WO2025097413A1

WO2025097413A1 - Csi payload processing indication for model based csi feedback

Info

Publication number: WO2025097413A1
Application number: PCT/CN2023/130895
Authority: WO
Inventors: Jay Kumar Sundararajan; Taesang Yoo; Chenxi HAO; Abdelrahman Mohamed Ahmed Mohamed IBRAHIM; Pavan Kumar Vitthaladevuni; Naga Bhushan
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2023-11-10
Filing date: 2023-11-10
Publication date: 2025-05-15
Anticipated expiration: 2026-05-10

Abstract

A user equipment (UE) receives one or more reference signals. The UE transmits a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals and provides a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. A network node provides one or more reference signals; The network node obtains a CSI message comprising a CSI payload based on measurements of the one or more reference signals and obtains a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model.

Description

CSI PAYLOAD PROCESSING INDICATION FOR MODEL BASED CSI FEEDBACK

TECHNICAL FIELD

The present disclosure relates generally to communication systems, and more particularly, to wireless communication including the measurement and reporting of channel state information (CSI) .

INTRODUCTION

Wireless communication systems are widely deployed to provide various 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. 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, and time division synchronous code division multiple access (TD-SCDMA) systems.

These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. An example telecommunication standard is 5G New Radio (NR) . 5G NR is part of a continuous mobile broadband evolution promulgated by Third Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with Internet of Things (IoT) ) , and other requirements. 5G NR includes services associated with enhanced mobile broadband (eMBB) , massive machine type communications (mMTC) , and ultra-reliable low latency communications (URLLC) . Some aspects of 5G NR may be based on the 4G Long Term Evolution (LTE) standard. There exists a need for further improvements in 5G NR technology. These improvements may also be applicable to other multi-access technologies and the telecommunication standards that employ these technologies.

BRIEF SUMMARY

The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects. This summary neither identifies key or critical elements of all aspects nor delineates the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided for wireless communication at a user equipment (UE) . The apparatus may include at least one memory and at least one processor. The at least one processor may be configured, based at least in part on information stored in the at least one memory, individually or in any combination to cause the UE to receive one or more reference signals; transmit a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals; and provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided for wireless communication at a network node. The apparatus may include at least one memory and at least one processor. The at least one processor may be configured, based at least in part on information stored in the at least one memory, individually or in any combination to cause the network node to provide one or more reference signals; obtain a CSI message comprising a CSI payload based on measurements of the one or more reference signals; and obtain a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model.

To the accomplishment of the foregoing and related ends, the one or more aspects may include the features hereinafter fully described and particularly pointed out in the claims. The following description and the drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative, however, of but a few of the various ways in which the principles of various aspects may be employed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a wireless communications system and an access network.

FIG. 2A is a diagram illustrating an example of a first frame, in accordance with various aspects of the present disclosure.

FIG. 2B is a diagram illustrating an example of downlink (DL) channels within a subframe, in accordance with various aspects of the present disclosure.

FIG. 2C is a diagram illustrating an example of a second frame, in accordance with various aspects of the present disclosure.

FIG. 2D is a diagram illustrating an example of uplink (UL) channels within a subframe, in accordance with various aspects of the present disclosure.

FIG. 3 is a diagram illustrating an example of a base station and user equipment (UE) in an access network.

FIG. 4 illustrates a diagram showing an example of compression and reconstruction of CSI feedback based on artificial intelligence (AI) or machine learning (ML) (AI/ML) based models.

FIG. 5 is an example of training and inference based on an AI/ML model for a method of wireless communication.

FIG. 6A illustrates an example of compression and reconstruction of CSI feedback based on AI/ML based models and including a CSI payload processing indication.

FIG. 6B illustrates an examples of different arrangements for a two part CSI message.

FIG. 7 is an example communication flow between a UE and a base station showing example aspects of CSI payload processing indication, in accordance with aspects presented herein.

FIG. 8 is a flowchart of a method of wireless communication.

FIG. 9 is a flowchart of a method of wireless communication.

FIG. 10 is a flowchart of a method of wireless communication.

FIG. 11 is a flowchart of a method of wireless communication.

FIG. 12 is a diagram illustrating an example of a hardware implementation for an apparatus.

FIG. 13 is a diagram illustrating an example of a hardware implementation for a network entity.

DETAILED DESCRIPTION

The measurement and reporting of CSI may be used to adjust and improve communication between two devices, such as communication between a UE and network or between two UEs. In order to balance the benefits of CSI with efficient use of resources, in some aspects, a UE may employ CSI compression to reduce the overhead for reporting the CSI. For example, the UE may use a trained AI/ML model to compress the CSI, and a base station may use a corresponding trained AI/ML model to reconstruct the compressed CSI. By compressing the CSI before transmission of the CSI feedback, the overhead can be reduced without reducing the amount or frequency of the CSI. Aspects presented herein further optimize the CSI reporting by enabling a UE to select between different types of processing of the compressed CSI when reporting CSI feedback to the base station.

For example, if a wireless channel is not changing very fast, the UE may convey the CSI feedback in an incremental manner. The UE may compute the successive difference of the CSI feedback relative to the CSI feedback from the previous occasion. Then, the UE may quantize the difference. The UE may then apply entropy coding to the quantized output before transmitting the compressed CSI feedback as a bit sequence. In contrast, if the channel has changed significantly, the UE may send CSI feedback as a current channel state without applying a successive difference operation. In order to enable the base station to apply the corresponding processing of the CSI payload to derive an input to the AI/ML model, the UE may convey its choice in a CSI payload processing indication that is transmitted with the CSI message.

Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. The indication of the payload processing enables flexibility by allowing a UE to dynamically select a suitable format or processing depending on the local conditions. For example, a UE may decide whether to use differential encoding of the payload dynamically, and the UE may indicate its choice to the network using the CSI payload processing indication. Such flexibility can help optimize the size of the payload to convey the CSI and thereby reduce feedback overhead.

The detailed description set forth below in connection with the drawings describes various configurations and does not represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts.

Several aspects of telecommunication systems are presented with reference to various apparatus and methods. These apparatus and methods are described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “elements” ) . These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.

By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. When multiple processors are implemented, the multiple processors may perform the functions individually or in combination. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs) , central processing units (CPUs) , application processors, digital signal processors (DSPs) , reduced instruction set computing (RISC) processors, systems on a chip (SoC) , baseband processors, field programmable gate arrays (FPGAs) , programmable logic devices (PLDs) , state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system may execute software. Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination thereof.

Accordingly, in one or more example aspects, implementations, and/or use cases, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, such computer-readable media can include a random-access memory (RAM) , a read-only memory (ROM) , an electrically erasable programmable ROM (EEPROM) , optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the types of computer-readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.

While aspects, implementations, and/or use cases are described in this application by illustration to some examples, additional or different aspects, implementations and/or use cases may come about in many different arrangements and scenarios. Aspects, implementations, and/or use cases described herein may be implemented across many differing platform types, devices, systems, shapes, sizes, and packaging arrangements. For example, aspects, implementations, and/or use cases may come about via integrated chip implementations and other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, artificial intelligence (AI) -enabled devices, etc. ) . While some examples may or may not be specifically directed to use cases or applications, a wide assortment of applicability of described examples may occur. Aspects, implementations, and/or use cases may range a spectrum from chip-level or modular components to non-modular, non-chip-level implementations and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more techniques herein. In some practical settings, devices incorporating described aspects and features may also include additional components and features for implementation and practice of claimed and described aspect. For example, transmission and reception of wireless signals necessarily includes a number of components for analog and digital purposes (e.g., hardware components including antenna, RF-chains, power amplifiers, modulators, buffer, processor (s) , interleaver, adders/summers, etc. ) . Techniques described herein may be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc. of varying sizes, shapes, and constitution.

Deployment of communication systems, such as 5G NR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element, or a network equipment, such as a base station (BS) , or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a BS (such as a Node B (NB) , evolved NB (eNB) , NR BS, 5G NB, access point (AP) , a transmission reception point (TRP) , or a cell, etc. ) may be implemented as an aggregated base station (also known as a standalone BS or a monolithic BS) or a disaggregated base station.

An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more central or centralized units (CUs) , one or more distributed units (DUs) , or one or more radio units (RUs) ) . In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU and RU can be implemented as virtual units, i.e., a virtual central unit (VCU) , a virtual distributed unit (VDU) , or a virtual radio unit (VRU) .

Base station operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as the network configuration sponsored by the O-RAN Alliance) ) , or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN) ) . Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.

FIG. 1 is a diagram 100 illustrating an example of a wireless communications system and an access network. The illustrated wireless communications system includes a disaggregated base station architecture. The disaggregated base station architecture may include one or more CUs 110 that can communicate directly with a core network 120 via a backhaul link, or indirectly with the core network 120 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 125 via an E2 link, or a Non-Real Time (Non-RT) RIC 115 associated with a Service Management and Orchestration (SMO) Framework 105, or both) . A CU 110 may communicate with one or more DUs 130 via respective midhaul links, such as an F1 interface. The DUs 130 may communicate with one or more RUs 140 via respective fronthaul links. The RUs 140 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, the UE 104 may be simultaneously served by multiple RUs 140.

Each of the units, i.e., the CUs 110, the DUs 130, the RUs 140, as well as the Near-RT RICs 125, the Non-RT RICs 115, and the SMO Framework 105, may include one or more interfaces or be coupled to one or more interfaces configured to receive or to transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or to transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter, or a transceiver (such as an RF transceiver) , configured to receive or to transmit signals, or both, over a wireless transmission medium to one or more of the other units.

In some aspects, the CU 110 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC) , packet data convergence protocol (PDCP) , service data adaptation protocol (SDAP) , or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 110. The CU 110 may be configured to handle user plane functionality (i.e., Central Unit –User Plane (CU-UP) ) , control plane functionality (i.e., Central Unit –Control Plane (CU-CP) ) , or a combination thereof. In some implementations, the CU 110 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as an E1 interface when implemented in an O-RAN configuration. The CU 110 can be implemented to communicate with the DU 130, as necessary, for network control and signaling.

The DU 130 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 140. In some aspects, the DU 130 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, demodulation, or the like) depending, at least in part, on a functional split, such as those defined by 3GPP. In some aspects, the DU 130 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 130, or with the control functions hosted by the CU 110.

Lower-layer functionality can be implemented by one or more RUs 140. In some deployments, an RU 140, controlled by a DU 130, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT) , inverse FFT (iFFT) , digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like) , or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU (s) 140 can be implemented to handle over the air (OTA) communication with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU (s) 140 can be controlled by the corresponding DU 130. In some scenarios, this configuration can enable the DU (s) 130 and the CU 110 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

The SMO Framework 105 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 105 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements that may be managed via an operations and maintenance interface (such as an O1 interface) . For virtualized network elements, the SMO Framework 105 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 190) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface) . Such virtualized network elements can include, but are not limited to, CUs 110, DUs 130, RUs 140 and Near-RT RICs 125. In some implementations, the SMO Framework 105 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 111, via an O1 interface. Additionally, in some implementations, the SMO Framework 105 can communicate directly with one or more RUs 140 via an O1 interface. The SMO Framework 105 also may include a Non-RT RIC 115 configured to support functionality of the SMO Framework 105.

The Non-RT RIC 115 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence (AI) /machine learning (ML) (AI/ML) workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 125. The Non-RT RIC 115 may be coupled to or communicate with (such as via an A1 interface) the Near-RT RIC 125. The Near-RT RIC 125 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 110, one or more DUs 130, or both, as well as an O-eNB, with the Near-RT RIC 125.

In some implementations, to generate AI/ML models to be deployed in the Near-RT RIC 125, the Non-RT RIC 115 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 125 and may be received at the SMO Framework 105 or the Non-RT RIC 115 from non-network data sources or from network functions. In some examples, the Non-RT RIC 115 or the Near-RT RIC 125 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 115 may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions through the SMO Framework 105 (such as reconfiguration via O1) or via creation of RAN management policies (such as A1 policies) .

At least one of the CU 110, the DU 130, and the RU 140 may be referred to as a base station 102. Accordingly, a base station 102 may include one or more of the CU 110, the DU 130, and the RU 140 (each component indicated with dotted lines to signify that each component may or may not be included in the base station 102) . The base station 102 provides an access point to the core network 120 for a UE 104. The base station 102 may include macrocells (high power cellular base station) and/or small cells (low power cellular base station) . The small cells include femtocells, picocells, and microcells. A network that includes both small cell and macrocells may be known as a heterogeneous network. A heterogeneous network may also include Home Evolved Node Bs (eNBs) (HeNBs) , which may provide service to a restricted group known as a closed subscriber group (CSG) . The communication links between the RUs 140 and the UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to an RU 140 and/or downlink (DL) (also referred to as forward link) transmissions from an RU 140 to a UE 104. The communication links may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links may be through one or more carriers. The base station 102 /UEs 104 may use spectrum up to Y MHz (e.g., 5, 10, 15, 20, 100, 400, etc. MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x component carriers) used for transmission in each direction. The carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL than for UL) . The component carriers may include a primary component carrier and one or more secondary component carriers. A primary component carrier may be referred to as a primary cell (PCell) and a secondary component carrier may be referred to as a secondary cell (SCell) .

Certain UEs 104 may communicate with each other using device-to-device (D2D) communication link 158. The D2D communication link 158 may use the DL/UL wireless wide area network (WWAN) spectrum. The D2D communication link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH) , a physical sidelink discovery channel (PSDCH) , a physical sidelink shared channel (PSSCH) , and a physical sidelink control channel (PSCCH) . D2D communication may be through a variety of wireless D2D communications systems, such as for example, Bluetooth^TM (Bluetooth is a trademark of the Bluetooth Special Interest Group (SIG) ) , Wi-Fi^TM (Wi-Fi is a trademark of the Wi-Fi Alliance) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.

The wireless communications system may further include a Wi-Fi AP 150 in communication with UEs 104 (also referred to as Wi-Fi stations (STAs) ) via communication link 154, e.g., in a 5 GHz unlicensed frequency spectrum or the like. When communicating in an unlicensed frequency spectrum, the UEs 104 /AP 150 may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

The electromagnetic spectrum is often subdivided, based on frequency/wavelength, into various classes, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz –7.125 GHz) and FR2 (24.25 GHz –52.6 GHz) . Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz –300 GHz) which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band.

The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz –24.25 GHz) . Frequency bands falling within FR3 may inherit FR1 characteristics and/or FR2 characteristics, and thus may effectively extend features of FR1 and/or FR2 into mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR2-2 (52.6 GHz –71 GHz) , FR4 (71 GHz –114.25 GHz) , and FR5 (114.25 GHz –300 GHz) . Each of these higher frequency bands falls within the EHF band.

With the above aspects in mind, unless specifically stated otherwise, the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Further, unless specifically stated otherwise, the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR2-2, and/or FR5, or may be within the EHF band.

The base station 102 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and/or antenna arrays to facilitate beamforming. The base station 102 may transmit a beamformed signal 182 to the UE 104 in one or more transmit directions. The UE 104 may receive the beamformed signal from the base station 102 in one or more receive directions. The UE 104 may also transmit a beamformed signal 184 to the base station 102 in one or more transmit directions. The base station 102 may receive the beamformed signal from the UE 104 in one or more receive directions. The base station 102 /UE 104 may perform beam training to determine the best receive and transmit directions for each of the base station 102 /UE 104. The transmit and receive directions for the base station 102 may or may not be the same. The transmit and receive directions for the UE 104 may or may not be the same.

The base station 102 may include and/or be referred to as a gNB, Node B, eNB, an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS) , an extended service set (ESS) , a TRP, network node, network entity, network equipment, or some other suitable terminology. The base station 102 can be implemented as an integrated access and backhaul (IAB) node, a relay node, a sidelink node, an aggregated (monolithic) base station with a baseband unit (BBU) (including a CU and a DU) and an RU, or as a disaggregated base station including one or more of a CU, a DU, and/or an RU. The set of base stations, which may include disaggregated base stations and/or aggregated base stations, may be referred to as next generation (NG) RAN (NG-RAN) .

The core network 120 may include an Access and Mobility Management Function (AMF) 161, a Session Management Function (SMF) 162, a User Plane Function (UPF) 163, a Unified Data Management (UDM) 164, one or more location servers 168, and other functional entities. The AMF 161 is the control node that processes the signaling between the UEs 104 and the core network 120. The AMF 161 supports registration management, connection management, mobility management, and other functions. The SMF 162 supports session management and other functions. The UPF 163 supports packet routing, packet forwarding, and other functions. The UDM 164 supports the generation of authentication and key agreement (AKA) credentials, user identification handling, access authorization, and subscription management. The one or more location servers 168 are illustrated as including a Gateway Mobile Location Center (GMLC) 165 and a Location Management Function (LMF) 166. However, generally, the one or more location servers 168 may include one or more location/positioning servers, which may include one or more of the GMLC 165, the LMF 166, a position determination entity (PDE) , a serving mobile location center (SMLC) , a mobile positioning center (MPC) , or the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients/applications (e.g., emergency services) for accessing UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute the position of the UE 104. The NG-RAN may utilize one or more positioning methods in order to determine the position of the UE 104. Positioning the UE 104 may involve signal measurements, a position estimate, and an optional velocity computation based on the measurements. The signal measurements may be made by the UE 104 and/or the base station 102 serving the UE 104. The signals measured may be based on one or more of a satellite positioning system (SPS) 170 (e.g., one or more of a Global Navigation Satellite System (GNSS) , global position system (GPS) , non-terrestrial network (NTN) , or other satellite position/location system) , LTE signals, wireless local area network (WLAN) signals, Bluetooth signals, a terrestrial beacon system (TBS) , sensor-based information (e.g., barometric pressure sensor, motion sensor) , NR enhanced cell ID (NR E-CID) methods, NR signals (e.g., multi-round trip time (Multi-RTT) , DL angle-of-departure (DL-AoD) , DL time difference of arrival (DL-TDOA) , UL time difference of arrival (UL-TDOA) , and UL angle-of-arrival (UL-AoA) positioning) , and/or other systems/signals/sensors.

Examples of UEs 104 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA) , a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player) , a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor/actuator, a display, or any other similar functioning device. Some of the UEs 104 may be referred to as IoT devices (e.g., parking meter, gas pump, toaster, vehicles, heart monitor, etc. ) . The UE 104 may also be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. In some scenarios, the term UE may also apply to one or more companion devices such as in a device constellation arrangement. One or more of these devices may collectively access the network and/or individually access the network.

Referring again to FIG. 1, in some aspects, the UE 104 and the base station 102 may each use a trained AI/ML model to exchange compressed CSI feedback, as described herein. In some aspects, the UE 104 may have a CSI payload processing indication component 198 that may be configured to receive one or more reference signals; transmit a CSI message comprising a CSI payload based on measurements of the one or more reference signals; and provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model, e.g., as described in more detail herein. In certain aspects, the base station 102 may have a CSI reconstruction component 199 that may be configured to provide one or more reference signals; obtain a CSI message comprising a CSI payload based on measurements of the one or more reference signals; and obtain a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model, e.g., as described in more detail herein.

FIG. 2A is a diagram 200 illustrating an example of a first subframe within a 5G NR frame structure. FIG. 2B is a diagram 230 illustrating an example of DL channels within a 5G NR subframe. FIG. 2C is a diagram 250 illustrating an example of a second subframe within a 5G NR frame structure. FIG. 2D is a diagram 280 illustrating an example of UL channels within a 5G NR subframe. The 5G NR frame structure may be frequency division duplexed (FDD) in which for a particular set of subcarriers (carrier system bandwidth) , subframes within the set of subcarriers are dedicated for either DL or UL, or may be time division duplexed (TDD) in which for a particular set of subcarriers (carrier system bandwidth) , subframes within the set of subcarriers are dedicated for both DL and UL. In the examples provided by FIGs. 2A, 2C, the 5G NR frame structure is assumed to be TDD, with subframe 4 being configured with slot format 28 (with mostly DL) , where D is DL, U is UL, and F is flexible for use between DL/UL, and subframe 3 being configured with slot format 1 (with all UL) . While subframes 3, 4 are shown with slot formats 1, 28, respectively, any particular subframe may be configured with any of the various available slot formats 0-61. Slot formats 0, 1 are all DL, UL, respectively. Other slot formats 2-61 include a mix of DL, UL, and flexible symbols. UEs are configured with the slot format (dynamically through DL control information (DCI) , or semi-statically/statically through radio resource control (RRC) signaling) through a received slot format indicator (SFI) . Note that the description infra applies also to a 5G NR frame structure that is TDD.

FIGs. 2A-2D illustrate a frame structure, and the aspects of the present disclosure may be applicable to other wireless communication technologies, which may have a different frame structure and/or different channels. A frame (10 ms) may be divided into 10 equally sized subframes (1 ms) . Each subframe may include one or more time slots. Subframes may also include mini-slots, which may include 7, 4, or 2 symbols. Each slot may include 14 or 12 symbols, depending on whether the cyclic prefix (CP) is normal or extended. For normal CP, each slot may include 14 symbols, and for extended CP, each slot may include 12 symbols. The symbols on DL may be CP orthogonal frequency division multiplexing (OFDM) (CP-OFDM) symbols. The symbols on UL may be CP-OFDM symbols (for high throughput scenarios) or discrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) symbols (for power limited scenarios; limited to a single stream transmission) . The number of slots within a subframe is based on the CP and the numerology. The numerology defines the subcarrier spacing (SCS) (see Table 1) . The symbol length/duration may scale with 1/SCS.

Table 1: Numerology, SCS, and CP

For normal CP (14 symbols/slot) , different numerologies μ 0 to 4 allow for 1, 2, 4, 8, and 16 slots, respectively, per subframe. For extended CP, the numerology 2 allows for 4 slots per subframe. Accordingly, for normal CP and numerology μ, there are 14 symbols/slot and 2^μ slots/subframe. The subcarrier spacing may be equal to 2^μ *15 kHz, where μ is the numerology 0 to 4. As such, the numerology μ=0 has a subcarrier spacing of 15 kHz and the numerology μ=4 has a subcarrier spacing of 240 kHz. The symbol length/duration is inversely related to the subcarrier spacing. FIGs. 2A-2D provide an example of normal CP with 14 symbols per slot and numerology μ=2 with 4 slots per subframe. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μs. Within a set of frames, there may be one or more different bandwidth parts (BWPs) (see FIG. 2B) that are frequency division multiplexed. Each BWP may have a particular numerology and CP (normal or extended) .

A resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as physical RBs (PRBs) ) that extends 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs) . The number of bits carried by each RE depends on the modulation scheme.

As illustrated in FIG. 2A, some of the REs carry reference (pilot) signals (RS) for the UE.The RS may include demodulation RS (DM-RS) (indicated as R for one particular configuration, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS may also include beam measurement RS (BRS) , beam refinement RS (BRRS) , and phase tracking RS (PT-RS) .

FIG. 2B illustrates an example of various DL channels within a subframe of a frame. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs) (e.g., 1, 2, 4, 8, or 16 CCEs) , each CCE including six RE groups (REGs) , each REG including 12 consecutive REs in an OFDM symbol of an RB. A PDCCH within one BWP may be referred to as a control resource set (CORESET) . A UE is configured to monitor PDCCH candidates in a PDCCH search space (e.g., common search space, UE-specific search space) during PDCCH monitoring occasions on the CORESET, where the PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs may be located at greater and/or lower frequencies across the channel bandwidth. A primary synchronization signal (PSS) may be within symbol 2 of particular subframes of a frame. The PSS is used by a UE 104 to determine subframe/symbol timing and a physical layer identity. A secondary synchronization signal (SSS) may be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI) . Based on the PCI, the UE can determine the locations of the DM-RS. The physical broadcast channel (PBCH) , which carries a master information block (MIB) , may be logically grouped with the PSS and SSS to form a synchronization signal (SS) /PBCH block (also referred to as SS block (SSB) ) . The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN) . The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs) , and paging messages.

As illustrated in FIG. 2C, some of the REs carry DM-RS (indicated as R for one particular configuration, but other DM-RS configurations are possible) for channel estimation at the base station. The UE may transmit DM-RS for the physical uplink control channel (PUCCH) and DM-RS for the physical uplink shared channel (PUSCH) . The PUSCH DM-RS may be transmitted in the first one or two symbols of the PUSCH. The PUCCH DM-RS may be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. The UE may transmit sounding reference signals (SRS) . The SRS may be transmitted in the last symbol of a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by a base station for channel quality estimation to enable frequency-dependent scheduling on the UL.

FIG. 2D illustrates an example of various UL channels within a subframe of a frame. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI) , a precoding matrix indicator (PMI) , a rank indicator (RI) , and hybrid automatic repeat request (HARQ) acknowledgment (ACK) (HARQ-ACK) feedback (i.e., one or more HARQ ACK bits indicating one or more ACK and/or negative ACK (NACK) ) . The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR) , a power headroom report (PHR) , and/or UCI.

FIG. 3 is a block diagram of a base station 310 in communication with a UE 350 in an access network. In the DL, Internet protocol (IP) packets may be provided to a controller/processor 375. The controller/processor 375 implements layer 3 and layer 2 functionality. Layer 3 includes a radio resource control (RRC) layer, and layer 2 includes a service data adaptation protocol (SDAP) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The controller/processor 375 provides RRC layer functionality associated with broadcasting of system information (e.g., MIB, SIBs) , RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release) , inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression /decompression, security (ciphering, deciphering, integrity protection, integrity verification) , and handover support functions; RLC layer functionality associated with the transfer of upper layer packet data units (PDUs) , error correction through ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs) , re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs) , demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

The transmit (TX) processor 316 and the receive (RX) processor 370 implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding/decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation/demodulation of physical channels, and MIMO antenna processing. The TX processor 316 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK) , quadrature phase-shift keying (QPSK) , M-phase-shift keying (M-PSK) , M-quadrature amplitude modulation (M-QAM) ) . The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and/or frequency domain, and then combined together using an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator 374 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the UE 350. Each spatial stream may then be provided to a different antenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx may modulate a radio frequency (RF) carrier with a respective spatial stream for transmission.

At the UE 350, each receiver 354Rx receives a signal through its respective antenna 352. Each receiver 354Rx recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 356. The TX processor 368 and the RX processor 356 implement layer 1 functionality associated with various signal processing functions. The RX processor 356 may perform spatial processing on the information to recover any spatial streams destined for the UE 350. If multiple spatial streams are destined for the UE 350, they may be combined by the RX processor 356 into a single OFDM symbol stream. The RX processor 356 then converts the OFDM symbol stream from the time-domain to the frequency domain using a Fast Fourier Transform (FFT) . The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 310. These soft decisions may be based on channel estimates computed by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the base station 310 on the physical channel. The data and control signals are then provided to the controller/processor 359, which implements layer 3 and layer 2 functionality.

The controller/processor 359 can be associated with at least one memory 360 that stores program codes and data. The at least one memory 360 may be referred to as a computer-readable medium. In the UL, the controller/processor 359 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets. The controller/processor 359 is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations.

Similar to the functionality described in connection with the DL transmission by the base station 310, the controller/processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression /decompression, and security (ciphering, deciphering, integrity protection, integrity verification) ; RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

Channel estimates derived by a channel estimator 358 from a reference signal or feedback transmitted by the base station 310 may be used by the TX processor 368 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor 368 may be provided to different antenna 352 via separate transmitters 354Tx. Each transmitter 354Tx may modulate an RF carrier with a respective spatial stream for transmission.

The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver function at the UE 350. Each receiver 318Rx receives a signal through its respective antenna 320. Each receiver 318Rx recovers information modulated onto an RF carrier and provides the information to a RX processor 370.

The controller/processor 375 can be associated with at least one memory 376 that stores program codes and data. The at least one memory 376 may be referred to as a computer-readable medium. In the UL, the controller/processor 375 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets. The controller/processor 375 is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations.

At least one of the TX processor 368, the RX processor 356, and the controller/processor 359 may be configured to perform aspects in connection with the CSI payload processing indication component 198 of FIG. 1.

At least one of the TX processor 316, the RX processor 370, and the controller/processor 375 may be configured to perform aspects in connection with the CSI reconstruction component 199 of FIG. 1.

The measurement and reporting of CSI may be used to adjust and improve communication between two devices, such as communication between a UE and network or between two UEs. In some aspects, such as high mobility situations, performance loss may occur based on channel variations that may occur more frequently than CSI updates. Although the CSI reporting rate can be increased, the increased uplink and downlink CSI overhead may reduce system throughput. Additionally, more frequent measurements, transmissions (e.g., of reference signals) , and/or reporting uses additional battery power at a UE.

In some aspects, the device providing the CSI may employ CSI compression to reduce the overhead for reporting the CSI. Reducing an overhead associated with CSI reporting may increase a performance of a UE and/or a base station, for example. While reducing a number of CSI measurements may increase a system throughput, the reduction may also reduce a quality of the CSI. More CSI measurements may provide increased measurement accuracy, yet increase the overhead. By compressing the CSI before transmission of the CSI feedback, the overhead can be reduced without reducing the amount or frequency of the CSI.

FIG. 4 illustrates a diagram 400 showing an example of compression and reconstruction of CSI feedback based on artificial intelligence (AI) or machine learning (ML) (AI/ML) based models. With an AI/ML based air interface, two wireless devices (e.g., such as a UE and a base station (e.g., gNB or other base station) or a first UE and a second UE) may use trained AI/ML models to implement a function.

For example, the UE 425 may intend to convey CSI to the base station 450. For example, the base station 450 may transmit one or more reference signals, which the UE 425 may measure to obtain the CSI feedback to provide to the base station 450. The UE 425 may use a CSI compression model 402, e.g., which may include a neural network, AI, and/or ML based model, to derive a compressed representation of the CSI to feed back in a transmission (e.g., of compressed CSI feedback 406) to the base station 450. The model 402 may also be referred to by other names, such as a CSI generation model, among other examples.

The compressed CSI feedback 406 corresponds to a complex representation of the CSI that can be transmitted with reduced overhead, for example.

The base station 450 may use another model (e.g., which may be referred to as a CSI reconstruction model 404, a CSI decompression model, etc. ) to reconstruct the target CSI from the compressed representation received from the UE at 406. In some aspects, the CSI reconstruction model 404 may include a neural network, AI, and/or ML based model.

One example of an AI/ML model CSI compression operation is the compression of precoder information. In this example, the CSI may include precoding vectors (e.g., precoding matrix indicator (PMI) ) that the UE 425 recommends to the base station 450, for each frequency subband.

The CSI compression model 402 and the CSI reconstruction model 404 may be associated AI/ML models. Both models may be trained AI/ML models, e.g., trained for compression and decompression of CSI feedback.

A UE and/or a base station or component (s) of a base station (e.g., including one or more of a CU, DU, and/or RU) may use machine-learning algorithms, deep-learning algorithms, neural networks, reinforcement learning, regression, boosting, and/or advanced signal processing methods for aspects of wireless communication.

Among others, example aspects of machine learning models or neural networks that may be included in the CSI compression model 402 and/or the CSI reconstruction model 404 may include artificial neural networks (ANN) , decision tree learning; convolutional neural networks (CNNs) , deep learning architectures in which an output of a first layer of neurons becomes an input to a second layer of neurons, support vector machines (SVM) (e.g., including a separating hyperplane (e.g., decision boundary) that categorizes data) , regression analysis, bayesian networks, genetic algorithms, deep convolutional networks (DCNs) configured with additional pooling and normalization layers, and/or deep belief networks (DBNs) , among other examples.

A machine learning model, such as an artificial neural network (ANN) , may include an interconnected group of artificial neurons (e.g., neuron models) , and may be a computational device or may represent a method to be performed by a computational device. The connections of the neuron models may be modeled as weights. Machine learning models may provide predictive modeling, adaptive control, and other applications through training via a dataset. The model may be adaptive based on external or internal information that is processed by the machine learning model. Machine learning may provide non-linear statistical data model or decision making and may model complex relationships between input data and output information.

A machine learning model may include multiple layers and/or operations that may be formed by concatenation of one or more of the referenced operations. Examples of operations that may be involved include extraction of various features of data, convolution operations, fully connected operations that may be activated or deactivated, compression, decompression, quantization, flattening, etc. As used herein, a “layer” of a machine learning model may be used to denote an operation on input data. For example, a convolution layer, a fully connected layer, and/or the like may be used to refer to associated operations on data that is input into a layer. A convolution AxB operation refers to an operation that converts a number of input features A into a number of output features B. “Kernel size” may refer to a number of adjacent coefficients that are combined in a dimension. As used herein, “weight” may be used to denote one or more coefficients used in the operations in the layers for combining various rows and/or columns of input data. For example, a fully connected layer operation may have an output y that is determined based at least in part on a sum of a product of input matrix x and weights A (which may be a matrix) and bias values B (which may be a matrix) . The term “weights” may be used herein to generically refer to both weights and bias values. Weights and biases are examples of parameters of a trained machine learning model. Different layers of a machine learning model may be trained separately.

Machine learning models may include a variety of connectivity patterns, e.g., including any of feed-forward networks, hierarchical layers, recurrent architectures, feedback connections, etc. The connections between layers of a neural network may be fully connected or locally connected. In a fully connected network, a neuron in a first layer may communicate its output to each neuron in a second layer, and each neuron in the second layer may receive input from every neuron in the first layer. In a locally connected network, a neuron in a first layer may be connected to a limited number of neurons in the second layer. In some aspects, a convolutional network may be locally connected and configured with shared connection strengths associated with the inputs for each neuron in the second layer. A locally connected layer of a network may be configured such that each neuron in a layer has the same, or similar, connectivity pattern, but with different connection strengths.

A machine learning model or neural network may be trained. For example, a machine learning model may be trained based on supervised learning. During training, the machine learning model may be presented with an input that the model uses to compute to produce an output. The actual output may be compared to a target output, and the difference may be used to adjust parameters (such as weights and biases) of the machine learning model in order to provide an output closer to the target output. Before training, the output may be incorrect or less accurate, and an error, or difference, may be calculated between the actual output and the target output. The weights of the machine learning model may then be adjusted so that the output is more closely aligned with the target. To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted slightly. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted so as to reduce the error or to move the output closer to the target. This manner of adjusting the weights may be referred to as back propagation through the neural network. The process may continue until an achievable error rate stops decreasing or until the error rate has reached a target level.

Reinforcement learning is a type of machine learning that involves the concept of taking actions in an environment in order to maximize a reward. Reinforcement learning is a machine learning paradigm; other paradigms include supervised learning and unsupervised learning. Basic reinforcement may be modeled as a Markov decision process (MDP) having a set of environment and agent states, and a set of actions of the agent. The process may include a probability of a state transition based on an action and a representation of a reward after the transition. The agent’s action selection may be modeled as a policy. The reinforcement learning may enable the agent to learn an optimal, or nearly-optimal, policy that maximizes a reward. Supervised learning may include learning a function that maps an input to an output based on example input-output pairs, which may be inferred from a set of training data, which may be referred to as training examples. The supervised learning algorithm analyzes the training data and provides an algorithm to map to new examples.

Regression analysis may include statistical processes for estimating the relationships between a dependent variable (e.g., which may be referred to as an outcome variable) and independent variable (s) . Linear regression is one example of regression analysis. Non-linear models may also be used. Regression analysis may include inferring causal relationships between variables in a dataset.

Boosting includes one or more algorithms for reducing bias and/or variance in supervised learning, such as machine learning algorithms that convert weak learners (e.g., a classifier that is slightly correlated with a true classification) to strong ones (e.g., a classifier that is more closely correlated with the true classification) . Boosting may include iterative learning based on weak classifiers with respect to a distribution that is added to a strong classifier. The weak learners may be weighted related to accuracy. The data weights may be readjusted through the process. In some aspects described herein, an encoding device (e.g., a UE, base station, or other network component) may train one or more neural networks to learn dependence of measured qualities on individual parameters.

The machine learning models may include computational complexity and substantial processor for training the machine learning model and may include a network of interconnected nodes. An output of one node may be connected as the input to another node. Connections between nodes may be referred to as edges, and weights may be applied to the connections/edges to adjust the output from one node that is applied as the input to another node. Nodes may apply thresholds in order to determine whether, or when, to provide output to a connected node. The output of each node may be calculated as a non-linear function of a sum of the inputs to the node. The AI/ML model may include any number of nodes and any type of connections between nodes. The model may include one or more hidden nodes. Nodes may be aggregated into layers, and different layers of the neural network may perform different kinds of transformations on the input. A signal may travel from input at a first layer through the multiple layers of the neural network to output at a last layer of the neural network and may traverse a layer multiple times.

FIG. 5 is an example of training and inference based on an AI/ML model 500 for a method of wireless communication. As illustrated at FIG. 5, data may be collected at 502. In some aspects, the data may be collected and input for model inference, e.g., at 506. As an example, the data collection may include measurement of one or more reference signals to obtain CSI feedback. The CSI may be provided to the model as input, e.g., as shown at 503. The model inference, e.g., at 506, may use the trained AI/ML model to generate compressed CSI feedback, e.g., a complex representation of the input CSI (which may also be referred to as a target CSI) having a reduced overhead as output using model inference. As illustrated at 507, the model inference outputs a compressed CSI, which a UE 508 may then transmit as compressed CSI feedback (e.g., as shown at 406 in FIG. 4) . In the example of CSI decompression or reconstruction based on model inference at 506, compressed CSI may be provided as input, as shown at 505, and reconstructed CSI may be output, as shown at 509, using the model inference function 506. The base station 511 may obtain the reconstructed CSI and may use the CSI to schedule communication, transmit, and/or receive communication with the UE.

The AI/ML model 500 may include various functions including a data collection 502, a model training function 504, a model inference function 506, and one or more actors (e.g., such as the UE 508 or base station 511) .

The data collection 502 may be a function that provides input data to the model training function 504 and/or the model inference function 506. The data collection 502 function may include any form of data preparation, and it may not be specific to the implementation of the AI/ML algorithm (e.g., data pre-processing and cleaning, formatting, and transformation) . The examples of input data may include, but are not limited to, gradient updates, from network entities including UEs or network nodes, feedback from actor, output from another AI/ML model, among other examples. The data collection 502 may include training data, which refers to the data to be sent as the input for the AI/ML model training function 504, and inference data, which refers to be sent as the input for the AI/ML model inference function 506.

The model training function 504 may be a function that performs the ML model training, validation, and testing, which may generate model performance metrics as part of the model testing procedure. The model training function 504 may also be responsible for data preparation (e.g. data pre-processing and cleaning, formatting, and transformation) based on the training data delivered or received from the data collection 502 function. The model training function 504 may deploy or update a trained, validated, and tested AI/ML model to the model inference function 506, and receive a model performance feedback from the model inference function 506.

The model inference function 506 may be a function that provides the AI/ML model inference output. The model inference function 506 may also perform data preparation (e.g. data pre-processing and cleaning, formatting, and transformation) based on the inference data delivered from the data collection 502 function. The output of the model inference function 506 may include the inference output of the AI/ML model produced by the model inference function 506. The details of the inference output may be use-case specific.

The model performance feedback may refer to information derived from the model inference function 506 that may be suitable for improvement of the AI/ML model trained in the model training function 504. The feedback from an actor or other network entities (via the data collection 502 function) may be implemented for the model inference function 506 to create the model performance feedback.

An actor may be a function that receives the output from the model inference function 506 and triggers or performs corresponding actions. The actor may trigger actions directed to network entities including the other network entities or itself. The actor may also provide feedback information that the model training function 504 or the model inference function 506 to derive training or inference data or performance feedback, e.g., as described herein. The feedback may be transmitted back to the data collection 502.

For a CSI reconstruction model, e.g., 404 as described in connection with FIG. 4, the format in which input is presented to the CSI reconstruction model may be established at the training of the model. For example, the input for the model may be trained as a multi-dimensional tensor of floating point numbers of specific dimensions. The UE may provide the base station with a sequence of bits comprising the compressed CSI payload. The base station may derive numbers corresponding to the sequence of bits in order to input the numbers to the CSI reconstruction model. As another example, the CSI reconstruction model may be trained with real numbers, and the CSI feedback received in the transmitted signal may include a quantized bit string representing the compressed CSI. As illustrated in diagram 600 in FIG. 6A, the base station may process the compressed CSI payload, at 608, in order to input the processed CSI payload 610 as input to the CSI reconstruction mode 604. FIG. 6A illustrates an example of CSI compression and reconstruction, similar to FIG. 4. The compression and reconstruction in FIG. 6A may include any of the aspects described in connection with FIG. 4 and/or FIG. 5. The compression and reconstruction in FIG. 6A enable a UE to adapt, e.g., change or otherwise select, among various types of processing to generate a compressed CSI payload, while continuing to enable the CSI reconstruction model 604 to process the compressed CSI payload for input to the model. In some aspects, the different combinations of processing of the CSI payload at the UE and/or corresponding processing to be performed at the network prior to input to the CSI reconstruction model 604 may be referred to as a CSI payload format.

For example, it may be useful for a UE to adapt the CSI payload processing based on certain conditions. As an example to illustrate the concept, in order to optimize the network resources for transmission of the CSI feedback, the UE may change or adjust the processing applied to the CSI feedback.

In order to enable the UE to select and/or change CSI payload processing for model based compressed CSI reporting, a mechanism is provided herein for the UE and the base station to have common understanding of the format/processing of the CSI feedback payload to enable the base station to correctly construct the input to the CSI reconstruction model 604 based on the compressed CSI feedback message, e.g., 606.

For example, the UE may report, or otherwise provide an indication of processing associated with the CSI payload (e.g., which may be referred to as a CSI feedback payload format in some aspects) . The UE may provide the indication of the CSI payload processing along with the CSI feedback payload, in some aspects. The CSI payload processing indication may provide information to the base station on how to use the CSI feedback payload (e.g., 606) to construct the input (e.g., 610) to the CSI reconstruction model 604. FIG. 6A illustrates that the UE may obtain CSI 601, e.g., based on measurements of one or more reference signals transmitted by a base station. The UE inputs the CSI 601 to the CSI compression model 602 (e.g., which may correspond to the CSI compression model 402 described in FIG. 4 and may include any of the aspects described in connection with FIG. 5) . The CSI compression model 602, based on the input CSI 601, provides a compressed CSI payload 603 for a CSI message to be transmitted to a base station. As illustrated in FIG. 6A, the compressed CSI payload 603 may include floating point numbers, e.g., in a floating point format. At 608, the UE processes the compressed CSI for transmission as a bit sequence, at 606. As described herein, the UE transmits the compressed CSI payload, e.g., 606, and a CSI payload processing indication 607 (which may also be referred to by other names such as CSI payload processing information, CSI payload processing indicator, compressed CSI format indicator, etc. ) to the base station, as shown at 606. The base station uses the CSI processing indication to determine the processing to perform, at 609, to the received CSI payload (e.g., 606) to place the CSI payload into a form that is ready to be input, at 610, to the CSI reconstruction model 604. For example, as illustrated at 610, the payload processor 609 at the base station may return the received bit sequence to a floating point format. The CSI reconstruction model 604 uses an AI/ML model to decompress the CSI and outputs reconstructed CSI 612. The reconstructed CSI 612 corresponds to the CSI 601 (which may be referred to as a target CSI) prior to compression. The CSI compression model 602 may correspond to, or have an association with, the CSI reconstruction model 604.

As an example, the CSI payload processing indication may provide information about the order in which the CSI payload is packed relative to the order in which the CSI payload is to be presented as input to the CSI reconstruction model 604. For example, in case of a matrix, the indication may indicate a row-wise or a column-wise order, among other examples of orders. Such information may be referred to as an ordering method.

As another example, the CSI payload processing indication may provide information about dequantization rules to map the bits of the signaled CSI payload to floating point numbers to be the input to the CSI reconstruction model 604. In some aspects, the indication may indicate that the quantization (or dequantization) is based on scalar or vector quantization.

As another example, the CSI payload processing indication may provide information about entropy coding related information. For example, the entropy coding related information informs the base station of the encoding used by the UE, which enables the network to interpret the received CSI payload.

As another example, the CSI payload processing indication may provide information about a dependency on previous CSI payloads 611, if any. For example, if the UE uses differential encoding of the CSI payload, the input to the CSI reconstruction model may be derived using the current payload and one or more past payloads. Thus, the UE may indicate that the CSI payload is based on differential encoding or is independent of a prior CSI payload.

The UE may indicate a combination of multiple types of processing information. In some aspects, the CSI payload processing indication may provide information about a sequence of compression processing. For example, the indication may indicate that quantization is performed before differential encoding for the CSI payload. This enables the base station to determine to process the CSI payload based on differential encoding prior to de-quantization. Similarly, the UE may indicate that differential encoding was performed prior to quantization.

The CSI payload processing indication may be layer-common or layer-specific. The indication may indicate that the processing information is common to multiple layers or that the processing information is for a single (or a particular) layer. The CSI payload processing indication may be subband-common or subband-specific. The indication may indicate that the processing information is common to multiple subbands or that the processing information is specific to a single (or a particular) subband.

In some aspects, there may be a mapping between CSI payload processing indications and CSI payload processing information. The mapping may be based on a defined table, such as a table that is defined in a wireless standard. In some aspects, the mapping may be aligned, e.g., signaling, during model identification between the UE and the base station.

The CSI payload processing information enables the UE to inform the network of a type of processing, among multiple options for a same CSI compression model. This enables the UE to use a different CSI payload processing while continuing to use a same CSI compression model. Similarly, the network may adjust the processing of the CSI payload prior to input to the same CSI reconstruction model 604.

For the same pair of compatible AI/ML models, e.g., for the same or corresponding logical CSI compression model 602 and CSI reconstruction model 604, the CSI payload format of the compressed CSI feedback 606 may be different based on different processing selected by the UE at 608. The CSI payload processing indication 607 indicates, as an example, the mapping from bits to floating point decoder input so that the base station can correctly identify the corresponding processing to apply at 609.

Although examples are described herein for a UE and a base station, the concepts may be applied between other devices reporting AI/ML model based compressed CSI, such as between two UEs.

The CSI feedback message may be arranged in two parts. A first part of the CSI feedback message may carry a rank indicator, CQI, and a payload size indication for the CSI payload. The second part of the CSI message may carry the CSI payload. FIG. 6B illustrates an example of two different arrangements for a two part CSI message. In the example CSI message 655, the first part 654 of the CSI message includes the CSI payload processing indication (e.g., 607) along with the payload size and other information, and the second part 656 includes the CSI payload (e.g., 606) . This would allow the base station to interpret the payload in the second part 656 with the correct processing format.

Alternately, as shown at 675, the second part 676 of the CSI message may include a header that carries the payload format indicator (e.g., 607) , followed by the compressed CSI payload (e.g., 606) itself. The first part 674 of the CSI message may indicate the rank, CQI, and payload size.

As an example of a UE selecting different processing/mapping for compressed CSI feedback, if a wireless channel is not changing very fast, the UE may convey the CSI feedback in an incremental manner. First, the UE may compute the successive difference of the CSI feedback relative to the CSI feedback from the previous occasion. Then, the UE may quantize the difference. The UE may then apply entropy coding to the quantized output before transmitting the compressed CSI feedback 606 as a bit sequence.

However, if the channel has changed significantly, the UE may determine to send CSI feedback with the current channel state without applying a successive difference operation. In order to enable the base station to apply the corresponding processing, at 609, the UE may convey its choice using the CSI payload processing indication 607.

At other times, the UE may determine to perform time domain compression or other types of processing on the CSI.

The indication of the payload processing, at 607, enables flexibility by allowing a UE to dynamically select a suitable format or processing depending on the local conditions. For example, a UE may decide whether to use differential encoding of the payload dynamically, and indicate its choice to the network using the proposed indication. Such flexibility can help optimize the size of the payload to convey the CSI and thereby reduce feedback overhead.

FIG. 7 is an example communication flow 700 between a UE and a base station showing example aspects of CSI payload processing indication, in accordance with aspects presented herein. Although this example illustrates CSI exchanged between a UE and a base station, the concepts can also be applied for the exchange of compressed CSI between two UEs, e.g., for sidelink communication. The aspects described for the base station 702 may be performed by a base station in aggregation or may be performed by one or more components of a base station, such as a CU, DU, and/or RU. The aspects described in connection with FIG. 7 allow a UE to dynamically select a format or processing for compressed CSI feedback depending on the local conditions, which can help the UE to optimize the size of the payload to convey the CSI and reduce feedback overhead. The UE may correspond to the UE 104, 350, 425, 508, or 625 or the apparatus 1204, and the base station may correspond to the base station 102, 310, 450, 511, or 650 or the network entity 1302.

As illustrated in FIG. 7, the UE 704 and the base station 702 may exchange signaling, as shown at 705, to coordinate the use of a trained AI/ML model for CSI compression and reconstruction. As an example, the UE and/or the base station may signal their support of a capability for AI/ML based CSI compression and reconstruction. Additionally, or alternatively, the base station and/or the UE may indicate a particular AI/ML for CSI compression so that the UE 704 and the base station 702 can use the same AI/ML models, or corresponding AI/ML models. In some aspects, the base station 702 may configure the UE 704 to send compressed CSI feedback using an AI/ML model. The base station 702 transmits one or more reference signals 708, and the UE 704 measures the reference signals to determine, or obtain, CSI, at 710. At 712, the UE uses an AI/ML model to compress the CSI, e.g., as described in connection with any of FIGs. 4-6A. At 713, the UE 704 may select a type of CSI payload processing, e.g., as described in connection with 608 in FIG. 6A. At 714, the UE generates the CSI payload for a CSI message, based on a CSI payload processing and compressed CSI payload. For example, the CSI message may include a CSI payload processing indication (e.g., 607) , as described in connection with FIG. 6A and/or 6B. The UE 704 transmits the CSI message with the compressed CSI payload and the CSI payload processing indication.

The CSI payload processing indication may indicate one or more of: an order in which the CSI payload is structured relative to an intended order for input to the CSI reconstruction model, one or more de-quantization rules to maps bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band. The CSI payload processing indication may indicate that the one or more de-quantization rules that are based on a scalar quantization or a vector quantization. The CSI payload processing indication for the CSI payload may be from a defined set of CSI payload processing indications, e.g., a set of defined indicators in wireless standard. Each of the defined indicators may correspond to one or more types of processing information. In some aspects, the UE and the base station may exchange information to map a set of indicators with a set of CSI payload processing operations. Then, the UE may select and send an indicator that corresponds to the set processing dynamically selected by the UE. For example, at 706, the UE and base station may transmit and/or receive mapping information associating a set of indications with a set of CSI payload processing operations, wherein the CSI payload processing indication is selected from the set of indications.

The CSI message may include a first part including the CSI payload processing indication, and a second part including the CSI payload. The first part may one or more of a rank indicator, a CQI, or a payload size indication, e.g., as illustrated in FIG. 6B. In some aspects, the first part of the CSI message may include one or more of a rank indicator, a CQI, or a payload size indication, and a second part of the CSI message may include the CSI payload and a header including the CSI payload processing indication.

The CSI payload processing indication may be comprised in one or more of uplink control information (UCI) , a medium access control-control element (MAC-CE) , or a physical uplink shared channel (PUSCH) transmission.

At 717, the base station 702 identifies the CSI payload processing, e.g., either identifying the processing performed by the UE or a processing to be performed by the base station, based on the CSI payload processing indication (e.g., 607) included in the CSI message. At 718, the base station 702 processes the CSI payload for input to an AI/ML model based on the processing identified at 717. For example, the base station 702 may derive floating point numbers for the compressed CSI from the bit sequence of the CSI payload, and may input the derived floating point numbers to the AI/ML model. At 720, the base station 702 uses the AI/ML model to reconstruct the CSI.

The communication flow between the UE 704 and the base station 702 may further include any of the aspects described in connection with FIGs. 4, 5, 6A, or 6B. The UE 704 may further perform any of the aspects described in connection with the flowcharts in FIGs. 8 and/or 9. The base station 702 may further perform any of the aspects described in connection with the flowcharts in FIGs. 10 and/or 11.

FIG. 8 is a flowchart 800 of a method of wireless communication. The method may be performed by wireless device such as a UE (e.g., the UE 104, 350, 425, 704; the apparatus 1204) . In some aspects, the UE may transmit or receive mapping information associating a set of indications with a set of CSI payload processing operations. In some aspects, a CSI payload processing indication may be selected from the set of indications. For example, referring to FIG. 7, the UE 704 may transmit or receive the mapping information, at 706 in FIG. 7.

At 804, the UE may receive one or more reference signals. For example, 804 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or the CSI payload processing indication component 198 of FIG. 12. Referring to FIG. 7, for example, the UE 704 may receive reference signals 708. In some aspects, the UE may measure the one or more reference signals to obtain CSI. Referring to FIG. 7, for example, the UE 704 may, at 710, measure the received reference signals 708 and obtain CSI. The UE, in some aspects, may compress the CSI based on a compression model to generate a CSI payload. In some aspects, the compression of the CSI may be based on an AI or ML model. For example, referring to FIG. 7, the UE 704 may, at 712, compress the CSI obtained and/or determined at 710.

At 810, the UE may transmit a CSI message including a CSI payload based on measurements of the one or more reference signals. For example, 810 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or the CSI payload processing indication component 198 of FIG. 12. Referring to FIG. 7, for example, the UE 704 may transmit a CSI message 716 including a compressed CSI payload and a CSI payload processing indication.

At 812, the UE may provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. For example, 812 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or the CSI payload processing indication component 198 of FIG. 12. In some aspects, the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to a CSI reconstruction model, one or more de-quantization rules to map bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band. The CSI payload processing indication, in some aspects, may indicate the one or more de-quantization rules that are based on a scalar quantization or a vector quantization. In some aspects, the CSI payload processing indication for the CSI payload may be from a defined set of CSI payload processing indications. The CSI payload processing indication, in some aspects, may be selected from a received set of indications (e.g., based on the mapping information associating the set of indications with a set of CSI payload processing operations) . In some aspects, the CSI payload processing indication may be included in one or more of UCI, a MAC-CE, or a PUSCH transmission.

In some aspects, the CSI payload processing indication may be included in the CSI message transmitted at 810. For example, the CSI message, in some aspects, may include a first part including the CSI payload processing indication, and a second part including the CSI payload. In some aspects, the first part may further include one or more of a rank indicator, a CQI, or a payload size indication. The CSI message, in some aspects, may include a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication. For example, referring to FIG. 7, the UE 704 may transmit the CSI message 716 including the indication indicating information about processing to apply to the CSI payload to derive an input to the CSI reconstruction model.

FIG. 9 is a flowchart 900 of a method of wireless communication. The method may be performed by wireless device such as a UE (e.g., the UE 104, 350, 425, 704; the apparatus 1204) . At 902, the UE may transmit or receive mapping information associating a set of indications with a set of CSI payload processing operations. For example, 902 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or the CSI payload processing indication component 198 of FIG. 12. In some aspects, a CSI payload processing indication may be selected from the set of indications. For example, referring to FIG. 7, the UE 704 may transmit or receive the mapping information, at 706 in FIG. 7.

At 904, the UE may receive one or more reference signals. For example, 904 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or CSI payload processing indication component 198 of FIG. 12. Referring to FIG. 7, for example, the UE 704 may receive reference signals 708.

At 906, the UE may measure the one or more reference signals to obtain CSI. For example, 906 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or CSI payload processing indication component 198 of FIG. 12. Referring to FIG. 7, for example, the UE 704 may, at 710, measure the received reference signals 708 and obtain CSI.

At 908, the UE may compress the CSI based on a compression model to generate a CSI payload. For example, 908 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, and/or CSI payload processing indication component 198 of FIG. 12. In some aspects, the compression of the CSI may be based on an AI or ML model. For example, referring to FIG. 7, the UE 704 may, at 712, compress the CSI obtained and/or determined at 710.

At 910, the UE may transmit a CSI message including a CSI payload based on measurements of the one or more reference signals. For example, 910 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or CSI payload processing indication component 198 of FIG. 12. Referring to FIG. 7, for example, the UE 704 may transmit the CSI message 716 including the indication indicating information about processing to apply to the CSI payload to derive an input to the CSI reconstruction model.

At 912, the UE may provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. For example, 912 may be performed by application processor (s) 1206, cellular baseband processor (s) 1224, transceiver (s) 1222, antenna (s) 1280, and/or CSI payload processing indication component 198 of FIG. 12. In some aspects, the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to a CSI reconstruction model, one or more de-quantization rules to map bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band. The CSI payload processing indication, in some aspects, may indicate the one or more de-quantization rules that are based on a scalar quantization or a vector quantization. In some aspects, the CSI payload processing indication for the CSI payload may be from a defined set of CSI payload processing indications. The CSI payload processing indication, in some aspects, may be selected from the set of indications received at 902 (e.g., based on the mapping information associating the set of indications with a set of CSI payload processing operations) . In some aspects, the CSI payload processing indication may be included in one or more of UCI, a MAC-CE, or a PUSCH transmission.

In some aspects, the CSI payload processing indication may be included in the CSI message transmitted at 910. For example, the CSI message, in some aspects, may include a first part including the CSI payload processing indication, and a second part including the CSI payload. In some aspects, the first part may further include one or more of a rank indicator, a CQI, or a payload size indication. The CSI message, in some aspects, may include a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication. For example, referring to FIG. 7, the UE 704 may transmit the CSI message 716 including the indication indicating information about processing to apply to the CSI payload to derive an input to the CSI reconstruction model.

FIG. 10 is a flowchart 1000 of a method of wireless communication. The method may be performed by network node such as a base station (e.g., the base station 102, 310, 450, 702; the network entity 1202, 1302) . In some aspects, the network node may transmit or receive mapping information associating a set of indications with a set of CSI payload processing operations. In some aspects, a CSI payload processing indication may be selected from the set of indications. For example, referring to FIG. 7, the base station 702 may transmit or receive the mapping information, at 706 in FIG. 7.

At 1004, the network node may provide one or more reference signals. For example, 1004 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. Referring to FIG. 7, for example, the base station 702 may transmit reference signals 708.

At 1006, the network node may obtain a CSI message including a CSI payload based on measurements of the one or more reference signals. For example, 1006 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. Referring to FIG. 7, for example, the base station 702 may receive the CSI message 716 including the indication indicating information about processing to apply to the CSI payload before input at the CSI reconstruction model.

At 1008, the network node may obtain a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. For example, 1008 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. In some aspects, the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to a CSI reconstruction model, one or more de-quantization rules to map bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band. The CSI payload processing indication, in some aspects, may indicate the one or more de-quantization rules that are based on a scalar quantization or a vector quantization. In some aspects, the CSI payload processing indication for the CSI payload may be from a defined set of CSI payload processing indications. The CSI payload processing indication, in some aspects, may be selected from the set of indications received at 1002 (e.g., based on the mapping information associating the set of indications with a set of CSI payload processing operations) . In some aspects, the CSI payload processing indication may be included in one or more of UCI, a MAC-CE, or a PUSCH transmission.

In some aspects, the CSI payload processing indication may be included in the CSI message obtained at 1006. For example, the CSI message, in some aspects, may include a first part including the CSI payload processing indication, and a second part including the CSI payload. In some aspects, the first part may further include one or more of a rank indicator, a CQI, or a payload size indication. The CSI message, in some aspects, may include a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication. For example, referring to FIG. 7, the base station 702 may receive the CSI message 716 including the indication indicating information about processing to apply to the CSI payload to derive an input to the CSI reconstruction model.

In some aspects, the network node may process the CSI payload based on the CSI payload processing indication prior to input to the CSI reconstruction model to reconstruct CSI for a UE. In some aspects, the CSI payload includes compressed CSI and a decompression of the CSI may be based on an AI or ML model (e.g., the CSI reconstruction model may be an AI or ML model) . For example, referring to FIG. 7, the base station 702 may, at 718, process the CSI payload for input to a CSI reconstruction model (e.g., an AI or ML model trained for CSI reconstruction) at 720.

FIG. 11 is a flowchart 1100 of a method of wireless communication. The method may be performed by network node such as a base station (e.g., the base station 102, 310, 450, 702; the network entity 1202, 1302) . At 1102, the network node may transmit or receive mapping information associating a set of indications with a set of CSI payload processing operations. For example, 1102 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. In some aspects, a CSI payload processing indication may be selected from the set of indications. For example, referring to FIG. 7, the base station 702 may transmit or receive the mapping information, at 706 in FIG. 7.

At 1104, the network node may provide one or more reference signals. For example, 1104 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. Referring to FIG. 7, for example, the base station 702 may transmit reference signals 708.

At 1106, the network node may obtain a CSI message including a CSI payload based on measurements of the one or more reference signals. For example, 1106 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. Referring to FIG. 7, for example, the base station 702 may receive the CSI message 716 including the indication indicating information about processing to apply to the CSI payload before input at the CSI reconstruction model.

At 1108, the network node may obtain a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. For example, 1108 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, transceiver (s) 1346, antenna (s) 1380, and/or CSI reconstruction component 199 of FIG. 13. In some aspects, the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to a CSI reconstruction model, one or more de-quantization rules to map bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band. The CSI payload processing indication, in some aspects, may indicate the one or more de-quantization rules that are based on a scalar quantization or a vector quantization. In some aspects, the CSI payload processing indication for the CSI payload may be from a defined set of CSI payload processing indications. The CSI payload processing indication, in some aspects, may be selected from the set of indications received at 1102 (e.g., based on the mapping information associating the set of indications with a set of CSI payload processing operations) . In some aspects, the CSI payload processing indication may be included in one or more of UCI, a MAC-CE, or a PUSCH transmission.

In some aspects, the CSI payload processing indication may be included in the CSI message obtained at 1106. For example, the CSI message, in some aspects, may include a first part including the CSI payload processing indication, and a second part including the CSI payload. In some aspects, the first part may further include one or more of a rank indicator, a CQI, or a payload size indication. The CSI message, in some aspects, may include a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication. For example, referring to FIG. 7, the base station 702 may receive the CSI message 716 including the indication indicating information about processing to apply to the CSI payload before input at the CSI reconstruction model.

At 1110, the network node may process the CSI payload based on the CSI payload processing indication prior to input to the CSI reconstruction model to reconstruct CSI for a UE. For example, 1110 may be performed by CU processor (s) 1312, DU processor (s) 1332, RU processor (s) 1342, and/or CSI reconstruction component 199 of FIG. 13. In some aspects, the CSI payload includes compressed CSI and a decompression of the CSI may be based on an AI or ML model (e.g., the CSI reconstruction model may be an AI or ML model) . For example, referring to FIG. 7, the base station 702 may, at 718, process the CSI payload for input to a CSI reconstruction model (e.g., an AI or ML model trained for CSI reconstruction) at 720.

FIG. 12 is a diagram 1200 illustrating an example of a hardware implementation for an apparatus 1204. The apparatus 1204 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus1204 may include at least one cellular baseband processor 1224 (also referred to as a modem) coupled to one or more transceivers 1222 (e.g., cellular RF transceiver) . The cellular baseband processor (s) 1224 may include at least one on-chip memory 1224'. In some aspects, the apparatus 1204 may further include one or more subscriber identity modules (SIM) cards 1220 and at least one application processor 1206 coupled to a secure digital (SD) card 1208 and a screen 1210. The application processor (s) 1206 may include on-chip memory 1206'. In some aspects, the apparatus 1204 may further include a Bluetooth module 1212, a WLAN module 1214, an SPS module 1216 (e.g., GNSS module) , one or more sensor modules 1218 (e.g., barometric pressure sensor /altimeter; motion sensor such as inertial measurement unit (IMU) , gyroscope, and/or accelerometer (s) ; light detection and ranging (LIDAR) , radio assisted detection and ranging (RADAR) , sound navigation and ranging (SONAR) , magnetometer, audio and/or other technologies used for positioning) , additional memory modules 1226, a power supply 1230, and/or a camera 1232. The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX) ) . The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include their own dedicated antennas and/or utilize one or more antennas 1280 for communication. The cellular baseband processor (s) 1224 communicates through the transceiver (s) 1222 via the one or more antennas 1280 with the UE 104 and/or with an RU associated with a network entity 1202. The cellular baseband processor (s) 1224 and the application processor (s) 1206 may each include a computer-readable medium /memory 1224', 1206', respectively. The additional memory modules 1226 may also be considered a computer-readable medium /memory. Each computer-readable medium /memory 1224', 1206', 1226 may be non-transitory. The cellular baseband processor (s) 1224 and the application processor (s) 1206 are each responsible for general processing, including the execution of software stored on the computer-readable medium /memory. The software, when executed by the cellular baseband processor (s) 1224 /application processor (s) 1206, causes the cellular baseband processor (s) 1224 /application processor (s) 1206 to perform the various functions described supra. The computer-readable medium /memory may also be used for storing data that is manipulated by the cellular baseband processor (s) 1224 /application processor (s) 1206 when executing software. The cellular baseband processor (s) 1224 /application processor (s) 1206 may be a component of the UE 350 and may include the at least one memory 360 and/or at least one of the TX processor 368, the RX processor 356, and the controller/processor 359. In one configuration, the apparatus 1204 may be at least one processor chip (modem and/or application) and include just the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, and in another configuration, the apparatus 1204 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1204.

As discussed supra, the CSI payload processing indication component 198 may be configured to receive one or more reference signals, transmit a CSI message comprising a CSI payload based on measurements of the one or more reference signals, and provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. The CSI payload processing indication component 198 may be within the cellular baseband processor (s) 1224, the application processor (s) 1206, or both the cellular baseband processor (s) 1224 and the application processor (s) 1206. The CSI payload processing indication component 198 may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes/algorithm individually or in combination. As shown, the apparatus 1204 may include a variety of components configured for various functions. In one configuration, the apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, may include means for receiving one or more reference signals. The apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, in some aspects, may include means for transmitting a CSI message comprising a CSI payload based on measurements of the one or more reference signals. The apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, in some aspects, may include means for providing a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model. The apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, in some aspects, may include means for measuring the one or more reference signals to obtain CSI. The apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, in some aspects, may include means for compressing the CSI based on a compression model to generate the CSI payload. The apparatus 1204, and in particular the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, in some aspects, may include means for transmitting or receiving mapping information associating a set of indications with a set of CSI payload processing operations. The apparatus 1204 may further include means for performing any of the aspects described in connection with the flowcharts in FIGs. 8 and 9, and/or performed by the UE 704 in the communication flow of FIG. 7. The means may be the CSI payload processing indication component 198 of the apparatus 1204 configured to perform the functions recited by the means. As described supra, the apparatus 1204 may include the TX processor 368, the RX processor 356, and the controller/processor 359. As such, in one configuration, the means may be the TX processor 368, the RX processor 356, and/or the controller/processor 359 configured to perform the functions recited by the means or as described in relation to FIGs. 8 and 9.

FIG. 13 is a diagram 1300 illustrating an example of a hardware implementation for a network entity 1302. The network entity 1302 may be a BS, a component of a BS, or may implement BS functionality. The network entity 1302 may include at least one of a CU 1310, a DU 1330, or an RU 1340. For example, depending on the layer functionality handled by the CSI reconstruction component 199, the network entity 1302 may include the CU 1310; both the CU 1310 and the DU 1330; each of the CU 1310, the DU 1330, and the RU 1340; the DU 1330; both the DU 1330 and the RU 1340; or the RU 1340. The CU 1310 may include at least one CU processor 1312. The CU processor (s) 1312 may include on-chip memory 1312'. In some aspects, the CU 1310 may further include additional memory modules 1314 and a communications interface 1318. The CU 1310 communicates with the DU 1330 through a midhaul link, such as an F1 interface. The DU 1330 may include at least one DU processor 1332. The DU processor (s) 1332 may include on-chip memory 1332'. In some aspects, the DU 1330 may further include additional memory modules 1334 and a communications interface 1338. The DU 1330 communicates with the RU 1340 through a fronthaul link. The RU 1340 may include at least one RU processor 1342. The RU processor (s) 1342 may include on-chip memory 1342'. In some aspects, the RU 1340 may further include additional memory modules 1344, one or more transceivers 1346, one or more antennas 1380, and a communications interface 1348. The RU 1340 communicates with the UE 104. The on-chip memory 1312', 1332', 1342'a nd the additional memory modules 1314, 1334, 1344 may each be considered a computer-readable medium /memory. Each computer-readable medium /memory may be non-transitory. Each of the processors 1312, 1332, 1342 is responsible for general processing, including the execution of software stored on the computer-readable medium /memory. The software, when executed by the corresponding processor (s) causes the processor (s) to perform the various functions described supra. The computer-readable medium /memory may also be used for storing data that is manipulated by the processor (s) when executing software.

As discussed supra, the CSI reconstruction component 199 may be configured to provide one or more reference signals, obtaining a CSI message comprising a CSI payload based on measurements of the one or more reference signals, and obtain a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model. The CSI reconstruction component 199 may be within one or more processors of one or more of the CU 1310, DU 1330, and the RU 1340. The CSI reconstruction component 199 may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes/algorithm individually or in combination. The network entity 1302 may include a variety of components configured for various functions. In one configuration, the network entity 1302 may include means for providing one or more reference signals. The network entity 1302 may include means for obtaining a CSI message comprising a CSI payload based on measurements of the one or more reference signals. The network entity 1302 may include means for obtaining a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model. The network entity 1302 may include means for processing the CSI payload based on the CSI payload processing indication prior to input to the CSI reconstruction model to reconstruct CSI for a user equipment. The network entity 1302 may include means for transmitting or receiving mapping information associating a set of indications with a set of CSI payload processing operations. The network entity 1302 may further include means for performing any of the aspects described in connection with the flowcharts in FIGs. 10 or 11, and/or performed by the UE in the communication flow of FIG. 7. The means may be the CSI reconstruction component 199 of the network entity 1302 configured to perform the functions recited by the means. As described supra, the network entity 1302 may include the TX processor 316, the RX processor 370, and the controller/processor 375. As such, in one configuration, the means may be the TX processor 316, the RX processor 370, and/or the controller/processor 375 configured to perform the functions recited by the means or as described in relation to FIGs. 10 and 11.

It is understood that the specific order or hierarchy of blocks in the processes /flowcharts disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes /flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not limited to the specific order or hierarchy presented.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not limited to the aspects described herein, but are to be accorded the full scope consistent with the language claims. Reference to an element in the singular does not mean “one and only one” unless specifically so stated, but rather “one or more. ” Terms such as “if, ” “when, ” and “while” do not imply an immediate temporal relationship or reaction. That is, these phrases, e.g., “when, ” do not imply an immediate action in response to or during the occurrence of an action, but simply imply that if a condition is met then an action will occur, but without requiring a specific or immediate time constraint for the action to occur. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration. ” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C, ” “one or more of A, B, or C, ” “at least one of A, B, and C, ” “one or more of A, B, and C, ” and “A, B, C, or any combination thereof” include any combination of A, B, and/or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C, ” “one or more of A, B, or C, ” “at least one of A, B, and C, ” “one or more of A, B, and C, ” and “A, B, C, or any combination thereof” may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. Sets should be interpreted as a set of elements where the elements number one or more. Accordingly, for a set of X, X would include one or more elements. When at least one processor is configured to perform a set of functions, the at least one processor, individually or in any combination, is configured to perform the set of functions. Accordingly, each processor of the at least one processor may be configured to perform a particular subset of the set of functions, where the subset is the full set, a proper subset of the set, or an empty subset of the set. A processor may be referred to as processor circuitry. A memory /memory module may be referred to as memory circuitry. If a first apparatus receives data from or transmits data to a second apparatus, the data may be received/transmitted directly between the first and second apparatuses, or indirectly between the first and second apparatuses through a set of apparatuses. A device configured to “output” data or “provide” data, such as a transmission, signal, or message, may transmit the data, for example with a transceiver, or may send the data to a device that transmits the data. A device configured to “obtain” data, such as a transmission, signal, or message, may receive, for example with a transceiver, or may obtain the data from a device that receives the data. Information stored in a memory includes instructions and/or data. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are encompassed by the claims. Moreover, nothing disclosed herein is dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module, ” “mechanism, ” “element, ” “device, ” and the like may not be a substitute for the word “means. ” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for. ”

As used herein, the phrase “based on” shall not be construed as a reference to a closed set of information, one or more conditions, one or more factors, or the like. In other words, the phrase “based on A” (where “A” may be information, a condition, a factor, or the like) shall be construed as “based at least on A” unless specifically recited differently.

The following aspects are illustrative only and may be combined with other aspects or teachings described herein, without limitation.

Aspect 1 is a method of wireless communication at a UE, comprising: receiving one or more reference signals; transmitting a CSI message comprising a CSI payload based on measurements of the one or more reference signals; and providing a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model.

Aspect 2 is the method of aspect 1, further comprising: measuring the one or more reference signals to obtain CSI; and compressing the CSI based on a compression model to generate the CSI payload.

Aspect 3 is the method of aspect 2, wherein compression of the CSI is based on an AI or ML model.

Aspect 4 is the method of any of aspects 1 to 3, wherein the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to the CSI reconstruction model, one or more de-quantization rules to map bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band.

Aspect 5 is the method of aspect 4, wherein the CSI payload processing indication indicates the one or more de-quantization rules that are based on a scalar quantization or a vector quantization.

Aspect 6 is the method of any of aspects 1 to 5, wherein the CSI payload processing indication for the CSI payload is from a defined set of CSI payload processing indications.

Aspect 7 is the method of any of aspects 1 to 6, further comprising: transmitting or receiving mapping information associating a set of indications with a set of CSI payload processing operations, wherein the CSI payload processing indication is selected from the set of indications.

Aspect 8 is the method of any of aspects 1 to 7, wherein the CSI message includes a first part including the CSI payload processing indication, and a second part including the CSI payload.

Aspect 9 is the method of aspect 8, wherein the first part further includes one or more of a rank indicator, a CQI, or a payload size indication.

Aspect 10 is the method of any of aspects 1 to 9, wherein the CSI message includes: a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication.

Aspect 11 is the method of any of aspects 1 to 10, wherein the CSI payload processing indication is comprised in one or more of UCI, a MAC-CE, or a PUSCH transmission.

Aspect 12 is a method of wireless communication at a network node, comprising: providing one or more reference signals; obtaining a CSI message comprising a CSI payload based on measurements of the one or more reference signals; and obtaining a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model.

Aspect 13 is the method of aspect 12, wherein the CSI payload includes compressed CSI, the method further comprising: processing the CSI payload based on the CSI payload processing indication prior to input to the CSI reconstruction model to reconstruct CSI for a user equipment.

Aspect 14 is the method of any of aspects 12 and 13, wherein reconstruction of the compressed CSI is based on an AI or ML model.

Aspect 15 is the method of any of aspects 12 to 14, wherein the CSI payload processing indication indicates one or more of: an order in which the CSI payload is structured relative to an intended order for input to the CSI reconstruction model, one or more de-quantization rules to maps bits of the CSI payload to floating point numbers for input to the CSI reconstruction model, entropy coding information for the CSI payload, a relationship to a prior CSI payload, a sequence for applying quantization and differential encoding, the CSI payload is common to multiple layers, the CSI payload is specific to a particular layer, the CSI payload is common to multiple sub-bands, or the CSI payload is specific to a particular sub-band.

Aspect 16 is the method of aspect 15, wherein the CSI payload processing indication indicates the one or more de-quantization rules that are based on a scalar quantization or a vector quantization.

Aspect 17 is the method of any of aspects 12 to 16, wherein the CSI payload processing indication for the CSI payload is from a defined set of CSI payload processing indications.

Aspect 18 is the method of any of aspects 12 to 17, further comprising: transmitting or receiving mapping information associating a set of indications with a set of CSI payload processing operations, wherein the CSI payload processing indication is from the set of indications.

Aspect 19 is the method of any of aspects 12 to 18, wherein the CSI message includes a first part including the CSI payload processing indication, and a second part including the CSI payload.

Aspect 20 is the method of any of aspects 19, wherein the first part further includes one or more of a rank indicator, a CQI, or a payload size indication.

Aspect 21 is the method of any of aspects 12 to 20, wherein the CSI message includes: a first part including one or more of a rank indicator, a CQI, or a payload size indication, and a second part including the CSI payload and a header including the CSI payload processing indication.

Aspect 22 is the method of any of aspects 12 to 21, wherein the CSI payload processing indication is comprised in one or more of UCI, a MAC-CE, or a PUSCH transmission.

Aspect 23 is an apparatus for wireless communication at a UE including a memory and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to cause the UE to implement any of aspects 1 to 11.

Aspect 24 is the apparatus of aspect 23, further including a transceiver or an antenna coupled to the at least one processor.

Aspect 25 is an apparatus for wireless communication at a device including means for implementing any of aspects 1 to 11.

Aspect 26 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code at a UE, where the code when executed by a processor causes the UE to implement any of aspects 1 to 11.

Aspect 27 is an apparatus for wireless communication at a network node including a memory and at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to cause the network node to implement any of aspects 12 to 22.

Aspect 28 is the apparatus of aspect 27, further including a transceiver or an antenna coupled to the at least one processor.

Aspect 29 is an apparatus for wireless communication at a device including means for implementing any of aspects 12 to 22.

Aspect 30 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code at a network node, where the code when executed by a processor causes the network node to implement any of aspects 12 to 22.

Claims

An apparatus for wireless communication at a user equipment (UE) , comprising:

at least one memory; and

at least one processor coupled to the at least one memory and, based at least in part on stored information that is stored in the at least one memory, the at least one processor, individually or in any combination, is configured to:

receive one or more reference signals;

transmit a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals; and

provide a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to:

measure the one or more reference signals to obtain CSI; and

compress the CSI based on a compression model to generate the CSI payload.
The apparatus of claim 2, wherein compression of the CSI is based on an artificial intelligence (AI) or machine learning (ML) model.
The apparatus of claim 1, wherein the CSI payload processing indication indicates one or more of:

an order in which the CSI payload is structured relative to an intended order for the input to the CSI reconstruction model,

one or more de-quantization rules to map bits of the CSI payload to floating point numbers for the input to the CSI reconstruction model,

entropy coding information for the CSI payload,

a relationship to a prior CSI payload,

a sequence for applying quantization and differential encoding,

the CSI payload is common to multiple layers,

the CSI payload is specific to a particular layer,

the CSI payload is common to multiple sub-bands, or

the CSI payload is specific to a particular sub-band.
The apparatus of claim 4, wherein the CSI payload processing indication indicates the one or more de-quantization rules that are based on a scalar quantization or a vector quantization.
The apparatus of claim 1, wherein the CSI payload processing indication for the CSI payload is from a defined set of CSI payload processing indications.
The apparatus of claim 1, further comprising a transceiver coupled to the at least one processor wherein the at least one processor, individually or in any combination, is further configured to:

transmit or receive, via the transceiver, mapping information associating a set of indications with a set of CSI payload processing operations, wherein the CSI payload processing indication is selected from the set of indications.
The apparatus of claim 1, wherein the CSI message includes a first part including the CSI payload processing indication, and a second part including the CSI payload.
The apparatus of claim 8, wherein the first part further includes one or more of a rank indicator, a channel quality indicator (CQI) , or a payload size indication.
The apparatus of claim 1, wherein the CSI message includes:

a first part including one or more of a rank indicator, a channel quality indicator (CQI) , or a payload size indication, and

a second part including the CSI payload and a header including the CSI payload processing indication.
The apparatus of claim 1, wherein the CSI payload processing indication is comprised in one or more of uplink control information (UCI) , a medium access control-control element (MAC-CE) , or a physical uplink shared channel (PUSCH) transmission.
An apparatus for wireless communication at a network node, comprising:

at least one memory; and

at least one processor coupled to the at least one memory and, based at least in part on stored information that is stored in the at least one memory, the at least one processor, individually or in any combination, is configured to:

provide one or more reference signals;

obtain a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals; and

obtain a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model.
The apparatus of claim 12, wherein the CSI payload includes compressed CSI, and the at least one processor, individually or in any combination, is further configured to:

process the CSI payload based on the CSI payload processing indication prior to the input to the CSI reconstruction model to reconstruct CSI for a user equipment.
The apparatus of claim 13, wherein reconstruction of the compressed CSI is based on an artificial intelligence (AI) or machine learning (ML) model.
The apparatus of claim 12, wherein the CSI payload processing indication indicates one or more of:

an order in which the CSI payload is structured relative to an intended order for the input to the CSI reconstruction model,

one or more de-quantization rules to maps bits of the CSI payload to floating point numbers for the input to the CSI reconstruction model,

entropy coding information for the CSI payload,

a relationship to a prior CSI payload,

a sequence for applying quantization and differential encoding,

the CSI payload is common to multiple layers,

the CSI payload is specific to a particular layer,

the CSI payload is common to multiple sub-bands, or

the CSI payload is specific to a particular sub-band.
The apparatus of claim 15, wherein the CSI payload processing indication indicates the one or more de-quantization rules that are based on a scalar quantization or a vector quantization.
The apparatus of claim 12, wherein the CSI payload processing indication for the CSI payload is from a defined set of CSI payload processing indications.
The apparatus of claim 12, further comprising a transceiver coupled to the at least one processor wherein the at least one processor, individually or in any combination, is further configured to:

transmit or receive, via the transceiver, mapping information associating a set of indications with a set of CSI payload processing operations, wherein the CSI payload processing indication is from the set of indications.
The apparatus of claim 12, wherein the CSI message includes a first part including the CSI payload processing indication, and a second part including the CSI payload.
The apparatus of claim 19, wherein the first part further includes one or more of a rank indicator, a channel quality indicator (CQI) , or a payload size indication.
The apparatus of claim 12, wherein the CSI message includes:

a first part including one or more of a rank indicator, a channel quality indicator (CQI) , or a payload size indication, and

a second part including the CSI payload and a header including the CSI payload processing indication.
The apparatus of claim 12, wherein the CSI payload processing indication is comprised in one or more of uplink control information (UCI) , a medium access control-control element (MAC-CE) , or a physical uplink shared channel (PUSCH) transmission.
A method of wireless communication at a user equipment (UE) , comprising:

receiving one or more reference signals;

transmitting a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals; and

providing a CSI payload processing indication indicating information about processing to apply to the CSI payload to derive an input to a CSI reconstruction model.
The method of claim 23, further comprising:

measuring the one or more reference signals to obtain CSI; and

compressing the CSI based on a compression model to generate the CSI payload, and wherein compression of the CSI is based on an artificial intelligence (AI) or machine learning (ML) model.
The method of claim 23, wherein the CSI payload processing indication indicates one or more of:

an order in which the CSI payload is structured relative to an intended order for the input to the CSI reconstruction model,

one or more de-quantization rules to map bits of the CSI payload to floating point numbers for the input to the CSI reconstruction model,

entropy coding information for the CSI payload,

a relationship to a prior CSI payload,

a sequence for applying quantization and differential encoding,

the CSI payload is common to multiple layers,

the CSI payload is specific to a particular layer,

the CSI payload is common to multiple sub-bands, or

the CSI payload is specific to a particular sub-band.
The method of claim 23, wherein the CSI message includes a first part including the CSI payload processing indication, and a second part including the CSI payload.
A method of wireless communication at a network node, comprising:

providing one or more reference signals;

obtaining a channel state information (CSI) message comprising a CSI payload based on measurements of the one or more reference signals; and

obtaining a CSI payload processing indication indicating information for processing to apply to the CSI payload to derive an input to a CSI reconstruction model.
The method of claim 27, wherein the CSI payload includes compressed CSI, the method further comprising:

processing the CSI payload based on the CSI payload processing indication prior to input to the CSI reconstruction model to reconstruct CSI for a user equipment.
The method of claim 28, wherein reconstruction of the compressed CSI is based on an artificial intelligence (AI) or machine learning (ML) model.
The method of claim 27, wherein the CSI payload processing indication indicates one or more of:

an order in which the CSI payload is structured relative to an intended order for the input to the CSI reconstruction model,

one or more de-quantization rules to maps bits of the CSI payload to floating point numbers for the input to the CSI reconstruction model,

entropy coding information for the CSI payload,

a relationship to a prior CSI payload,

a sequence for applying quantization and differential encoding,

the CSI payload is common to multiple layers,

the CSI payload is specific to a particular layer,

the CSI payload is common to multiple sub-bands, or

the CSI payload is specific to a particular sub-band.