WO2025065703A1

WO2025065703A1 - Data compression in channel state feedback reporting

Info

Publication number: WO2025065703A1
Application number: PCT/CN2023/123026
Authority: WO
Inventors: Abdelrahman Mohamed Ahmed Mohamed IBRAHIM; Jay Kumar Sundararajan; Chenxi HAO; Taesang Yoo
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2023-09-29
Filing date: 2023-09-29
Publication date: 2025-04-03
Anticipated expiration: 2026-03-29

Abstract

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS). The UE may transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters. Numerous other aspects are described.

Description

DATA COMPRESSION IN CHANNEL STATE FEEDBACK REPORTING

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to wireless communication and to techniques and apparatuses for data compression in channel state feedback (CSF) reporting.

BACKGROUND

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 (e.g., bandwidth, transmit power, or the like) . Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and Long Term Evolution (LTE) . LTE/LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP) .

A wireless network may include one or more network nodes that support communication for wireless communication devices, such as a user equipment (UE) or multiple UEs. A UE may communicate with a network node via downlink communications and uplink communications. “Downlink” (or “DL” ) refers to a communication link from the network node to the UE, and “uplink” (or “UL” ) refers to a communication link from the UE to the network node. Some wireless networks may support device-to-device communication, such as via a local link (e.g., a sidelink (SL) , a wireless local area network (WLAN) link, and/or a wireless personal area network (WPAN) link, among other examples) .

The above multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate on a municipal, national, regional, and/or global level. New Radio (NR) , which may be referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by the 3GPP. NR is designed to better support mobile broadband internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards using orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) (CP-OFDM) on the downlink, using CP-OFDM and/or single-carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM) ) on the uplink, as well as supporting beamforming, multiple-input multiple-output (MIMO) antenna technology, and carrier aggregation. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR, and other radio access technologies remain useful.

SUMMARY

Some aspects described herein relate to a method of wireless communication performed by a user equipment (UE) . The method may include communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) . The method may include transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to a method of wireless communication performed by a network node. The method may include communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The method may include receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to a UE for wireless communication. The UE may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The one or more processors may be configured to transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to a network node for wireless communication. The network node may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The one or more processors may be configured to receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a UE. The set of instructions, when executed by one or more processors of the UE, may cause the UE to communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The set of instructions, when executed by one or more processors of the UE, may cause the UE to transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a network node. The set of instructions, when executed by one or more processors of the network node, may cause the network node to communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The set of instructions, when executed by one or more processors of the network node, may cause the network node to receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The apparatus may include means for transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The apparatus may include means for receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Aspects generally include a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, network entity, network node, wireless communication device, and/or processing system as substantially described herein with reference to and as illustrated by the drawings and specification.

The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed herein, both their organization and method of operation, together with associated advantages, will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims.

While aspects are described in the present disclosure by illustration to some examples, those skilled in the art will understand that such aspects may be implemented in many different arrangements and scenarios. Techniques described herein may be implemented using different platform types, devices, systems, shapes, sizes, and/or packaging arrangements. For example, some aspects may be implemented via integrated chip embodiments or other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, and/or artificial intelligence devices) . Aspects may be implemented in chip-level components, modular components, non-modular components, non-chip-level components, device-level components, and/or system-level components. Devices incorporating described aspects and features may include additional components and features for implementation and practice of claimed and described aspects. For example, transmission and reception of wireless signals may include one or more components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffers, processors, interleavers, adders, and/or summers) . It is intended that aspects described herein may be practiced in a wide variety of devices, components, systems, distributed arrangements, and/or end-user devices of varying size, shape, and constitution.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the above-recited features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, may be had by reference to aspects, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects. The same reference numbers in different drawings may identify the same or similar elements.

Fig. 1 is a diagram illustrating an example of a wireless network, in accordance with the present disclosure.

Fig. 2 is a diagram illustrating an example of a network node in communication with a user equipment (UE) in a wireless network, in accordance with the present disclosure.

Fig. 3 is a diagram illustrating an example disaggregated base station architecture, in accordance with the present disclosure.

Fig. 4 is a diagram illustrating an example of physical channels and reference signals in a wireless network, in accordance with the present disclosure.

Fig. 5 is a diagram illustrating an example architecture of a functional framework for radio access network (RAN) intelligence enabled by data collection, in accordance with the present disclosure.

Figs. 6A-6C are diagrams illustrating an example associated with data compression in channel state feedback (CSF) reporting, in accordance with the present disclosure.

Figs. 7A-7B are diagrams illustrating examples associated with data compression in CSF reporting, in accordance with the present disclosure.

Fig. 8 is a diagram illustrating an example process performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure.

Fig. 9 is a diagram illustrating an example process performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure.

Fig. 10 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

Fig. 11 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

DETAILED DESCRIPTION

Various aspects relate generally to wireless communication. Some aspects more specifically relate to data compression for channel state feedback (CSF) . Some aspects more specifically relate to data compression techniques using entropy encoding and vector quantization. In some examples, a user equipment (UE) may apply entropy coding and vector quantization to a machine learning CSF embedding vector to generate a CSF report. The UE may transmit a compressed CSF report to a network node, which may apply entropy coding for decoding and vector quantization based dequantization to recover the CSF embedding vector. In some examples, the UE may have an encoder that is matched to a decoder of the network node. For example, the UE may be configured with a plurality of artificial intelligence or machine learning (AI/ML) models (e.g., a plurality of neural network models) and may receive or transmit an indication of which of the plurality of AI/ML models to use for encoding. In this example, the network node may transmit or receive the indication and use a corresponding AI/ML model for decoding. In some examples, an AI/ML model may have a plurality of precoding matrix functions (PMFs) corresponding to a plurality of different datasets (e.g., for a plurality of different scenarios) or a single PMF may be applicable to a plurality of AI/ML models. As such, the UE and the network node may communicate to synchronize which PMF the UE and the network node are to use for encoding and decoding, respectively.

Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. Data compression for CSF reduces a utilization of network resources. By configuring the UE with a plurality of AI/ML models, from which a single AI/ML model is selected, the UE and the network node can more accurately use AI/ML based data compression for generating a compressed PMF report. By configuring the UE with a plurality of PMFs, from which a single PMF is selected, the UE and the network node can further improve data compression and an accuracy of data recovery when using a compressed PMF report. By enabling a reduction in data traffic associated with communicating a PMF report, the network node and the UE may increase a frequency with which PMF reports are transmitted, thereby increasing an accuracy of determinations, such as beam selections or communication configuration parameter selections, which may improve communication performance.

Various aspects of the disclosure are described more fully hereinafter with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. One skilled in the art should appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether implemented independently of or combined with any other aspect of the disclosure. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

Several aspects of telecommunication systems will now be presented with reference to various apparatuses and techniques. These apparatuses and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, or the like (collectively referred to as “elements” ) . These elements may be implemented using hardware, software, or combinations thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.

While aspects may be described herein using terminology commonly associated with a 5G or New Radio (NR) radio access technology (RAT) , aspects of the present disclosure can be applied to other RATs, such as a 3G RAT, a 4G RAT, and/or a RAT subsequent to 5G (e.g., 6G) .

Fig. 1 is a diagram illustrating an example of a wireless network 100, in accordance with the present disclosure. The wireless network 100 may be or may include elements of a 5G (e.g., NR) network and/or a 4G (e.g., Long Term Evolution (LTE) ) network, among other examples. The wireless network 100 may include one or more network nodes 110 (shown as a network node 110a, a network node 110b, a network node 110c, and a network node 110d) , a UE 120 or multiple UEs 120 (shown as a UE 120a, a UE 120b, a UE 120c, a UE 120d, and a UE 120e) , and/or other entities. A network node 110 is a network node that communicates with UEs 120. As shown, a network node 110 may include one or more network nodes. For example, a network node 110 may be an aggregated network node, meaning that the aggregated network node is configured to utilize a radio protocol stack that is physically or logically integrated within a single radio access network (RAN) node (e.g., within a single device or unit) . As another example, a network node 110 may be a disaggregated network node (sometimes referred to as a disaggregated base station) , meaning that the network node 110 is configured to utilize a protocol stack that is physically or logically distributed among two or more nodes (such as one or more central units (CUs) , one or more distributed units (DUs) , or one or more radio units (RUs)) .

In some examples, a network node 110 is or includes a network node that communicates with UEs 120 via a radio access link, such as an RU. In some examples, a network node 110 is or includes a network node that communicates with other network nodes 110 via a fronthaul link or a midhaul link, such as a DU. In some examples, a network node 110 is or includes a network node that communicates with other network nodes 110 via a midhaul link or a core network via a backhaul link, such as a CU. In some examples, a network node 110 (such as an aggregated network node 110 or a disaggregated network node 110) may include multiple network nodes, such as one or more RUs, one or more CUs, and/or one or more DUs. A network node 110 may include, for example, an NR base station, an LTE base station, a Node B, an eNB (e.g., in 4G) , a gNB (e.g., in 5G) , an access point, a transmission reception point (TRP) , a DU, an RU, a CU, a mobility element of a network, a core network node, a network element, a network equipment, a RAN node, or a combination thereof. In some examples, the network nodes 110 may be interconnected to one another or to one or more other network nodes 110 in the wireless network 100 through various types of fronthaul, midhaul, and/or backhaul interfaces, such as a direct physical connection, an air interface, or a virtual network, using any suitable transport network.

In some examples, a network node 110 may provide communication coverage for a particular geographic area. In the Third Generation Partnership Project (3GPP) , the term “cell” can refer to a coverage area of a network node 110 and/or a network node subsystem serving this coverage area, depending on the context in which the term is used. A network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, and/or another type of cell. A macro cell may cover a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by UEs 120 with service subscriptions. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs 120 with service subscriptions. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by UEs 120 having association with the femto cell (e.g., UEs 120 in a closed subscriber group (CSG) ) . A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. A network node 110 for a femto cell may be referred to as a femto network node or an in-home network node. In the example shown in Fig. 1, the network node 110a may be a macro network node for a macro cell 102a, the network node 110b may be a pico network node for a pico cell 102b, and the network node 110c may be a femto network node for a femto cell 102c. A network node may support one or multiple (e.g., three) cells. In some examples, a cell may not necessarily be stationary, and the geographic area of the cell may move according to the location of a network node 110 that is mobile (e.g., a mobile network node) .

In some aspects, the terms “base station” or “network node” may refer to an aggregated base station, a disaggregated base station, an integrated access and backhaul (IAB) node, a relay node, or one or more components thereof. For example, in some aspects, “base station” or “network node” may refer to a CU, a DU, an RU, a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) , or a Non-Real Time (Non-RT) RIC, or a combination thereof. In some aspects, the terms “base station” or “network node” may refer to one device configured to perform one or more functions, such as those described herein in connection with the network node 110. In some aspects, the terms “base station” or “network node” may refer to a plurality of devices configured to perform the one or more functions. For example, in some distributed systems, each of a quantity of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to duplicate performance of at least a portion of the function, and the terms “base station” or “network node” may refer to any one or more of those different devices. In some aspects, the terms “base station” or “network node” may refer to one or more virtual base stations or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the terms “base station” or “network node” may refer to one of the base station functions and not another. In this way, a single device may include more than one base station.

The wireless network 100 may include one or more relay stations. A relay station is a network node that can receive a transmission of data from an upstream node (e.g., a network node 110 or a UE 120) and send a transmission of the data to a downstream node (e.g., a UE 120 or a network node 110) . A relay station may be a UE 120 that can relay transmissions for other UEs 120. In the example shown in Fig. 1, the network node 110d (e.g., a relay network node) may communicate with the network node 110a (e.g., a macro network node) and the UE 120d in order to facilitate communication between the network node 110a and the UE 120d. A network node 110 that relays communications may be referred to as a relay station, a relay base station, a relay network node, a relay node, a relay, or the like.

The wireless network 100 may be a heterogeneous network that includes network nodes 110 of different types, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, or the like. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and/or different impacts on interference in the wireless network 100. For example, macro network nodes may have a high transmit power level (e.g., 5 to 40 watts) whereas pico network nodes, femto network nodes, and relay network nodes may have lower transmit power levels (e.g., 0.1 to 2 watts) .

A network controller 130 may couple to or communicate with a set of network nodes 110 and may provide coordination and control for these network nodes 110. The network controller 130 may communicate with the network nodes 110 via a backhaul communication link or a midhaul communication link. The network nodes 110 may communicate with one another directly or indirectly via a wireless or wireline backhaul communication link. In some aspects, the network controller 130 may be a CU or a core network device, or may include a CU or a core network device.

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

Some UEs 120 may be considered machine-type communication (MTC) or evolved or enhanced machine-type communication (eMTC) UEs. An MTC UE and/or an eMTC UE may include, for example, a robot, an unmanned aerial vehicle, a remote device, a sensor, a meter, a monitor, and/or a location tag, that may communicate with a network node, another device (e.g., a remote device) , or some other entity. Some UEs 120 may be considered Internet-of-Things (IoT) devices, and/or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered a Customer Premises Equipment. A UE 120 may be included inside a housing that houses components of the UE 120, such as processor components and/or memory components. In some examples, the processor components and the memory components may be coupled together. For example, the processor components (e.g., one or more processors) and the memory components (e.g., a memory) may be operatively coupled, communicatively coupled, electronically coupled, and/or electrically coupled.

In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a particular RAT and may operate on one or more frequencies. A RAT may be referred to as a radio technology, an air interface, or the like. A frequency may be referred to as a carrier, a frequency channel, or the like. Each frequency may support a single RAT in a given geographic area in order to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more sidelink channels (e.g., without using a network node 110 as an intermediary to communicate with one another) . For example, the UEs 120 may communicate using peer-to-peer (P2P) communications, device-to-device (D2D) communications, a vehicle-to-everything (V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure (V2I) protocol, or a vehicle-to-pedestrian (V2P) protocol) , and/or a mesh network. In such examples, a UE 120 may perform scheduling operations, resource selection operations, and/or other operations described elsewhere herein as being performed by the network node 110.

Devices of the wireless network 100 may communicate using the electromagnetic spectrum, which may be subdivided by frequency or wavelength into various classes, bands, channels, or the like. For example, devices of the wireless network 100 may communicate using one or more operating bands. 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) . It should be understood that 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 FR4a or FR4-1 (52.6 GHz –71 GHz) , FR4 (52.6 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 examples in mind, unless specifically stated otherwise, it should be understood that 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, it should be understood that 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, FR4-aor FR4-1, and/or FR5, or may be within the EHF band. It is contemplated that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and/or FR5) may be modified, and techniques described herein are applicable to those modified frequency ranges.

In some aspects, the UE 120 may include a communication manager 140. As described in more detail elsewhere herein, the communication manager 140 may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a channel state information (CSI) reference signal (CSI-RS) ; and transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters. Additionally, or alternatively, the communication manager 140 may perform one or more other operations described herein.

In some aspects, the network node 110 may include a communication manager 150. As described in more detail elsewhere herein, the communication manager 150 may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS; and receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters. Additionally, or alternatively, the communication manager 150 may perform one or more other operations described herein.

As indicated above, Fig. 1 is provided as an example. Other examples may differ from what is described with regard to Fig. 1.

Fig. 2 is a diagram illustrating an example 200 of a network node 110 in communication with a UE 120 in a wireless network 100, in accordance with the present disclosure. The network node 110 may be equipped with a set of antennas 234a through 234t, such as T antennas (T ≥ 1) . The UE 120 may be equipped with a set of antennas 252a through 252r, such as R antennas (R ≥ 1) . The network node 110 of example 200 includes one or more radio frequency components, such as antennas 234 and a modem 232. In some examples, a network node 110 may include an interface, a communication component, or another component that facilitates communication with the UE 120 or another network node. Some network nodes 110 may not include radio frequency components that facilitate direct communication with the UE 120, such as one or more CUs, or one or more DUs.

At the network node 110, a transmit processor 220 may receive data, from a data source 212, intended for the UE 120 (or a set of UEs 120) . The transmit processor 220 may select one or more modulation and coding schemes (MCSs) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from that UE 120. The network node 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS (s) selected for the UE 120 and may provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource partitioning information (SRPI) ) and control information (e.g., CQI requests, grants, and/or upper layer signaling) and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., a cell-specific reference signal (CRS) or a demodulation reference signal (DMRS) ) and synchronization signals (e.g., a primary synchronization signal (PSS) or a secondary synchronization signal (SSS) ) . A transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) , shown as modems 232a through 232t. For example, each output symbol stream may be provided to a modulator component (shown as MOD) of a modem 232. Each modem 232 may use a respective modulator component to process a respective output symbol stream (e.g., for OFDM) to obtain an output sample stream. Each modem 232 may further use a respective modulator component to process (e.g., convert to analog, amplify, filter, and/or upconvert) the output sample stream to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (e.g., T downlink signals) via a corresponding set of antennas 234 (e.g., T antennas) , shown as antennas 234a through 234t.

At the UE 120, a set of antennas 252 (shown as antennas 252a through 252r) may receive the downlink signals from the network node 110 and/or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) , shown as modems 254a through 254r. For example, each received signal may be provided to a demodulator component (shown as DEMOD) of a modem 254. Each modem 254 may use a respective demodulator component to condition (e.g., filter, amplify, downconvert, and/or digitize) a received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modems 254, may perform MIMO detection on the received symbols if applicable, and may provide detected symbols. A receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to a data sink 260, and may provide decoded control information and system information to a controller/processor 280. The term “controller/processor” may refer to one or more controllers, one or more processors, or a combination thereof. A channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and/or a CQI parameter, among other examples. In some examples, one or more components of the UE 120 may be included in a housing 284.

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

One or more antennas (e.g., antennas 234a through 234t and/or antennas 252a through 252r) may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and/or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, and/or an antenna array may include one or more antenna elements (within a single housing or multiple housings) , a set of coplanar antenna elements, a set of non-coplanar antenna elements, and/or one or more antenna elements coupled to one or more transmission and/or reception components, such as one or more components of Fig. 2.

On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information (e.g., for reports that include RSRP, RSSI, RSRQ, and/or CQI) from the controller/processor 280. The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be precoded by a TX MIMO processor 266 if applicable, further processed by the modems 254 (e.g., for DFT-s-OFDM or CP-OFDM) , and transmitted to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of the antenna (s) 252, the modem (s) 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, and/or the TX MIMO processor 266. The transceiver may be used by a processor (e.g., the controller/processor 280) and the memory 282 to perform aspects of any of the methods described herein (e.g., with reference to Figs. 6A-11) .

At the network node 110, the uplink signals from UE 120 and/or other UEs may be received by the antennas 234, processed by the modem 232 (e.g., a demodulator component, shown as DEMOD, of the modem 232) , detected by a MIMO detector 236 if applicable, and further processed by a receive processor 238 to obtain decoded data and control information sent by the UE 120. The receive processor 238 may provide the decoded data to a data sink 239 and provide the decoded control information to the controller/processor 240. The network node 110 may include a communication unit 244 and may communicate with the network controller 130 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and/or uplink communications. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of the antenna (s) 234, the modem (s) 232, the MIMO detector 236, the receive processor 238, the transmit processor 220, and/or the TX MIMO processor 230. The transceiver may be used by a processor (e.g., the controller/processor 240) and the memory 242 to perform aspects of any of the methods described herein (e.g., with reference to Figs. 6A-11) .

The controller/processor 240 of the network node 110, the controller/processor 280 of the UE 120, and/or any other component (s) of Fig. 2 may perform one or more techniques associated with data compression for CSF reporting, as described in more detail elsewhere herein. For example, the controller/processor 240 of the network node 110, the controller/processor 280 of the UE 120, and/or any other component (s) of Fig. 2 may perform or direct operations of, for example, process 800 of Fig. 8, process 900 of Fig. 9, and/or other processes as described herein. The memory 242 and the memory 282 may store data and program codes for the network node 110 and the UE 120, respectively. In some examples, the memory 242 and/or the memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and/or program code) for wireless communication. For example, the one or more instructions, when executed (e.g., directly, or after compiling, converting, and/or interpreting) by one or more processors of the network node 110 and/or the UE 120, may cause the one or more processors, the UE 120, and/or the network node 110 to perform or direct operations of, for example, process 800 of Fig. 8, process 900 of Fig. 9, and/or other processes as described herein. In some examples, executing instructions may include running the instructions, converting the instructions, compiling the instructions, and/or interpreting the instructions, among other examples.

In some aspects, the UE 120 includes means for communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS; and/or means for transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters. The means for the UE 120 to perform operations described herein may include, for example, one or more of communication manager 140, antenna 252, modem 254, MIMO detector 256, receive processor 258, transmit processor 264, TX MIMO processor 266, controller/processor 280, or memory 282.

In some aspects, the network node 110 includes means for communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS; and/or means for receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters. The means for the network node to perform operations described herein may include, for example, one or more of communication manager 150, transmit processor 220, TX MIMO processor 230, modem 232, antenna 234, MIMO detector 236, receive processor 238, controller/processor 240, memory 242, or scheduler 246.

In some aspects, an individual processor may perform all of the functions described as being performed by the one or more processors. In some aspects, one or more processors may collectively perform a set of functions. For example, a first set of (one or more) processors of the one or more processors may perform a first function described as being performed by the one or more processors, and a second set of (one or more) processors of the one or more processors may perform a second function described as being performed by the one or more processors. The first set of processors and the second set of processors may be the same set of processors or may be different sets of processors. Reference to “one or more processors” should be understood to refer to any one or more of the processors described in connection with Fig. 2. Reference to “one or more memories” should be understood to refer to any one or more memories of a corresponding device, such as the memory described in connection with Fig. 2. For example, functions described as being performed by one or more memories can be performed by the same subset of the one or more memories or different subsets of the one or more memories.

While blocks in Fig. 2 are illustrated as distinct components, the functions described above with respect to the blocks may be implemented in a single hardware, software, or combination component or in various combinations of components. For example, the functions described with respect to the transmit processor 264, the receive processor 258, and/or the TX MIMO processor 266 may be performed by or under the control of the controller/processor 280.

As indicated above, Fig. 2 is provided as an example. Other examples may differ from what is described with regard to Fig. 2.

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 RAN node, a core network node, a network element, a base station, or a network equipment may be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB) , an evolved NB (eNB) , an NR base station, a 5G NB, an access point (AP) , a TRP, or a cell, among other examples) , or one or more units (or one or more components) performing base station functionality, may be implemented as an aggregated base station (also known as a standalone base station or a monolithic base station) or a disaggregated base station. “Network entity” or “network node” may refer to a disaggregated base station, or to one or more units of a disaggregated base station (such as one or more CUs, one or more DUs, one or more RUs, or a combination thereof) .

An aggregated base station (e.g., an aggregated network node) may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node (e.g., within a single device or unit) . A disaggregated base station (e.g., a disaggregated network node) may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more CUs, one or more DUs, or one or more RUs) . In some examples, a CU may be implemented within a network 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 network nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU also can be implemented as virtual units, such as a virtual central unit (VCU) , a virtual distributed unit (VDU) , or a virtual radio unit (VRU) , among other examples.

Base station-type operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an 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) ) to facilitate scaling of communication systems by separating base station functionality into one or more units that can be individually deployed. A disaggregated base station may include functionality implemented across two or more units at various physical locations, as well as functionality implemented for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station can be configured for wired or wireless communication with at least one other unit of the disaggregated base station.

Fig. 3 is a diagram illustrating an example disaggregated base station architecture 300, in accordance with the present disclosure. The disaggregated base station architecture 300 may include a CU 310 that can communicate directly with a core network 320 via a backhaul link, or indirectly with the core network 320 through one or more disaggregated control units (such as a Near-RT RIC 325 via an E2 link, or a Non-RT RIC 315 associated with a Service Management and Orchestration (SMO) Framework 305, or both) . A CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as through F1 interfaces. Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective radio frequency (RF) access links. In some implementations, a UE 120 may be simultaneously served by multiple RUs 340.

Each of the units, including the CUs 310, the DUs 330, the RUs 340, as well as the Near-RT RICs 325, the Non-RT RICs 315, and the SMO Framework 305, may include one or more interfaces or be coupled with one or more interfaces configured to receive or 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 one or multiple communication interfaces of the respective unit, can be configured to communicate with one or more of the other units via the transmission medium. In some examples, each of the units can include a wired interface, configured to receive or transmit signals over a wired transmission medium to one or more of the other units, and a wireless interface, which may include a receiver, a transmitter or transceiver (such as an RF transceiver) , configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

In some aspects, the CU 310 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, or service data adaptation protocol (SDAP) functions, among other examples. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 310. The CU 310 may be configured to handle user plane functionality (for example, Central Unit –User Plane (CU-UP) functionality) , control plane functionality (for example, Central Unit –Control Plane (CU-CP) functionality) , or a combination thereof. In some implementations, the CU 310 can be logically split into one or more CU-UP units and one or more CU-CP units. A CU-UP unit can communicate bidirectionally with a CU-CP unit via an interface, such as the E1 interface when implemented in an O-RAN configuration. The CU 310 can be implemented to communicate with a DU 330, as necessary, for network control and signaling.

Each DU 330 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 340. In some aspects, the DU 330 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 depending, at least in part, on a functional split, such as a functional split defined by the 3GPP. In some aspects, the one or more high PHY layers may be implemented by one or more modules for forward error correction (FEC) encoding and decoding, scrambling, and modulation and demodulation, among other examples. In some aspects, the DU 330 may further host one or more low PHY layers, such as implemented by one or more modules for a fast Fourier transform (FFT) , an inverse FFT (iFFT) , digital beamforming, or physical random access channel (PRACH) extraction and filtering, among other examples. Each layer (which also may be referred to as a module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 330, or with the control functions hosted by the CU 310.

Each RU 340 may implement lower-layer functionality. In some deployments, an RU 340, controlled by a DU 330, may correspond to a logical node that hosts RF processing functions or low-PHY layer functions, such as performing an FFT, performing an iFFT, digital beamforming, or PRACH extraction and filtering, among other examples, based on a functional split (for example, a functional split defined by the 3GPP) , such as a lower layer functional split. In such an architecture, each RU 340 can be operated to handle over the air (OTA) communication with one or more UEs 120. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU (s) 340 can be controlled by the corresponding DU 330. In some scenarios, this configuration can enable each DU 330 and the CU 310 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

The SMO Framework 305 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operations and maintenance interface (such as an O1 interface) . For virtualized network elements, the SMO Framework 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) platform 390) 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 310, DUs 330, RUs 340, non-RT RICs 315, and Near-RT RICs 325. In some implementations, the SMO Framework 305 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 311, via an O1 interface. Additionally, in some implementations, the SMO Framework 305 can communicate directly with each of one or more RUs 340 via a respective O1 interface. The SMO Framework 305 also may include a Non-RT RIC 315 configured to support functionality of the SMO Framework 305.

The Non-RT RIC 315 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, AI/ML workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 325. The Non-RT RIC 315 may be coupled to or communicate with (such as via an A1 interface) the Near-RT RIC 325. The Near-RT RIC 325 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 310, one or more DUs 330, or both, as well as an O-eNB, with the Near-RT RIC 325.

In some implementations, to generate AI/ML models to be deployed in the Near-RT RIC 325, the Non-RT RIC 315 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 325 and may be received at the SMO Framework 305 or the Non-RT RIC 315 from non-network data sources or from network functions. In some examples, the Non-RT RIC 315 or the Near-RT RIC 325 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 315 may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions through the SMO Framework 305 (such as reconfiguration via an O1 interface) or via creation of RAN management policies (such as A1 interface policies) .

As indicated above, Fig. 3 is provided as an example. Other examples may differ from what is described with regard to Fig. 3.

Fig. 4 is a diagram illustrating an example 400 of physical channels and reference signals in a wireless network, in accordance with the present disclosure. As shown in Fig. 4, downlink channels and downlink reference signals may carry information from a network node 110 to a UE 120, and uplink channels and uplink reference signals may carry information from a UE 120 to a network node 110.

As shown, a downlink channel may include a physical downlink control channel (PDCCH) that carries downlink control information (DCI) , a physical downlink shared channel (PDSCH) that carries downlink data, or a physical broadcast channel (PBCH) that carries system information, among other examples. In some aspects, PDSCH communications may be scheduled by PDCCH communications. As further shown, an uplink channel may include a physical uplink control channel (PUCCH) that carries uplink control information (UCI) , a physical uplink shared channel (PUSCH) that carries uplink data, or a PRACH used for initial network access, among other examples. In some aspects, the UE 120 may transmit acknowledgement (ACK) or negative acknowledgement (NACK) feedback (e.g., ACK/NACK feedback or ACK/NACK information) in UCI on the PUCCH and/or the PUSCH.

As further shown, a downlink reference signal may include a synchronization signal block (SSB) , a CSI-RS, a DMRS, a positioning reference signal (PRS) , or a phase tracking reference signal (PTRS) , among other examples. As also shown, an uplink reference signal may include a sounding reference signal (SRS) , a DMRS, or a PTRS, among other examples.

An SSB may carry information used for initial network acquisition and synchronization, such as a PSS, an SSS, a PBCH, and a PBCH DMRS. An SSB is sometimes referred to as a synchronization signal/PBCH (SS/PBCH) block. In some aspects, the network node 110 may transmit multiple SSBs on multiple corresponding beams, and the SSBs may be used for beam selection.

A CSI-RS may carry information used for downlink channel estimation (e.g., downlink CSI acquisition) , which may be used for scheduling, link adaptation, or beam management, among other examples. The network node 110 may configure a set of CSI-RSs for the UE 120, and the UE 120 may measure the configured set of CSI-RSs. Based at least in part on the measurements, the UE 120 may perform channel estimation and may report channel estimation parameters to the network node 110 (e.g., in a CSI report) , such as a CQI, a precoding matrix indicator (PMI) , a CSI-RS resource indicator (CRI) , a layer indicator (LI) , a rank indicator (RI) , or an RSRP, among other examples. The network node 110 may use the CSI report to select transmission parameters for downlink communications to the UE 120, such as a number of transmission layers (e.g., a rank) , a precoding matrix (e.g., a precoder) , an MCS, or a refined downlink beam (e.g., using a beam refinement procedure or a beam management procedure) , among other examples. In some examples, the UE 120 may transmit CSF to report one or more channel parameters determined in connection with a measurement of a CSI-RS (or another reference signal) .

A DMRS may carry information used to estimate a radio channel for demodulation of an associated physical channel (e.g., PDCCH, PDSCH, PBCH, PUCCH, or PUSCH) . The design and mapping of a DMRS may be specific to a physical channel for which the DMRS is used for estimation. DMRSs are UE-specific, can be beamformed, can be confined in a scheduled resource (e.g., rather than transmitted on a wideband) , and can be transmitted only when necessary. As shown, DMRSs are used for both downlink communications and uplink communications.

A PTRS may carry information used to compensate for oscillator phase noise. Typically, the phase noise increases as the oscillator carrier frequency increases. Thus, PTRS can be utilized at high carrier frequencies, such as millimeter wave frequencies, to mitigate phase noise. The PTRS may be used to track the phase of the local oscillator and to enable suppression of phase noise and common phase error (CPE) . As shown, PTRSs are used for both downlink communications (e.g., on the PDSCH) and uplink communications (e.g., on the PUSCH) .

A PRS may carry information used to enable timing or ranging measurements of the UE 120 based on signals transmitted by the network node 110 to improve observed time difference of arrival (OTDOA) positioning performance. For example, a PRS may be a pseudo-random Quadrature Phase Shift Keying (QPSK) sequence mapped in diagonal patterns with shifts in frequency and time to avoid collision with cell-specific reference signals and control channels (e.g., a PDCCH) . In general, a PRS may be designed to improve detectability by the UE 120, which may need to detect downlink signals from multiple neighboring network nodes in order to perform OTDOA-based positioning. Accordingly, the UE 120 may receive a PRS from multiple cells (e.g., a reference cell and one or more neighbor cells) , and may report a reference signal time difference (RSTD) based on OTDOA measurements associated with the PRSs received from the multiple cells. In some aspects, the network node 110 may then calculate a position of the UE 120 based on the RSTD measurements reported by the UE 120.

An SRS may carry information used for uplink channel estimation, which may be used for scheduling, link adaptation, precoder selection, or beam management, among other examples. The network node 110 may configure one or more SRS resource sets for the UE 120, and the UE 120 may transmit SRSs on the configured SRS resource sets. An SRS resource set may have a configured usage, such as uplink CSI acquisition, downlink CSI acquisition for reciprocity-based operations, uplink beam management, among other examples. The network node 110 may measure the SRSs, may perform channel estimation based at least in part on the measurements, and may use the SRS measurements to configure communications with the UE 120.

As indicated above, Fig. 4 is provided as an example. Other examples may differ from what is described with regard to Fig. 4.

Fig. 5 is a diagram illustrating an example architecture 500 of a functional framework for RAN intelligence enabled by data collection, in accordance with the present disclosure. In some scenarios, the functional framework for RAN intelligence may be enabled by further enhancement of data collection through use cases and/or examples. For example, principles or algorithms for RAN intelligence enabled by an AI/ML and the associated functional framework (e.g., the artificial intelligence (AI) functionality and/or the input/output of the component for AI enabled optimization) have been utilized or studied to identify the benefits of AI enabled RAN through possible use cases (e.g., beam management, energy saving, load balancing, mobility management, data compression, and/or coverage optimization, among other examples) . In one example, as shown by the architecture 500, a functional framework for RAN intelligence may include multiple logical entities, such as a model training host 502, a model inference host 504, data sources 506, and an actor 508.

The model inference host 504 may be configured to run an AI/ML model (e.g., a neural network model) based on inference data provided by the data sources 506, and the model inference host 504 may produce an output (e.g., a prediction) with the inference data input to the actor 508. The actor 508 may be an element or an entity of a core network or a RAN. For example, the actor 508 may be a UE, a network node, base station (e.g., a gNB) , a CU, a DU, and/or an RU, among other examples. In some examples, the entities described herein may be components of a single device or of a plurality of devices. The actor 508 may also depend on the type of tasks performed by the model inference host 504, type of inference data provided to the model inference host 504, and/or type of output produced by the model inference host 504. For example, if the output from the model inference host 504 is associated with position determination, the actor 508 may be a UE, a DU or an RU. In some examples, the model inference host 504 may be hosted on the actor 508. For example, a UE may be the actor 508 and may host the model inference host 504. In some aspects, a UE (e.g., the actor 508) may be a data source 506. For example, the UE may perform a measurement (e.g., an NR measurement) , may input the measurement to the AI/ML model at the model inference host 504 (or may provide the measurement to the model inference host 504) , and may act based on an output of the AI/ML model (e.g., generating a CSF report) .

After the actor 508 receives an output from the model inference host 504, the actor 508 may determine whether to act based on the output. For example, if the actor 508 is a UE and the output from the model inference host 504 is associated with position information, the actor 508 may determine whether to report the position information, reconfigure a beam, among other examples. If the actor 508 determines to act based on the output, in some examples, the actor 508 may indicate the action to at least one subject of action 510.

The data sources 506 may also be configured for collecting data that is used as training data for training a machine learning (ML) model or as inference data for feeding an ML model inference operation. For example, the data sources 506 may collect data from one or more core network and/or RAN entities, which may include the actor 508 or the subject of action 510, and provide the collected data to the model training host 502 for ML model training. In some aspects, the model training host 502 may be co-located with the model inference host 504 and/or the actor 508. For example, the actor 508 or the subject of action 510 may provide performance feedback associated with the beam configuration to the data sources 506, where the performance feedback may be used by the model training host 502 for monitoring or evaluating the ML model performance, such as whether the output (e.g., prediction) provided to the actor 508 is accurate. In some examples, the model training host 502 may monitor or evaluate ML model performance using a training position value, which may be provided by a node (e.g., a UE 120 or a network node 110) , as described elsewhere herein. In some examples, if the output provided by the actor 508 is inaccurate (or the accuracy is below an accuracy threshold) , then the model training host 502 may determine to modify or retrain the ML model used by the model inference host, such as via an ML model deployment/update.

As indicated above, Fig. 5 is provided as an example. Other examples may differ from what is described with regard to Fig. 5.

In some communications systems, a CSI report configuration can include a codebook, which is used as a PMI from which a UE can select a set of best PMI codewords that the UE indicates as a bit sequence transmitted to a network node. In other communications systems, a UE may use an AI technique to encode CSI feedback and a network node may use a corresponding AI technique to decode the encoded CSI feedback. For example, the UE may use a downlink channel matrix, a set of downlink precoders, or an interference covariance matrix as an input to an AI model and the network node may identify the downlink channel matrix, a transmit covariance matrix, the set of downlink precoders, the interference covariance matrix R_nn, a raw downlink channel, or a whitened downlink channel as outputs from processing received CSI feedback using a corresponding AI model.

Some UEs may be configured with a plurality of different AI/ML models for a plurality of different scenarios. For example, a UE may be configurable with an AI/ML model for an indoor or outdoor scenario, a line of sight or non-light of sight scenario, a geographical location, a serving cell, a channel statistic (e.g., a particular delay spread or signal to noise ratio) , or a particular type of the UE. The different AI/ML models may be trained with different datasets or data samples, which may provide more accurate performance when a UE uses, in a particular scenario, an AI/ML model trained for the particular scenario (e.g., using data collected from the same or similar scenarios) . In other words, for example, a UE that is operating outdoors and using an encoder AI/ML model trained on datasets of CSI feedback from outdoor observations may provide CSF that can be more accurately recovered by a network node with a corresponding decoder AI/ML model than if the UE was operating outdoors using an encoder AI/ML model trained on datasets of CSI feedback from indoor observations.

Some AI/ML models may be referred to as “hyper-local models” . A hyper-local model may include an AI/ML model trained using a dataset with a particular level of granularity. For example, a non-hyper-local model may be trained using AI/ML model datasets on a city-scale (e.g., a 10 kilometer (km) × 10 km geographical area of measurements) , whereas a hyper-local model may be trained using AI/ML model datasets on a block-scale (e.g., a 25 meter (m) × 25 m geographical area of measurements) . As a result of spatial consistency, samples collected from a hyper-local region may result in a higher degree of correlation, which may provide more compressibility compared to data for a non-hyper-local region.

When a UE uses an AI/ML model for data compression of CSF data, the UE may perform a set of procedures to compress data. One procedure that the UE can perform is vector quantization (VQ) . In VQ, the UE quantizes each input vector and maps each quantized, input vector to a vector of a codebook (e.g., with a configured vector size) . In other words, the UE may receive an input V_in, divide the input into a set of input vectors Z_e, divide Z_e into a set of sub-vectors of size d (e.g., [Z_e0, Z_e1] ) , select a codeword from a vector of a quantization codebook Z_embd, map the sub-vectors to the vector of the quantization codebook (e.g., [Z_q0, Z_q1] ) , and combine the mapped sub-vectors to generate an output, quantized vector Z_q. Another technique the UE may perform for data compression is entropy coding (EC) , such as Huffman coding and Arithmetic coding. In performing EC, the UE estimates a PMF of a random variable and generates a variable length codeword as an output using the PMF.

Combining VQ and EC, a UE may quantize an output of an encoder (e.g., a latent vector Z) using scalar quantization, such that Z is mapped to an embedding vector Z_embd (e.g., with entries from {0, …, K} , where K is a codebook size) . EC can be applied to further compress the output of the encoder. This results in a generated CSF report that can be transmitted to the network node with reduced data size relative to an uncompressed CSF report. The PMF is a discrete random variable over the space {0, …, K} that is non-uniform and is dependent on which AI/ML model is used for the encoder and probability distribution of an input dataset (e.g., accordingly, hyper-local datasets correspond to different PMFs) . However, the network node may not be able to decode the generated CSF without alignment between the PMF used for EC at the UE and at the network node.

Some aspects described herein enable data compression for CSF. For example, a UE and a network node may communicate to determine a set of parameters for EC and VQ, such as an AI/ML model used for CSF generation and/or a PMF associated therewith. In this case, by coordinating configuration of the set of parameters, the UE and the network node enable data compression and recovery of CSF, thereby reducing a throughput associated with conveying CSF. Additionally, or alternatively, by coordinating the set of parameters, the UE and the network node enable use of hyper-local datasets for AI/ML based CSF generation, thereby improving an accuracy of CSF generation and recovery.

Figs. 6A-6C are diagrams illustrating an example 600 associated with data compression in CSF reporting, in accordance with the present disclosure. As shown in Fig. 6A, example 600 includes communication between a network node 110 and a UE 120.

As further shown in Fig. 6A, and by reference number 610, the UE 120 may receive entropy encoding configuration information from the network node 110. For example, the UE 120 may receive RRC configuration information (e.g., in a CSI report configuration) that is associated with conveying one or more parameters of EC and/or VQ for CSF generation. In some aspects, the UE 120 may receive an indication of whether EC and/or VQ are to be enabled or disabled for CSF generation. For example, a network node 110 may configure the UE 120 to use EC and/or VQ for data compression in a first scenario (e.g., first channel conditions, such as a congested channel) and to use a codebook technique for CSI feedback (or a different EC and/or VQ configuration for data compression) in a second scenario (e.g., second channel conditions, such as a non-congested channel) , as described in more detail with regard to Figs. 7A and 7B.

In some aspects, the UE 120 may determine a model identifier for an AI/ML model (e.g., a neural network model) for encoding and/or the network node 110 may determine a model identifier for a corresponding AI/ML model for decoding. For example, the UE 120 may receive configuration information identifying an encoder/decoder pairing identifier indicating that the UE 120 is to use an encoder corresponding to a decoder of the network node 110. In some aspects, the UE 120 may receive RRC configuration information associated with configuring a single PMF and/or AI/ML model. For example, the UE 120 may receive RRC configuration information associating a CSI report for ML CSF with a single neural network model identifier. In this case, the CSI report may also be associated with a single PMF of a plurality of PMFs configured for hyper-local datasets, as described below. As such, when the UE 120 moves or a scenario changes, the UE 120 may change to a different PMF via RRC reconfiguration signaling. Alternatively, the UE 120 may receive RRC configuration signaling identifying a plurality of PMFs and may receive layer 1 (L1) or layer 2 (L2) signaling to activate or select a PMF of the plurality of PMFs. In this case, a type of signaling that the UE 120 receives may correspond to a type of CSI report (e.g., whether the CSI report is aperiodic, semi-persistent, or periodic) .

As further shown in Fig. 6A, and by reference number 620, the UE 120 may transmit UE capability signaling. For example, the UE 120 may transmit a confirmation that the UE has a capability of performing EC and/or VQ as a response to the network node 110 transmitting entropy encoding configuration information attempting to configure the UE for performing EC and/or VQ. Additionally, or alternatively, the UE 120 may transmit information indicating a PMF that the UE 120 is to use for EC, a scenario that the UE 120 is observing (e.g., from which the UE 120 may select an AI/ML model of a plurality of configured AI/ML models) , or another parameter. In this way, the UE 120 and the network node 110 maintain an encoder/decoder pairing between the UE 120 and the network node 110 and avoid a mismatch between an estimated PMF (e.g., based on an initial training dataset) and an actual PMF (e.g., based on an inference dataset) , thereby reducing a likelihood of poor performance in entropy encoding and decoding.

As further shown in Fig. 6A, and by reference number 630, the UE 120 may receive a CSI-RS. For example, the network node 110 may transmit a set of CSI-RSs in a set of CSI-RS resources and the UE 120 may attempt to receive the set of CSI-RSs and/or measure a channel condition associated with the set of CSI-RSs. As shown by reference number 640, the UE 120 may generate a CSI report and/or CSF relating to the CSI report. For example, the UE 120 may apply an encoding technique, such an AI/ML model to compress data obtained from receiving the set of CSI-RSs and generate a CSF report of the compressed data for transmission.

For example, as shown in Fig. 6B, and by reference number 642, the UE 120 may input data to an encoder, which may be an AI/ML model. For example, the UE 120 may input a set of channel metrics to the encoder, such as a downlink channel matrix (H) , a transmit covariance matrix, a downlink precoder (V) , an interference covariance matrix (R_nn) , a raw downlink channel, or a whitened downlink channel. In this case, the UE 120 may use the encoder to generate a latent message that is to be decoded at the network node 110 (e.g., to recover H, V, or R_nn, among other examples) . As shown by reference number 644, the UE 120 may use a quantizer to perform VQ on an output of the encoder (e.g., a latent vector Z) . For example, the UE 120 may use a VQ codebook to quantize the output of the encoder and generate an embedding vector Z_embd. As shown by reference number 646, the UE 120 may use an EC encoder to encode the embedding vector. For example, the UE 120 may generate a CSF using the embedding vector Z_embd and an EC PMF. In this case, by applying entropy coding on an AI/ML CSF embedding vector, the UE 120 may improve compression gain relative to transmitting with only VQ applied. In some aspects, the UE 120 may have a variable payload size for each layer of the entropy encoding. For example, for precoder compression, the UE 120 can represent each single quantized vector by a different quantity of bits. In this case, the UE 120 may communicate a payload size for each layer in the CSI report (e.g., in a first part of a CSI report, which includes decoding information for a second part of the CSI report) .

As shown in Fig. 6C, and by reference numbers 646a and 646b, in some aspects, the UE 120 may use different PMFs associated with different hyper-local datasets. For example, the UE 120 may use a first local EC encoder with a first PMF for encoding an embedding vector in a first scenario and may use a second local EC encoder with a second PMF for encoding an embedding vector in a second scenario. In some aspects, the UE 120 may select a hyper-local dataset and associated PMF and local encoding AI/ML model based at least in part on a scenario. For example, the UE 120 may determine a geographic location, a use case, a channel metric, or another scenario and select an AI/ML model trained using a dataset corresponding to the scenario, as described above. In this case, the UE 120 may indicate the scenario to the network node 110 to enable the network node 110 to select a corresponding decoder and PMF. In some aspects, an AI/ML encoding model may be associated with a plurality of different PMFs. For example, the plurality of PMFs, configured for a single AI/ML encoding model, may correspond to a plurality of different scenarios and the UE 120 may select a PMF corresponding to an observed scenario for input to an AI/ML encoding model. Additionally, or alternatively, a plurality of AI/ML encoding models may share a single PMF. For example, a single PMF may be usable as input to a plurality of AI/ML encoding models trained with a plurality of different datasets and/or using a plurality of different training parameters.

As further shown in Fig. 6A, and by reference number 650, the UE 120 may transmit a CSI report to the network node 110. For example, the UE 120 may transmit a compressed CSF report to the network node 110 for decompression. As shown by reference number 660, the network node 110 may perform decompression to recover data of the CSF report. For example, the network node 110 may use a decoder (e.g., a decoder AI/ML model corresponding to the encoder AI/ML model of the UE) to decode the compressed CSF.

For example, as shown in Fig. 6B, and by reference number 662, the network node 110 may use an EC decoder with the EC PMF (e.g., the same EC PMF used by the UE 120 for encoding) to decode the CSF and recover the latent embedding vector Z_embd. As shown in Fig. 6C, and by reference number 662a and 662b, when using hyper-local datasets, the network node 110 may apply a first PMF to a first local EC decoder (e.g., a first AI/ML decoding model) and may apply a second PMF to a second EC decoder (e.g., a second AI/ML decoding model) to process the received information from the UE 120 and recover the latent embedding vector. As shown in Fig. 6B, and by reference number 664, the network node 110 may use a de-quantizer to de-quantize the latent embedding vector (e.g., using a VQ codebook) and recover the latent vector Z and the CSF associated therewith, as shown by reference number 666.

As indicated above, Figs. 6A-6C are provided as examples. Other examples may differ from what is described with respect to Figs. 6A-6C.

Figs. 7A-7B are diagrams illustrating examples 700/700'a ssociated with data compression in CSF reporting, in accordance with the present disclosure. As shown in Fig. 7A, example 700 includes communication between a network node 110 and a UE 120.

As further shown in Fig. 7A, and by reference numbers 710 and 720, the UE 120 may identify a scenario and configure entropy encoding for the scenario. For example, the UE 120 may identify the scenario, identify an EC configuration corresponding to the scenario, and activate the EC configuration to use for EC, as described above. In this case, the EC configuration may include a PMF or codebook that is to be used for EC or VQ. In some aspects, the UE 120 and the network node 110 may share a preconfigured list of EC configurations and associated parameters. For example, the network node 110 may configure the UE 120 with a set of possible EC configurations, and the UE 120 may select a configuration from the set of possible EC configurations and indicate the selected configuration to the network node 110. Additionally, or alternatively, the UE 120 may use L2 or layer 3 (L3) signaling to communicate updated EC configuration parameters (e.g., an updated PMF) for a particular identified scenario.

As further shown in Fig. 7A, and by reference numbers 730 and 740, the UE 120 may transmit signaling to indicate the EC configuration and the network node 110 may activate the EC configuration to use for EC decoding, as described above. For example, the UE 120 may transmit an indication of an EC configuration update, the network node 110 may receive the indication and identify the EC configuration included therein, and may activate the EC configuration (e.g., configure one or more parameters) for decoding (e.g., select a particular AI/ML model and PMF for use in arithmetic decoding) . In some aspects, the network node 110 may send a confirmation that the EC configuration and/or a parameter thereof is updated, thereby maintaining synchronization with the UE 120.

In example 700', as shown in Fig. 7B, and by reference number 710', the UE 120 may identify the scenario and, as shown by reference number 750, transmit an indication of an EC configuration update. For example, rather than the UE 120 identifying a new EC configuration corresponding to the scenario, the UE 120 indicates the scenario to the network node 110. In some aspects, the UE 120 may be configured with a set of scenario identifiers for different scenarios (e.g., a list with a set of index values) , as described above, and may transmit a parameter identifying a scenario identifier to indicate which scenario the UE 120 has detected. Accordingly, as shown by reference numbers 760 and 770, the network node 110 may accept (or reject) the UE 120 request to update the scenario and (if accepted) identifies EC configurations for the network node 110 and for the UE 120. The network node 110 activates the EC configuration for the network node 110. As shown by reference number 780, the network node 110 transmits an instruction to the UE 120 to cause the UE 120 to switch to the new EC configuration that has been determined by the network node 110. For example, the network node 110 may transmit information identifying an index value of a preconfigured EC setting, configuration or parameter. Additionally, or alternatively, the network node 110 may use L2 or L3 signaling to explicitly identify an EC parameter. Based on receiving the instruction, the UE 120 switches to the new EC configuration, as shown by reference number 790.

As indicated above, Figs. 7A-7B is provided as examples. Other examples may differ from what is described with respect to Figs. 7A-7B.

Fig. 8 is a diagram illustrating an example process 800 performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure. Example process 800 is an example where the apparatus or the UE (e.g., UE 120) performs operations associated with data compression in CSF reporting.

As shown in Fig. 8, in some aspects, process 800 may include communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS (block 810) . For example, the UE (e.g., using reception component 1002, transmission component 1004, and/or communication manager 1006, depicted in Fig. 10) may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS, as described above.

As further shown in Fig. 8, in some aspects, process 800 may include transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters (block 820) . For example, the UE (e.g., using transmission component 1004 and/or communication manager 1006, depicted in Fig. 10) may transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters, as described above.

Process 800 may include additional aspects, such as any single aspect or any combination of aspects described below and/or in connection with one or more other processes described elsewhere herein.

In a first aspect, process 800 includes receiving the CSI-RS, and wherein transmitting the CSF message comprises transmitting the CSF message to report one or more characteristics of the CSI-RS.

In a second aspect, alone or in combination with the first aspect, process 800 includes encoding, using entropy coding and a machine learning encoder, data, in accordance with the set of parameters, to generate an encoded CSF message, and wherein transmitting the CSF message comprises transmitting the encoded CSF message.

In a third aspect, alone or in combination with one or more of the first and second aspects, process 800 includes transmitting UE capability signaling indicating a capability for entropy coding, and wherein transmitting the CSF message comprises transmitting the CSF message encoded with entropy coding in association with the capability.

In a fourth aspect, alone or in combination with one or more of the first through third aspects, process 800 includes transmitting information identifying a model identifier of an encoding model for the CSF message to enable decoding of the CSF message.

In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the CSF message is encoded using a probability mass function derived from a dataset associated with the CSI-RS.

In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, a payload size is a first size for a first layer associated with the CSF message and is a second size for a second layer associated with the CSF message.

In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, process 800 includes transmitting an indication of a payload size associated with the CSF message in connection with transmitting the CSF message.

In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, a dataset for a probability mass function associated with the CSF message corresponds to a UE location with a threshold level of granularity.

In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, each model identifier, from which a model identifier of a model is selected for generating the CSF message, is associated with one or more probability mass functions for one or more localized datasets.

In a tenth aspect, alone or in combination with one or more of the first through ninth aspects, a probability mass function associated with the CSF message is configured, in association with a type of a channel state information report of the CSF message, via at least one of radio resource control configuration signaling, radio resource control reconfiguration signaling, L1 signaling, or L2 signaling.

In an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, communicating the entropy encoding configuration comprises communicating the entropy encoding configuration to convey at least one parameter, of the set of parameters, that is associated with a communication scenario.

In a twelfth aspect, alone or in combination with one or more of the first through eleventh aspects, the entropy encoding configuration conveys at least one of an indicator of a pre-configured value, of a set of pre-configured values, for a parameter, of the set of parameters, or a numerical value for the parameter.

In a thirteenth aspect, alone or in combination with one or more of the first through twelfth aspects, communicating the entropy encoding configuration comprises transmitting information identifying a communication scenario, and receiving the entropy encoding configuration as a response to the transmitted information identifying the communication scenario.

Although Fig. 8 shows example blocks of process 800, in some aspects, process 800 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 8. Additionally, or alternatively, two or more of the blocks of process 800 may be performed in parallel.

Fig. 9 is a diagram illustrating an example process 900 performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure. Example process 900 is an example where the apparatus or the network node (e.g., network node 110) performs operations associated with data compression in CSF reporting.

As shown in Fig. 9, in some aspects, process 900 may include communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS (block 910) . For example, the network node (e.g., using reception component 1102, transmission component 1104, and/or communication manager 1106, depicted in Fig. 11) may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS, as described above.

As further shown in Fig. 9, in some aspects, process 900 may include receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters (block 920) . For example, the network node (e.g., using reception component 1102 and/or communication manager 1106, depicted in Fig. 11) may receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters, as described above.

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

In a first aspect, process 900 includes transmitting the CSI-RS, and wherein receiving the CSF message comprises receiving the CSF message with reporting of one or more characteristics of the CSI-RS.

In a second aspect, alone or in combination with the first aspect, process 900 includes decoding, using entropy coding and a machine learning decoder, the CSF message, in accordance with the set of parameters, to identify underlying data of the CSF message.

In a third aspect, alone or in combination with one or more of the first and second aspects, process 900 includes receiving UE capability signaling indicating a capability for entropy coding, and wherein receiving the CSF message comprises receiving the CSF message encoded with entropy coding that is in association with the capability.

In a fourth aspect, alone or in combination with one or more of the first through third aspects, process 900 includes receiving information identifying a model identifier of an encoding model for the CSF message, and decoding of the CSF message using a machine learning model associated with the model identifier.

In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, process 900 includes receiving an indication of a payload size associated with the CSF message in connection with receiving the CSF message.

In a thirteenth aspect, alone or in combination with one or more of the first through twelfth aspects, communicating the entropy encoding configuration comprises receiving information identifying a communication scenario, and transmitting the entropy encoding configuration as a response to the transmitted information identifying the communication scenario.

Although Fig. 9 shows example blocks of process 900, in some aspects, process 900 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 9. Additionally, or alternatively, two or more of the blocks of process 900 may be performed in parallel.

Fig. 10 is a diagram of an example apparatus 1000 for wireless communication, in accordance with the present disclosure. The apparatus 1000 may be a UE, or a UE may include the apparatus 1000. In some aspects, the apparatus 1000 includes a reception component 1002, a transmission component 1004, and/or a communication manager 1006, which may be in communication with one another (for example, via one or more buses and/or one or more other components) . In some aspects, the communication manager 1006 is the communication manager 140 described in connection with Fig. 1. As shown, the apparatus 1000 may communicate with another apparatus 1008, such as a UE or a network node (such as a CU, a DU, an RU, or a base station) , using the reception component 1002 and the transmission component 1004.

In some aspects, the apparatus 1000 may be configured to perform one or more operations described herein in connection with Figs. 6A-7B. Additionally, or alternatively, the apparatus 1000 may be configured to perform one or more processes described herein, such as process 800 of Fig. 8. In some aspects, the apparatus 1000 and/or one or more components shown in Fig. 10 may include one or more components of the UE described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 10 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component.

The reception component 1002 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1008. The reception component 1002 may provide received communications to one or more other components of the apparatus 1000. In some aspects, the reception component 1002 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples) , and may provide the processed signals to the one or more other components of the apparatus 1000. In some aspects, the reception component 1002 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers/processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2.

The transmission component 1004 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1008. In some aspects, one or more other components of the apparatus 1000 may generate communications and may provide the generated communications to the transmission component 1004 for transmission to the apparatus 1008. In some aspects, the transmission component 1004 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples) , and may transmit the processed signals to the apparatus 1008. In some aspects, the transmission component 1004 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers/processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2. In some aspects, the transmission component 1004 may be co-located with the reception component 1002 in one or more transceivers.

The communication manager 1006 may support operations of the reception component 1002 and/or the transmission component 1004. For example, the communication manager 1006 may receive information associated with configuring reception of communications by the reception component 1002 and/or transmission of communications by the transmission component 1004. Additionally, or alternatively, the communication manager 1006 may generate and/or provide control information to the reception component 1002 and/or the transmission component 1004 to control reception and/or transmission of communications.

The reception component 1002 and/or the transmission component 1004 may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The transmission component 1004 may transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

The reception component 1002 may receive the CSI-RS. The communication manager 1006 may encode, using entropy coding and a machine learning encoder, data, in accordance with the set of parameters, to generate an encoded CSF message. The transmission component 1004 may transmit UE capability signaling indicating a capability for entropy coding. The transmission component 1004 may transmit information identifying a model identifier of an encoding model for the CSF message to enable decoding of the CSF message. The transmission component 1004 may transmit an indication of a payload size associated with the CSF message in connection with transmitting the CSF message.

The number and arrangement of components shown in Fig. 10 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 10. Furthermore, two or more components shown in Fig. 10 may be implemented within a single component, or a single component shown in Fig. 10 may be implemented as multiple, distributed components. Additionally, or alternatively, a set of (one or more) components shown in Fig. 10 may perform one or more functions described as being performed by another set of components shown in Fig. 10.

Fig. 11 is a diagram of an example apparatus 1100 for wireless communication, in accordance with the present disclosure. The apparatus 1100 may be a network node, or a network node may include the apparatus 1100. In some aspects, the apparatus 1100 includes a reception component 1102, a transmission component 1104, and/or a communication manager 1106, which may be in communication with one another (for example, via one or more buses and/or one or more other components) . In some aspects, the communication manager 1106 is the communication manager 150 described in connection with Fig. 1. As shown, the apparatus 1100 may communicate with another apparatus 1108, such as a UE or a network node (such as a CU, a DU, an RU, or a base station) , using the reception component 1102 and the transmission component 1104.

In some aspects, the apparatus 1100 may be configured to perform one or more operations described herein in connection with Figs. 6A-7B. Additionally, or alternatively, the apparatus 1100 may be configured to perform one or more processes described herein, such as process 900 of Fig. 9. In some aspects, the apparatus 1100 and/or one or more components shown in Fig. 11 may include one or more components of the network node described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 11 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component.

The reception component 1102 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1108. The reception component 1102 may provide received communications to one or more other components of the apparatus 1100. In some aspects, the reception component 1102 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples) , and may provide the processed signals to the one or more other components of the apparatus 1100. In some aspects, the reception component 1102 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers/processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the reception component 1102 and/or the transmission component 1104 may include or may be included in a network interface. The network interface may be configured to obtain and/or output signals for the apparatus 1100 via one or more communications links, such as a backhaul link, a midhaul link, and/or a fronthaul link.

The transmission component 1104 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1108. In some aspects, one or more other components of the apparatus 1100 may generate communications and may provide the generated communications to the transmission component 1104 for transmission to the apparatus 1108. In some aspects, the transmission component 1104 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples) , and may transmit the processed signals to the apparatus 1108. In some aspects, the transmission component 1104 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers/processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the transmission component 1104 may be co-located with the reception component 1102 in one or more transceivers.

The communication manager 1106 may support operations of the reception component 1102 and/or the transmission component 1104. For example, the communication manager 1106 may receive information associated with configuring reception of communications by the reception component 1102 and/or transmission of communications by the transmission component 1104. Additionally, or alternatively, the communication manager 1106 may generate and/or provide control information to the reception component 1102 and/or the transmission component 1104 to control reception and/or transmission of communications.

The reception component 1102 and/or the transmission component 1104 may communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of CSF associated with a CSI-RS. The reception component 1102 may receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

The transmission component 1104 may transmit the CSI-RS. The communication manager 1106 may decode, using entropy coding and a machine learning decoder, the CSF message, in accordance with the set of parameters, to identify underlying data of the CSF message. The reception component 1102 may receive UE capability signaling indicating a capability for entropy coding. The reception component 1102 may receive information identifying a model identifier of an encoding model for the CSF message. The communication manager 1106 may decode of the CSF message using a machine learning model associated with the model identifier. The reception component 1102 may receive an indication of a payload size associated with the CSF message in connection with receiving the CSF message.

The number and arrangement of components shown in Fig. 11 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 11. Furthermore, two or more components shown in Fig. 11 may be implemented within a single component, or a single component shown in Fig. 11 may be implemented as multiple, distributed components. Additionally, or alternatively, a set of (one or more) components shown in Fig. 11 may perform one or more functions described as being performed by another set of components shown in Fig. 11.

The following provides an overview of some Aspects of the present disclosure:

Aspect 1: A method of wireless communication performed by a user equipment (UE) , comprising: communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) ; and transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Aspect 2: The method of Aspect 1, further comprising: receiving the CSI-RS; and wherein transmitting the CSF message comprises: transmitting the CSF message to report one or more characteristics of the CSI-RS.

Aspect 3: The method of any of Aspects 1-2, further comprising: encoding, using entropy coding and a machine learning encoder, data, in accordance with the set of parameters, to generate an encoded CSF message; and wherein transmitting the CSF message comprises: transmitting the encoded CSF message.

Aspect 4: The method of any of Aspects 1-3, further comprising: transmitting UE capability signaling indicating a capability for entropy coding; and wherein transmitting the CSF message comprises: transmitting the CSF message encoded with entropy coding in association with the capability.

Aspect 5: The method of any of Aspects 1-4, further comprising: transmitting information identifying a model identifier of an encoding model for the CSF message to enable decoding of the CSF message.

Aspect 6: The method of any of Aspects 1-5, wherein the CSF message is encoded using a probability mass function derived from a dataset associated with the CSI-RS.

Aspect 7: The method of any of Aspects 1-6, wherein a payload size is a first size for a first layer associated with the CSF message and is a second size for a second layer associated with the CSF message.

Aspect 8: The method of any of Aspects 1-7, further comprising: transmitting an indication of a payload size associated with the CSF message in connection with transmitting the CSF message.

Aspect 9: The method of any of Aspects 1-8, wherein a dataset for a probability mass function associated with the CSF message corresponds to a UE location with a threshold level of granularity.

Aspect 10: The method of Aspect 9, wherein each model identifier, from which a model identifier of a model is selected for generating the CSF message, is associated with one or more probability mass functions for one or more localized datasets.

Aspect 11: The method of any of Aspects 1-10, wherein a probability mass function associated with the CSF message is configured, in association with a type of a channel state information report of the CSF message, via at least one of: radio resource control configuration signaling, radio resource control reconfiguration signaling, layer 1 (L1) signaling, or layer 2 (L2) signaling.

Aspect 12: The method of any of Aspects 1-11, wherein communicating the entropy encoding configuration comprises: communicating the entropy encoding configuration to convey at least one parameter, of the set of parameters, that is associated with a communication scenario.

Aspect 13: The method of any of Aspects 1-12, wherein the entropy encoding configuration conveys at least one of: an indicator of a pre-configured value, of a set of pre-configured values, for a parameter, of the set of parameters, or a numerical value for the parameter.

Aspect 14: The method of any of Aspects 1-13, wherein communicating the entropy encoding configuration comprises: transmitting information identifying a communication scenario; and receiving the entropy encoding configuration as a response to the transmitted information identifying the communication scenario.

Aspect 15: A method of wireless communication performed by a network node, comprising: communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) ; and receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.

Aspect 16: The method of Aspect 15, further comprising: transmitting the CSI-RS; and wherein receiving the CSF message comprises: receiving the CSF message with reporting of one or more characteristics of the CSI-RS.

Aspect 17: The method of any of Aspects 15-16, further comprising: decoding, using entropy coding and a machine learning decoder, the CSF message, in accordance with the set of parameters, to identify underlying data of the CSF message.

Aspect 18: The method of any of Aspects 15-17, further comprising: receiving UE capability signaling indicating a capability for entropy coding; and wherein receiving the CSF message comprises: receiving the CSF message encoded with entropy coding that is in association with the capability.

Aspect 19: The method of any of Aspects 15-18, further comprising: receiving information identifying a model identifier of an encoding model for the CSF message; and decoding of the CSF message using a machine learning model associated with the model identifier.

Aspect 20: The method of any of Aspects 15-19, wherein the CSF message is encoded using a probability mass function derived from a dataset associated with the CSI-RS.

Aspect 21: The method of any of Aspects 15-20, wherein a payload size is a first size for a first layer associated with the CSF message and is a second size for a second layer associated with the CSF message.

Aspect 22: The method of any of Aspects 15-21, further comprising: receiving an indication of a payload size associated with the CSF message in connection with receiving the CSF message.

Aspect 23: The method of any of Aspects 15-22, wherein a dataset for a probability mass function associated with the CSF message corresponds to a UE location with a threshold level of granularity.

Aspect 24: The method of Aspect 23, wherein each model identifier, from which a model identifier of a model is selected for generating the CSF message, is associated with one or more probability mass functions for one or more localized datasets.

Aspect 25: The method of any of Aspects 15-24, wherein a probability mass function associated with the CSF message is configured, in association with a type of a channel state information report of the CSF message, via at least one of: radio resource control configuration signaling, radio resource control reconfiguration signaling, layer 1 (L1) signaling, or layer 2 (L2) signaling.

Aspect 26: The method of any of Aspects 15-25, wherein communicating the entropy encoding configuration comprises: communicating the entropy encoding configuration to convey at least one parameter, of the set of parameters, that is associated with a communication scenario.

Aspect 27: The method of any of Aspects 15-26, wherein the entropy encoding configuration conveys at least one of: an indicator of a pre-configured value, of a set of pre-configured values, for a parameter, of the set of parameters, or a numerical value for the parameter.

Aspect 28: The method of any of Aspects 15-27, wherein communicating the entropy encoding configuration comprises: receiving information identifying a communication scenario; and transmitting the entropy encoding configuration as a response to the transmitted information identifying the communication scenario.

Aspect 29: An apparatus for wireless communication at a device, the apparatus comprising one or more processors; one or more memories coupled with the one or more processors; and instructions stored in the one or more memories and executable by the one or more processors to cause the apparatus to perform the method of one or more of Aspects 1-28.

Aspect 30: An apparatus for wireless communication at a device, the apparatus comprising one or more memories and one or more processors coupled to the one or more memories, the one or more processors configured to cause the device to perform the method of one or more of Aspects 1-28.

Aspect 31: An apparatus for wireless communication, the apparatus comprising at least one means for performing the method of one or more of Aspects 1-28.

Aspect 32: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by one or more processors to perform the method of one or more of Aspects 1-28.

Aspect 33: A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform the method of one or more of Aspects 1-28.

Aspect 34: A device for wireless communication, the device comprising a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause the device to perform the method of one or more of Aspects 1-28.

Aspect 35: An apparatus for wireless communication at a device, the apparatus comprising one or more memories and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to cause the device to perform the method of one or more of Aspects 1-28.

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

As used herein, the term “component” is intended to be broadly construed as hardware and/or a combination of hardware and software. “Software” shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, and/or functions, among other examples, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. As used herein, a “processor” is implemented in hardware and/or a combination of hardware and software. It will be apparent that systems and/or methods described herein may be implemented in different forms of hardware and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the aspects. Thus, the operation and behavior of the systems and/or methods are described herein without reference to specific software code, since those skilled in the art will understand that software and hardware can be designed to implement the systems and/or methods based, at least in part, on the description herein.

The hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose single-or multi-chip processor, a digital signal processor (DSP) , an application specific integrated circuit (ASIC) , a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. A processor also may be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some aspects, particular processes and methods may be performed by circuitry that is specific to a given function.

As used herein, “satisfying a threshold” may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, or the like.

Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. The disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set. As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a + b, a + c, b + c, and a + b + c, as well as any combination with multiples of the same element (e.g., a + a, a + a + a, a + a + b, a + a + c, a + b + b, a + c + c, b + b, b + b + b, b + b + c, c + c, and c + c + c, or any other ordering of a, b, and c) .

No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more. ” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more. ” Furthermore, as used herein, the terms “set” and “group” are intended to include one or more items and may be used interchangeably with “one or more. ” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has, ” “have, ” “having, ” or the like are intended to be open-ended terms that do not limit an element that they modify (e.g., an element “having” A may also have B) . Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and/or, ” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of” ) .

Claims

A user equipment (UE) for wireless communication, comprising:

one or more memories; and

one or more processors, coupled to the one or more memories, configured to cause the UE to:

communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS); and

transmit, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.
The UE of claim 1, wherein the one or more processors are further configured to cause the UE to:

receive the CSI-RS; and

wherein the one or more processors, to cause the UE to transmit the CSF message, are configured to cause the UE to:

transmit the CSF message to report one or more characteristics of the CSI-RS.
The UE of claim 1, wherein the one or more processors are further configured to cause the UE to:

encode, using entropy coding and a machine learning encoder, data, in accordance with the set of parameters, to generate an encoded CSF message; and

wherein the one or more processors, to cause the UE to transmit the CSF message, are configured to cause the UE to:

transmit the encoded CSF message.
The UE of claim 1, wherein the one or more processors are further configured to cause the UE to:

transmit UE capability signaling indicating a capability for entropy coding; and

wherein the one or more processors, to cause the UE to transmit the CSF message, are configured to cause the UE to:

transmit the CSF message encoded with entropy coding in association with the capability.
The UE of claim 1, wherein the one or more processors are further configured to cause the UE to:

transmit information identifying a model identifier of an encoding model for the CSF message to enable decoding of the CSF message.
The UE of claim 1, wherein the CSF message is encoded using a probability mass function derived from a dataset associated with the CSI-RS.
The UE of claim 1, wherein a payload size is a first size for a first layer associated with the CSF message and is a second size for a second layer associated with the CSF message.
The UE of claim 1, wherein the one or more processors are further configured to cause the UE to:

transmit an indication of a payload size associated with the CSF message in connection with transmitting the CSF message.
The UE of claim 1, wherein a dataset for a probability mass function associated with the CSF message corresponds to a UE location with a threshold level of granularity.
The UE of claim 9, wherein each model identifier, from which a model identifier of a model is selected for generating the CSF message, is associated with one or more probability mass functions for one or more localized datasets.
The UE of claim 1, wherein a probability mass function associated with the CSF message is configured, in association with a type of a channel state information report of the CSF message, via at least one of:

radio resource control configuration signaling,

radio resource control reconfiguration signaling,

layer 1 (L1) signaling, or

layer 2 (L2) signaling.
The UE of claim 1, wherein the one or more processors, to cause the UE to communicate the entropy encoding configuration, are configured to cause the UE to:

communicate the entropy encoding configuration to convey at least one parameter, of the set of parameters, that is associated with a communication scenario.
The UE of claim 1, wherein the entropy encoding configuration conveys at least one of:

an indicator of a pre-configured value, of a set of pre-configured values, for a parameter, of the set of parameters, or

a numerical value for the parameter.
The UE of claim 1, wherein the one or more processors, to cause the UE to communicate the entropy encoding configuration, are configured to cause the UE to:

transmit information identifying a communication scenario; and

receive the entropy encoding configuration as a response to the transmitted information identifying the communication scenario.
A network node for wireless communication, comprising:

one or more memories; and

one or more processors, coupled to the one or more memories, configured to cause the network node to:

communicate an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) ; and

receive, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.
The network node of claim 15, wherein the one or more processors are further configured to cause the network node to:

transmit the CSI-RS; and

wherein the one or more processors, to cause the network node to receive the CSF message, are configured to cause the network node to:

receive the CSF message with reporting of one or more characteristics of the CSI-RS.
The network node of claim 15, wherein the one or more processors are further configured to cause the network node to:

decode, using entropy coding and a machine learning decoder, the CSF message, in accordance with the set of parameters, to identify underlying data of the CSF message.
The network node of claim 15, wherein the one or more processors are further configured to cause the network node to:

receive UE capability signaling indicating a capability for entropy coding; and

wherein the one or more processors, to cause the network node to receive the CSF message, are configured to cause the network node to:

receive the CSF message encoded with entropy coding that is in association with the capability.
The network node of claim 15, wherein the one or more processors are further configured to cause the network node to:

receive information identifying a model identifier of an encoding model for the CSF message; and

decode of the CSF message using a machine learning model associated with the model identifier.
The network node of claim 15, wherein the CSF message is encoded using a probability mass function derived from a dataset associated with the CSI-RS.
The network node of claim 15, wherein a payload size is a first size for a first layer associated with the CSF message and is a second size for a second layer associated with the CSF message.
The network node of claim 15, wherein the one or more processors are further configured to cause the network node to:

receive an indication of a payload size associated with the CSF message in connection with receiving the CSF message.
The network node of claim 15, wherein a dataset for a probability mass function associated with the CSF message corresponds to a UE location with a threshold level of granularity.
The network node of claim 23, wherein each model identifier, from which a model identifier of a model is selected for generating the CSF message, is associated with one or more probability mass functions for one or more localized datasets.
The network node of claim 15, wherein a probability mass function associated with the CSF message is configured, in association with a type of a channel state information report of the CSF message, via at least one of:

radio resource control configuration signaling,

radio resource control reconfiguration signaling,

layer 1 (L1) signaling, or

layer 2 (L2) signaling.
The network node of claim 15, wherein the one or more processors, to cause the network node to communicate the entropy encoding configuration, are configured to cause the network node to:

communicate the entropy encoding configuration to convey at least one parameter, of the set of parameters, that is associated with a communication scenario.
The network node of claim 15, wherein the entropy encoding configuration conveys at least one of:

an indicator of a pre-configured value, of a set of pre-configured values, for a parameter, of the set of parameters, or

a numerical value for the parameter.
The network node of claim 15, wherein the one or more processors, to cause the network node to communicate the entropy encoding configuration, are configured to cause the network node to:

receive information identifying a communication scenario; and

transmit the entropy encoding configuration as a response to the received information identifying the communication scenario.
A method of wireless communication performed by a user equipment (UE) , comprising:

communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) ; and

transmitting, in connection with receiving the CSI-RS, a CSF message encoded in accordance with the set of parameters.
A method of wireless communication performed by a network node, comprising:

communicating an entropy encoding configuration identifying a set of parameters for vector quantization and entropy coding for machine learning compression of channel state feedback (CSF) associated with a channel state information reference signal (CSI-RS) ; and

receiving, in connection with transmitting the CSI-RS, a CSF message encoded in accordance with the set of parameters.