WO2025043380A1

WO2025043380A1 - Csf for multi-resolution csi feedback

Info

Publication number: WO2025043380A1
Application number: PCT/CN2023/114919
Authority: WO
Inventors: Abdelrahman Mohamed Ahmed Mohamed IBRAHIM; Jay Kumar Sundararajan; Chenxi HAO; Taesang Yoo; Pavan Kumar Vitthaladevuni; Runxin WANG
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2023-08-25
Filing date: 2023-08-25
Publication date: 2025-03-06
Anticipated expiration: 2026-02-25

Abstract

Apparatus, methods, and computer program products for wireless communication are provided. An example method may include training an encoder at the UE. The example method may further include receiving, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder. The example method may further include transmitting the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission comprising at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission comprising at least a second subset of the second part of the CSI feedback.

Description

CSF FOR MULTI-RESOLUTION CSI FEEDBACK

TECHNICAL FIELD

The present disclosure relates generally to communication systems, and more particularly, to wireless communication systems with channel state information (CSI) feedback.

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 at a user equipment (UE) are provided. The apparatus may include at least one memory and at least one processor coupled to the at least one memory. Based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to train an encoder at the UE. Based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to receive, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder. Based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

In another aspect of the disclosure, a method, a computer-readable medium, and an apparatus at a network entity are provided. The apparatus may include at least one memory and at least one processor coupled to the at least one memory. Based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to transmit CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. Based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

To the accomplishment of the foregoing and related ends, the one or more aspects 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, in accordance with various aspects of the present disclosure. FIG. 4 is a diagram illustrating an example of CSI encoder and CSI decoder, in accordance with various aspects of the present disclosure.

FIG. 5 is a diagram illustrating example communications between a network entity and a UE, in accordance with various aspects of the present disclosure.

FIG. 6 is a diagram illustrating an example multi-resolution CSI feedback, in accordance with various aspects of the present disclosure.

FIG. 7 is a diagram illustrating example training for an encoder at the UE, in accordance with various aspects of the present disclosure.

FIG. 8A is a diagram illustrating a first example multi-resolution CSI report, in accordance with various aspects of the present disclosure.

FIG. 8B is a diagram illustrating a second example multi-resolution CSI report, in accordance with various aspects of the present disclosure.

FIG. 9 is a flowchart of a method of wireless communication, in accordance with various aspects of the present disclosure.

FIG. 10 is a flowchart of a method of wireless communication, in accordance with various aspects of the present disclosure.

FIG. 11 is a flowchart of a method of wireless communication, in accordance with various aspects of the present disclosure.

FIG. 12 is a flowchart of a method of wireless communication, in accordance with various aspects of the present disclosure.

FIG. 13 is a diagram illustrating an example of a hardware implementation for an example apparatus and/or network entity, in accordance with various aspects of the present disclosure.

FIG. 14 is a diagram illustrating an example of a hardware implementation for an example network entity, in accordance with various aspects of the present disclosure.

FIG. 15 illustrates example aspects of machine learning or artificial intelligence model training and inference for wireless communication, in accordance with various aspects of the present disclosure.

DETAILED DESCRIPTION

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.

In some wireless communication systems, a first part and a second part of the CSI feedback are configured to be transmitted together. Aspects provided herein enable multi-resolution CSI feedback where a first part and a second part of a same CSI feedback may be configured to be transmitted in multiple different CSI transmissions, allowing more flexibility and efficiency in transmitting the CSI feedback. For scenarios where semi-persistent (SP) /periodic (P) CSI-RS is relatively sparse and the channel is slow-varying, subband CSI may have large payload depending on report configuration. By introducing multi-resolution CSI feedback, the network node may indicate, to the UE, whether to continue to transmit the CSI feedback before all of the CSI feedback is transmitted. Therefore, signaling overhead may be reduced in situations where a portion of the CSI feedback, such as the first part and a portion of the second part, may be received by the network and determined to be sufficient.

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 may include a CSF component 198. In some aspects, the CSF component 198 may be configured to train an encoder at the UE. In some aspects, the CSF component 198 may be further configured to receive, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder. In some aspects, the CSF component 198 may be further configured to transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

In certain aspects, the base station 102 may include a CSF component 199. In some aspects, the CSF component 199 may be configured to transmit CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. In some aspects, the CSF component 199 may be further configured to reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

Although the following description may be focused on 5G NR, the concepts described herein may be applicable to other similar areas, such as LTE, LTE-A, CDMA, GSM, and other wireless technologies.

As described herein, a node (which may be referred to as a node, a network node, a network entity, or a wireless node) may include, be, or be included in (e.g., be a component of) a base station (e.g., any base station described herein) , a UE (e.g., any UE described herein) , a network controller, an apparatus, a device, a computing system, an integrated access and backhauling (IAB) node, a distributed unit (DU) , a central unit (CU) , a remote/radio unit (RU) (which may also be referred to as a remote radio unit (RRU) ) , and/or another processing entity configured to perform any of the techniques described herein. For example, a network node may be a UE. As another example, a network node may be a base station or network entity. As another example, a first network node may be configured to communicate with a second network node or a third network node. In one aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a UE. In another aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a base station. In yet other aspects of this example, the first, second, and third network nodes may be different relative to these examples. Similarly, reference to a UE, base station, apparatus, device, computing system, or the like may include disclosure of the UE, base station, apparatus, device, computing system, or the like being a network node. For example, disclosure that a UE is configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node. Consistent with this disclosure, once a specific example is broadened in accordance with this disclosure (e.g., a UE is configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node) , the broader example of the narrower example may be interpreted in the reverse, but in a broad open-ended way. In the example above where a UE is configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node, the first network node may refer to a first UE, a first base station, a first apparatus, a first device, a first computing system, a first set of one or more one or more components, a first processing entity, or the like configured to receive the information; and the second network node may refer to a second UE, a second base station, a second apparatus, a second device, a second computing system, a second set of one or more components, a second processing entity, or the like.

As described herein, communication of information (e.g., any information, signal, or the like) may be described in various aspects using different terminology. Disclosure of one communication term includes disclosure of other communication terms. For example, a first network node may be described as being configured to transmit information to a second network node. In this example and consistent with this disclosure, disclosure that the first network node is configured to transmit information to the second network node includes disclosure that the first network node is configured to provide, send, output, communicate, or transmit information to the second network node. Similarly, in this example and consistent with this disclosure, disclosure that the first network node is configured to transmit information to the second network node includes disclosure that the second network node is configured to receive, obtain, or decode the information that is provided, sent, output, communicated, or transmitted by the first network node.

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 CSF 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 CSF component 199 of FIG. 1.

In wireless communication systems, CSI parameters are quantities related to the state of a channel that are extracted from the channel estimate array. A device such, as a UE, may report CSI parameters in a CSI feedback to a device transmitting wireless communication to the UE, such as a base station or other UE. The CSI feedback may include several parameters, such as the channel quality indication (CQI) , the precoding matrix indices (PMI) with different codebook sets, the rank indicator (RI) , or the like. A UE may use various different RSs, such as CSI-RS, to measure and compute the CSI parameters. The CSI parameters may be reported by the UE to the network as CSI feedback. Upon receiving CSI feedback, the network may schedule, based on the CSI feedback, downlink data transmissions with attributes such as modulation scheme, code rate, number of transmission layers, and precoding. The UE may report the CSI feedback based on a CSI report configuration, which may be used as a PMI dictionary from which UE would report one or more PMI codewords (e.g., best performing PMI codewords) , and use a sequence of bits (e.g., in a CSI report) to report the PMI.

In some wireless communication systems, for some CSI feedback, instead of using codebook, a machine learning or artificial intelligence (AI) based CSI encoder and CSI decoder may be used to encode/decode wireless communication. The CSI encoder may be used as a PMI searching algorithm and the CSI decoder may be used as a PMI codebook that may be able to convert PMI codeword to CSI reporting bits or vice versa.

For codebook-based transmission, a UE may be configured with one SRS resource set with “usage” set to “codebook. ” For example, a maximum of 4 SRS resources within the set may be configured for the UE. Each SRS resource may be radio resource control (RRC) configured with a number of ports, such as one or more ports. The SRS resource indicator (SRI) field in the UL DCI scheduling the PUSCH may indicate one SRS resource. The number of ports configured for the indicated SRS resource may determine number of antenna ports for the PUSCH. The PUSCH may be transmitted with the same spatial domain filter (which may be otherwise referred to as a “beam” ) as the indicated SRS resources. The number of layers (i.e., rank) or transmitted precoding matrix indicator (TPMI) (e.g., for precoder) for the scheduled PUSCH may be determined from a separate DCI field “Precoding information and number of layers. ” The TPMI may be used for indicating a precoding matrix. In some aspects, one TPMI may correspond to one precoding matrix. By transmitting the TPMI, a network may communicate with a UE regarding a particular precoding matrix is selected for an uplink transmission. For example, for each PUSCH, in the UL grant, the network may signal a precoder to use in the codebook based on TPMI. In some aspects, for each PUSCH slot, in the UL grant, the network may signal a precoder to use in the codebook based on TPMI. In some aspects, precoder may be different for each PUSCH slot.

In codebook-based uplink shared-channel transmission, the network selects the transmission rank and the corresponding precoding matrix and informs the device through uplink scheduling grant. The term “a configured set of codebooks” may refer to a set of configured precoding matrices (that may each correspond to a precoder) at a UE and a network used for precoding (e.g., which may also be referred to as “a whole codebook, ” “an entire codebook, ” or “a codebook” ) . The configured set of codebooks may be configured without signaling between the UE and the network.

Precoding is the process of preprocessing of transmit signals based on a precoder. Based on a precoder, a wireless device may apply weights on to the antenna element that includes amplitude and phase for each antenna element. The term “precoder” may correspond to a “precoding matrix” and may refer to parameters used in the precoding process and may be indicated as a “codebook candidate” or “codebook” in the configured set of codebooks. With the help of weights, the antenna can be electronically steered to radiate in the intended direction by suppressing the power in the other directions. In some wireless communication systems, a UE may support eight transmission channels (8 Tx) UL operation to support 4 and more layers per UE. To support such operations, the configured set of codebooks configured for the UE may include a large amount of precoders.

FIG. 4 is a diagram 400 illustrating an example of CSI encoder and CSI decoder, in accordance with various aspects of the present disclosure. As illustrated in FIG. 4, an input 402 may be the input for an encoder 404 at the UE. The encoder input 402 may include a downlink channel matrix (e.g., represented by the parameter H) , one or more downlink precoders (e.g., represented by the parameter V) , and an interference covariance matrix (represented by the parameter Rnn) . The encoder 404 may encode based on the encoder input 402 and transmit a latent message 406 to a decoder 408 at network node. A decoder output 410 may include one or more of the downlink channel matrix (H) , a transmit covariance matrix, one or more downlink precoders, an interference covariance matrix, and a representation of a raw versus whitened downlink channel. The decoder output 410 may include a representation of a raw downlink channel (e.g., a raw downlink channel matrix) , a representation of a whitened downlink channel (e.g., a whitened downlink channel) , or a representation based on a scattering matrix associated with the whitened downlink channel matrix. As illustrated in FIG. 4, in a left end 420 of a continuum of a message between a UE and a network node, RI, PMI, and CQI may be represented. in a right end 422 of a continuum, the full channel may be represented.

AI-based CSI feedback may be based on CSI feedback signaling mechanisms, such as CSI-RS configurations, CSI-RS reporting configurations, CSI report uplink control information (UCI) mapping, priority, and omission, CSI processing procedures, or the like, that may also be associated with non-AI based CSI feedback. For a UCI payload carrying CSI feedback, the UCI may be transmitted on PUCCH or PUSCH and may include (e.g., consists of) one part or two parts depending on reporting quantity and type, such as whether the CSI feedback is associated with wideband or subband. For UCI including two parts of CSI feedback, a first part (e.g., CSI part 1) may be a fixed payload size part (e.g., based on zero padding that may fill zeros to bits that do not include CSI information) , and a second part (e.g., CSI part 2) may be configured to be derived based on information in the first part. In some aspects, a one part CSI feedback may include CSI part 1 without including CSI part 2. In some aspects, the first part is configured to be used for decoding the second part, such as information for identifying a number of information bits in the second part. The first part and the second part may be encoded separately.

As used herein, the term “CSI feedback” may refer to CSI parameters transmitted from a UE to the network that may be included in one or more CSI transmissions. In some aspects, the term “multi-resolution CSI feedback” may refer to a CSI feedback that is transmitted in multiple CSI transmissions. As an example, in some aspects, after a first CSI report transmission of the multiple CSI transmissions, the UE may receive an indication from the network to continue with the subsequent CSI transmissions of multiple CSI transmissions or cease transmission of the subsequent CSI transmissions. As used herein, the term “first part of the CSI feedback” may refer to a CSI part 1 that may be configured to identify a number of information bits in “a second part of the CSI feedback” and may be configured to have a fixed payload size. As used herein, the term “a second part of the CSI feedback” may refer to a part of the CSI feedback with non-fixed payload size and carry a number of information bits identified by CSI part 1. In some aspects, the first part of the CSI feedback may include a CSI reference signal resource indicator (CRI) or a synchronization signal physical broadcast channel (SS/PBCH) block resource indicator (SSBRI) , an indicator of a number of non-zero amplitude coefficients, a first channel quality indicator (CQI) associated with a first transport block (TB) , or a rank indicator, and the second part may include a second CQI associated with a second TB or precoding matrix indicator (PMI) information. In some aspects, the first part of the CSI feedback may not include PMI information.

As an example, for CSI on PUSCH, two UCI bit sequences are generated, andThe CSI fields of all CSI reports, in the order from upper part to lower part in Table 6.3.2.1.2-6, are mapped to the UCI bit sequencestarting withThe CSI fields of all CSI reports, in the order from upper part to lower part in, are mapped to the UCI bit sequencestarting withThe mapping order of CSI fields of one report for CSI reference signal resource indicator (CRI) CRI/reference signal received power (RSRP) or synchronization signal physical broadcast channel (SS/PBCH) block resource indicator (SSBRI) /RSRP or CRI/RSRP/CapabilityIndex or SSBRI/RSRP/CapabilityIndex reporting may be configured (e.g., configured without signaling) . The mapping order of CSI fields of one report for inter-cell SSBRI/RSRP reporting may also be configured. The mapping order of CSI fields of one report for CRI/SINR or SSBRI/SINR or CRI/SINR/CapabilityIndex or SSBRI/SINR/CapabilityIndex reporting may also be configured. . The mapping order of CSI fields of one report for group-based CRI/RSRP or SSBRI/RSRP reporting may also be configured. An example mapping order of CSI fields of one CSI report, CSI part 1, is provided below:

Table 2

As another example, mapping order of CSI fields of one CSI report, CSI part 1, in a first CSI report mode (e.g., associated with wideband CQI) , is provided below:

Table 3

As another example, mapping order of CSI fields of one CSI report, CSI part 1, in a second CSI report mode (e.g., associated with subband CQI) , is provided below:

Table 4

As an example, mapping order of CSI fields of one CSI report, CSI part 2, is provided below:

Table 5

As another example, mapping order of CSI fields of one CSI report, CSI part 2 wideband, in a first CSI reporting mode, is provided below:

Table 6

There may be other examples of CSI part 1 and CSI part 2. CSI feedback with CSI part 1 and CSI part 2 being separate may be used for PUSCH based CSI reporting or PUCCH based CSI reporting. As another example, for PUCCH based CSI reporting, an example mapping order of CSI fields of one CSI report is provided below:

Table 7

CSI report dropping may be performed. For example, when CSI reporting on PUSCH includes two parts, the UE may omit a portion of the second part. Omission of the second part may be based on a priority order, an example of which is provided below (priority 0 being the highest priority) :

Table 8

The parameter N_Rep is the number of CSI reports configured to be carried on the PUSCH. Priority 0 is the highest priority and priority 2N_Rep is the lowest priority, lower priorities may be dropped first. The subbands for a given CSI report n indicated by the higher layer parameter csi-ReportingBand may be numbered continuously in increasing order with the lowest subband of csi-ReportingBand as subband 0. When omitting Part 2 CSI information for a particular priority level, the UE may omit all of the information at that priority level.

FIG. 5 is a diagram 500 illustrating example communications between a network node 504 and a UE 502, in accordance with various aspects of the present disclosure. As illustrated in FIG. 5, the UE 502 and the network node 504 may train an encoder at the UE 502 in 506. In some aspects, the UE 502 may train the encoder at the UE 502 in 506 with another entity different from the network node 504. In some aspects, the training in 506 facilitates multi-resolution CSI feedback.

Referring now to FIG. 6, FIG. 6 is a diagram 600 illustrating an example multi-resolution CSI feedback, in accordance with various aspects of the present disclosure. As illustrated in FIG. 6, CSI-RS 602 may be transmitted from a network node to a UE and report #1 based on the CSI-RS 602 may include various portions, including portion #1-1 604A transmitted in a first CSI report transmission, portion #1-2 604B transmitted in a second CSI report transmission. The two portions constituting report #1 may be collectively referred to as one multi-resolution CSI feedback, which may be transmitted in three separate transmissions. There may be another CSI-RS 606 after the one multi-resolution CSI feedback for separate CSI feedback.

Referring back to FIG. 5, the training in 506 may include several different parts, such as a first part (Z1) for achieving a baseline performance and subsequent parts (e.g., Z2) for improving the baseline performance. The encoder training may be based on a joint loss function capturing L1 and L2, a linear combination of L1 and L2, or the like. L1 may correspond to minimal performance guarantee and L2 may correspond to improved performance after receiving a second part of a latent vector (e.g., associated with a latent message) . In some aspects, additional performance improvements, referred to as L3, may also be captured. In some aspects, L3 may be captured for self-decodability based on receiving Z2 only (without Z1) . Referring now to FIG. 7, FIG. 7 is a diagram 700 illustrating example training for an encoder 702 at the UE, in accordance with various aspects of the present disclosure. As illustrated in FIG. 7, an output of the encoder 702 may be captured in two different latent vectors, one of which correspond to Z1 and fed into a first decoder 704A and a first loss function 706A and a second one of which correspond to (Z1, Z2) and fed into a second decoder 704B and a second loss function 706B.

Referring back to FIG. 5, after training the encoder at the UE 502 in 506, the network node 504 may transmit CSI-RS (s) 508 to the UE 502. Based on the UE 502, the trained encoder at the UE 502 may generate CSI feedback, which may include AI-based CSI feedback including one or more parameters such as CRI, CQI, PMI, RI, other parameters such as the parameters included in tables 2-8, or the like. After generating the CSI feedback, the UE 502 may transmit the CSI feedback in multiple CSI transmissions. For example, the UE 502 may transmit a first CSI report transmission 510A, and one or more subsequent CSI transmissions including a second CSI report transmission 510B, a third CSI report transmission 510C, or the like.

In some aspects, a first part of the CSI feedback (CSI part 1) is transmitted in the first CSI report transmission 510A and not transmitted in the one or more subsequent CSI transmissions. In such aspects, the first CSI report transmission 510A may be used for decoding the one or more subsequent CSI transmissions because the first part of the CSI feedback (CSI part 1) may include information for decoding the one or more subsequent CSI transmissions. In such aspects, the CSI reporting overhead may be uniformized. In some aspects, a second part of the CSI feedback may include multiple groups and each of the one or more subsequent CSI transmissions (e.g., including a second CSI report transmission 510B, a third CSI report transmission 510C, or the like) may each include more of groups (e.g., represented by Gx) in the second part of the CSI feedback. FIG. 8A is a diagram 800 illustrating a first example multi-resolution CSI report, in accordance with various aspects of the present disclosure. As illustrated in FIG. 8A, a first CSI report transmission 802A (which may correspond to 510A) may include the first part of the CSI report 804 and one or more groups in the second part of the CSI feedback as part 2-1 806A. A second CSI report transmission 802B (which may correspond to 510B) may include one or more groups in the second part of the CSI feedback as part 2-2 806B. A third CSI report transmission 802C (which may correspond to 510C) may include one or more groups in the second part of the CSI feedback as part 2-3 806C.

In some aspects, a first group of the groups in the second part of the CSI feedback may be group 0 (G0) and may be used for decoding the subsequent groups. In some aspects, G0 may be transmitted on all CSI report transmissions associated with the CSI feedback, such as the first CSI report transmission 802A, the second CSI report transmission 802B, and the third CSI report transmission 802C. In some aspects, the CSI feedback may be divided into more than one part such that each part may be transmitted in one of the CSI report transmissions.

In some aspects, a first part of the CSI feedback (CSI part 1) is transmitted in the first CSI report transmission 510A and also transmitted in the one or more subsequent CSI transmissions. In such aspects, each of the CSI report transmissions may be self-contained and decodable regardless of whether the previous parts are received or not. In such aspects, if G0 is used for decoding other groups, G0 may also be transmitted on all of the CSI report transmissions. Referring now to FIG. 8B, FIG. 8B is a diagram 850 illustrating a second example multi-resolution CSI report, in accordance with various aspects of the present disclosure. As illustrated in FIG. 8B, a first CSI report transmission 852A (which may correspond to 510A) may include the first part of the CSI report 854 and G0 856A, and another group G1 856B. A second CSI report transmission 852B (which may correspond to 510B) may include the first part of the CSI report 854 and G0 856A, and another group G2 856C. In some aspects, the second part of the CSI feedback may be divided into more than portions such that each portion is transmitted in one of the CSI report transmissions.

In some aspects, the network node 504 may be configured with an algorithm or learning model for performance monitoring based on partial CSI feedback. In some aspects, the network node 504 may determine that a partial CSI feedback may be sufficient or not and whether subsequent CSI report transmissions may be transmitted or not after receiving the first CSI report transmission 510A. For example, to determine whether subsequent CSI report transmissions may be transmitted or not after receiving the first CSI report transmission 510A, for precoder feedback, the network node 504 may check some of the properties of the constructed downlink precoders such as orthogonality over the layers, structure/continuity of precoder of different subbands, and comparison with other CSI reports (e.g., past CSI reports) . In some aspects, upon determining that subsequent CSI report transmissions may be transmitted after receiving the first CSI report transmission 510A, the network node 504 may transmit an indication 511A to indicate transmission the one or more subsequent CSI report transmissions to the UE 502. After receiving the indication 511A, the UE 502 may proceed with transmitting the one or more subsequent CSI report transmissions including the CSI report transmission 510B and the CSI report transmission 510C. In some aspects, upon determining that subsequent CSI report transmissions may not be transmitted after receiving the first CSI report transmission 510A, the network node 504 may transmit an indication 511B to indicate cease transmission of the one or more subsequent CSI report transmissions to the UE 502. After receiving the indication 511B, the UE 502 may cease transmission of the one or more subsequent CSI report transmissions including the CSI report transmission 510B and the CSI report transmission 510C.

At 512, after receiving the CSI report transmission (s) carrying the CSI feedback, the network node 504 may reconstruct the CSI feedback and consider the CSI feedback in scheduling future communications with the UE 502 or another UE.

FIG. 9 is a flowchart 900 of a method of wireless communication. The method may be performed by a UE (e.g., the UE 104, the UE 502; the apparatus 1304) . The method may enable multi-resolution CSI feedback where the network node may indicate, to the UE, whether to continue to transmit the CSI feedback before all of the CSI feedback is transmitted. Therefore, signaling overhead may be reduced in situations where a portion of the CSI feedback, such as the first part and a portion of the second part, may be received by the network and determined to be sufficient.

At 902, the UE may train an encoder at the UE. For example, the UE 502 may train (e.g., at 506) , with a network node 504, an encoder at the UE. In some aspects, 902 may be performed by CSF component 198.

At 904, the UE may receive, from a network node, CSI-RS associated with a CSI feedback configured to be generated based on the trained encoder. For example, the UE 502 may receive, from the network node 504, CSI-RS (e.g., 508) associated with a CSI feedback configured to be generated based on the trained encoder. In some aspects, 904 may be performed by CSF component 198.

At 906, the UE may transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. For example, the UE 502 may transmit the CSI feedback to the network node in at least a first CSI report transmission (e.g., 510A) and a second CSI report transmission (e.g., 510B) , the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. In some aspects, 906 may be performed by CSF component 198.

FIG. 10 is a flowchart 1000 of a method of wireless communication. The method may be performed by a UE (e.g., the UE 104, the UE 502; the apparatus 1304) . The method may enable multi-resolution CSI feedback where the network node may indicate, to the UE, whether to continue to transmit the CSI feedback before all of the CSI feedback is transmitted. Therefore, signaling overhead may be reduced in situations where a portion of the CSI feedback, such as the first part and a portion of the second part, may be received by the network and determined to be sufficient.

At 1002, the UE may train an encoder at the UE. For example, the UE 502 may train (e.g., at 506) , with a network node 504, an encoder at the UE. In some aspects, 1002 may be performed by CSF component 198. In some aspects, as part of 1002, at 1003A, the UE may train the encoder based on a joint loss function, where a first input of the joint loss function is a first part corresponding to a baseline performance and a second input of the joint loss function is a second part corresponding to an improved performance. In some aspects, a third input of the joint loss function is a third part. In some aspects, as part of 1002, at 1003B, the UE may train the encoder based on a linear combination of a first part corresponding to a baseline performance and a second part corresponding to an improved performance or train the encoder based on a linear combination of a first part corresponding to a baseline performance, a second part corresponding to an improved performance, and a third part.

At 1004, the UE may receive, from a network node, CSI-RS associated with a CSI feedback configured to be generated based on the trained encoder. For example, the UE 502 may receive, from the network node 504, CSI-RS (e.g., 508) associated with a CSI feedback configured to be generated based on the trained encoder. In some aspects, 1004 may be performed by CSF component 198.

At 1006, the UE may transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. For example, the UE 502 may transmit the CSI feedback to the network node in at least a first CSI report transmission (e.g., 510A) and a second CSI report transmission (e.g., 510B) , the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. In some aspects, 1006 may be performed by CSF component 198.

In some aspects, the second CSI report transmission does not include the first part of the CSI feedback (e.g., as illustrated in FIG. 8A) . In some aspects, the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes a second group associated with the CSI feedback, where the first group enables decoding of the second group. In some aspects, the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group. In some aspects, the first subset and the second subset are non-overlapping.

In some aspects, the second CSI report transmission includes the first part of the CSI feedback (e.g., as illustrated in FIG. 8B) . In some aspects, the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group. In some aspects, the first subset and the second subset are non-overlapping.

In some aspects, as part of 1006, at 1008, the UE may receive, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission. For example, the UE 502 may receive, from the network node 504 after the first CSI report transmission 510A and before the second CSI report transmission 510B, an indication 511B to drop one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, 1008 may be performed by CSF component 198.

In some aspects, as part of 1006, at 1010, the UE may cease the second CSI report transmission. For example, the UE 502 may cease the second CSI report transmission 510B based on receiving the indication 511B. In some aspects, 1010 may be performed by CSF component 198.

In some aspects, as part of 1006, at 1012, the UE may receive, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. For example, the UE 502 may receive, from the network node 504 after the first CSI report transmission 510A and before the second CSI report transmission 510B, an indication 511A to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, 1012 may be performed by CSF component 198.

In some aspects, as part of 1006, at 1014, the UE may transmit the second CSI report transmission based on reception of the indication. For example, the UE 502 may transmit the second CSI report transmission 510B based on reception of the indication 511A. In some aspects, 1014 may be performed by CSF component 198.

In some aspects, as part of 1006, at 1016, the UE may transmit the CSI feedback to the network node in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. For example, the UE 502 may transmit the CSI feedback to the network node 504 in a third CSI report transmission 510C, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. In some aspects, 1016 may be performed by CSF component 198.

In some aspects, the CSI feedback is multiplexed on a physical uplink shared channel (PUSCH) , where the first part includes a CSI reference signal resource indicator (CRI) or a synchronization signal physical broadcast channel (SS/PBCH) block resource indicator (SSBRI) , an indicator of a number of non-zero amplitude coefficients, a first channel quality indicator (CQI) associated with a first transport block (TB) , or a rank indicator, and where the second part includes a second CQI associated with a second TB or precoding matrix indicator (PMI) information. In some aspects, a payload size of the first part is fixed.

FIG. 11 is a flowchart 1100 of a method of wireless communication. The method may be performed by a network entity (e.g., the base station 102, the network node 504, the network entity 1302, the network entity 1402) . The method may enable multi-resolution CSI feedback where the network node may indicate, to the UE, whether to continue to transmit the CSI feedback before all of the C SI feedback is transmitted. Therefore, signaling overhead may be reduced in situations where a portion of the CSI feedback, such as the first part and a portion of the second part, may be received by the network and determined to be sufficient.

At 1102, the network entity may transmit CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. For example, the network node 504 may transmit CSI-RS (e.g., 508) associated with a CSI feedback configured to be generated based on a trained encoder at a UE 502. In some aspects, 1102 may be performed by CSF component 199.

At 1104, the network entity may reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. For example, the network node 504 may reconstruct (e.g., at 512) the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission (e.g., 511A) and a second CSI report transmission (e.g., 511B) , the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. In some aspects, 1104 may be performed by CSF component 199.

FIG. 12 is a flowchart 1200 of a method of wireless communication. The method may be performed by a network entity (e.g., the base station 102, the network node 504, the network entity 1302, the network entity 1402) . The method may enable multi-resolution CSI feedback where the network node may indicate, to the UE, whether to continue to transmit the CSI feedback before all of the C SI feedback is transmitted. Therefore, signaling overhead may be reduced in situations where a portion of the CSI feedback, such as the first part and a portion of the second part, may be received by the network and determined to be sufficient.

At 1202, the network entity may transmit CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. For example, the network node 504 may transmit CSI-RS (e.g., 508) associated with a CSI feedback configured to be generated based on a trained encoder at a UE 502. In some aspects, 1202 may be performed by CSF component 199.

At 1204, the network entity may reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. For example, the network node 504 may reconstruct (e.g., at 512) the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission (e.g., 511A) and a second CSI report transmission (e.g., 511B) , the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. In some aspects, 1204 may be performed by CSF component 199.

As part of 1204, at 1206, the network entity may transmit, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. For example, the network node 504 may transmit, for the UE after the first CSI report transmission 510A and before the second CSI report transmission 510B, an indication 511A to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, 1206 may be performed by CSF component 199.

As part of 1204, at 1208, the network entity may receive the second CSI report transmission based on transmission of the indication. For example, the network node 504 may receive the second CSI report transmission 510B based on transmission of the indication 511A. In some aspects, 1208 may be performed by CSF component 199.

As part of 1204, at 1210, the network entity may transmit, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission. For example, the network node 504 may transmit, for the UE after the first CSI report transmission 510A and before the second CSI report transmission 510B, an indication 511B to drop one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, 1210 may be performed by CSF component 199.

As part of 1204, at 1212, the network entity may receive the CSI feedback from the UE in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. For example, the network node 504 may receive the CSI feedback from the UE 502 in a third CSI report transmission 510C, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. In some aspects, 1212 may be performed by CSF component 199.

FIG. 13 is a diagram 1300 illustrating an example of a hardware implementation for an apparatus 1304. The apparatus 1304 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus1304 may include at least one cellular baseband processor 1324 (also referred to as a modem) coupled to one or more transceivers 1322 (e.g., cellular RF transceiver) . The cellular baseband processor (s) 1324 may include at least one on-chip memory 1324'. In some aspects, the apparatus 1304 may further include one or more subscriber identity modules (SIM) cards 1320 and at least one application processor 1306 coupled to a secure digital (SD) card 1308 and a screen 1310. The application processor (s) 1306 may include on-chip memory 1306'. In some aspects, the apparatus 1304 may further include a Bluetooth module 1312, a WLAN module 1314, an SPS module 1316 (e.g., GNSS module) , one or more sensor modules 1318 (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 1326, a power supply 1330, and/or a camera 1332. The Bluetooth module 1312, the WLAN module 1314, and the SPS module 1316 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX) ) . The Bluetooth module 1312, the WLAN module 1314, and the SPS module 1316 may include their own dedicated antennas and/or utilize the antennas 1380 for communication. The cellular baseband processor (s) 1324 communicates through the transceiver (s) 1322 via one or more antennas 1380 with the UE 104 and/or with an RU associated with a network entity 1302. The cellular baseband processor (s) 1324 and the application processor (s) 1306 may each include a computer-readable medium /memory 1324', 1306', respectively. The additional memory modules 1326 may also be considered a computer-readable medium /memory. Each computer-readable medium /memory 1324', 1306', 1326 may be non-transitory. The cellular baseband processor (s) 1324 and the application processor (s) 1306 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) 1324 /application processor (s) 1306, causes the cellular baseband processor (s) 1324 /application processor (s) 1306 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) 1324 /application processor (s) 1306 when executing software. The cellular baseband processor (s) 1324 /application processor (s) 1306 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 1304 may be at least one processor chip (modem and/or application) and include just the cellular baseband processor (s) 1324 and/or the application processor (s) 1306, and in another configuration, the apparatus 1304 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1304.

As discussed supra, the CSF component 198 may be configured to train an encoder at the UE. In some aspects, the CSF component 198 may be further configured to receive, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder. In some aspects, the CSF component 198 may be further configured to transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. The CSF component 198 may be within the cellular baseband processor (s) 1324, the application processor (s) 1306, or both the cellular baseband processor (s) 1324 and the application processor (s) 1306. The 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 1304 may include a variety of components configured for various functions. In one configuration, the apparatus 1304, and in particular the cellular baseband processor (s) 1324 and/or the application processor (s) 1306, may include means for training the encoder based on a joint loss function, where a first input of the joint loss function corresponds to a baseline performance and a second input of the joint loss function corresponds to an improved performance. In some aspects, the apparatus 1304 may include means for training the encoder based on a linear combination of a first input corresponding to a baseline performance and a second input corresponding to an improved performance. In some aspects, the apparatus 1304 may include means for training the encoder based on a linear combination of a first input corresponding to a baseline performance, a second input corresponding to an improved performance, and a third input. In some aspects, the apparatus 1304 may include means for receiving, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, the apparatus 1304 may include means for ceasing the second CSI report transmission. In some aspects, the apparatus 1304 may include means for receiving, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, the apparatus 1304 may include means for transmitting the second CSI report transmission based on reception of the indication. In some aspects, the apparatus 1304 may include means for transmitting the CSI feedback to the network node in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. The means may be the component 198 of the apparatus 1304 configured to perform the functions recited by the means. As described supra, the apparatus 1304 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.

FIG. 14 is a diagram 1400 illustrating an example of a hardware implementation for a network entity 1402. The network entity 1402 may be a BS, a component of a BS, or may implement BS functionality. The network entity 1402 may include at least one of a CU 1410, a DU 1430, or an RU 1440. For example, depending on the layer functionality handled by the component 199, the network entity 1402 may include the CU 1410; both the CU 1410 and the DU 1430; each of the CU 1410, the DU 1430, and the RU 1440; the DU 1430; both the DU 1430 and the RU 1440; or the RU 1440. The CU 1410 may include at least one CU processor 1412. The CU processor (s) 1412 may include on-chip memory 1412'. In some aspects, the CU 1410 may further include additional memory modules 1414 and a communications interface 1418. The CU 1410 communicates with the DU 1430 through a midhaul link, such as an F1 interface. The DU 1430 may include at least one DU processor 1432. The DU processor (s) 1432 may include on-chip memory 1432'. In some aspects, the DU 1430 may further include additional memory modules 1434 and a communications interface 1438. The DU 1430 communicates with the RU 1440 through a fronthaul link. The RU 1440 may include at least one RU processor 1442. The RU processor (s) 1442 may include on-chip memory 1442'. In some aspects, the RU 1440 may further include additional memory modules 1444, one or more transceivers 1446, antennas 1480, and a communications interface 1448. The RU 1440 communicates with the UE 104. The on-chip memory 1412', 1432', 1442' and the additional memory modules 1414, 1434, 1444 may each be considered a computer-readable medium /memory. Each computer-readable medium /memory may be non-transitory. Each of the processors 1412, 1432, 1442 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 CSF component 199 may be configured to transmit CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. In some aspects, the CSF component 199 may be further configured to reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. The CSF component 199 may be within one or more processors of one or more of the CU 1410, DU 1430, and the RU 1440. The 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 1402 may include a variety of components configured for various functions. In one configuration, the network entity 1402 may include means for transmitting CSI-RS associated with a CSI feedback configured to be generated based on a trained encoder at a UE. In some aspects, the network entity 1402 may include means for reconstructing the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback. In some aspects, the network entity 1402 may include means for transmitting, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, the network entity 1402 may include means for transmitting, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission. In some aspects, the network entity 1402 may include means for receiving the second CSI report transmission based on transmission of the indication. In some aspects, the network entity 1402 may include means for receiving the CSI feedback to the network node in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback. The means may be the component 199 of the network entity 1402 configured to perform the functions recited by the means. As described supra, the network entity 1402 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.

Some wireless communication may include the use of AI or ML at a transmitter and receiver (e.g., encoder and decoder) . As an example, the encoder and decoder may be at the network and the UE. Among various examples, AI/ML may be used for CSI compression at a UE and/or a network. The use of an AI/ML model may enable more efficient CSI feedback. Models may be provided that support various levels of network and UE collaboration and to support various use cases. The use of an AI/ML model may include various aspects such as model training, model deployment, model inference, model monitoring, and model updated.

FIG. 15 is an example of the AI/ML algorithm 1500 for CSI feedback in wireless communication and illustrates various aspects model training, model inference, model feedback, and model update. The AI/ML algorithm 1500 may include various aspects including a data collection 1502, a model training 1504, model inference 1506, and an actor 1508 that receives and uses output based on the model inference.

The data collection 1502 may be a function that provides input data for the model training 1504 and the model inference 1506. The data collection 1502 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, measurements, such as channel measurements, such as CSI from entities including UEs or network nodes, feedback from the actor 1508 (e.g., which may be a UE or network node) , output from another AI/ML model. The data collection 1502 may include training data, which refers to the data to be sent as the input for the AI/ML model training 1504, and inference data, which refers to data input for the AI/ML model inference (e.g., 1506) .

The model training 1504 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 1504 may also include data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the training data delivered or received from the data collection 1502 function. The model training 1504 component may deploy or update a trained, validated, and tested AI/ML model to the model inference 1506 component, and receive a model performance feedback from the model inference 1506 component. As described above, there may be various functionalities to be performed by an AI/ML model for wireless communication

The model inference 1506 may be a function that provides the AI/ML model inference output (e.g., predictions or decisions) . The model inference 1506 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 1502 function. The output of the model inference 1506 may include the inference output of the AI/ML model produced by the model inference 1506. The details of the inference output may be use case specific. As an example, the output may include compressed CSI. The prediction may be for the transmitter or the receiver and may be for the network or the UE. In some aspects, the actor may be a component of the base station or of a core network. In other aspects, the actor may be a UE in communication with a wireless network.

The model performance feedback may refer to information derived from the model inference 1506 function that may be suitable for the improvement of the AI/ML model trained in the model training 1504. The feedback from the actor 1508 or other network entities (via the data collection 1502 function) may be implemented for the model inference 1506 to create the model performance feedback.

The actor 1508 may be a function that receives the output from the model inference 1506 and triggers or performs corresponding actions. The actor may trigger actions directed to network entities including the other network entities or itself. The actor 1508 may also provide a feedback information that the model training 1504 or the model inference 1506 to derive training or inference data or performance feedback. The feedback may be transmitted back to the data collection 1502.

A network or UE may use machine-learning algorithms, deep-learning algorithms, neural networks, reinforcement learning, regression, boosting, or advanced signal processing methods for aspects of wireless communication including the various functionalities such as beam management, CSF, or positioning, among other examples.

In some aspects described herein, the network may train one or more neural networks to learn the dependence of measured qualities on individual parameters. Among others, examples of machine learning models or neural networks that may be included in the network entity 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, and so forth; 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 Deep belief networks (DBNs) .

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 the 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., any 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 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.

The machine learning models may include computational complexity and substantial processor for training the machine learning model. An output of one node is 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 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 neural network may include any number of nodes and any type of connections between nodes. The neural network 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 the last layer of the neural network and may traverse layers multiple times.

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. 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, 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 for wireless communication performed by a user equipment (UE) , including: training an encoder at the UE; receiving, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder; and transmitting the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

Aspect 2 is the method of aspect 1, where training the encoder further includes: training the encoder based on a joint loss function, where a first input of the joint loss function corresponds to a baseline performance and a second input of the joint loss function corresponds to an improved performance.

Aspect 3 is the method of aspect 2, where a third input of the joint loss function is separate from the first input and the second input.

Aspect 4 is the method of aspect 1, where training the encoder further includes: train the encoder based on a linear combination of a first input corresponding to a baseline performance and a second input corresponding to an improved performance.

Aspect 5 is the method of aspect 1, where training the encoder further includes: train the encoder based on a linear combination of a first input corresponding to a baseline performance, a second input corresponding to an improved performance, and a third input.

Aspect 6 is the method of any of aspects 1-5, where the second CSI report transmission does not include the first part of the CSI feedback.

Aspect 7 is the method of aspect 6, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 8 is the method of aspect 6, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 9 is the method of any of aspects 6-8, where the first subset and the second subset are non-overlapping.

Aspect 10 is the method of any of aspects 1-9, where the second CSI report transmission includes the first part of the CSI feedback.

Aspect 11 is the method of aspect 10, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 12 is the method of any of aspects 10-11, where the first subset and the second subset are non-overlapping.

Aspect 13 is the method of any of aspects 1-12, further including: receiving, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission; and ceasing the second CSI report transmission.

Aspect 14 is the method of any of aspects 1-13, further including: receiving, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission; and transmitting the second CSI report transmission based on reception of the indication.

Aspect 15 is the method of any of aspects 1-14, further including: transmitting the CSI feedback to the network node in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback.

Aspect 16 is the method of any of aspects 1-15, where the CSI feedback is multiplexed on a physical uplink shared channel (PUSCH) , where the first part includes a CSI reference signal resource indicator (CRI) or a synchronization signal physical broadcast channel (SS/PBCH) block resource indicator (SSBRI) , an indicator of a number of non-zero amplitude coefficients, a first channel quality indicator (CQI) associated with a first transport block (TB) , or a rank indicator, and where the second part includes a second CQI associated with a second TB or precoding matrix indicator (PMI) information.

Aspect 17 is the method of any of aspects 1-16, where a payload size of the first part is fixed.

Aspect 18 is a method for wireless communication performed by a network node, including: transmitting channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on a trained encoder at a user equipment (UE) ; and reconstructing the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission including at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission including at least a second subset of the second part of the CSI feedback.

Aspect 19 is the method of aspect 18, where the second CSI report transmission does not include the first part of the CSI feedback.

Aspect 20 is the method of aspect 19, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 21 is the method of aspect 19, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 22 is the method of any of aspects 19-21, where the first subset and the second subset are non-overlapping.

Aspect 23 is the method of any of aspects 18-22, where the second CSI report transmission includes the first part of the CSI feedback.

Aspect 24 is the method of aspect 23, where the first subset of the second part of the CSI feedback includes a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback includes the first group and a second group associated with the CSI feedback, where the first group enables decoding of the second group.

Aspect 25 is the method of any of aspects 23-24, where the first subset and the second subset are non-overlapping.

Aspect 26 is the method of any of aspects 18-25, further including: transmitting, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission.

Aspect 27 is the method of any of aspects 18-26, further including: transmitting, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission; and receiving the second CSI report transmission based on transmission of the indication.

Aspect 28 is the method of any of aspects 18-27, further including: receiving the CSI feedback from the UE in a third CSI report transmission, the third CSI report transmission including at least a third subset of the second part of the CSI feedback.

Aspect 29 is an apparatus for wireless communication at a UE including at least one memory and at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor is configured, individually or in combination, to implement any of aspects 1 to 17.

Aspect 30 is the apparatus of aspect 29, further including one or more transceivers or one or more antennas coupled to the at least one processor.

Aspect 31 is an apparatus for wireless communication at a UE including means for implementing any of aspects 1 to 17.

Aspect 32 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code, where the code when executed by at least one processor causes the at least one processor to implement any of aspects 1 to 17.

Aspect 33 is an apparatus for wireless communication at a network node including at least one memory and at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor is configured, individually or in combination, to implement any of aspects 18 to 28.

Aspect 34 is the apparatus of aspect 23, further including one or more transceivers or one or more antennas coupled to the at least one processor.

Aspect 35 is an apparatus for wireless communication at a network node including means for implementing any of aspects 18 to 28.

Aspect 36 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code, where the code when executed by at least one processor causes the at least one processor to implement any of aspects 18 to 28.

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 information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to cause the UE to:

train an encoder at the UE;

receive, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder; and

transmit the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission comprising at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission comprising at least a second subset of the second part of the CSI feedback.
The apparatus of claim 1, wherein to train the encoder, the at least one processor, individually or in any combination, is further configured to cause the UE to:

train the encoder based on a joint loss function, wherein a first input of the joint loss function corresponds to a baseline performance and a second input of the joint loss function corresponds to an improved performance.
The apparatus of claim 2, wherein a third input of the joint loss function is separate from the first input and the second input.
The apparatus of claim 1, wherein to train the encoder, the at least one processor, individually or in any combination, is further configured to cause the UE to:

train the encoder based on a linear combination of a first input corresponding to a baseline performance and a second input corresponding to an improved performance.
The apparatus of claim 1, wherein to train the encoder, the at least one processor, individually or in any combination, is further configured to cause the UE to:

train the encoder based on a linear combination of a first input corresponding to a baseline performance, a second input corresponding to an improved performance, and a third input.
The apparatus of claim 1, wherein the second CSI report transmission does not include the first part of the CSI feedback.
The apparatus of claim 6, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 6, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises the first group and a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 6, wherein the first subset and the second subset are non-overlapping.
The apparatus of claim 1, wherein the second CSI report transmission includes the first part of the CSI feedback.
The apparatus of claim 10, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises the first group and a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 10, wherein the first subset and the second subset are non-overlapping.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to cause the UE to:

receive, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission; and

cease the second CSI report transmission.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to cause the UE to:

receive, from the network node after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission; and

transmit the second CSI report transmission based on reception of the indication.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to cause the UE to:

transmit the CSI feedback to the network node in a third CSI report transmission, the third CSI report transmission comprising at least a third subset of the second part of the CSI feedback.
The apparatus of claim 1, the CSI feedback is multiplexed on a physical uplink shared channel (PUSCH) , wherein the first part comprises a CSI reference signal resource indicator (CRI) or a synchronization signal physical broadcast channel (SS/PBCH) block resource indicator (SSBRI) , an indicator of a number of non-zero amplitude coefficients, a first channel quality indicator (CQI) associated with a first transport block (TB) , or a rank indicator, and wherein the second part comprises a second CQI associated with a second TB or precoding matrix indicator (PMI) information.
The apparatus of claim 1, wherein a payload size of the first part is fixed.
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 information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to cause the network node to:

transmit channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on a trained encoder at a user equipment (UE) ; and

reconstruct the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission comprising at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission comprising at least a second subset of the second part of the CSI feedback.
The apparatus of claim 18, wherein the second CSI report transmission does not include the first part of the CSI feedback.
The apparatus of claim 19, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 19, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises the first group and a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 19, wherein the first subset and the second subset are non-overlapping.
The apparatus of claim 18, wherein the second CSI report transmission includes the first part of the CSI feedback.
The apparatus of claim 23, wherein the first subset of the second part of the CSI feedback comprises a first group associated with the CSI feedback and the second subset of the second part of the CSI feedback comprises the first group and a second group associated with the CSI feedback, wherein the first group enables decoding of the second group.
The apparatus of claim 23, wherein the first subset and the second subset are non-overlapping.
The apparatus of claim 18, wherein the at least one processor, individually or in any combination, is further configured to cause the network node to:

transmit, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to drop one or more subsequent CSI report transmissions including the second CSI report transmission.
The apparatus of claim 18, wherein the at least one processor, individually or in any combination, is further configured to cause the network node to:

transmit, for the UE after the first CSI report transmission and before the second CSI report transmission, an indication to transmit one or more subsequent CSI report transmissions including the second CSI report transmission; and

receive the second CSI report transmission based on transmission of the indication.
The apparatus of claim 18, wherein the at least one processor, individually or in any combination, is further configured to cause the network node to:

receive the CSI feedback from the UE in a third CSI report transmission, the third CSI report transmission comprising at least a third subset of the second part of the CSI feedback.
A method for wireless communication at a user equipment (UE) , comprising:

training an encoder at the UE;

receiving, from a network node, channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on the trained encoder; and

transmitting the CSI feedback to the network node in at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission comprising at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission comprising at least a second subset of the second part of the CSI feedback.
A method for wireless communication performed by a network node, comprising:

transmitting channel state information (CSI) reference signal (CSI-RS) associated with a CSI feedback configured to be generated based on a trained encoder at a user equipment (UE) ; and

reconstructing the CSI feedback, using a decoder at the network node, based on at least a first CSI report transmission and a second CSI report transmission, the first CSI report transmission comprising at least a first part of the CSI feedback and a first subset of a second part of the CSI feedback, the second CSI report transmission comprising at least a second subset of the second part of the CSI feedback.