with "one or more". In addition, as used herein, the article "the" is intended to include one or more projects cited in conjunction with the article "the", and can be used interchangeably with "one or more". In addition, as used herein, the terms "set (set)" and "group" are intended to include one or more projects (for example, related items, non-related items, or a combination of related items and non-related items), and can be used interchangeably with "one or more". In the case of being intended to have only one project, the phrase "only one" or similar language is used. Moreover, as used herein, the terms "having", "containing", "comprising" etc. are intended to be open terms. In addition, the phrase "based on" is intended to mean "at least partially based on", unless otherwise explicitly stated. Furthermore, as used herein, the term "or" when used in a sequence is intended to be inclusive and used interchangeably with "and / or" unless expressly stated otherwise (e.g., when used in conjunction with "either of" or "only one of").

Claims

1. A first apparatus for wirelessly reporting weight updates to a neural network, comprising: Memory; as well as one or more processors coupled to the memory, the one or more processors configured to: receiving a request to report updates to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message, wherein the request comprises one or more of: an indication to the first device of the neural network of one or more layers for which the updates are to be reported, or an indication to the first device of the neural network of a subset of weights including the one or more weights within the one or more layers for which the updates are to be reported, wherein the request indicates that a first updated subset associated with a first layer of the neural network is to be reported at a first periodicity, and wherein the request indicates that a second updated subset associated with a second layer of the neural network is to be reported at a second periodicity; as well as A report is transmitted indicating the update to the one or more weights.

2. The first device of claim 1 , wherein the one or more processors are further configured to: transmitting an indication that the one or more weights have been updated, Wherein receiving the request is based at least in part on transmitting the indication.

3. The first device of claim 1, wherein the neural network is based at least in part on federated learning.

4. The first device of claim 3, wherein the one or more processors are configured to transmit the report: transmitting the report to the second device, transmitting the report to a user equipment (UE), or The report is transmitted to the second device and the UE. The first device of claim 3 , wherein the request indicates that the updates to the one or more weights are reported at a configured periodicity.

6. The first device of claim 3, wherein the request indicates reporting the updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

7. The first device of claim 1 , wherein the one or more processors are further configured to: An indication is received to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

8. The first device of claim 7, wherein the one or more processors are further configured to: Transmit instructions regarding: a change in the environment at the first device, a request to reset all weights of the neural network, or The environmental change at the first device and the request to reset all weights of the neural network.

9. The first device of claim 1 , wherein the request comprises one or more of: an indication to report the update as a differential update to the one or more weights, or An indication of a differential time period to be used in determining the differential update for the one or more weights.

10. The first device of claim 9, wherein the one or more processors are further configured to: An indication is received to use an additional neural network to determine the differential update for the one or more weights.

11. The first device of claim 10, wherein the one or more processors are further configured to: transmitting an indication of the ability of the first device to use the neural network to determine the differential update for the one or more weights, Wherein receiving the indication to use the additional neural network to determine the differential update for the one or more weights is based at least in part on transmitting the indication of the capabilities of the first device.

12. A second apparatus for wirelessly reporting weight updates to a neural network, comprising: Memory; as well as one or more processors coupled to the memory, the one or more processors configured to: transmitting, to a first device, a request to report updates to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message, wherein the request comprises one or more of: an indication of one or more layers of the neural network for which the first device is to report the updates, or an indication of a subset of weights within the one or more layers of the neural network for which the first device is to report the updates that include the one or more weights, wherein the request indicates that a first subset of updates associated with a first layer of the neural network is to be reported at a first periodicity, wherein the request indicates that a second subset of updates associated with a second layer of the neural network is to be reported at a second periodicity; as well as A report is received indicating the update to the one or more weights.

13. The second device of claim 12, wherein the one or more processors are further configured to: receiving an indication that the one or more weights have been updated, Wherein transmitting the request is based at least in part on receiving the indication.

14. The second device of claim 12, wherein the neural network is based at least in part on federated learning.

15. The second device of claim 14, wherein the request indicates that the updates to the one or more weights are reported at a configured periodicity.

16. The second device of claim 14, wherein the request indicates reporting the updates associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

17. The second device of claim 12, wherein the one or more processors are further configured to: An indication is transmitted to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

18. The second device of claim 17, wherein the one or more processors are further configured to: Receive instructions on: a change in the environment at the first device, a request to reset all weights of the neural network, or The environmental change at the first device and the request to reset all weights of the neural network.

19. The second device of claim 12, wherein the request comprises one or more of: an indication to report the update as a differential update to the one or more weights, or An indication of a differential time period to be used in determining the differential update for the one or more weights.

20. The second device of claim 19, wherein the one or more processors are further configured to: An indication is transmitted to use an additional neural network to determine the differential update for the one or more weights.

21. The second device of claim 20, wherein the one or more processors are further configured to: receiving an indication of an ability of the first device to use the neural network to determine the differential update for the one or more weights, Wherein transmitting the indication to use the additional neural network to determine the differential update for the one or more weights is based at least in part on receiving the indication of the capabilities of the first device.

22. A wireless communication method, performed by a first device, for reporting weight updates to a neural network, comprising: receiving a request to report updates to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message, wherein the request comprises one or more of: an indication to the first device of the neural network of one or more layers for which the updates are to be reported, or an indication to the first device of the neural network of a subset of weights including the one or more weights within the one or more layers for which the updates are to be reported, wherein the request indicates that a first updated subset associated with a first layer of the neural network is to be reported at a first periodicity, and wherein the request indicates that a second updated subset associated with a second layer of the neural network is to be reported at a second periodicity; as well as A report is transmitted indicating the update to the one or more weights.

23. The method of claim 22, further comprising: transmitting an indication that the one or more weights have been updated, Wherein receiving the request is based at least in part on transmitting the indication.

24. The method of claim 22, wherein the neural network is based at least in part on federated learning.

25. The method of claim 24, further comprising: transmitting the report to the second device, transmitting the report to a user equipment (UE), or The report is transmitted to the second device and the UE.

26. The method of claim 24, wherein the request indicates that the updates to the one or more weights are reported at a configured periodicity.

27. The method of claim 24, wherein the request indicates reporting the update associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

28. The method of claim 22, further comprising: An indication is received to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

29. The method of claim 28, further comprising: Transmit instructions regarding: a change in the environment at the first device, a request to reset all weights of the neural network, or The environmental change at the first device and the request to reset all weights of the neural network.

30. The method of claim 22, wherein the request comprises one or more of: an indication to report the update as a differential update to the one or more weights, or An indication of a differential time period to be used in determining the differential update for the one or more weights.

31. The method of claim 30, further comprising: An indication is received to use an additional neural network to determine the differential update for the one or more weights.

32. The method of claim 31 , further comprising: transmitting an indication of the ability of the first device to use the neural network to determine the differential update for the one or more weights, Wherein receiving the indication to use the additional neural network to determine the differential update for the one or more weights is based at least in part on transmitting the indication of the capabilities of the first device.

33. A wireless communication method, performed by a second device, for reporting weight updates to a neural network, comprising: transmitting, to a first device, a request to report updates to one or more weights of a neural network configured to encode a channel state information feedback (CSF) message, wherein the request comprises one or more of: an indication of one or more layers of the neural network for which the first device is to report the updates, or an indication of a subset of weights within the one or more layers of the neural network for which the first device is to report the updates that include the one or more weights, wherein the request indicates that a first subset of updates associated with a first layer of the neural network is to be reported at a first periodicity, wherein the request indicates that a second subset of updates associated with a second layer of the neural network is to be reported at a second periodicity; as well as A report is received indicating the update to the one or more weights.

34. The method of claim 33, further comprising: receiving an indication that the one or more weights have been updated, Wherein transmitting the request is based at least in part on receiving the indication.

35. The method of claim 33, wherein the neural network is based at least in part on federated learning.

36. The method of claim 35, wherein the request indicates that the updates to the one or more weights are reported at a configured periodicity.

37. The method of claim 35, wherein the request indicates reporting the update associated with one or more layers of the neural network based at least in part on a Doppler metric of the first device.

38. The method of claim 33, further comprising: An indication is transmitted to reset all weights of the neural network based at least in part on a dynamic radio access network mode update.

39. The method of claim 38, further comprising: Receive instructions on: a change in the environment at the first device, a request to reset all weights of the neural network, or The environmental change at the first device and the request to reset all weights of the neural network.

40. The method of claim 33, wherein the request comprises one or more of: an indication to report the update as a differential update to the one or more weights, or An indication of a differential time period to be used in determining the differential update for the one or more weights.

41. The method of claim 40, further comprising: An indication is transmitted to use an additional neural network to determine the differential update for the one or more weights.

42. The method of claim 41, further comprising: receiving an indication of an ability of the first device to use the neural network to determine the differential update for the one or more weights, Wherein transmitting the indication to use the additional neural network to determine the differential update for the one or more weights is based at least in part on receiving the indication of the capabilities of the first device.

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