WO2025236195A1

WO2025236195A1 - Configuration of identifiers for network-side conditions

Info

Publication number: WO2025236195A1
Application number: PCT/CN2024/093269
Authority: WO
Inventors: Qiaoyu Li; Juan Zhang; Hamed Pezeshki; Mahmoud Taherzadeh Boroujeni; Aziz Gholmieh; Taesang Yoo; Rajeev Kumar
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2024-05-15
Filing date: 2024-05-15
Publication date: 2025-11-20
Anticipated expiration: 2026-11-15

Abstract

A network entity (e.g., an operations, administration, and maintenance (OAM) entity) may configure a set of candidate-associated identifiers (IDs). Each of the set of candidate-associated IDs may be associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality. The network entity may transmit a first indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions. The network node may receive the first indicator of the set of candidate-associated IDs, may configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality, and may transmit the configured configuration and the corresponding candidate-associated ID to a corresponding UE.

Description

CONFIGURATION OF IDENTIFIERS FOR NETWORK-SIDE CONDITIONS

TECHNICAL FIELD

The present disclosure relates generally to communication systems, and more particularly, to a wireless signal calculation system.

INTRODUCTION

Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.

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

BRIEF SUMMARY

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

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may include a network entity. The network entity may be, for example, an operations, administration, and maintenance (OAM) entity or a server that may be configured to manage candidate-associated identifiers (IDs) . The apparatus may configure a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality. The apparatus may transmit an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may include a network node. The network node may be, for example, a base station, a transmission reception point (TRP) , a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity. The apparatus may receive a first indicator of a set of candidate-associated identifiers (IDs) . Each of the set of candidate-associated IDs may be associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality. The apparatus may configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The apparatus may transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE.

In some aspects, the techniques described herein relate to a method of wireless communication at a network entity, including: configuring a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; and transmitting an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.

In some aspects, the techniques described herein relate to a method, further including: receiving a first vendor ID from the network node and a second vendor ID from a second network node, where configuring the set of candidate-associated IDs includes configuring the set of candidate-associated IDs to be associated with the first vendor ID; configuring a second set of candidate-associated IDs to be orthogonal to the set of candidate-associated IDs in response to the first vendor ID being different than the second vendor ID and configuring the second set of candidate-associated IDs to be associated with the second vendor ID, where each of the second set of candidate-associated IDs is associated with a second consistent assumption by a second UE of a second set of network-side additional conditions for both training and inference procedures of a second UE-side AI/ML functionality; and transmitting a second indicator of the configured second set of candidate-associated IDs to the second network node for indicating a second associated set of consistent assumptions.

In some aspects, the techniques described herein relate to a method, further including: receiving the first vendor ID from a third network node; and transmitting the indicator of the configured set of candidate-associated IDs to the third network node in response to the reception of the first vendor ID from the third network node.

In some aspects, the techniques described herein relate to a method, further including: receiving a first number of requested candidate-associated IDs, where configuring the set of candidate-associated IDs includes: configuring a second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, further including: receiving a third number of requested candidate-associated IDs from a second network node, where receiving the first number of requested candidate-associated IDs includes: receiving the first number of requested candidate-associated IDs from the network node, where configuring the set of candidate-associated IDs further includes: configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, further including: receiving a vendor ID from the network node; and receiving the vendor ID from the second network node, where configuring the second number of the set of candidate-associated IDs includes: configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs in response to the reception of the vendor ID from both the network node and the second network node.

In some aspects, the techniques described herein relate to a method, where receiving the first number of requested candidate-associated IDs includes: receiving the first number of requested candidate-associated IDs from a second network node.

In some aspects, the techniques described herein relate to a method, further including: receiving a vendor ID from the network node; and receiving the vendor ID from the second network node, where transmitting the indicator of the configured set of candidate-associated IDs to the network node includes: transmitting the indicator of the configured set of candidate-associated IDs to the network node in response to the reception of the vendor ID from both the network node and the second network node.

In some aspects, the techniques described herein relate to a method, where configuring the set of candidate-associated IDs includes: configuring the set of candidate-associated IDs based on an initial candidate-associated ID and a number of candidate-associated IDs, where the set of candidate-associated IDs includes consecutive integers, where the indicator includes the initial candidate-associated ID and the number of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where configuring the set of candidate-associated IDs includes: configuring the set of candidate-associated IDs based on a randomizer, a randomizer seed, and a number of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where the indicator includes the randomizer seed.

In some aspects, the techniques described herein relate to a method, where the set of candidate-associated IDs includes non-consecutive integers, where the indicator includes each of the set of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where the set of candidate-associated IDs includes a single candidate-associated ID.

In some aspects, the techniques described herein relate to a method, where configuring the set of candidate-associated IDs includes: identifying a second consistent assumption associated with the network node; and configuring the set of candidate-associated IDs to include the single candidate-associated ID based on the identified second consistent assumption.

In some aspects, the techniques described herein relate to a method, where the set of network-side additional conditions include at least one of: a number of a set of prediction targets for a corresponding UE-side AI/ML functionality; an order of the set of prediction targets for the corresponding UE-side AI/ML functionality; an index for the set of prediction targets for the corresponding UE-side AI/ML functionality; a second indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality; a third indicator of a quasi-co-location (QCL) relationship associated with at least two of the set of RSs; or a set of temporal parameters associated with the corresponding UE-side AI/ML functionality.

In some aspects, the techniques described herein relate to a method, where the network entity includes an operations, administration, and maintenance (OAM) entity.

In some aspects, the techniques described herein relate to a method, where the network node includes at least one of a base station, a transmission reception point (TRP) , a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity.

In some aspects, the techniques described herein relate to a method, where the set of UE-side AI/ML functionality includes at least one of: a first set of beam prediction calculations; a second set of positioning calculations; or a third set of channel state information (CSI) reference signal (CSI-RS) feedback calculations. A beam prediction calculation may include beam prediction and reporting processes. A CSI-RS feedback calculation may include CSI compression and feedback processes.

In some aspects, the techniques described herein relate to a method of wireless communication at a network node, including: receiving a first indicator of a set of candidate-associated identifiers (IDs) , where each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; configuring a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality; and transmitting a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE.

In some aspects, the techniques described herein relate to a method, further including: transmitting a first vendor ID associated with the network node before the reception of the first indicator of the set of candidate-associated IDs, where the set of candidate-associated IDs are orthogonal to a second set of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, further including: transmitting a first number of requested candidate-associated IDs before the reception of the first indicator of the set of candidate-associated IDs, where a second number of the set of candidate-associated IDs is greater or equal to the transmitted first number of requested candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where the set of candidate-associated IDs includes consecutive integers, where the first indicator includes an initial candidate-associated ID and a number of candidate-associated IDs associated with the set of candidate-associated IDs, further including: calculating the consecutive integers of the set of candidate-associated IDs based on the initial candidate-associated ID and the number of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where the first indicator includes a randomizer seed, further including: calculating the set of candidate-associated IDs based on the randomizer seed.

In some aspects, the techniques described herein relate to a method, where the set of candidate-associated IDs includes non-consecutive integers, where the first indicator includes each of the set of candidate-associated IDs.

In some aspects, the techniques described herein relate to a method, where receiving the first indicator of the set of candidate-associated IDs includes: receiving the first indicator of the set of candidate-associated IDs from an operations, administration, and maintenance (OAM) entity.

In some aspects, the techniques described herein relate to a method, where the network node includes at least one of a base station, a transmission reception point, a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity.

In some aspects, the techniques described herein relate to a method, where the corresponding UE-side AI/ML functionality includes at least one of: a first set of beam prediction calculations; a second set of positioning calculations; or a third set of channel state information (CSI) reference signal (CSI-RS) feedback calculations.

In some aspects, the techniques described herein relate to a method, where the UE-side AI/ML functionality configuration includes at least one of a first configuration for training the UE-side AI/ML functionality or a second configuration for calculating a prediction target based on the UE-side AI/ML functionality.

In some aspects, the techniques described herein relate to a method, where the corresponding set of network-side additional conditions include at least one of: a number of a set of prediction targets for the corresponding UE-side AI/ML functionality; an order of the set of prediction targets for the corresponding UE-side AI/ML functionality; an index for the set of prediction targets for the corresponding UE-side AI/ML functionality; a fourth indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality; a fifth indicator of a quasi-co-location (QCL) relationship associated with at least two of the set of RSs; or a set of temporal parameters associated with the corresponding UE-side AI/ML functionality.

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

BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

FIG. 4 is an illustrative block diagram of an example machine learning (ML) model represented by an artificial neural network (ANN) , in accordance with various aspects of the present disclosure.

FIG. 5 is an illustrative block diagram of an example ML architecture 500 that may be used for wireless communications in any of the various implementations, processes, environments, networks, or use cases, in accordance with various aspects of the present disclosure.

FIG. 6 is an illustrative block diagram of an example ML architecture of first wireless device in communication with second wireless device, in accordance with various aspects of the present disclosure.

FIG. 7 is a diagram illustrating an example of a UE and a network node configured to perform UE-side AI/ML functionality, in accordance with various aspects of the present disclosure.

FIG. 8 is a diagram illustrating an example of a plurality of network nodes corresponding with different vendors using candidate-associated IDs to perform UE- side AI/ML functionality with a common UE, in accordance with various aspects of the present disclosure.

FIG. 9 is a connection flow diagram illustrating an example of a plurality of network nodes configured to use candidate-associated IDs to perform UE-side AI/ML functionality, in accordance with various aspects of the present disclosure.

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

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

FIG. 12 is a diagram illustrating an example of a hardware implementation for an example apparatus and/or network entity.

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

FIG. 14 is a diagram illustrating an example of a hardware implementation for an example network entity.

FIG. 15 is a diagram illustrating an example of a UE and a network node configured to perform UE-side AI/ML functionality, in accordance with various aspects of the present disclosure.

DETAILED DESCRIPTION

The following description is directed to examples for the purposes of describing innovative aspects of this disclosure. However, a person having ordinary skill in the art may recognize that the teachings herein may be applied in a multitude of ways. Some or all of the described examples may be implemented in any device, system or network that is capable of transmitting and receiving radio frequency (RF) signals according to one or more of the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards, the IEEE 802.15 standards, the standards as defined by the Bluetooth Special Interest Group (SIG) , or the Long Term Evolution (LTE) , 3G, 4G or 5G (New Radio (NR) ) standards promulgated by the 3rd Generation Partnership Project (3GPP) , among others. The described examples may be implemented in any device, system or network that is capable of transmitting and receiving RF signals according to one or more of the following technologies or techniques: code division multiple access (CDMA) , time division multiple access (TDMA) , frequency division multiple access (FDMA) , orthogonal FDMA (OFDMA) , single-carrier FDMA (SC-FDMA) , spatial division multiple access (SDMA) , rate-splitting multiple access (RSMA) , multi-user shared access (MUSA) , single-user (SU) multiple-input multiple-output (MIMO) and multi-user (MU) -MIMO. The described examples also may be implemented using other wireless communication protocols or RF signals suitable for use in one or more of a wireless personal area network (WPAN) , a wireless local area network (WLAN) , a wireless wide area network (WWAN) , a wireless metropolitan area network (WMAN) , or an internet of things (IoT) network.

Various aspects relate generally to configuring user equipment (UE) -side artificial intelligence machine learning (AI/ML) functionality. Some aspects more specifically relate to configuring candidate-associated identifiers that may be used by a UE to make consistent assumptions for both training procedures and inference procedures of AI/ML functionality. In some examples, a network entity may configure a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality. The network entity may transmit an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions to a UE.

In some examples, a network node may receive a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The network node may configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The network node may transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. The UE may assume the set of network-side additional conditions associated with the candidate-associated ID to be the same for a set of training procedures for an AI/ML functionality and for a set of inference procedures for the AI/ML functionality.

Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by configuring a candidate-associated ID to be used by a UE to identify a consistent assumption of network-side conditions for both training and inference procedures of UE-side AI/ML functionality, the described techniques can be used to identify network-side conditions without transmitting them over-the-air (OTA) , improving security and privacy of such network-side conditions. Furthermore, if a network entity and a network node belong to the same infrastructure vendor (infra-vendor) , then the network entity may configure a single candidate-associated ID for a network node (e.g., a gNB) for a particular AI/ML functionality or AI/ML sub-functionality instead of a plurality of candidate-associated IDs.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Referring again to FIG. 1, in certain aspects, the base station 102 may have a candidate-associated ID association component 199 that may be configured to receive a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The candidate-associated ID association component 199 may be configured to configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The candidate-associated ID association component 199 may be configured to transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. In certain aspects, a server 104 may have a candidate-associated ID configuration component 198. The server 104 may be configured to manage candidate-associated IDs. For example, the server 104 may be an operations, administration, and maintenance (OAM) entity. The server 104 may communicate with the base station 102 via online means (e.g., OTA signals) or via offline beams (e.g., a backhaul link, an Internet connection) . The candidate-associated ID configuration component 198 may be configured to configure a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The candidate-associated ID configuration component 198 may be configured to transmit an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.

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

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

Table 1: Numerology, SCS, and CP

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A candidate-associated ID may be used to maintain consistency of NW-side additional conditions (i.e., conditions associated with a network node/base station/TRP) between training procedures and inference procedures for a UE-side AI/ML functionality. When the UE identifies that the same candidate-associated ID signaled by a network is used for both a training procedure and an inference procedure, the UE may assume that a set of network-side additional conditions are consistent for both the training procedure associated with the candidate-associated ID and the inference procedure associated with the candidate-associated ID.

An AI/ML functionality may include any suitable calculation of a prediction target using AI/ML, for example a beam prediction and reporting, a positioning calculation, or a channel state information (CSI) compression and feedback. A training procedure for UE-side AI/ML may be a process to train UE-side AI/ML models associated with an AI/ML functionality and its corresponding associated ID. In other words, the trained UE-side AI/ML model may be associated with a corresponding ID, such as a candidate-associated ID. An inference procedure for UE-side AI/ML may be a process at the UE-side (e.g., by a UE processing AI/ML functionality) to carry out inference of the AI/ML models associated with an AI/ML functionality and its corresponding candidate-associated ID and feedback to NW information based at least on output of the AI/ML inference results. For example, a UE processing AI/ML functionality may calculate a set of outputs using a trained AI/ML model based on its corresponding candidate-associated ID. The UE may provide feedback to a network node or a network entity based on at least one of the calculated set of outputs.

In some aspects, the candidate-associated ID may be interpreted as an identifier for any dataset, configuration, scenario, codebook, functionality, and/or model associated with an AI/ML functionality. The candidate-associated ID may identify a consistent set of network-side additional conditions related with UE assumptions associated with AI/ML life cycle management (LCM) , for example data collection, training, deployment, inference, performance monitoring, activation, deactivation, and/or switching. In fact, any network entity, including a network node, a UE, or an offline server, may use a candidate-associated ID to make a consistent assumption about a set of network-side additional conditions for a set of AI/ML LCM associated with an AI/ML functionality. For example, with respect to beam prediction AI/ML functionality, the network-side additional conditions may include a number of Set-A beams (i.e., prediction targets calculated by AI/ML functionality) , a number of Set-B beams (i.e., measured reference signals (RSs) used by AI/ML functionality) , an order of Set-A beams, an order of Set-B beams, indexing of Set-A beams, indexing of Set-B beams, absolute pointing directions, relative pointing directions, beam shapes (i.e., angular specific beam forming gains) , quasi co-location (QCL) relationships across Set-A beams, QCL relationships within Set-A beams, QCL relationships across Set- B beams, QCL relationships within Set-B beams, and/or temporal parameters (e.g., periodicity of beams, target future occasions for temporal prediction) . In another example, an AI/ML functionality may calculate a set of spatial domain DL Tx beam prediction targets for Set-A beams based on measurements results of Set-B beams measured in a different spatial domain than the Set-A beams. In another example, an AI/ML functionality may calculate a set of temporal domain DL Tx beam prediction targets for Set-A beams based on historic measurements results of Set-B beams. A UE and network node may facilitate LCM operations specific to beam management use cases via signaling of a candidate-associate ID that may be used to make a consistent assumption about network-side additional conditions for both training and inference procedures. Use of such candidate-associate IDs may enable methods to ensure consistency between training procedures and inference procedures for UE-side AI/ML functionality.

In some aspects, a training procedure may include a process of training an AI/ML functionality for spatial or temporal domain DL Tx beam prediction for Set-A beams based on measurement results of Set-B beams measured in a different spatial domain than the Set-A beams. Such Set-B beams may include a set of measurement resources associated with one or more spatial filters (e.g., absolute direction, relative direction, shape) that may be used to train an AI/ML functionality at a UE to predict/infer DL Tx beams associated with a set of prediction targets (e.g., Set-A beams) . Such measurements may be measurements of reference signals (RSs) , such as a synchronization signal block (SSB) , a channel state information (CSI) -reference signal (CSI-RS) , or a demodulation reference signal (DM-RS) . An inference procedure may include a process of using an AI/ML functionality for spatial or temporal domain DL Tx beam prediction for Set-A beams based on historical measurement results of Set-B beams. A prediction target, or a prediction result, may refer to a set of calculated metrics based on inferences using AI/ML functionality, such as predicted reference signal received power (RSRP) or other metrics (e.g., a channel quality indicator (CQI) , a signal-to-noise ratio (SNR) , a signal-to-interference plus noise ratio (SINR) , a signal-to-noise-plus-distortion ratio (SNDR) , a received signal strength indicator (RSSI) , or a reference signal received quality (RSRQ) , and/or a block error rate (BLER) ) associated with prediction target (s) .

In some aspects, network side additional conditions may include number (e.g., quantity) , ordering, or indexing of the measurement resources and the prediction targets. In some aspects, network side additional conditions may also include absolute or relative pointing directions (e.g., with regard to boresight direction relative to the center of Tx antenna panel) . In some aspects, network side additional conditions may also include beam shapes (e.g., angular specific beam forming gains) . In some aspects, network side additional conditions may also include quasi-co-location (QCL) relationships across or within the measurement resources or the prediction targets. In some aspects, network side additional conditions may also include temporal parameters (e.g., periodicity of the measurement resources or the prediction targets, target future occasions for temporal prediction, or the like) . QCL relationships may be specified in terms of QCL types. Regarding the QCL types, QCL type A may include the Doppler shift, the Doppler spread, the average delay, and the delay spread; QCL type B may include the Doppler shift and the Doppler spread; QCL type C may include the Doppler shift and the average delay; and QCL type D may include the spatial Rx parameters (e.g., associated with beam information such as beamforming properties for finding a beam) . In some aspects, network side additional conditions may impact UE side assumptions when a same associated ID, and may be received by the UE during training and inference.

Certain aspects and techniques as described herein may be implemented, at least in part, using an AI program, such as a program that includes a ML or artificial neural network (ANN) model. An example ML model may include mathematical representations or define computing capabilities for making inferences from input data based on patterns or relationships identified in the input data. As used herein, the term “inferences” can include one or more of decisions, predictions, determinations, or values, which may represent outputs of the ML model. The computing capabilities may be defined in terms of certain parameters of the ML model, such as weights and biases. Weights may indicate relationships between certain input data and certain outputs of the ML model, and biases are offsets which may indicate a starting point for outputs of the ML model. An example ML model operating on input data may start at an initial output based on the biases and then update its output based on a combination of the input data and the weights.

In some aspects, an ML model may be configured to provide computing capabilities for wireless communications. Such an ML model may be configured with weights and biases to perform spatial domain or temporal domain DL Tx beam prediction. Thus, during operation of a device, the ML model may receive input data (such as measurements on the measurement resources) and make inferences (such as spatial domain or temporal domain DL Tx beam prediction on the prediction targets, including reference signal received power (RSRP) or other metric prediction on the prediction targets) based on the weights and biases. ML models may be deployed in one or more devices (for example, network entities and user equipments (UEs) ) and may be configured to enhance various aspects of a wireless communication system. For example, an ML model may be trained to identify patterns or relationships in data corresponding to a network, a device, an air interface, or the like. An ML model may support operational decisions relating to one or more aspects associated with wireless communications devices, networks, or services. For example, an ML model may be utilized for supporting or improving aspects such as signal coding/decoding, network routing, energy conservation, transceiver circuitry controls, frequency synchronization, timing synchronization, channel state estimation, channel equalization, channel state feedback, modulation, demodulation, device positioning, beamforming, load balancing, operations and management functions, security, or the like.

ML models may be characterized in terms of types of learning that generate specific types of learned models that perform specific types of tasks. For example, different types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc. ML models may be used to perform different tasks such as classification or regression, where classification refers to determining one or more discrete output values from a set of predefined output values, and regression refers to determining continuous values which are not bounded by predefined output values. Some example ML models configured for performing such tasks include ANNs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) , transformers, diffusion models, regression analysis models (such as statistical models) , large language models (LLMs) , decision tree learning (such as predictive models) , support vector networks (SVMs) , and probabilistic graphical models (such as a Bayesian network) , or the like.

The description herein illustrates, by way of some examples, how one or more tasks or problems in wireless communications may benefit from the application of one or more ML models. For example, based on aspects provided herein, performance of beams may be predicted and the UE may be able to more efficiently perform beam management. To facilitate the discussion, an ML model configured using an ANN is used, but it may be understood, that other types of ML models may be used instead of an ANN. Hence, unless expressly recited, subject matter regarding an ML model is not necessarily intended to be an ANN solution without other solutions. Further, it may be understood that, unless otherwise specifically stated, terms such “AI/ML model, ” “ML model, ” “trained ML model, ” “ANN, ” “model, ” “algorithm, ” or the like are intended to be interchangeable.

FIG. 4 is an illustrative block diagram of an example machine learning (ML) model represented by an artificial neural network (ANN) , in accordance with various aspects of the present disclosure. ANN 400 may receive input data 406 which may include one or more bits of data 402, pre-processed data output from pre-processor 404 (optional) , or some combination thereof. Here, data 402 may include training data, verification data, application-related data, or the like, based, for example, on the stage of deployment of ANN 400. The data 402 may include measurement data, for example measurement of RSs. Pre-processor 404 may be included within ANN 400 in some other implementations. Pre-processor 404 may, for example, process all or a portion of data 402 which may result in some of data 402 being changed, replaced, deleted, etc. In some implementations, pre-processor 404 may add additional data to data 402. In some implementations, the pre-processor 404 may be a ML model, such as an ANN.

ANN 400 includes at least one first layer 408 of artificial neurons 410 to process input data 406 and provide resulting first layer data via connections or “edges” such as edges 412 to at least a portion of at least one second layer 414. Second layer 414 processes data received via edges 412 and provides second layer output data via edges 416 to at least a portion of at least one third layer 418. Third layer 418 processes data received via edges 416 and provides third layer output data via edges 420 to at least a portion of a final layer 422 including one or more neurons to provide output data 424. All or part of output data 424 may be further processed in some manner by (optional) post-processor 426. Thus, in certain examples, ANN 400 may provide output data 428 that is based on output data 424, post-processed data output from post-processor 426, or some combination thereof.

Post-processor 426 may be included within ANN 400 in some other implementations. Post-processor 426 may, for example, process all or a portion of output data 424 which may result in output data 428 being different, at least in part, to output data 424, as result of data being changed, replaced, deleted, etc. In some implementations, post-processor 426 may be configured to add additional data to output data 424. In this example, second layer 414 and third layer 418 represent intermediate or hidden layers that may be arranged in a hierarchical or other like structure. Although not explicitly shown, there may be one or more further intermediate layers between the second layer 414 and the third layer 418. In some implementations, the post-processor 426 may be a ML model, such as an ANN.

The structure and training of artificial neurons 410 in the various layers may be tailored to specific requirements of an application. Within a given layer such as first layer 408, second layer 414, or third layer 418 of ANN 400, some or all of the neurons may be configured to process information provided to the layer and output corresponding transformed information from the layer. For example, transformed information from a layer may represent a weighted sum of the input information associated with or otherwise based on a non-linear activation function or other activation function used to “activate” artificial neurons of a next layer. Artificial neurons in such a layer may be activated by or be responsive to parameters such as the previously described weights and biases of ANN 400. The weights and biases of ANN 400 may be adjusted during a training process or during operation of ANN 400. The weights of the various artificial neurons may control a strength of connections between layers or artificial neurons, while the biases may control a direction of connections between the layers or artificial neurons. An activation function may select or determine whether an artificial neuron transmits its output to the next layer or not in response to its received data.

Different activation functions may be used to model different types of non-linear relationships. By introducing non-linearity into an ML model, an activation function allows the configuration for the ML model to change in response to identifying or detecting complex patterns and relationships in the input data 406. Some non-exhaustive example activation functions include a sigmoid based activation function, a hyperbolic tangent (tanh) based activation function, a convolutional activation function, up-sampling, pooling, and a rectified linear unit (ReLU) based activation function.

Training of an ML model, such as ANN 400, may be conducted using training data. Training data may include one or more datasets which ANN 400 may use to identify patterns or relationships. Training data may represent various types of information, including written, visual, audio, environmental context, operational properties, etc. During training, the parameters (such as the weights and biases) of artificial neurons 410 may be changed, such as to minimize or otherwise reduce a loss function or a cost function. A training process may be repeated multiple times to fine-tune ANN 400 with each iteration.

Various ANN model structures are available for consideration. For example, in a feedforward ANN structure, each artificial neuron 410 in layer 414 receives information from the previous layer (such as, one or more artificial neurons 410 in layer 408) and produces information for the next layer (such as, one or more artificial neurons 410 in layer 418) . In a convolutional ANN structure, some layers may be organized into filters that extract features from data, such as the training data or the input data. In a recurrent ANN structure, some layers may have connections that allow for processing of data across time, such as for processing information having a temporal structure, such as time series data forecasting.

ANN 400 or other ML models may be implemented in various types of processing circuits along with memory and applicable instructions therein. For example, general-purpose hardware circuits, such as, such as one or more central processing units (CPUs) , one or more graphics processing units (GPUs) , or suitable combinations thereof, may be employed to implement a model. In some implementations, one or more tensor processing units (TPUs) , neural processing units (NPUs) , or other special-purpose processors, field-programmable gate arrays (FPGAs) , application-specific integrated circuits (ASICs) , or the like may also be employed. In some implementations, the ML model may be implemented by a NPU or a TPU embedded in a system on chip (SoC) along with other components, such as one or more CPUs, GPUs, etc. A SoC includes several components manufactured on a shared semiconductor substrate. The NPU or TPU may be controlled by the one or more CPUs by configuring the ML model implemented by the NPU or TPU with weights and biases, providing certain training data to the ML model to configure the ML model, or providing input data to the ML model to obtain related inferences. The one or more CPUs may also receive the inferences and be configured to perform certain actions based on the inferences produced by the ML model. The actions performed by the one or more CPUs may include sending commands to other components of the SoC or components external to the SoC to perform certain actions. For example, the CPU may send commands to a RF transceiver based on the outputs or inferences obtained from an ML model to cause the RF transceiver to operate on a wireless network in accordance with the ML model. For example, the UE may be more inclined to use a particular set of spatial filters from the prediction targets that are associated with a better performing metric during DL reception. As another example, the UE may also predict when may the DL transmission arrive (e.g., as part of the prediction result) and adjust its RF transceiver accordingly.

In example aspects, an ML model may be trained prior to, or at some point following, operation of the ML model, such as ANN 400, on input data. When training the ML model, information in the form of applicable training data may be gathered or otherwise created for use in training an ANN accordingly. For example, training data may be gathered or otherwise created regarding information associated with received/transmitted signal strengths, interference, and resource usage data, as well as any other relevant data that might be useful for training a model to address one or more problems or issues in a communication system. In certain instances, all or part of the training data may originate in a user equipment (UE) or other device in a wireless communication system, or one or more network entities, or aggregated from multiple sources (such as a UE and a network entity/entities, one or more other UEs, the Internet, or the like) . As a particular example, during the training stage, reference signals and measured metrics associated with the measurement resources or the prediction targets may be used as input for the model training. Offline training may refer to creating and using a static training dataset, such as, in a batched manner, whereas online training may refer to a real-time collection and use of training data. For example, an ML model at a network device (such as, a UE) may be trained or fine-tuned using online or offline training. For offline training, data collection and training can occur in an offline manner at the network side (such as, at a base station or other network entity) or at the UE side. For online training, the training of a UE- side ML model may be performed locally at the UE or by a server device (such as, a server hosted by a UE vendor) in a real-time or near-real-time manner based on data provided to the server device from the UE (e.g., measurement of RSs, labels calculated using sensors at the UE) . Once an ANN has been configured by setting parameters, including weights and biases, from training data, the ANN’s performance may be evaluated. In some scenarios, evaluation/verification tests may use a validation dataset, which may include data not in the training data, to compare the model’s performance to baseline or other benchmark information. The ANN configuration may be further refined, for example, by changing its architecture, re-training it on the data, or using different optimization techniques, or the like.

As part of a training process, parameters affecting the functioning of the artificial neurons and layers may be adjusted. For example, backpropagation techniques may be used to train an ANN by iteratively adjusting weights or biases of certain artificial neurons associated with errors between a predicted output of the model and a desired output that may be known or otherwise deemed acceptable. Backpropagation may include a forward pass, a loss function, a backward pass, and a parameter update that may be performed in training iteration. The process may be repeated for a certain number of iterations for each set of training data until the weights of the artificial neurons/layers are adequately tuned. Backpropagation techniques associated with a loss function may measure how well a model is able to predict a desired output for a given input. An optimization algorithm may be used during a training process to adjust weights and biases as needed to reduce or minimize the loss function which should improve the performance of the model. There are a variety of optimization algorithms that may be used along with backpropagation techniques or other training techniques. Some initial examples include a gradient descent based optimization algorithm and a stochastic gradient descent based optimization algorithm. A stochastic gradient descent technique may be used to adjust weights/biases in order to minimize or otherwise reduce a loss function. A mini-batch gradient descent technique, which is a variant of gradient descent, may involve updating weights/biases using a small batch of training data rather than the entire dataset. A momentum technique may accelerate an optimization process by adding a momentum term to update or otherwise affect certain weights/biases. An adaptive learning rate technique may adjust a learning rate of an optimization algorithm associated with one or more characteristics of the training data. A batch normalization technique may be used to normalize inputs to a model in order to stabilize a training process and potentially improve the performance of the model. A “dropout” technique may be used to randomly drop out some of the artificial neurons from a model during a training process, for example, in order to reduce overfitting and potentially improve the generalization of the model. An “early stopping” technique may be used to stop an on-going training process early, such as when a performance of the model using a validation dataset starts to degrade. Another example technique includes data augmentation to generate additional training data by applying transformations to all or part of the training information. A transfer learning technique may be used which involves using a pre-trained model as a starting point for training a new model, which may be useful when training data is limited or when there are multiple tasks that are related to each other. A multi-task learning technique may be used which involves training a model to perform multiple tasks simultaneously to potentially improve the performance of the model on one or more of the tasks. Hyperparameters or the like may be input and applied during a training process in certain instances. Another example technique that may be useful with regard to an ANN is a “pruning” technique. A pruning technique, which may be performed during a training process or after a model has been trained, involves the removal of unnecessary or less necessary, or possibly redundant features from a model. In certain instances, a pruning technique may reduce the complexity of a model or improve efficiency of a model without undermining the intended performance of the model. One or more of the example training techniques presented above may be employed as part of a training process. Some example training processes that may be used to train an ANN include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning technique. With supervised learning, a model is trained on a labeled training dataset, where the input data is accompanied by a correct or otherwise acceptable output. With unsupervised learning, a model is trained on an unlabeled training dataset, such that the model will need to learn to identify patterns and relationships in the data without the explicit guidance of a labeled training dataset. With semi-supervised learning, a model is trained using some combination of supervised and unsupervised learning processes, for example, when the amount of labeled data is somewhat limited. With reinforcement learning, a model may learn from interactions with its operation/environment, such as in the form of feedback akin to rewards or penalties. Reinforcement learning may be particularly beneficial when used to improve or attempt to optimize a behavior of a model deployed in a dynamically changing environment, such as a wireless communication network. Distributed, shared, or collaborative learning techniques may be used for the training process. For example, techniques such as federated learning may be used to decentralize the training process and rely on multiple devices, network entities, or organizations for training various versions or copies of a ML model, without relying on a centralized training mechanism. Federated learning may be particularly useful in scenarios where data is sensitive or subject to privacy constraints, or where it is impractical, inefficient, or expensive to centralize data. In the context of wireless communication, for example, federated learning may be used to improve performance by allowing an ANN to be trained on data collected from a wide range of devices and environments. For example, an ANN may be trained on data collected from a large number of wireless devices in a network, such as distributed wireless communication nodes, smartphones, or internet-of-things (IoT) devices, to improve the network's performance and efficiency. With federated learning, a user equipment (UE) or other device may receive a copy of all or part of a global or shared model and perform local training on the local model using locally available training data. The UE may provide update information regarding the locally trained model to one or more other devices (such as a network entity or a server) where the updates from other-like devices (such as other UEs) may be aggregated and used to provide an update to global or shared model. A federated learning process may be repeated iteratively until all or part of a model obtains a satisfactory level of performance. Federated learning may enable devices to protect the privacy and security of local data, while supporting collaboration regarding training and updating of all or part of a shared model.

In some implementations, one or more devices or services may support processes relating to a ML model’s usage, maintenance, activation, reporting, or the like. In certain instances, all or part of a dataset or model may be shared across multiple devices, to provide or otherwise augment or improve processing. In some examples, signaling mechanisms may be utilized at various nodes of wireless network to signal the capabilities for performing specific functions related to ML model, support for specific ML models, capabilities for gathering, creating, transmitting training data, or other ML related capabilities. ML models in wireless communication systems may, for example, be employed to support decisions or improve performance relating to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy savings, or modulation or coding schemes, etc. In some implementations, model deployment may occur jointly or separately at various network levels, such as, a UE, a network entity such as a base station, or a disaggregated network entity such as a central unit (CU) , a distributed unit (DU) , a radio unit (RU) , or the like.

FIG. 5 is an illustrative block diagram of an example ML architecture 500 that may be used for wireless communications in any of the various implementations, processes, environments, networks, or use cases, in accordance with various aspects of the present disclosure. As illustrated, architecture 500 includes multiple logical entities, such as model training host 502, model inference host 504, data source (s) 506, and agent 508. Model inference host 504 is configured to run an ML model based on inference data 512 provided by data source (s) 506. Model inference host 504 may produce output 514, which may include a prediction or inference, such as a discrete or continuous value based on inference data 512, which may then be provided as input to the agent 508. Agent 508 may represent an element or an entity of a wireless communication system including, for example, a radio access network (RAN) , a wireless local area network, a device-to-device (D2D) communications system, etc. As an example, agent 508 may be an UE, such as the UE 104 in FIG. 1. Additionally, agent 508 also may be a type of agent that depends on the type of tasks performed by model inference host 504, the type of inference data 512 provided to model inference host 504, or the type of output 514 produced by model inference host 504. Agent 508 may perform one or more actions associated with receiving output 514 from model inference host 504. For example, if the agent 508 determines to change or modify a transmit or receive beam for a communication between agent 508 and the subject of action 510, agent 508 may adjust reception beam. As an example, agent 508 may be a UE and output 514 from model inference host 504 may one or more predicted channel characteristics for one or more beams. For example, model inference host 504 may predict channel characteristics for a set of beam based on the measurements of another set of beams. Based on the predicted channel characteristics, agent 508, the UE, may send, to the BS, a request to switch to a different beam for communications. In some cases, agent 508 and the subject of action 510 are the same entity. Data can be collected from data sources 506, and may be used as training data 516 for training an ML model, or as inference data 512 for feeding an ML model inference operation. Data sources 506 may collect data from various subject of action 510 entities (such as, the UE or the network entity) , and provide the collected data to a model training host 502 for ML model training. As an example, the data collected may include measured metrics associated with the measurement resources or the prediction targets.

Model training host 502 may be deployed at the same or a different entity than that in which model inference host 504 is deployed. For example, in order to offload model training processing, which can impact the performance of model inference host 504, model training host 502 may be deployed at a model server.

FIG. 6 is an illustrative block diagram 600 of an example ML architecture of first wireless device 602 in communication with second wireless device 604, in accordance with various aspects of the present disclosure. First wireless device 602 may be, or may include, a chip, system on chip (SoC) , chipset, package or device that includes one or more processors, processing blocks or processing elements (collectively “processor 610” ) and one or more memory blocks or elements (collectively “memory 620” ) . Processor 610 may be coupled to transceiver 640, which includes radio frequency (RF) circuitry 642 coupled to antennas 646 via interface 644, for transmitting or receiving signals.

One or more ML models 630 (collectively “ML model 630” ) may be stored in memory 620 and accessible to processor (s) 610. Individual or groups of ML models 630 may be associated with respective model identifiers. In some aspects, different ML models 630, which may optionally be associated with different model identifiers, may have different characteristics. One or more ML models 630 may be selected based on respective features, characteristics, or applications, as well as characteristics or conditions of first wireless device 602 (such as, a power state, a mobility state, a battery reserve, a temperature, etc. ) . For example, ML models 630 may have different inference data and output pairings (such as, different types of inference data produce different types of output) , different levels of accuracies associated with the predictions, different latencies associated with producing the predictions, different ML model sizes, different coefficients, different parameters, or the like.

Processor 610 may deploy ML models 630 to produce respective output data based on input data. For example, the ML models 630 may output predicted metric (s) , such as predicted reference signal received power (RSRP) or other metrics associated with prediction target (s) based on measurements on the measurement resources. In some aspects, model server 650 may perform various ML management tasks for first wireless device 602 and/or second wireless device 604. For example, model server 650 may host various types and/or versions of ML models 630 for first wireless device 602 and/or second wireless device 604 to download. Model server 650 may monitor and evaluate the performance of ML model 630. Model server 650 may transmit signals or provide indications/instructions to activate or deactivate the use of a particular ML model at first wireless device 602 or second wireless device 604. Model server 650 may switch to a different ML model being used at first wireless device 602 or second wireless device 604, and model server 650 may provide such an instruction to the respective first wireless device 602 or second wireless device 604. Model server 650 may operate as a model training host (such as model training host 502) and update ML model 630 using training data. In some cases, the model server 650 may operate as a data source (such as data source 506) to collect and host training data, inference data, performance feedback, etc., associated with ML model 630.

FIG. 7 is a diagram 700 illustrating an example of a UE 706 and a network node 704 configured to perform UE-side AI/ML functionality. A server 702 may be used to store AI/ML functionality, for example AI/ML models that have been trained to perform beam prediction based on measurements of Set-B beams. The server 702 may be an offline server that is trained with respect to different candidate-associated IDs. Each AI/ML trained AI/ML functionality may be associated with a candidate-associated ID that may be used to make a consistent assumption about network-side additional conditions for both training procedures and inference procedures of a UE-side AI/ML functionality. The parameter consistence for the candidate-associated ID may be guaranteed by the network node 704 for both training procedures and inference procedures, allowing the UE 706 to safely make consistent assumptions based on the candidate-associated ID.

The network node 704 may transmit a CSI-report schedule 708 to the UE 706. The UE 706 may receive the CSI-report schedule 708 from the UE 706. The CSI-report schedule 708 may schedule a set of CSI reports 714 to be transmitted by the UE 706 based on AI/ML functionality, for example training an AI/ML functionality or inferring a set of prediction targets based on trained AI/ML functionality. The network node 704 may transmit ID and beam information 710 to the UE 706. The UE 706 may receive the ID and beam information 710 from the UE 706. The ID and beam information 710 may include a candidate-associated ID associated with a consistent assumption by the UE 706 of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The ID and beam information 710 may include indicators of associated Set-A and/or Set-B beams for training, and/or inferring prediction targets using the UE-side AI/ML functionality.

At 712, the UE 706 may retrieve AI/ML functionality saved on the server 702 based on the candidate-associated ID received from the network node 704. The UE 706 may measure a set of Set-B beams (e.g., L1-RSRPs) and may infer predicted targets based on the measurements. The UE 706 may make consistent assumptions about a set of network-side additional conditions when inferring predicted targets using the AI/ML functionality, for example a number and order of measurement RSs. The UE 706 may transmit a set of CSI reports 714 to the network node 704. The set of CSI reports 714 may include feedback beam prediction results calculated based on the AI/ML functionality associated with the candidate-associated ID.

Dynamic configuration of such candidate-associated IDs avoids such IDs from being hard-coded in standards, which further improves the security to prevent malevolent actors from accessing sensitive network-side additional conditions. Infra-vendors may feel more secure in avoiding details on how a specific location's environment is deployed using a particular network infrastructure. By dynamically assigning candidate-associated IDs to networks, networks may enable a UE to make a consistent assumption about a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality without sharing such data OTA. Furthermore, hard-coding such IDs in standards may limit future upgrade capabilities of infrastructures that adopt ever-changing standards.

Some network vendors may wish to avoid OTA or offline coordination with one another, as hardware from one network vendor may not function optimally with hardware from another network vendor. For example, network vendors may not use the same set of network parameters (e.g., same codebook information) , or may not wish to transmit such codebook information to other vendors for security purposes, or to protect proprietary information. Dynamic configuration of candidate-associated IDs may allow a network entity to provide such security with minimum impact on standards. Candidate-associated IDs assigned to one vendor may be designed to be orthogonal to candidate-associated IDs assigned to another vendor, to minimize the chance of disclosing network-side additional conditions of one NW-vendor towards another NW-vendor and improving security between network vendors that operate in overlapping locations. For example, a network entity that configures candidate-associated IDs may configure a first set of candidate-associated IDs for a first set of network nodes associated with a first network vendor, and may configure a second set of candidate-associated IDs for a second set of network nodes associated with a second network vendor, where the first set of candidate-associated IDs are orthogonal to the second set of candidate-associated IDs.

FIG. 8 is a diagram 800 illustrating an example of a plurality of network nodes corresponding with different vendors using candidate-associated IDs to perform UE-side AI/ML functionality with a common UE. The server 802 may include a candidate-associated ID configuration component 198. In other words, the server 802 may be configured to perform aspects in connection with the candidate-associated ID configuration component 198 of FIG. 1. The candidate-associated ID configuration component 198 may be configured to configure candidate-associated IDs to applicable network nodes, for example gNBs that communicate with UEs that perform UE-side AI/ML functionality (or sub-functionality) . The server 802 may include means for configuring a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The server 802 may include means for transmitting an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions. The server 802 may include means for receiving a first vendor ID from the network node and a second vendor ID from a second network node. Configuring the set of candidate-associated IDs may include configuring the set of candidate-associated IDs to be associated with the first vendor ID. The server 802 may include means for configuring a second set of candidate-associated IDs to be orthogonal to the set of candidate-associated IDs in response to the first vendor ID being different than the second vendor ID. The server 802 may include means for configuring the second set of candidate-associated IDs to be associated with the second vendor ID in response to the reception of the second vendor ID. Each of the second set of candidate-associated IDs may be associated with a second consistent assumption by a second UE of a second set of network-side additional conditions for both training and inference procedures of a second UE-side AI/ML functionality. The server 802 may include means for transmitting a second indicator of the configured second set of candidate-associated IDs to the second network node for indicating a second associated set of consistent assumptions. The server 802 may include means for receiving the first vendor ID from a third network node. The server 802 may include means for transmitting the indicator of the configured set of candidate-associated IDs to the third network node in response to the reception of the first vendor ID from the third network node. The server 802 may include means for receiving a first number of requested candidate-associated IDs. The server 802 may include means for configuring the set of candidate-associated IDs by configuring a second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs. The server 802 may include means for receiving a third number of requested candidate-associated IDs from a second network node. The server 802 may include means for receiving the first number of requested candidate-associated IDs by receiving the first number of requested candidate-associated IDs from the network node. The server 802 may include means for configuring the set of candidate-associated IDs further by configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs. The server 802 may include means for receiving a vendor ID from the network node. The server 802 may include means for receiving the vendor ID from the second network node. The server 802 may include means for configuring the second number of the set of candidate-associated IDs comprises: configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs in response to the reception of the vendor ID from both the network node and the second network node. The server 802 may include means for receiving the first number of requested candidate-associated IDs by receiving the first number of requested candidate-associated IDs from a second network node. The server 802 may include means for receiving a vendor ID from the network node. The server 802 may include means for receiving the vendor ID from the second network node. The server 802 may include means for transmitting the indicator of the configured set of candidate-associated IDs to the network node by transmitting the indicator of the configured set of candidate-associated IDs to the network node in response to the reception of the vendor ID from both the network node and the second network node. The server 802 may include means for configuring the set of candidate-associated IDs by configuring the set of candidate-associated IDs based on an initial candidate-associated ID and a number of candidate-associated IDs. The set of candidate-associated IDs may include consecutive integers. The indicator may include the initial candidate-associated ID and the number of candidate-associated IDs. The server 802 may include configuring the set of candidate-associated IDs by configuring the set of candidate-associated IDs based on a randomizer, a randomizer seed, and a number of candidate-associated IDs. The indicator may include the randomizer seed. The set of candidate-associated IDs may include non-consecutive integers. The indicator may include each of the set of candidate-associated IDs. The set of candidate-associated IDs may include a single candidate-associated ID. The server 802 may include configuring the set of candidate-associated IDs by identifying a second consistent assumption associated with the network node. The server 802 may include configuring the set of candidate-associated IDs to be the single candidate-associated ID based on the identified second consistent assumption. The set of network-side additional conditions may include a number of a set of prediction targets for a corresponding UE-side AI/ML functionality. The set of network-side additional conditions may include an order of the set of prediction targets for the corresponding UE-side AI/ML functionality. The set of network-side additional conditions may include an index for the set of prediction targets for the corresponding UE-side AI/ML functionality. The set of network-side additional conditions may include a second indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality. The set of network-side additional conditions may include a third indicator of a QCL relationship associated with at least two of the set of RSs. The set of network-side additional conditions may include a set of temporal parameters associated with the corresponding UE-side AI/ML functionality. The set of UE-side AI/ML functionality may include a first set of beam prediction calculations. The set of UE-side AI/ML functionality may include a second set of positioning calculations. The set of UE-side AI/ML functionality may include a third set of CSI-RS feedback calculations. The means may include the candidate-associated ID configuration component 198 of the server 802 configured to perform the functions recited by the means.

The server 802 may be configured to manage candidate-associated IDs. The server 802 may be an OAM. The set of network nodes 804 may include one or more network nodes associated with a first vendor. The first vendor may be identified by a common vendor ID. The set of network nodes 804 may include at least one candidate-associated ID association component 199. In some aspects, each of the set of network nodes 804 have a candidate-associated ID association component 199. In other aspects, a subset (one or more) of the set of network nodes 804 may have a candidate-associated ID association component 199 configured to assign candidate-associated IDs to others of the set of network nodes 804. In other words, at least one of the set of network nodes 804 may be configured to perform aspects in connection with the candidate-associated ID association component 199 of FIG. 1. The set of network nodes 806 may include one or more network nodes associated with a second vendor different than the first vendor. The second vendor may be identified by a common vendor ID. Similar to the set of network nodes 804, at least one of the set of network nodes 806 may be configured to perform aspects in connection with the candidate-associated ID association component 199 of FIG. 1.

In some aspects, the server 802 may assign a set of candidate-associated IDs to a set of network nodes (e.g., a set of network nodes that are associated with the same vendor ID) and may transmit or broadcast sets of candidate-associated IDs to at least one of the set of network nodes. For example, the server 802 may broadcast a set of candidate-associated IDs 814 along with a vendor ID associated with the set of network nodes 804, and may broadcast a set of candidate-associated IDs 816 along with a vendor ID associated with the set of network nodes 806. The set of network nodes 804 may receive the broadcast of the set of candidate-associated IDs 814 along with a vendor ID associated with the set of network nodes 804 and at least one of the set of network nodes 804 may save the set of candidate-associated IDs 814 for use to transmit to a UE, such as the UE 810, for UE-side AI/ML functionality. The set of network nodes 804 may receive the broadcast of the set of candidate-associated IDs 816 along with a vendor ID associated with the set of network nodes 806 and ignore the broadcast. Similarly, the set of network nodes 806 may receive the broadcast of the set of candidate-associated IDs 816 along with a vendor ID associated with the set of network nodes 806 and at least one of the set of network nodes 806 may save the set of candidate-associated IDs 816 for use to transmit to a UE, such as the UE 810, for UE-side AI/ML functionality. The set of network nodes 806 may receive the broadcast of the set of candidate-associated IDs 814 along with a vendor ID associated with the set of network nodes 804 and ignore the broadcast. The server 802 may configure the set of candidate-associated IDs 814 to be orthogonal to the set of candidate-associated IDs 816.

In some aspects, the server 802 may be configured to respond to a request for a set of candidate-associated IDs from a network node, and, in response to the request, the server 802 may transmit a set of candidate-associated IDs to the network node. For example, at least one of the set of network nodes 804 may signal an indicator of its vendor ID to the server 802. In response, the server 802 may transmit the set of candidate-associated IDs 814 associated with the vendor ID associated with the set of network nodes 804 to at least one of the set of network nodes 804. If any of the set of network nodes 804 similarly transmit an indicator of the same vendor ID to the server 802, the server 802 may transmit the same set of candidate-associated IDs to the requesting network node of the set of network nodes 804.

In some aspects, a request for a set of candidate-associated IDs may include a number of requested IDs. For example, at least one of the set of network nodes 804 may transmit a request to the server 802 that includes a number of requested IDs. In response, the server 802 may configure the set of candidate-associated IDs 814 such that the number of the set of candidate-associated IDs 814 is greater or equal to the requested number. In some aspects, a plurality of the set of network nodes 804 may transmit a request to the server 802 that includes a number of requested IDs. In response, the server 802 may sum up the total requested number of requested IDs, and may configure the set of candidate-associated IDs 814 such that the number of the set of candidate-associated IDs 814 is greater or equal to the sum of each of the requested numbers. In some aspects, other network entities (e.g., others of the set of network nodes 804, other core network entities, or a server operated by the same vendor associated with the set of network nodes 804, such as the server 808) may transmit a request to the server 802 (e.g., via OTA signaling or via offline or backhaul signaling) , signaling a total number of requested IDs along with a vendor ID. In response, the server 802 may configure the set of candidate-associated IDs 814 such that the number of the set of candidate-associated IDs 814 is greater or equal to the requested number received by the other network entity.

In some aspects, the server 802 may configure a contiguous set of candidate-associated IDs (i.e., the associated IDs are consecutive integers) . In such aspects, the server 802 may indicate a set of candidate-associated IDs by transmitting a starting candidate-associated ID. For example, at least one of the set of network nodes 804 may understand that the server 802 is configuring 10 candidate-associated IDs (e.g., may be specified in a standard) , and the server 802 may transmit the first or the last of the set of candidate-associated IDs, enabling the receiving network node of the set of network nodes 804 to calculate the remaining candidate-associated IDs based on the received ID. In some aspects, the server 802 may transmit the set of candidate-associated IDs as a starting ID together with a total number of IDs, which enables the receiving network node of the set of network nodes 804 to calculate the remaining candidate-associated IDs based on the received ID together with the total number of IDs.

In some aspects, the server 802 may configure a non-contiguous set of candidate-associated IDs (i.e., the associated IDs are non-consecutive integers) . For example, the server 802 may configure the set of candidate-associated IDs based on a randomizer, a seed used to seed the randomizer, and a number of IDs to configure. In such aspects, the server 802 may indicate a set of candidate-associated IDs by transmitting at least the seed. In some aspects, the server 802 may also transmit an indicator of the randomizer used, and/or the number of IDs that were configured by the server 802. For example, at least one of the set of network nodes 804 may understand that the server 802 is configuring 10 candidate-associated IDs with a given randomizer (e.g., may be specified in a standard) , and the server 802 may transmit the randomizer seed to at least one of the set of network nodes 804, enabling the receiving network node of the set of network nodes 804 to calculate the candidate-associated IDs based on the received randomizer seed. In other aspects, the server 802 may transmit an indicator of the randomizer used and/or the number of IDs configured along with the randomizer seed, which enables the receiving network node of the set of network nodes 804 to calculate the candidate-associated IDs based on the received data. In other aspects, the server 802 may transmit a set of candidate-associated IDs by transmitting an indicator of the set of candidate-associated IDs themselves, such as an enumerated list of each of the IDs, or an index to such an enumerated list.

The server 808 may be operated by the first vendor. The set of network nodes 804 may use ID determination signaling 818 to determine associated IDs that may be signaled to UEs for making consistent assumptions. For example, the set of network nodes 804 and the server 808 may apply proprietary schemes to determine which of the set of candidate-associated IDs 814 should be used with specified UEs, such as the UE 810. In some aspects, the server 808 may down-select an ID from the set of candidate-associated IDs 814 for the UE 810. The server 808 may be available via a reserved IP address, such that at least one of the set of network nodes 804 may communicate with the server 808 via the ID determination signaling 818 to determine which of the set of candidate-associated IDs 814 to use with the UE 810. At least one of the set of network nodes 804 may transmit the selected ID as the candidate-associated ID 822 to the UE 810 for the UE 810 to make a consistent assumption of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. For example, where the UE 810 identifies that the candidate-associated ID 822 is the same for both a training procedure and an inference procedure, the UE 810 may assume that the set of additional conditions are the same for both the training procedure and the inference procedure. The UE 810 may associate any LCM procedure, for example a training procedure, or an inference procedure, that is associated with the candidate-associated ID 822 with a consistent assumption of the same set of network-side additional conditions, for example a number of Set-A beams (i.e., prediction targets calculated by AI/ML functionality) , a number of Set-B beams (i.e., measured RSs used by AI/ML functionality) , an order of Set-A beams, an order of Set-B beams, indexing of Set-A beams, indexing of Set-B beams, absolute pointing directions, relative pointing directions, beam shapes (i.e., angular specific beam forming gains) , QCL relationships across Set-A beams, QCL relationships within Set-A beams, QCL relationships across Set-B beams, QCL relationships within Set-B beams, and/or temporal parameters (e.g., periodicity of beams, target future occasions for temporal prediction) .

The server 812 may be operated by the second vendor. The set of network nodes 806 may use ID determination signaling 820 to determine associated IDs that may be signaled to UEs for making consistent assumptions. For example, the set of network nodes 806 and the server 812 may apply proprietary schemes to determine which of the set of candidate-associated IDs 816 should be used with specified UEs, such as the UE 810. In some aspects, the server 812 may down-select an ID from the set of candidate-associated IDs 816 for the UE 810. The server 812 may be available via a reserved IP address, such that at least one of the set of network nodes 806 may communicate with the server 812 via the ID determination signaling 820 to determine which of the set of candidate-associated IDs 816 to use with the UE 810. At least one of the set of network nodes 806 may transmit the selected ID as the candidate-associated ID 824 to the UE 810 for the UE 810 to make a consistent assumption of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. For example, where the UE 810 identifies that the candidate-associated ID 822 is the same for both a training procedure and an inference procedure, the UE 810 may assume that the set of additional conditions are the same for both the training procedure and the inference procedure. The UE 810 may associate any LCM procedure, for example a training procedure, or an inference procedure, that is associated with the candidate-associated ID 824 with a consistent assumption of the same set of network-side additional conditions, for example a number of Set-A beams (i.e., prediction targets calculated by AI/ML functionality) , a number of Set-B beams (i.e., measured RSs used by AI/ML functionality) , an order of Set-A beams, an order of Set-B beams, indexing of Set-A beams, indexing of Set-B beams, absolute pointing directions, relative pointing directions, beam shapes (i.e., angular specific beam forming gains) , QCL relationships across Set-A beams, QCL relationships within Set-A beams, QCL relationships across Set-B beams, QCL relationships within Set-B beams, and/or temporal parameters (e.g., periodicity of beams, target future occasions for temporal prediction) .

The candidate-associated ID 822 may be orthogonal to the candidate-associated ID 824, minimizing the chances of accidentally letting sensitive vendor information leak via communications with the UE 810. Use of the candidate-associated ID 822 may also minimize the likelihood of disclosing proprietary information to either the UE 810 or other network vendors, as the UE 810 may make consistent assumptions without receiving signaling explicitly disclosing network-side additional conditions.

In some aspects, a server that configures candidate-associated IDs may be from the same vendor as a set of network nodes. For example, the set of network nodes 804, the set of network nodes 806, and the server 802 may all belong to the same infra-vendor. In such aspects, the vendor ID may not be transmitted between the set of network nodes 804, the set of network nodes 806, and the server 802, as they all know they belong to the same vendor. The server 802 may directly configure a single associated ID to each of the set of network nodes 804 and the set of network nodes 806 for a particular AI/ML functionality instead of a range. For example, the server 802 may know the base configuration for one of the set of network nodes 804, and thus may identify which consistent assumption applies to the one of the set of network nodes 804, and may configure a single candidate-associated ID for the one of the set of network nodes 804 based on its knowledge of the set of network-side additional conditions. The server 802 may not configure the set of network nodes 804 and the set of network nodes 806 to be orthogonal to one another if both of the set of network nodes 804 and the set of network nodes 806 belong to a common vendor ID.

FIG. 9 is a connection flow diagram 900 illustrating an example of a plurality of network nodes configured to use candidate-associated IDs to perform UE-side AI/ML functionality. The network entity 902 may be a server that may be configured to manage candidate-associated IDs, such as an OAM entity. The network node 904 may be a base station, a TRP, a gNB, or an NG-RAN entity configured to ensure consistent assumptions with respect to a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality using a candidate-associated ID. The network node 906 may be a base station, a TRP, a gNB, or an NG-RAN entity configured to ensure consistent assumptions with respect to a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality using a candidate-associated ID. The UE 908 may be a UE configured to make consistent assumptions for a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality using a candidate-associated ID.

The network node 904 may transmit an indicator of a vendor ID 910 to the network entity 902. The network entity 902 may receive the indicator of the vendor ID 910 from the network node 904. The indicator may include an index to the vendor ID 910. The vendor ID 910 may identify a vendor that the network node 904 is associated with. In some aspects, the network node 904 may periodically transmit the indicator of the vendor ID 910, for example as a periodic broadcast.

The network node 906 may transmit an indicator of a vendor ID 912 to the network entity 902. The network entity 902 may receive the indicator of the vendor ID 912 from the network node 906. The indicator may include an index to the vendor ID 912. The vendor ID 912 may identify a vendor that the network node 906 is associated with. In some aspects, the network node 906 may periodically transmit the indicator of the vendor ID 912, for example as a periodic broadcast.

The network node 904 may transmit a request 914 for a number of candidate-associated IDs to the network entity 902. The network entity 902 may receive the request 914 from the network node 904. The request 914 may include an indicator of a request from the network node 904 for the network entity 902 to configure a set of candidate associated IDs for the network node 904. In some aspects, the request 914 may include the vendor ID 910, such that the network node 904 does not transmit a separate signal that includes the vendor ID 910. In some aspects, the request 914 may include an indicator of a number of candidate-associated IDs for the network entity 902 to configure (e.g., 10 IDs, 20 IDs, 100 IDs) . In some aspects, the request 914 may not include a vendor ID.

The network node 906 may transmit a request 916 for a number of candidate-associated IDs to the network entity 902. The network entity 902 may receive the request 916 from the network node 906. The request 916 may include an indicator of a request from the network node 906 for the network entity 902 to configure a set of candidate associated IDs for the network node 906. In some aspects, the request 916 may include the vendor ID 910, such that the network node 906 does not transmit a separate signal that includes the vendor ID 910. In some aspects, the request 916 may include an indicator of a number of candidate-associated IDs for the network entity 902 to configure (e.g., 10 IDs, 20 IDs, 100 IDs) . In some aspects, the request 916 may not include a vendor ID.

At 918, the network entity 902 may configure a set of candidate-associated IDs based on the received transmissions. For example, the network entity 902 may configure the set of candidate-associated IDs 922 based on the vendor ID 910 and/or the request 914. Similarly, the network entity 902 may configure the set of candidate-associated IDs 920 based on the vendor ID 912 and/or the request 916.

If the vendor ID 910 and the vendor ID 912 are the same, then at 918 the network entity 902 may configure a set of candidate-associated IDs for both the network node 904 and the network node 906. If the request 914 includes a number of requested IDs and the request 916 does not include a number of requested IDs, the network entity 902 may configure a number of the set of candidate-associated IDs for both the network node 904 and the network node 906 that is greater or equal to the requested number in the request 914. If the request 916 includes a number of requested IDs and the request 914 does not include a number of requested IDs, the network entity 902 may configure a number of the set of candidate-associated IDs for both the network node 904 and the network node 906 that is greater or equal to the requested number in the request 916. If the request 914 and the request 916 includes a number of requested IDs, the network entity 902 may configure a number of the set of candidate-associated IDs for both the network node 904 and the network node 906 that is greater or equal to the larger of the numbers transmitted in the request 914 and the request 916 (greater or equal to the greatest number requested by network nodes having a common vendor ID) , or may configure a number that is greater or equal to the sum of both numbers transmitted in the requests. In some aspects, another network entity, for example a different network node or a server associated with the same vendor ID (e.g., network entity operated by the same vendor) , may request a number of candidate-associated IDs, and the network entity 902 may configure a number of the set of candidate-associated IDs for both the network node 904 and the network node 906 that is greater or equal to the number requested by the other entity. In such aspects, the network entity 902 may not receive requests from the network node 904 or the network node 906. In aspects where the vendor ID 910 and the vendor ID 912 are the same, the set of candidate-associated IDs 920 and the set of candidate-associated IDs 922 may be the same set. In aspects where the vendor for the network entity 902 is the same as the vendor for the network node 904 and the network node 906, then the network node 904 and the network node 906 may not transmit their vendor ID, as the network entity 902 already knows which vendor is associated with the network node 904 and the network node 906. At 918, the network entity 902 may directly configure a single associated ID for the network node 904 and a single associated ID for the network node 906 rather than a range, as the network entity 902 knows the sets of network-side additional conditions associated with each respective network node.

If the vendor ID 910 and the vendor ID 912 are different, then at 918 the network entity 902 may configure the set of candidate-associated IDs 922 to be orthogonal to the set of candidate-associated IDs 920. If the request 914 includes a number of requested IDs, the network entity 902 may configure a number of the set of candidate-associated IDs 922 for the network node 904 to be greater or equal to the requested number in the request 914. If the request 916 includes a number of requested IDs, the network entity 902 may configure a number of the set of candidate-associated IDs 920 for the network node 906 to be greater or equal to the requested number in the request 916. In some aspects, another network entity, for example a different network node or a server associated with the same vendor ID (e.g., network entity operated by the same vendor) , may request a number of candidate-associated IDs for the network node 904 or the network node 906, and the network entity 902 may configure a number of the set of candidate-associated IDs for the respective network node to be greater or equal to the number requested by the other entity. In aspects where the vendor for the network entity 902 is the same as the vendor for the network node 904 or the network node 906, then the respective network node may not transmit its vendor ID, as the network entity 902 already knows which vendor is associated with the respective network node. At 918, the network entity 902 may directly configure a single associated ID for the respective network node rather than a range, as the network entity 902 knows the sets of network-side additional conditions associated with the respective network node.

The network entity 902 may transmit the set of candidate-associated IDs 922 to the network node 904 based on the configuration at 918. The network node 904 may receive the set of candidate-associated IDs 922 from the network entity 902. At 924, the network node 904 may configure a set of UE-side AI/ML functionality based on at least one of the set of candidate-associated IDs 922 received from the network entity 902. The network node 904 may transmit a configuration and ID 928 to the UE 908 for performing UE-side AI/ML functionality. The configuration and ID 928 may include the candidate-associated ID configured at 924 for the UE 908 to use for a consistent assumption of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality performed by the UE 908 (e.g., training or inferring of a predicted target) . In some aspects, the configuration and ID 928 may include a configuration for the UE 908 to perform an AI/ML functionality, for example a training of a beam prediction model, an inference using a beam prediction model, a training of a positioning model, an inference using a positioning model, a training of a CSI-RS feedback model, or an inference using a CSI-RS feedback model.

The network entity 902 may transmit the set of candidate-associated IDs 922 to the network node 906 based on the configuration at 918. The network node 906 may receive the set of candidate-associated IDs 922 from the network entity 902. At 926, the network node 906 may configure a set of UE-side AI/ML functionality based on at least one of the set of candidate-associated IDs 922 received from the network entity 902. The network node 906 may transmit a configuration and ID 930 to the UE 908 for performing UE-side AI/ML functionality. The configuration and ID 930 may include the candidate-associated ID configured at 926 for the UE 908 to use for a consistent assumption of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality performed by the UE 908 (e.g., training or inferring of a predicted target) . In some aspects, the configuration and ID 930 may include a configuration for the UE 908 to perform an AI/ML functionality, for example a training of a beam prediction model, an inference using a beam prediction model, a training of a positioning model, an inference using a positioning model, a training of a CSI-RS feedback model, or an inference using a CSI-RS feedback model.

At 932, the UE 908 may perform AI/ML functionality using the corresponding candidate-associated ID received from the network node 904 and the network node 906, respectively. For example, the UE 908 may perform AI/ML functionality based on the configuration &ID 928 received from the network node 904, and may make consistent assumptions that the set of network-side additional conditions for both training and inference procedures of the AI/ML functionality are the same whenever the same candidate-associated ID is used. The UE 908 may learn such network-side conditions during training, for example a number and ordering of beams, or beam shapes used during the training of the AI/ML functionality. Similarly, the UE 908 may perform AI/ML functionality based on the configuration &ID 930 received from the network node 906, and may make consistent assumptions that the set of network-side additional conditions for both training and inference procedures of the AI/ML functionality are the same whenever the same candidate-associated ID is used. The UE 908 may learn such network-side conditions during training, for example a number and ordering of beams, or beam shapes used during the training of the AI/ML functionality. The relative candidate-associated IDs may be orthogonal to one another if the vendor ID of the network node 904 is different from the vendor ID of the network node 906, or may be non-orthogonal to one another if the vendor ID is the same for both the network node 904 and the network node 906.

FIG. 10 is a flowchart 1000 of a method of wireless communication. The method may be performed by a network entity (e.g., the server 104, the server 802, the network entity 902) . At 1002, the network entity may configure a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. For example, 1002 may be performed by the network entity 902 in FIG. 9, which may, at 918, configure a set of candidate-associated IDs, such as the set of candidate-associated IDs 920 and the set of candidate-associated IDs 922. Each of the set of candidate-associated IDs configured at 918 may be associated with a consistent assumption by a UE (e.g., at least one ID may be used by the UE 908) of a set of network-side additional conditions (e.g., conditions associated with the network node 904 based on the ID of the configuration and ID 928, conditions associated with the network node 906 based on the ID of the configuration and ID 930) for both training and inference procedures of a UE-side AI/ML functionality (e.g., the functionality performed at 932) . Moreover, 1002 may be performed by the component 198 in FIGs. 1 and 8.

At 1004, the network entity may transmit an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions. For example, 1004 may be performed by the network entity 902 in FIG. 9, which may transmit the set of candidate-associated IDs 922 to the network node 904 for indicating an associated set of consistent assumptions. The set of candidate-associated IDs 922 may include an indicator of at least one of the set of candidate-associated IDs configured at 918. Moreover, 1004 may be performed by the component 198 in FIGs. 1 and 8.

FIG. 11 is a flowchart 1100 of a method of wireless communication. The method may be performed by a network node (e.g., the base station 102, the base station 310; a network node of the set of network nodes 804, a network node of the set of network nodes 806; the network node 904, the network node 906; the network entity 1202, the network entity 1302, the network entity 1460) . At 1102, the network node may receive a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. For example, 1102 may be performed by the network node 904 in FIG. 9, which may receive the set of candidate-associated IDs 922 from the network entity 902. The set of candidate-associated IDs 922 may include a first indicator of at least one of the set of candidate-associated IDs configured at 918. Each of the set of candidate-associated IDs 922 may be associated with a consistent assumption by a UE (e.g., at least one ID may be used by the UE 908) of a set of network-side additional conditions (e.g., conditions associated with the network node 904 based on the ID of the configuration and ID 928, conditions associated with the network node 906 based on the ID of the configuration and ID 930) for both training and inference procedures of a UE-side AI/ML functionality (e.g., the functionality performed at 932) . Moreover, 1102 may be performed by the component 199 in FIGs. 1, 3, 8, 13, or 14.

At 1104, the network node may configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. For example, 1104 may be performed by the network node 904 in FIG. 9, which may, at 924, configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID (e.g., the ID of the configuration &ID 928) of the set of candidate-associated IDs, a corresponding set of network-side additional conditions (e.g., conditions at the network node 904 associated with the ID of the configuration &ID 928) , and a corresponding UE-side AI/ML functionality (e.g., the configuration of the UE configured at 924) . Moreover, 1104 may be performed by the component 199 in FIGs. 1, 3, 8, 13, or 14.

At 1106, the network node may transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. For example, 1106 may be performed by the network node 904 in FIG. 9, which may transmit the configuration &ID 928 to the UE 908. The configuration &ID 928 may include a second indicator of the configured UE-side AI/ML functionality configuration (configured at 924) and a third indicator of the corresponding candidate-associated ID to the UE 908. Moreover, 1106 may be performed by the component 199 in FIGs. 1, 3, 8, 13, or 14.

FIG. 12 is a diagram 1200 illustrating an example of a hardware implementation for an apparatus 1204. The apparatus 1204 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1204 may include at least one cellular baseband processor 1224 (also referred to as a modem) coupled to one or more transceivers 1222 (e.g., cellular RF transceiver) . The cellular baseband processor (s) 1224 may include at least one on-chip memory 1224'. In some aspects, the apparatus 1204 may further include one or more subscriber identity modules (SIM) cards 1220 and at least one application processor 1206 coupled to a secure digital (SD) card 1208 and a screen 1210. The application processor (s) 1206 may include on-chip memory 1206'. In some aspects, the apparatus 1204 may further include a Bluetooth module 1212, a WLAN module 1214, an SPS module 1216 (e.g., GNSS module) , one or more sensor modules 1218 (e.g., barometric pressure sensor /altimeter; motion sensor such as inertial measurement unit (IMU) , gyroscope, and/or accelerometer (s) ; light detection and ranging (LIDAR) , radio assisted detection and ranging (RADAR) , sound navigation and ranging (SONAR) , magnetometer, audio and/or other technologies used for positioning) , additional memory modules 1226, a power supply 1230, and/or a camera 1232. The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX) ) . The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include their own dedicated antennas and/or utilize the antennas 1280 for communication. The cellular baseband processor (s) 1224 communicates through the transceiver (s) 1222 via one or more antennas 1280 with the UE 104 and/or with an RU associated with a network entity 1202. The cellular baseband processor (s) 1224 and the application processor (s) 1206 may each include a computer-readable medium /memory 1224', 1206', respectively. The additional memory modules 1226 may also be considered a computer-readable medium /memory. Each computer-readable medium /memory 1224', 1206', 1226 may be non-transitory. The cellular baseband processor (s) 1224 and the application processor (s) 1206 are each responsible for general processing, including the execution of software stored on the computer-readable medium /memory. The software, when executed by the cellular baseband processor (s) 1224 /application processor (s) 1206, causes the cellular baseband processor (s) 1224 /application processor (s) 1206 to perform the various functions described supra. The cellular baseband processor (s) 1224 and the application processor (s) 1206 are configured to perform the various functions described supra based at least in part of the information stored in the memory. That is, the cellular baseband processor (s) 1224 and the application processor (s) 1206 may be configured to perform a first subset of the various functions described supra without information stored in the memory and may be configured to perform a second subset of the various functions described supra based on the information stored in the memory. The computer-readable medium /memory may also be used for storing data that is manipulated by the cellular baseband processor (s) 1224 /application processor (s) 1206 when executing software. The cellular baseband processor (s) 1224 /application processor (s) 1206 may be a component of the UE 350 and may include the at least one memory 360 and/or at least one of the TX processor 368, the RX processor 356, and the controller/processor 359. In one configuration, the apparatus 1204 may be at least one processor chip (modem and/or application) and include just the cellular baseband processor (s) 1224 and/or the application processor (s) 1206, and in another configuration, the apparatus 1204 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1204.

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

As discussed supra, the component 199 may be configured to receive a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The component 199 may be configured to configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The component 199 may be configured to transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. The component 199 may be within one or more processors of one or more of the CU 1310, DU 1330, and the RU 1340. The component 199 may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes/algorithm individually or in combination. The network entity 1302 may include a variety of components configured for various functions. In one configuration, the network entity 1302 may include means for receiving a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-AI/ML functionality. The network entity 1302 may include means for configuring a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The network entity 1302 may include means for transmitting a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. The network entity 1302 may include means for transmitting a first vendor ID associated with the network node before the reception of the first indicator of the set of candidate-associated IDs. The set of candidate-associated IDs may be orthogonal to a second set of candidate-associated IDs. The network entity 1302 may include means for transmitting a first number of requested candidate-associated IDs before the reception of the first indicator of the set of candidate-associated IDs. A second number of the set of candidate-associated IDs may be greater or equal to the transmitted first number of requested candidate-associated IDs. The set of candidate-associated IDs may include consecutive integers. The first indicator may include an initial candidate-associated ID and a number of candidate-associated IDs associated with the set of candidate-associated IDs. The network entity 1302 may include means for calculating the consecutive integers of the set of candidate-associated IDs based on the initial candidate-associated ID and the number of candidate-associated IDs. The first indicator may include a randomizer seed. The network entity 1302 may include means for calculating the set of candidate-associated IDs based on the randomizer seed. The set of candidate-associated IDs may include non-consecutive integers. The first indicator may include each of the set of candidate-associated IDs. The set of candidate-associated IDs may include a single candidate-associated ID. The network entity 1302 may include means for receiving the first indicator of the set of candidate-associated IDs by receiving the first indicator of the set of candidate-associated IDs from an OAM entity. The network entity 1302 may include a base station. The network entity 1302 may include a TRP. The network entity 1302 may be a gNB. The network entity 1302 may be an NG-RAN entity. The corresponding UE-side AI/ML functionality may include a first set of beam prediction calculations. The corresponding UE-side AI/ML functionality may include a second set of positioning calculations. The corresponding UE-side AI/ML functionality may include a third set of CSI-RS feedback calculations. The UE-side AI/ML functionality configuration may include a first configuration for training the UE-side AI/ML functionality. The UE-side AI/ML functionality configuration may include a second configuration for calculating a prediction target based on the UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a number of a set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include an order of the set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include an index for the set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a fourth indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a fifth indicator of a QCL relationship associated with at least two of the set of RSs. The corresponding set of network-side additional conditions may include a set of temporal parameters associated with the corresponding UE-side AI/ML functionality. The means may be the component 199 of the network entity 1302 configured to perform the functions recited by the means. As described supra, the network entity 1302 may include the TX processor 316, the RX processor 370, and the controller/processor 375. As such, in one configuration, the means may be the TX processor 316, the RX processor 370, and/or the controller/processor 375 configured to perform the functions recited by the means.

FIG. 14 is a diagram 1400 illustrating an example of a hardware implementation for a network entity 1460. In one example, the network entity 1460 may be within the core network 120. The network entity 1460 may include at least one network processor 1412. The network processor (s) 1412 may include on-chip memory 1412'. In some aspects, the network entity 1460 may further include additional memory modules 1414. The network entity 1460 communicates via the network interface 1480 directly (e.g., backhaul link) or indirectly (e.g., through a RIC) with the CU 1402. The on-chip memory 1412' and the additional memory modules 1414 may each be considered a computer-readable medium /memory. Each computer-readable medium /memory may be non-transitory. The network processor (s) 1412 is responsible for general processing, including the execution of software stored on the computer-readable medium /memory. The software, when executed by the corresponding processor (s) causes the processor (s) to perform the various functions described supra. The computer-readable medium /memory may also be used for storing data that is manipulated by the processor (s) when executing software.

As discussed supra, the component 199 may be configured to receive a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-side AI/ML functionality. The component 199 may be configured to configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The component 199 may be configured to transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. The component 199 may be within the network processor (s) 1412. The component 199 may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by one or more processors configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes/algorithm individually or in combination. The network entity 1460 may include a variety of components configured for various functions. In one configuration, the network entity 1460 may include means for receiving a first indicator of a set of candidate-associated IDs. Each of the set of candidate-associated IDs may be associated with a consistent assumption by a UE of a set of network-side additional conditions for both training and inference procedures of a UE-AI/ML functionality. The network entity 1460 may include means for configuring a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality. The network entity 1460 may include means for transmitting a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE. The network entity 1460 may include means for transmitting a first vendor ID associated with the network node before the reception of the first indicator of the set of candidate-associated IDs. The set of candidate-associated IDs may be orthogonal to a second set of candidate-associated IDs. The network entity 1460 may include means for transmitting a first number of requested candidate-associated IDs before the reception of the first indicator of the set of candidate-associated IDs. A second number of the set of candidate-associated IDs may be greater or equal to the transmitted first number of requested candidate-associated IDs. The set of candidate-associated IDs may include consecutive integers. The first indicator may include an initial candidate-associated ID and a number of candidate-associated IDs associated with the set of candidate-associated IDs. The network entity 1460 may include means for calculating the consecutive integers of the set of candidate-associated IDs based on the initial candidate-associated ID and the number of candidate-associated IDs. The first indicator may include a randomizer seed. The network entity 1460 may include means for calculating the set of candidate-associated IDs based on the randomizer seed. The set of candidate-associated IDs may include non-consecutive integers. The first indicator may include each of the set of candidate-associated IDs. The set of candidate-associated IDs may include a single candidate-associated ID. The network entity 1460 may include means for receiving the first indicator of the set of candidate-associated IDs by receiving the first indicator of the set of candidate-associated IDs from an OAM entity. The network entity 1460 may include a base station. The network entity 1460 may include a TRP. The network entity 1460 may be a gNB. The network entity 1460 may be an NG-RAN entity. The corresponding UE-side AI/ML functionality may include a first set of beam prediction calculations. The corresponding UE-side AI/ML functionality may include a second set of positioning calculations. The corresponding UE-side AI/ML functionality may include a third set of CSI-RS feedback calculations. The UE-side AI/ML functionality configuration may include a first configuration for training the UE-side AI/ML functionality. The UE-side AI/ML functionality configuration may include a second configuration for calculating a prediction target based on the UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a number of a set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include an order of the set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include an index for the set of prediction targets for the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a fourth indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality. The corresponding set of network-side additional conditions may include a fifth indicator of a QCL relationship associated with at least two of the set of RSs. The corresponding set of network-side additional conditions may include a set of temporal parameters associated with the corresponding UE-side AI/ML functionality. The means may be the component 199 of the network entity 1460 configured to perform the functions recited by the means.

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

FIG. 15 is a diagram 1500 illustrating an example of a UE 1506 and a network node 1504 configured to perform UE-side AI/ML functionality. A network entity 1502, such as an OAM, may be configured to register a candidate-associated ID. The candidate-associated ID may be associated with a consistent assumption by the UE 1506 of a set of network-side additional conditions of the network node 1504 for both training and inference procedures of a UE-side AI/ML functionality. The network node 1504 and the network entity 1502 may exchange candidate-associated ID registration signals 1508 to configure a candidate-associated ID. For example, the network node 1504 may transmit a request to the network entity 1502, and the network entity 1502 may transmit a set of candidate-associated IDs to the network node 1504 in response to the request.

The network node 1504 may configure training of AI/ML functionality at the UE 1506, for example a configuration for the UE 1506 to train an AI/ML model to calculate CSI feedback based on a set of CSI-RS received and measured by the UE 1506. The network node 1504 may transmit a configuration &ID 1510 to the UE 1506. The UE 1506 may receive the configuration &ID 1510 from the network node 1504. The configuration &ID 1510 may include a configuration for the UE 908 to train an AI/ML functionality, for example a training of a beam prediction model, a training of a positioning model, or a training of a CSI-RS feedback model. The configuration &ID 1510 may include the candidate-associate ID registered with the network entity 1502. At 1512, the UE 1506 may train AI/ML functionality based on the configuration of the configuration &ID 1510. The UE 1506 may save a set of network-side additional conditions, and may associate those network-side additional conditions with the candidate-associate ID. The network-side additional conditions may include, for example, a number/ordering/indexing of Set-A or Set-B beams, absolute/relative pointing directions associated with the Set-A/B beams, QCL relationships across/within the Set-A/B beams, and/or temporal parameters of the Set-A/B beams. The UE 1506 may transmit a set of training reports 1514 to the network node 1504. The set of training reports 1514 may indicate that the UE 1506 associates the candidate-associated ID with a consistent assumption by the UE 1506 of the saved set of network-side additional conditions of the network node 1504 for both training and inference procedures of the UE-side AI/ML functionality trained at 1512.

The network node 1504 may configure an inference by the UE 1506 based on the AI/ML functionality trained at 1512, for example a configuration for the UE 1506 to infer a set of outputs using an AI/ML model trained at 1512 to calculate CSI feedback based on a set of CSI-RS received and measured by the UE 1506. The network node 1504 may transmit a configuration &ID 1516 to the UE 1506. The UE 1506 may receive the configuration &ID 1516 from the network node 1504. The configuration &ID 1516 may include a configuration for the UE 908 to infer a set of outputs based on the AI/ML functionality trained at 1512, for example, inferring a set of outputs based on a trained beam prediction model, a trained positioning model, or a trained CSI-RS feedback model. The configuration &ID 1516 may include the candidate- associate ID used to train the AI/ML functionality at 1512. The network entity 1502 may guarantee network-side parameter consistency for the same candidate-associate ID between data collection/training and inference. At 1518, the UE 1506 may infer a set of outputs based on the trained AI/ML functionality. The UE 1506 may infer the set of outputs based on the saved set of network-side additional conditions (e.g., at least a subset of the conditions may be used as inputs to an AI/ML model, or may be used to calculate an input to the AI/ML model) . The UE 1506 may transmit a set of feedback reports 1520 to the network node 1504. The set of feedback reports 1520 may include, for example, at least some of the calculated set of outputs, or a set of information calculated based on at least some of the calculated set of outputs.

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

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

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

Aspect 1 is a method of wireless communication at a network entity, comprising: configuring a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; and transmitting an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.

Aspect 2 is the method of aspect 1, further comprising: receiving a first vendor ID from the network node and a second vendor ID from a second network node, wherein configuring the set of candidate-associated IDs comprises configuring the set of candidate-associated IDs to be associated with the first vendor ID; configuring a second set of candidate-associated IDs to be orthogonal to the set of candidate-associated IDs in response to the first vendor ID being different than the second vendor ID and configuring the second set of candidate-associated IDs to be associated with the second vendor ID, wherein each of the second set of candidate-associated IDs is associated with a second consistent assumption by a second UE of a second set of network-side additional conditions for both training and inference procedures of a second UE-side AI/ML functionality; and transmitting a second indicator of the configured second set of candidate-associated IDs to the second network node for indicating a second associated set of consistent assumptions.

Aspect 3 is the method of aspect 2, further comprising: receiving the first vendor ID from a third network node; and transmitting the indicator of the configured set of candidate-associated IDs to the third network node in response to the reception of the first vendor ID from the third network node.

Aspect 4 is the method of any of aspects 1 to 3, further comprising: receiving a first number of requested candidate-associated IDs, wherein configuring the set of candidate-associated IDs comprises: configuring a second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs.

Aspect 5 is the method of aspect 4, further comprising: receiving a third number of requested candidate-associated IDs from a second network node, wherein receiving the first number of requested candidate-associated IDs comprises: receiving the first number of requested candidate-associated IDs from the network node, wherein configuring the set of candidate-associated IDs further comprises: configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs.

Aspect 6 is the method of aspect 5, further comprising: receiving a vendor ID from the network node; and receiving the vendor ID from the second network node, wherein configuring the second number of the set of candidate-associated IDs comprises: configuring the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs in response to the reception of the vendor ID from both the network node and the second network node.

Aspect 7 is the method of any of aspects 4 to 6, wherein receiving the first number of requested candidate-associated IDs comprises: receiving the first number of requested candidate-associated IDs from a second network node.

Aspect 8 is the method of aspect 7, further comprising: receiving a vendor ID from the network node; and receiving the vendor ID from the second network node, wherein transmitting the indicator of the configured set of candidate-associated IDs to the network node comprises: transmitting the indicator of the configured set of candidate-associated IDs to the network node in response to the reception of the vendor ID from both the network node and the second network node.

Aspect 9 is the method of any of aspects 1 to 8, wherein configuring the set of candidate-associated IDs comprises: configuring the set of candidate-associated IDs based on an initial candidate-associated ID and a number of candidate-associated IDs, wherein the set of candidate-associated IDs comprises consecutive integers, wherein the indicator comprises the initial candidate-associated ID and the number of candidate-associated IDs.

Aspect 10 is the method of any of aspects 1 to 9, wherein configuring the set of candidate-associated IDs comprises: configuring the set of candidate-associated IDs based on a randomizer, a randomizer seed, and a number of candidate-associated IDs.

Aspect 11 is the method of aspect 10, wherein the indicator comprises the randomizer seed.

Aspect 12 is the method of any of aspects 1 to 11, wherein the set of candidate-associated IDs comprises non-consecutive integers, wherein the indicator comprises each of the set of candidate-associated IDs.

Aspect 13 is the method of any of aspects 1 to 12, wherein the set of candidate-associated IDs comprises a single candidate-associated ID.

Aspect 14 is the method of aspect 13, wherein configuring the set of candidate-associated IDs comprises: identifying a second consistent assumption associated with the network node; and configuring the set of candidate-associated IDs to comprise the single candidate-associated ID based on the identified second consistent assumption.

Aspect 15 is the method of any of aspects 1 to 14, wherein the set of network-side additional conditions comprise at least one of: a number of a set of prediction targets for a corresponding UE-side AI/ML functionality; an order of the set of prediction targets for the corresponding UE-side AI/ML functionality; an index for the set of prediction targets for the corresponding UE-side AI/ML functionality; a second indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality; a third indicator of a quasi-co-location (QCL) relationship associated with at least two of the set of RSs; or a set of temporal parameters associated with the corresponding UE-side AI/ML functionality.

Aspect 16 is the method of any of aspects 1 to 15, wherein the network entity comprises an operations, administration, and maintenance (OAM) entity.

Aspect 17 is the method of any of aspects 1 to 16, wherein the network node comprises at least one of a base station, a transmission reception point (TRP) , a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity.

Aspect 18 is the method of any of aspects 1 to 17, wherein the set of UE-side AI/ML functionality comprises at least one of: a first set of beam prediction calculations; a second set of positioning calculations; or a third set of channel state information (CSI) reference signal (CSI-RS) feedback calculations. A beam prediction calculation may include beam prediction and reporting processes. A CSI-RS feedback calculation may include CSI compression and feedback processes.

Aspect 19 is a method of wireless communication at a network node, comprising: receiving a first indicator of a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; configuring a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality; and transmitting a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE.

Aspect 20 is the method of aspect 19, further comprising: transmitting a first vendor ID associated with the network node before the reception of the first indicator of the set of candidate-associated IDs, wherein the set of candidate-associated IDs are orthogonal to a second set of candidate-associated IDs.

Aspect 21 is the method of either of aspects 19 or 20, further comprising: transmitting a first number of requested candidate-associated IDs before the reception of the first indicator of the set of candidate-associated IDs, wherein a second number of the set of candidate-associated IDs is greater or equal to the transmitted first number of requested candidate-associated IDs.

Aspect 22 is the method of any of aspects 19 to 21, wherein the set of candidate-associated IDs comprises consecutive integers, wherein the first indicator comprises an initial candidate-associated ID and a number of candidate-associated IDs associated with the set of candidate-associated IDs, further comprising: calculating the consecutive integers of the set of candidate-associated IDs based on the initial candidate-associated ID and the number of candidate-associated IDs.

Aspect 23 is the method of any of aspects 19 to 22, wherein the first indicator comprises a randomizer seed, further comprising: calculating the set of candidate-associated IDs based on the randomizer seed.

Aspect 24 is the method of any of aspects 19 to 23, wherein the set of candidate-associated IDs comprises non-consecutive integers, wherein the first indicator comprises each of the set of candidate-associated IDs.

Aspect 25 is the method of any of aspects 19 to 24, wherein the set of candidate-associated IDs comprises a single candidate-associated ID.

Aspect 26 is the method of any of aspects 19 to 25, wherein receiving the first indicator of the set of candidate-associated IDs comprises: receiving the first indicator of the set of candidate-associated IDs from an operations, administration, and maintenance (OAM) entity.

Aspect 27 is the method of any of aspects 19 to 26, wherein the network node comprises at least one of a base station, a transmission reception point (TRP) , a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity.

Aspect 28 is the method of any of aspects 19 to 27, wherein the corresponding UE-side AI/ML functionality comprises at least one of: a first set of beam prediction calculations; a second set of positioning calculations; or a third set of channel state information (CSI) reference signal (CSI-RS) feedback calculations.

Aspect 29 is the method of any of aspects 19 to 28, wherein the UE-side AI/ML functionality configuration comprises at least one of a first configuration for training the UE-side AI/ML functionality or a second configuration for calculating a prediction target based on the UE-side AI/ML functionality.

Aspect 30 is the method of any of aspects 19 to 29, wherein the corresponding set of network-side additional conditions comprise at least one of: a number of a set of prediction targets for the corresponding UE-side AI/ML functionality; an order of the set of prediction targets for the corresponding UE-side AI/ML functionality; an index for the set of prediction targets for the corresponding UE-side AI/ML functionality; a fourth indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality; a fifth indicator of a quasi-co-location (QCL) relationship associated with at least two of the set of RSs; or a set of temporal parameters associated with the corresponding UE-side AI/ML functionality.

Aspect 31 is an apparatus for wireless communication, comprising: at least one memory; and at least one processor coupled to the at least one memory and, based at least in part on information stored in the at least one memory, the at least one processor, individually or in any combination, is configured to perform the method of any of aspects 1 to 30.

Aspect 32 is an apparatus for wireless communication, comprising means for performing each step in the method of any of aspects 1 to 30.

Aspect 33 is the apparatus of any of aspects 1 to 30, further comprising a transceiver (e.g., functionally connected to the at least one processor of Aspect 31) configured to receive or to transmit in association with the method of any of aspects 1 to 30.

Aspect 34 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code, the code when executed by at least one processor causes the at least one processor, individually or in any combination, to perform the method of any of aspects 1 to 30.

Claims

An apparatus for wireless communication at a network entity, comprising:

at least one memory; and

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

configure a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; and

transmit an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to:

receive a first vendor ID from the network node and a second vendor ID from a second network node, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to configure the set of candidate-associated IDs to be associated with the first vendor ID;

configure a second set of candidate-associated IDs to be orthogonal to the set of candidate-associated IDs in response to the first vendor ID being different than the second vendor ID and configure the second set of candidate-associated IDs to be associated with the second vendor ID, wherein each of the second set of candidate-associated IDs is associated with a second consistent assumption by a second UE of a second set of network-side additional conditions for both training and inference procedures of a second UE-side AI/ML functionality; and

transmit a second indicator of the configured second set of candidate-associated IDs to the second network node for indicating a second associated set of consistent assumptions.
The apparatus of claim 2, wherein the at least one processor, individually or in any combination, is further configured to:

receive the first vendor ID from a third network node; and

transmit the indicator of the configured set of candidate-associated IDs to the third network node in response to reception of the first vendor ID from the third network node.
The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to:

receive a first number of requested candidate-associated IDs, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

configure a second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs.
The apparatus of claim 4, wherein the at least one processor, individually or in any combination, is further configured to:

receive a third number of requested candidate-associated IDs from a second network node, wherein, to receive the first number of requested candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

receive the first number of requested candidate-associated IDs from the network node, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is further configured to:

configure the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs.
The apparatus of claim 5, wherein the at least one processor, individually or in any combination, is further configured to:

receive a vendor ID from the network node; and

receive the vendor ID from the second network node, wherein, to configure the second number of the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

configure the second number of the set of candidate-associated IDs to be greater or equal to the received first number of requested candidate-associated IDs plus the received third number of requested candidate-associated IDs in response to reception of the vendor ID from both the network node and the second network node.
The apparatus of claim 4, wherein, to receive the first number of requested candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

receive the first number of requested candidate-associated IDs from a second network node.
The apparatus of claim 7, wherein the at least one processor, individually or in any combination, is further configured to:

receive a vendor ID from the network node; and

receive the vendor ID from the second network node, wherein, to transmit the indicator of the configured set of candidate-associated IDs to the network node, the at least one processor, individually or in any combination, is configured to:

transmit the indicator of the configured set of candidate-associated IDs to the network node in response to reception of the vendor ID from both the network node and the second network node.
The apparatus of claim 1, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

configure the set of candidate-associated IDs based on an initial candidate-associated ID and a number of candidate-associated IDs, wherein the set of candidate-associated IDs comprises consecutive integers, wherein the indicator comprises the initial candidate-associated ID and the number of candidate-associated IDs.
The apparatus of claim 1, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

configure the set of candidate-associated IDs based on a randomizer, a randomizer seed, and a number of candidate-associated IDs.
The apparatus of claim 10, wherein the indicator comprises the randomizer seed.
The apparatus of claim 1, wherein the set of candidate-associated IDs comprises non-consecutive integers, wherein the indicator comprises each of the set of candidate-associated IDs.
The apparatus of claim 1, wherein the set of candidate-associated IDs comprises a single candidate-associated ID.
The apparatus of claim 13, wherein, to configure the set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

identify a second consistent assumption associated with the network node; and

configure the set of candidate-associated IDs to comprise the single candidate-associated ID based on the identified second consistent assumption.
The apparatus of claim 1, wherein the set of network-side additional conditions comprise at least one of:

a number of a set of prediction targets for a corresponding UE-side AI/ML functionality;

an order of the set of prediction targets for the corresponding UE-side AI/ML functionality;

an index for the set of prediction targets for the corresponding UE-side AI/ML functionality;

a second indicator of a first spatial filter associated with a first RS of a set of RSs associated with the corresponding UE-side AI/ML functionality;

a third indicator of a quasi-co-location (QCL) relationship associated with at least two of the set of RSs; or

a set of temporal parameters associated with the corresponding UE-side AI/ML functionality.
The apparatus of claim 1, wherein the network entity comprises an operations, administration, and maintenance (OAM) entity, and wherein the network node comprises at least one of a base station, a transmission reception point (TRP) , a next generation node B (gNB) , or a new generation (NG) radio access network (NG-RAN) entity.
The apparatus of claim 1, further comprising at least one transceiver coupled to the at least one processor, wherein, to transmit the indicator of the configured set of candidate-associated IDs, the at least one processor, individually or in any combination, is configured to:

transmit, via the at least one transceiver, the indicator of the configured set of candidate-associated IDs.
The apparatus of claim 1, wherein the set of UE-side AI/ML functionality comprises at least one of:

a first set of beam prediction calculations;

a second set of positioning calculations; or

a third set of channel state information (CSI) reference signal (CSI-RS) feedback calculations.
An apparatus for wireless communication at a network node, comprising:

at least one memory; and

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

receive a first indicator of a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality;

configure a UE-side AI/ML functionality configuration based on a corresponding candidate-associated ID of the set of candidate-associated IDs, a corresponding set of network-side additional conditions, and a corresponding UE-side AI/ML functionality; and

transmit a second indicator of the configured UE-side AI/ML functionality configuration and a third indicator of the corresponding candidate-associated ID to a corresponding UE.
A method of wireless communication at a network entity, comprising:

configuring a set of candidate-associated identifiers (IDs) , wherein each of the set of candidate-associated IDs is associated with a consistent assumption by a user equipment (UE) of a set of network-side additional conditions for both training and inference procedures of a UE-side artificial intelligence machine learning (AI/ML) functionality; and

transmitting an indicator of the configured set of candidate-associated IDs to a network node for indicating an associated set of consistent assumptions.