WO2024213187A1

WO2024213187A1 - Indication of network side additional condition

Info

Publication number: WO2024213187A1
Application number: PCT/CN2024/106232
Authority: WO
Inventors: Yinghao ZHANG; Bingchao LIU; Hongmei Liu; Haiming Wang; Jianfeng Wang
Original assignee: Lenovo (Beijing) Limited
Priority date: 2024-07-18
Filing date: 2024-07-18
Publication date: 2024-10-17

Abstract

Various aspects of the present disclosure relate to consistency of network side additional condition. In an aspect, a UE receives, via the transceiver, a configuration comprising a first indication for a network side additional condition. And the UE transmits, via the transceiver, a beam report based on the configuration. The beam report is determined based on the first indication.

Description

INDICATION OF NETWORK SIDE ADDITIONAL CONDITION

TECHNICAL FIELD

The present disclosure relates to wireless communications, and more specifically to a user equipment, a base station, processors, methods for indicating a network (NW) -side additional condition, especially for ensuring consistency of NW-side additional condition for an artificial intelligence (AI) /machine learning (ML) model or an AI/ML functionality.

BACKGROUND

A wireless communications system may include one or multiple network communication devices, such as base stations, which may be otherwise known as an eNodeB (eNB) , a next-generation NodeB (gNB) , or other suitable terminology. Each network communication devices, such as a base station may support wireless communications for one or multiple user communication devices, which may be otherwise known as user equipment (UE) , or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing resources of the wireless communication system (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers) . Additionally, the wireless communications system may support wireless communications across various radio access technologies including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, among other suitable radio access technologies beyond 5G (e.g., sixth generation (6G) ) .

The 3rd Generation Partnership Project (3GPP) is working on the study of ensuring consistency of NW-side additional condition between model training and inference for AI/ML model (s) or functionality (ies) . Some methods are proposed but no touch details.

SUMMARY

The present disclosure relates to devices, methods, and apparatuses that support indicating a NW-side additional condition, especially ensuring consistency of NW-side additional condition. By performing the related process of present disclosure, the consistency of NW-side additional condition with a first indication can be supported.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a configuration comprising a first indication for a network side additional condition; and transmitting, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

In some implementations of the method and apparatuses described herein, the first indication is associated with at least one functionality or model of an artificial intelligence (AI) /machine learning (ML) .

In some implementations of the method and apparatuses described herein, the first indication identifies at least one of following network side additional conditions: a downlink (DL) transmit (Tx) beam codebook; at least one DL spatial domain transmission filter corresponding to beams in a first beam set and a second beam set for an AI/ML-enabled feature or feature group (FG) ; a relationship of the first beam set and the second beam set; an order of resources for the first beam set; an order of resources for the second beam set; DL transmission power; an antenna height of a base station; or network side beam shape information.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, an indication of reporting at least one applicable functionality or model of the UE associated with the first indication; and transmitting, via the transceiver, information of the at least one applicable functionality or model based on the configuration.

In some implementations of the method and apparatuses described herein, the configuration comprises a second indication which is determined from a set of first indications supported by the UE.

Some implementations of the method and apparatuses described herein may further include transmitting, via the transceiver, a report of the set of first indications supported by the UE and at least one functionality or model related to the set of first indications.

In some implementations of the method and apparatuses described herein, the network side additional condition is applied to a configuration level; and a network side additional condition for the configuration level is consistent with the network side additional condition associated with the first indication.

In some implementations of the method and apparatuses described herein, the configuration level comprises one of the following: a level of channel state information (CSI) report setting, a level of CSI resource setting, a level of reference signal (RS) resource set, or a level of RS resource.

In some implementations of the method and apparatuses described herein, the beam report is determined based on a functionality or model among the at least one functionality or model, and the functionality or model is determined based on performance monitoring on the at least one functionality or model.

In some implementations of the method and apparatuses described herein, the performance monitoring is performed based on a performance monitoring configuration comprising at least one of the following: a type of performance metric; a target performance; or a duration for monitoring in which one or more pairs of RS resources for a first beam set and RS resources for a second beam set are transmitted, wherein the first beam set and the second beam set are associated with the AI/ML-enabled feature or FG.

Some implementations of the method and apparatuses described herein may further include reporting, via the transceiver, a result of the performance monitoring.

In some implementations of the method and apparatuses described herein, the reporting of the result is based on a CSI report configuration associated with the first indication.

In some implementations of the method and apparatuses described herein, a report quantity for monitoring is set such that the UE reports the result of the performance monitoring in a CSI report.

In some implementations of the method and apparatuses described herein, the reporting of the result of the performance monitoring is based on at least one resource configured by a base station.

In some implementations of the method and apparatuses described herein, the at least one resource comprises at least one scheduling request (SR) resource. And some implementations of the method and apparatuses described herein may further include based on detecting at least one functionality or model is valid based on the performance monitoring, transmitting, via the transceiver, a positive SR on the at least one scheduling request SR resource, and receiving, via the transceiver, scheduling information of at least one physical uplink shared channel (PUSCH) resource, wherein the result of the performance monitoring is reported in the scheduled at least one PUSCH resource.

Some implementations of the method and apparatuses described herein may further include based on detecting no functionality or model is valid based on the performance monitoring, transmitting a negative SR on the at least one scheduling request SR resource; and determining to fall back to non-AI/ML beam management.

In some implementations of the method and apparatuses described herein, the at least one resource comprises at least one physical uplink control channel (PUCCH) resources which correspond to at least one functionalities or models; wherein the result of the performance monitoring is reported on one PUCCH resource corresponding to the at least one functionalities or models; or the result of performance monitoring is reported on a plurality of PUCCH resources corresponding to a plurality of functionalities or models, respectively.

In some implementations of the method and apparatuses described herein, the result of the performance monitoring comprises at least one of the following: an indication of a functionality or model life cycle management (LCM) decision comprising functionality or model switching, functionality or model update, or fallback to non-AI/ML beam management; an indication that at least one functionality or model is applicable in the network side additional condition indicated by the first indication; an indication of which functionality or model is applicable in the network side additional condition indicated by the first indication; or an indication of a suggested functionality or model.

Some implementations of the method and apparatuses described herein may further include transmitting, via the transceiver, a request for the performance monitoring.

In some implementations of the method and apparatuses described herein, the request for the performance monitoring comprises at least one of the following: the first indication, or at least one preferred RS resource configuration required by at least one functionality or model.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a configuration for performance monitoring on at least one functionality or model; and reporting, via the transceiver, a result of the performance monitoring.

In some implementations of the method and apparatuses described herein, the performance monitoring configuration comprises at least one of the following: a type of performance metric; a target performance; or a duration for monitoring in which one or more pairs of RS resources for a first beam set and RS resources for a second beam set are transmitted, wherein the first beam set and the second beam set are associated with a AI/ML-enabled feature or FG.

In some implementations of the method and apparatuses described herein, the at least one resource comprises at least one physical uplink control channel (PUCCH) resources which correspond to at least one functionalities or models; wherein the result of the performance monitoring is reported on one PUCCH resource corresponding the at least one functionalities or models; or the result of performance monitoring is reported on a plurality of PUCCH resources corresponding to a plurality of functionalities or models, respectively.

In some implementations of the method and apparatuses described herein, the result of the performance monitoring comprises at least one of the following: an indication of a functionality or model life cycle management (LCM) decision comprising functionality or model switching, functionality or model update, or fallback to non-AI/ML beam management; or an indication of a suggested functionality or model.

In some implementations of the method and apparatuses described herein, the request for the performance monitoring comprises at least one preferred RS resource configuration required by at least one functionality or model.

Some implementations of the method and apparatuses described herein may further include transmitting, via the transceiver, a configuration comprising a first indication for a network side additional condition; and receiving, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

In some implementations of the method and apparatuses described herein, the first indication identifies at least one of following network side additional conditions: a downlink (DL) transmit (Tx) beam codebook; at least one DL spatial domain transmission filter corresponding to beams in a first beam set and a second beam set for a AI/ML-enabled feature or FG; a relationship of the first beam set and the second beam set; an order of resources for the first beam set; an order of resources for the second beam set; DL transmission power; an antenna height of a base station; or network side beam shape information.

Some implementations of the method and apparatuses described herein may further include transmitting, via the transceiver, an indication of reporting at least one applicable functionality or model of the UE associated with the first indication; and receiving, via the transceiver, information of the at least one applicable functionality or model based on the configuration.

In some implementations of the method and apparatuses described herein, the configuration comprises a second indication which is determined from a set of first indications supported by a UE.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, the set of first indications supported by the UE and at least one functionality or model related to the set of first indications from another base station.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a result of the performance monitoring.

In some implementations of the method and apparatuses described herein, the at least one resource comprises at least one scheduling request (SR) resource. And some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a positive SR on the at least one scheduling request SR resource in case of the UE detecting at least one functionality or model is valid based on the performance monitoring, and transmitting, via the transceiver, scheduling information of at least one physical uplink shared channel (PUSCH) resource, wherein the result of the performance monitoring is received in the scheduled at least one PUSCH resource.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a negative SR on the at least one scheduling request SR resource in case of the UE detecting no functionality or model is valid based on the performance monitoring; and determining to fall back to non-AI/ML beam management.

Some implementations of the method and apparatuses described herein may further include receiving, via the transceiver, a request for the performance monitoring.

Some implementations of the method and apparatuses described herein may further include transmitting, via the transceiver, a configuration for performance monitoring on at least one functionality or model; and receiving, via the transceiver, a result of the performance monitoring.

In some implementations of the method and apparatuses described herein, the receiving of the result of the performance monitoring is based on at least one resource configured by a base station.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a wireless communications system that supports AI/ML for communication in accordance with aspects of the present disclosure.

FIG. 2 illustrates an example of signaling flow that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 3 illustrates an example of a procedure that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 4 illustrates an example of a basic associated ID assignment in accordance with aspects of the present disclosure.

FIG. 5 illustrates an example of basic associated IDs with NW-side additional conditions in accordance with aspects of the present disclosure.

FIGS. 6A-6C illustrate examples of signaling flow that indicates the basic associated ID in accordance with aspects of the present disclosure.

FIG. 7 illustrates an example of reporting performance monitoring in accordance with aspects of the present disclosure.

FIG. 8 illustrates an example of a device that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 9 illustrates an example of a device that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 10 illustrates an example of a processor that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 11 illustrates an example of a processor that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 12 illustrates flowchart of method that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

FIG. 13 illustrates flowchart of method that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

Principles of the present disclosure will now be described with reference to some embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein may be implemented in various manners other than the ones described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

References in the present disclosure to “one embodiment, ” “an example embodiment, ” “an embodiment, ” “some embodiments, ” and the like indicate that the embodiment (s) described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment (s) . Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

It shall be understood that although the terms “first” and “second” or the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could also be termed as a second element, and similarly, a second element could also be termed as a first element, without departing from the scope of embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a” , “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” , “comprising” , “has” , “having” , “includes” and/or “including” , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof.

As used herein, the term “communication network” refers to a network following any suitable communication standards, such as, 5G NR, long term evolution (LTE) , LTE-advanced (LTE-A) , wideband code division multiple access (WCDMA) , high-speed packet access (HSPA) , narrow band internet of things (NB-IoT) , and so on. Further, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will also be future type communication technologies and systems in which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned systems.

As used herein, the term “network device” generally refers to a node in a communication network via which a terminal device can access the communication network and receive services therefrom. The network device may refer to a base station (BS) or an access point (AP) , for example, a node B (NodeB or NB) , a radio access network (RAN) node, an evolved NodeB (eNodeB or eNB) , a NR NB (also referred to as a gNB) , a remote radio unit (RRU) , a radio header (RH) , an infrastructure device for a V2X (vehicle-to- everything) communication, a transmission and reception point (TRP) , a reception point (RP) , a remote radio head (RRH) , a relay, an integrated access and backhaul (IAB) node, a low power node such as a femto BS, a pico BS, and so forth, depending on the applied terminology and technology.

As used herein, the term “terminal device” generally refers to any end device that may be capable of wireless communications. By way of example rather than a limitation, a terminal device may also be referred to as a communication device, a user equipment (UE) , an end user device, a subscriber station (SS) , an unmanned aerial vehicle (UAV) , a portable subscriber station, a mobile station (MS) , or an access terminal (AT) . The terminal device may include, but is not limited to, a mobile phone, a cellular phone, a smart phone, a voice over IP (VoIP) phone, a wireless local loop phone, a tablet, a wearable terminal device, a personal digital assistant (PDA) , a portable computer, a desktop computer, an image capture terminal device such as a digital camera, a gaming terminal device, a music storage and playback appliance, a vehicle-mounted wireless terminal device, a wireless endpoint, a mobile station, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , a USB dongle, a smart device, wireless customer-premises equipment (CPE) , an internet of things (loT) device, a watch or other wearable, a head-mounted display (HMD) , a vehicle, a drone, a medical device (for example, a remote surgery device) , an industrial device (for example, a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts) , a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. In the following description, the terms: “terminal device, ” “communication device, ” “terminal, ” “user equipment” and “UE, ” may be used interchangeably.

3GPP is working on the study on ensuring consistency of NW-side additional condition between model training and inference. For an AI/ML-enabled feature/feature group (FG) , additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI/ML-enabled feature/FG. Additional conditions can be divided into two categories: NW-side additional condition and UE-side additional condition. For inference of UE-side models, NW-side additional condition is invisible for UE which causes UE is not able to identify an appropriate AI model trained with the NW-side additional condition. Thus, it is an essential issue for model inference at UE side that ensuring consistency of NW-additional condition between model training and inference.

In release 18 of 3GPP, some methods are provided by companies but no touch details. In release 19 of 3GPP, some agreements is achieved for this topic. Based on the agreements, two methods to ensuring consistency of NW-side additional condition for AI beam prediction is proposed. One is associated ID based method and another is performance monitoring-based method. But there are disadvantages for the two methods, respectively.

For performance monitoring-based method, different models may require different RS configurations. If all supported models need to be monitored for selecting a valid model, the RS overhead for monitoring is huge. On the other hand, UE has to consume huge budget to perform monitoring on all its supported AI models, and there is a high delay from triggering the monitoring to applying the valid model.

For associated ID-based methods, models of different UE vendors vary in their generalization abilities and have different requirement on NW-side additional conditions. On one hand, if an ID is linked with a coarse additional conditions which corresponds to several scenarios/configurations. In this case, it requires strong generalization capability of trained model so that the model can applied in these scenarios/configurations. However, the ID is not sufficient for weaker generalization model. On the other hand, finer granularity leads to huge overhead to maintain the associated ID across NW vendors or cells. Signaling overhead for ID exchange is non-negligible, especially for per cell group ID or even global ID. If the associated ID applies across cell, there are so many scenarios/configurations. Assigning associated ID for each scenario/configuration requires a large number of IDs. Before AI functionality activation, supported IDs would be aligned between UE and gNB, which leads to high signaling overhead. Even within a same cell, the NW-side additional condition changes across time. Even there is minor change of NW-side additional condition, the ID would be updated and aligned between UE and gNB. In addition, there is also proprietary information disclosure risks for private NW configuration.

Some embodiments of the present application provide detailed solutions of ensuring consistency of NW-side additional condition between model training and inference. For example, a first indication is linked with a NW-side additional condition when data collection for model training. When model inference, the first indication is indicated to UE for determining a group of candidate models in UE. Then UE performs monitoring on the group of candidate models to select a valid model.

Aspects of the present disclosure are described in the context of a wireless communications system.

FIG. 1 illustrates an example of a wireless communications system 100 that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure. The wireless communications system 100 may include one or more network entities 102 (also referred to as network equipment (NE) ) , one or more UEs 104, a core network 106, and a packet data network 108. The wireless communications system 100 may support various radio access technologies. In some implementations, the wireless communications system 100 may be a 4G network, such as an LTE network or an LTE-Advanced (LTE-A) network. In some other implementations, the wireless communications system 100 may be a 5G network, such as an NR network. In other implementations, the wireless communications system 100 may be a combination of a 4G network and a 5G network, or other suitable radio access technology including Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi) , IEEE 802.16 (WiMAX) , IEEE 802.20. The wireless communications system 100 may support radio access technologies beyond 5G. Additionally, the wireless communications system 100 may support technologies, such as time division multiple access (TDMA) , frequency division multiple access (FDMA) , or code division multiple access (CDMA) , etc.

The one or more network entities 102 may be dispersed throughout a geographic region to form the wireless communications system 100. One or more of the network entities 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a radio access network (RAN) , a base transceiver station, an access point, a NodeB, an eNodeB (eNB) , a next-generation NodeB (gNB) , or other suitable terminology. A network entity 102 and a UE 104 may communicate via a communication link 110, which may be a wireless or wired connection. For example, a network entity 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.

A network entity 102 may provide a geographic coverage area 112 for which the network entity 102 may support services (e.g., voice, video, packet data, messaging, broadcast, etc. ) for one or more UEs 104 within the geographic coverage area 112. For example, a network entity 102 and a UE 104 may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc. ) according to one or multiple radio access technologies. In some implementations, a network entity 102 may be moveable, for example, a satellite associated with a non-terrestrial network. In some implementations, different geographic coverage areas 112 associated with the same or different radio access technologies may overlap, but the different geographic coverage areas 112 may be associated with different network entities 102. Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

The one or more UEs 104 may be dispersed throughout a geographic region of the wireless communications system 100. A UE 104 may include or may be referred to as a mobile device, a wireless device, a remote device, a remote unit, a handheld device, or a subscriber device, or some other suitable terminology. In some implementations, the UE 104 may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE 104 may be referred to as an Internet-of-Things (IoT) device, an Internet-of-Everything (IoE) device, or machine-type communication (MTC) device, among other examples. In some implementations, a UE 104 may be stationary in the wireless communications system 100. In some other implementations, a UE 104 may be mobile in the wireless communications system 100.

The one or more UEs 104 may be devices in different forms or having different capabilities. Some examples of UEs 104 are illustrated in FIG. 1. A UE 104 may be capable of communicating with various types of devices, such as the network entities 102, other UEs 104, or network equipment (e.g., the core network 106, the packet data network 108, a relay device, an integrated access and backhaul (IAB) node, or another network equipment) , as shown in FIG. 1. Additionally, or alternatively, a UE 104 may support communication with other network entities 102 or UEs 104, which may act as relays in the wireless communications system 100.

A UE 104 may also be able to support wireless communication directly with other UEs 104 over a communication link 114. For example, a UE 104 may support wireless communication directly with another UE 104 over a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link 114 may be referred to as a sidelink. For example, a UE 104 may support wireless communication directly with another UE 104 over a PC5 interface.

A network entity 102 may support communications with the core network 106, or with another network entity 102, or both. For example, a network entity 102 may interface with the core network 106 through one or more backhaul links 116 (e.g., via an S1, N2, N2, or another network interface) . The network entities 102 may communicate with each other over the backhaul links 116 (e.g., via an X2, Xn, or another network interface) . In some implementations, the network entities 102 may communicate with each other directly (e.g., between the network entities 102) . In some other implementations, the network entities 102 may communicate with each other or indirectly (e.g., via the core network 106) . In some implementations, one or more network entities 102 may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC) . An ANC may communicate with the one or more UEs 104 through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or transmission-reception points (TRPs) .

In some implementations, a network entity 102 may be configured in a disaggregated architecture, which may be configured to utilize a protocol stack physically or logically distributed among two or more network entities 102, such as an integrated access backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance) , or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN) ) . For example, a network entity 102 may include one or more of a central unit (CU) , a distributed unit (DU) , a radio unit (RU) , a RAN Intelligent Controller (RIC) (e.g., a Near-Real Time RIC (Near-RT RIC) , a Non-Real Time RIC (Non-RT RIC) ) , a Service Management and Orchestration (SMO) system, or any combination thereof.

An RU may also be referred to as a radio head, a smart radio head, a remote radio head (RRH) , a remote radio unit (RRU) , or a transmission reception point (TRP) . One or more components of the network entities 102 in a disaggregated RAN architecture may be co-located, or one or more components of the network entities 102 may be located in distributed locations (e.g., separate physical locations) . In some implementations, one or more network entities 102 of a disaggregated RAN architecture may be implemented as virtual units (e.g., a virtual CU (VCU) , a virtual DU (VDU) , a virtual RU (VRU) ) .

Split of functionality between a CU, a DU, and an RU may be flexible and may support different functionalities depending upon which functions (e.g., network layer functions, protocol layer functions, baseband functions, radio frequency functions, and any combinations thereof) are performed at a CU, a DU, or an RU. For example, a functional split of a protocol stack may be employed between a CU and a DU such that the CU may support one or more layers of the protocol stack and the DU may support one or more different layers of the protocol stack. In some implementations, the CU may host upper protocol layer (e.g., a layer 3 (L3) , a layer 2 (L2) ) functionality and signaling (e.g., Radio Resource Control (RRC) , service data adaption protocol (SDAP) , Packet Data Convergence Protocol (PDCP) ) . The CU may be connected to one or more DUs or RUs, and the one or more DUs or RUs may host lower protocol layers, such as a layer 1 (L1) (e.g., physical (PHY) layer) or an L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160.

Additionally, or alternatively, a functional split of the protocol stack may be employed between a DU and an RU such that the DU may support one or more layers of the protocol stack and the RU may support one or more different layers of the protocol stack. The DU may support one or multiple different cells (e.g., via one or more RUs) . In some implementations, a functional split between a CU and a DU, or between a DU and an RU may be within a protocol layer (e.g., some functions for a protocol layer may be performed by one of a CU, a DU, or an RU, while other functions of the protocol layer are performed by a different one of the CU, the DU, or the RU) .

A CU may be functionally split further into CU control plane (CU-CP) and CU user plane (CU-UP) functions. A CU may be connected to one or more DUs via a midhaul communication link (e.g., F1, F1-c, F1-u) , and a DU may be connected to one or more RUs via a fronthaul communication link (e.g., open fronthaul (FH) interface) . In some implementations, a midhaul communication link or a fronthaul communication link may be implemented in accordance with an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities 102 that are in communication via such communication links.

The core network 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The core network 106 may be an evolved packet core (EPC) , or a 5G core (5GC) , which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME) , an access and mobility management functions (AMF) ) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW) , a Packet Data Network (PDN) gateway (P-GW) , or a user plane function (UPF) ) . In some implementations, the control plane entity may manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management (e.g., data bearers, signal bearers, etc. ) for the one or more UEs 104 served by the one or more network entities 102 associated with the core network 106.

The core network 106 may communicate with the packet data network 108 over one or more backhaul links 116 (e.g., via an S1, N2, N2, or another network interface) . The packet data network 108 may include an application server 118. In some implementations, one or more UEs 104 may communicate with the application server 118. A UE 104 may establish a session (e.g., a protocol data unit (PDU) session, or the like) with the core network 106 via a network entity 102. The core network 106 may route traffic (e.g., control information, data, and the like) between the UE 104 and the application server 118 using the established session (e.g., the established PDU session) . The PDU session may be an example of a logical connection between the UE 104 and the core network 106 (e.g., one or more network functions of the core network 106) .

In the wireless communications system 100, the network entities 102 and the UEs 104 may use resources of the wireless communications system 100 (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers) ) to perform various operations (e.g., wireless communications) . In some implementations, the network entities 102 and the UEs 104 may support different resource structures. For example, the network entities 102 and the UEs 104 may support different frame structures. In some implementations, such as in 4G, the network entities 102 and the UEs 104 may support a single frame structure. In some other implementations, such as in 5G and among other suitable radio access technologies, the network entities 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures) . The network entities 102 and the UEs 104 may support various frame structures based on one or more numerologies.

One or more numerologies may be supported in the wireless communications system 100, and a numerology may include a subcarrier spacing and a cyclic prefix. A first numerology (e.g., μ=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a normal cyclic prefix. In some implementations, the first numerology (e.g., μ=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., μ=1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., μ=2) may be associated with a third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., μ=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., μ=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

A time interval of a resource (e.g., a communication resource) may be organized according to frames (also referred to as radio frames) . Each frame may have a duration, for example, a 10 millisecond (ms) duration. In some implementations, each frame may include multiple subframes. For example, each frame may include 10 subframes, and each subframe may have a duration, for example, a 1 ms duration. In some implementations, each frame may have the same duration. In some implementations, each subframe of a frame may have the same duration.

Additionally or alternatively, a time interval of a resource (e.g., a communication resource) may be organized according to slots. For example, a subframe may include a number (e.g., quantity) of slots. The number of slots in each subframe may also depend on the one or more numerologies supported in the wireless communications system 100. For instance, the first, second, third, fourth, and fifth numerologies (i.e., μ=0, μ=1, μ=2, μ=3, μ=4) associated with respective subcarrier spacings of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz may utilize a single slot per subframe, two slots per subframe, four slots per subframe, eight slots per subframe, and 16 slots per subframe, respectively. Each slot may include a number (e.g., quantity) of symbols (e.g., OFDM symbols) . In some implementations, the number (e.g., quantity) of slots for a subframe may depend on a numerology. For a normal cyclic prefix, a slot may include 14 symbols. For an extended cyclic prefix (e.g., applicable for 60 kHz subcarrier spacing) , a slot may include 12 symbols. The relationship between the number of symbols per slot, the number of slots per subframe, and the number of slots per frame for a normal cyclic prefix and an extended cyclic prefix may depend on a numerology. It should be understood that reference to a first numerology (e.g., μ=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

In the wireless communications system 100, an electromagnetic (EM) spectrum may be split, based on frequency or wavelength, into various classes, frequency bands, frequency channels, etc. By way of example, the wireless communications system 100 may support one or multiple operating frequency bands, such as frequency range designations FR1 (410 MHz –7.125 GHz) , FR2 (24.25 GHz –52.6 GHz) , FR3 (7.125 GHz –24.25 GHz) , FR4 (52.6 GHz –114.25 GHz) , FR4a or FR4-1 (52.6 GHz –71 GHz) , and FR5 (114.25 GHz –300 GHz) .

In some implementations, the network entities 102 and the UEs 104 may perform wireless communications over one or more of the operating frequency bands. In some implementations, FR1 may be used by the network entities 102 and the UEs 104, among other equipment or devices for cellular communications traffic (e.g., control information, data) . In some implementations, FR2 may be used by the network entities 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.

FR1 may be associated with one or multiple numerologies (e.g., at least three numerologies) . For example, FR1 may be associated with a first numerology (e.g., μ=0) , which includes 15 kHz subcarrier spacing; a second numerology (e.g., μ=1) , which includes 30 kHz subcarrier spacing; and a third numerology (e.g., μ=2) , which includes 60 kHz subcarrier spacing. FR2 may be associated with one or multiple numerologies (e.g., at least 2 numerologies) . For example, FR2 may be associated with a third numerology (e.g., μ=2) , which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., μ=3) , which includes 120 kHz subcarrier spacing.

FIG. 2 illustrates an example of signaling flow that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure. As shown in FIG. 2, the UE 104 receives configuration 202 comprising a first indication from base station 102. The first indication is for a network side additional condition, the UE 104 transmits a beam report 204 to the base station 102. The beam report is based on the configuration. And the beam report is determined based on the first indication.

In this way, the pre-alignment for the network side conditions is supported with the first indication. And the beam report can be determined based on the first indication. Thus, the performance of communication is improved.

FIG. 3 illustrates an example of a procedure that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure. Generally, as shown in FIG. 3, when model training, NW (such as the base station 102 in FIG. 1) indicates configuration relevant to data collection and its basic associated ID (Corresponding to the first indication) . Based on data set collected with the configuration, UE (such as the UE 104 in FIG. 1) trains AI/ML model for beam prediction and reports information of the model to NW.

Specifically, as shown in FIG. 3, in phase 1, the gNB (such as the base station 102 in FIG. 1) indicates basic associated ID 1. For data collection, NW signals the data collection related configuration and its basic associated ID. The basic associated ID is linked with the basic NW-sided additional conditions, like ordering of model input/output, Tx spatial filter assumption, etc. And a same basic associated ID will be assigned if NW-side additional conditions of cells are same.

In some example implementations, the first indication identifies at least one of following network side additional conditions: a downlink (DL) transmit (Tx) beam codebook; at least one DL spatial domain transmission filter corresponding to beams in a first beam set and a second beam set for a AI/ML-enabled feature or FG; a relationship of the first beam set and the second beam set; an order of resources for the first beam set; an order of resources for the second beam set; DL transmission power; an antenna height of a base station; or network side beam shape information.

Specifically, the basic associated ID is per UE, cell group, per NW vendor or global ID. With same basic associated ID, UE shall assume at least one of following NW-side additional condition in cell is same: DL Tx beam codebook; DL spatial domain transmission filters corresponding to the beams in Set A and Set B; Relationship of Set A and Set B, e.g., QCL relation of RS with Set A beams and RS with Set B beams; Order of resources for Set B, e.g., order of CSI-RS resources corresponding to beams for Set B; Order of resources for Set A, e.g., order of CSI-RS resources corresponding to beams for Set A; DL transmission power; gNB antenna height; NW-side beam shape information, such as, range of 3dB beamwidth, range of beam boresight direction, range of Tx beam angle.

In some example implementations, the first indication is associated with at least one functionality or model. Specifically, the UE (s) (such as the UE 104 in FIG. 1) collects the data corresponding to the basic associated ID. And AI/ML models are developed (e.g., trained, updated) at UE side based on the collected data corresponding to the basic associated ID.As shown in FIG. 3, the model 1 and model 2 are determined based on the basic associated ID 1. Since non-NW-side additional conditions are different across cells, cell groups or NW vendors, the models that are trained by UE 104 with even the same basic associated ID may have different performance. Thus, there will be multiple different models is associated with a same basic associated ID.

As shown in FIG. 3, in phase 2, the UE 104 performs monitoring on the models determined by the basic associated ID1. Specifically, the UE reports information of its AI/ML models corresponding to basic associated IDs to the NW.

The NW-side additional condition will be changed. For example, NW-side additional condition within a cell changes over time, or NW-side additional condition changes as the UE 104 moves to new cell. In some example implementations, to facilitate UE selecting a functionality/model appropriated for current NW-side additional condition, the basic associated ID aligned with current NW-side additional condition is indicated to UE. If AI/ML functionalities/models linked with the basic associated ID are supported to UE then a performance monitoring will be performed to assess the functionality/model indicated by the associated ID.

In some example implementations, the UE reports the monitoring result to NW. As shown in FIG. 3, the UE 104 selects the valid model after the performance monitoring. The information of the model comprises at least model input information (e.g., model input content and size of model input) and model output information (e.g., model output content and size of model output) .

FIG. 4 illustrates an example of basic associated ID assignment in accordance with aspects of the present disclosure. The basic associated ID is linked with basic NW-side additional condition. In this example, the basic NW-side additional condition is DL Tx beam codebook. When model training, Cell A indicates configuration relevant to data collection to UE 104, including RS resource for Set A and RS resource for Set B. Meanwhile, a basic associated ID 1 will be also assigned to the UE 104 by the Cell A, which implies a DL Tx beam codebook1 applied currently in the Cell A. The UE104 will train an AI/ML model for beam prediction based on the data set collected with the configuration in the Cell A. Surely the trained AI/ML model is linked with the basic associated ID 1. According to the similar way, the UE104 can train multiple AI/ML models where each model is linked with a basic associated ID, e.g., basic associated ID 1, basic associated ID 2…Since there may happen that a same Tx beam codebook applies in two or more cells, but other NW-side additional conditions are different among these cells, UE may train multiple models which are linked with a same basic associated ID, e.g., {model1, model2} linked with basic associated ID1 as shown in FIG. 4.

FIG. 5 illustrates an example of basic associated IDs with NW-side additional conditions in accordance with aspects of the present disclosure. An AI/ML functionality may contain one or multiple AI/ML models. An AI/ML functionality may be linked with one or multiple basic associated IDs. And a basic associated ID may be linked with one or multiple AI/ML functionalities. UE 104 reports information of its supported functionalities/models and associated IDs. The information of its supported models comprises at least model input information (e.g., model input content and size of model input) and model output information (e.g., model output content and size of model output) of each model.

To activate a functionality/mode for model inference, a basic associated ID associated with current NW-side additional condition shall be indicated to UE 104 to pre-align the basic NW-side additional condition. After pre-alignment with basic associated ID, UE 104 and gNB 102 have common understanding about candidate functionalities/models corresponding to the indicated basic associated ID. But which one functionality/model will be activated for inference is determined based on a candidate functionality/model sweeping with performance monitoring.

As shown in FIG. 5 , there are 15 different combinations of NW-side additional condition and each combination comprise a DL Tx beam codebook and other condition, e.g., DL Tx beam codebook1 and Condition1. Based on the 15 different combinations, 3 individual DL Tx beam codebooks can be selected and linked with three basic associated IDs, i.e., DL Tx beam codebook1 is linked with basic associated ID1, DL Tx beam codebook2 is linked with basic associated ID2 and DL Tx beam codebook3 is linked with basic associated ID3. Since other NW-side additional condition maybe different for a same basic associate ID, multiple models may be trained with a same, e.g., Model1～Model5 are trained with basic associated ID1. If UE 104 is indicated with a basic associated ID and receives configuration for monitoring from NW 102, the UE could execute performance monitoring of the models corresponding to the basic associated ID and select a valid model based on result of the performance monitoring.

In some example implementations, the performance monitoring is performed based on a performance monitoring configuration comprising a type of performance metric, a target performance, or a duration for monitoring in which one or more pairs of RS resources for a first beam set and RS resources for a second beam set are transmitted. The first beam set and the second beam set are associated with the AI/ML-enabled feature or FG. Specifically, the performance monitoring can be triggered by NW configuration. The NW configuration for monitoring may contain the type of performance metric, a target performance, a window for validation in which one or more than one pair of RS resources for Set A and RS resources for Set B are transmitted, and report resource for monitoring. Based on the NW configuration, UE 104 monitors performance of the candidate functionalities/models.

In some example implementations, the monitoring can be based on UE requesting. The request contains the first indication, and/or preferred RS resource configuration (s) . And different RS resource configurations may be required by different functionalities/models.

In some example implementations, the UE reports monitoring results of the candidate functionalities/models. For example, if performance of a functionalities/models which is calculated based on measurements within the window is better than the target performance, UE reports that the functionality/model can be applied with the NW additional condition indicated by the basic associated ID.

In some example implementations, the beam report is determined based on a functionality or model among the at least one functionality or model, and the functionality or model is determined based on the performance monitoring on the at least one functionality or model. Specifically, with the functionality or model determined by the basic associated ID and the performance monitoring, base station 102 configures resource for model inference, e.g., CSI resource configuration and CSI report configuration. For example, NW configures a CSI report configuration CSI-ReportConfig and corresponding CSI resource configuration CSI-ResourceConfig, where the CSI-ReportConfig contains the basic associated ID. The UE 104 determines a functionality or model based on the associated ID and obtain inference result using the functionality or model based on measurement corresponding to the CSI-ResourceConfig. The inference result is reported in a beam report corresponding to the CSI-ReportConfig.

In some example implementations, the UE 104 receives an indication of reporting at least one applicable functionality or model of the UE 104 associated with the first indication. And the UE 104 transmits information of the at least one applicable functionality or model based on the configuration.

As shown in FIG. 6A, the base station 102 indicates directly basic associated ID (Corresponding to the first indication) 602 linked with current NW-side additional condition. If functionalities/models which are linked with the ID are supported in the UE 104, then UE 104 reports these functionalities/models.

Specifically, the base station 102 sends NW configurations to initiate UE 104 to report its functionalities/models. And the NW configuration comprises a basic associated ID supported by the base station 102 and an indication of initiating UE to report its functionalities/models. Then UE 104 reports functionalities/models which are linked with the basic associated ID based on the NW configuration and requests monitoring of these functionalities/models. For example, DL Tx beam codebook1 applies in the base station 102, then the NW configurations sent by the base station 102 comprise basic associated ID1. If at least one functionality/model supported in the UE 104 are linked with the basic associated ID1, then the UE 104 reports identifier of these functionalities/models 604 and requests monitoring of these functionalities/models. The NW configuration is sent using the radio resource control (RRC) reconfiguration signaling and the functionalities/models are reported with UE assistance information (UAI) or RRC reconfiguration complete signaling.

In some example implementations, the UE 104 transmit a report of the set of first indications supported by the UE 104 and at least one functionality or model related to the set of first indications. And the configuration received by the UE 104 comprises a second indication which is determined from the set of first indications supported by the UE 104.

As shown in FIG. 6B, the UE 104 reports its supported basic associated IDs (Corresponding to the first indication) and corresponding functionalities/models 612 to base station 102 by UAI. And base station 102 indicates one from these reported IDs. The NW-side additional condition associated with the indicated ID is applied in the serving cell. Specifically, UE 104 reports its supported basic associated ID (s) and information of corresponding functionalities/models to gNB using UE capability signaling/UAI signaling. And the report is based on NW configuration that comprises an indication of allowing UE 104 to report. gNB determines one from the UE supported basic associated ID (s) . Then the base station 102 indicates it and/or configuration for monitoring of these functionalities/models to UE using a RRC (re-) configuration. And the indicated basic associated ID is associated with NW-side additional condition applied in the base station 102. For example, UE 104 reports its supported basic associated IDs and identifier related to corresponding functionalities/models to base station 102 by UAI, i.e., {Basic associated ID1, (functionality1, functionality2) } , {Basic associated ID2, (functionality3, functionality4) } , etc. The base station 102 determines one based on these reported basic associated IDs and its current NW-side additional condition, e.g., basic associated ID1 if current NW-side additional condition is DL Tx beam codebook1. The base station 102 indicates the basic associated ID1 and/or send configuration for monitoring of functionality1 and functionality2 to the UE 104.

In some example implementations, the base station 102-1 receives the set of first indications supported by the UE 104 and at least one functionality or model related to the set of first indications from another base station 102-2. And the configuration received by the UE 104 comprises a second indication which is determined from the set of first indications supported by the UE 104.

As shown in FIG. 6C, source base station 102-2 sends supported associated IDs (Corresponding to the first indication) and information of corresponding functionalities/models of the UE 104 to target base statin 102-1. The target base station 102-1 determines a basic associated ID based on these IDs and current NW-side additional condition, e.g., NW-side additional condition associated with the indicated ID is same with current NW-side addition condition.

In some example implementations, the network side additional condition is applied to a configuration level. And a network side additional condition for the configuration level is consistent with the network side additional condition associated with the first indication. The configuration level comprises a level of channel state information (CSI) report setting, a level of CSI resource setting, a level of reference signal (RS) resource set, or a level of RS resource.

Specifically, the basic associated ID can be introduced in CSI report framework. NW configures a CSI report configuration linked with the basic associated ID to UE 104 for model training, inference, or monitoring. The basic associated ID can be introduced at level of CSI report setting, CSI resource setting, RS resource set, or RS resource. With different levels, the basic associated ID has different meanings. For example, if the basic associated ID is introduced at level of CSI report setting, NW-side additional condition for the CSI report setting (or RS resources corresponding to the CSI report setting) is consistent with that is indicated by the basic associated ID.

In some example implementations, a report quantity for monitoring is set such that the UE 104 reports the result of the performance monitoring in a CSI report. Specifically, a report quantity can be set according to purpose of the CSI report configuration. If the CSI report configuration is configured for model training, the report quantity is set to “none” . UE 104 will collect data set based on the CSI report configuration and train model (s) based on the data set. The trained model is associated with the basic associated ID. If the CSI report configuration is configured for model inference, a report quantity for inference is set so that UE 104 reports inference result in the corresponding CSI report, e.g., report quantity set to “PredictedBeam” . The inference result is determined based on the beam measurement of CSI-RS/SSB resource corresponding to the CSI report configuration. UE 104 shall assume the NW-side additional condition for the CSI report setting, CSI resource setting, RS resource set or RS resource is consistent with that is associated with the basic associated ID.

In some example implementations, the monitoring result should be reported to NW for determining a valid functionality/model. And the reporting of the result is based on a CSI report configuration associated with the first indication. Specifically, the reporting of monitoring result is based on CSI report framework. The base station 102 configures a CSI report configuration linked with the basic associated ID to UE 104 for monitoring. And a resource configuration corresponding to the CSI report configuration comprises RS resource for Set A and RS resource for Set B.

In addition, a report quantity for monitoring is set so that UE 104 reports monitoring result in the corresponding CSI report, e.g., report quantity set to “AIBeamPredictionMonitoring” .

In some example implementations, the configuration for monitoring is configured to UE 104, including: the type of performance metric, a target performance, a duration for monitoring in which one or more than one pair of RS resources for Set A and RS resources for Set B are transmitted. The performance metric type contains one of following: beam prediction accuracy related metric, e.g., Top N/1 beam prediction accuracy, indicating that the top one measured beam is one of N best predicted beams; Top 1/M beam prediction accuracy, indicating that the top one predicted beam is one of N best predicted beams; RSRP related metric, e.g., L1-RSRP difference between predicted RSRP of a predicted beam (e.g., the best predicted beam) and measured L1-RSRP of same beam; L1-RSRP difference between measured L1-RSRP of the best predicted beam and the measured L1-RSRP of the best measured beam. And the best predicted beam is obtained by AI/ML model inference output and the best measured beam is obtained based on measurement of all beams of Set A, e.g., the beam with the largest measured L1-RSRP among beams of Set A.

In some example implementations, the UE 104 shall report the monitoring result based on performance assessment of it supported AI/ML models associated with the basic associated ID. The monitoring result comprises an indication for indicating whether the AI beam prediction is applicable in the NW-side additional condition indicated by the basic associated ID and/or an indication for indicating which model (s) are applicable in the NW-side additional condition indicated by the basic associated ID and/or suggested model.

In some example implementations, the reporting of the result is based on a CSI report configuration associated with the basic associated ID. Specifically, a CSI report setting and corresponding CSI resource setting are configured to UE 104. And the CSI report setting is linked with a basic associated ID, and the report quantity set to “none” . An indication for explicitly triggering a monitoring is carried by signaling configuring/triggering/activating the CSI report corresponding to the CSI report setting, or the CSI report setting and the CSI resource setting are dedicated for a monitoring (the monitoring is triggered implicitly when the CSI report setting is configured/triggered/activated) . Monitoring result is obtained based on the measurement on the triggered CSI-RS resources.

In some example implementations, the reporting of the result of the performance monitoring is based on at least one resource configured by a base station. The at least one resource comprises at least one scheduling request (SR) resource. And based on detecting at least one functionality or model is valid based on the performance monitoring, the UE 104 transmits, a positive SR on the at least one scheduling request SR resource. And the UE 104 receives the scheduling information of at least one physical uplink shared channel (PUSCH) resource. And the result of the performance monitoring is reported in the scheduled at least one PUSCH resource. In addition, based on detecting no functionality or model is valid based on the performance monitoring, the UE 104 transmits a negative SR on the at least one scheduling request SR resource. And the UE 104 determines to fall back to non-AI/ML beam management.

Specifically, FIG. 7 illustrates an example of reporting performance monitoring in accordance with aspects of the present disclosure. As shown in FIG. 7, the base station 102 configures dedicated SR resources for reporting monitoring, e.g., SchedulingRequestForAIMonitoring. If UE 104 detects that at least one functionality/model is valid based on the monitoring, UE 104 reports a positive SR with the dedicated SR resource. Further, the detail monitoring results are reported by PUSCH MAC CE scheduled based on the positive SR. The detail monitoring results may contain validation of each assessed functionality/model and/or suggested functionality/model. Otherwise, UE 104 reports a negative SR with the dedicated SR resource and fallback to non-AI beam management.

In some example implementations, the at least one resource comprises at least one physical uplink control channel (PUCCH) resources which correspond to at least one functionalities or models; wherein the result of the performance monitoring is reported on one PUCCH resource corresponding to the at least one functionalities or models. Or the result of performance monitoring is reported on a plurality of PUCCH resources corresponding to a plurality of functionalities or models, respectively.

Specifically, the base station 102 configures a list of PUCCH resources for reporting monitoring results of applicable functionalities/models. And the list of PUCCH resources is linked with the CSI report setting and its time behavior is same as the CSI report setting. Each PUCCH resource corresponds to an applicable functionality/model. The UE 104 reports monitoring result of an applicable functionality/model in the corresponding PUCCH resource. If time behaviors of the CSI report setting and the corresponding PUCCH resource are configured with aperiodic time behavior, the PUCCH resource is triggered by a same DCI triggering CSI report corresponding to the CSI report setting.

With the present solution, the pre-alignment for NW-side conditions with basic associated ID is supported. And the methods to indicate the basic associated ID is provided. In addition, the candidate functionality/model sweeping with performance monitoring is supported. The solution also comprise methods for monitoring result reporting methods, which including method based on CSI report framework and method based on dedicated resource. Thus, the performance of communication is improved.

FIG. 8 illustrates an example of a device that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure. The device 800 may be an example of the UE 104 as described herein. The device 800 may support wireless communication with one or more network entities 102, UEs 104, or any combination thereof. The device 800 may include components for bi-directional communications including components for transmitting and receiving communications, such as a processor 802, a memory 804, a transceiver 806, and, optionally, an I/O controller 808. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 802, the memory 804, the transceiver 806, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. For example, the processor 802, the memory 804, the transceiver 806, or various combinations or components thereof may support a method for performing one or more of the operations described herein.

In some implementations, the processor 802, the memory 804, the transceiver 806, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry) . The hardware may include a processor, a digital signal processor (DSP) , an application-specific integrated circuit (ASIC) , a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some implementations, the processor 802 and the memory 804 coupled with the processor 802 may be configured to perform one or more of the functions described herein (e.g., executing, by the processor 802, instructions stored in the memory 804) .

For example, the processor 802 may support wireless communication at the device 800 in accordance with examples as disclosed herein. The processor 802 may be configured to operable to support a means for receiving, via the transceiver, a configuration comprising a first indication for a network side additional condition; and means for transmitting, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

The processor 802 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof) . In some implementations, the processor 802 may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the processor 802. The processor 802 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 804) to cause the device 800 to perform various functions of the present disclosure.

The memory 804 may include random access memory (RAM) and read-only memory (ROM) . The memory 804 may store computer-readable, computer-executable code including instructions that, when executed by the processor 802 cause the device 800 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some implementations, the code may not be directly executable by the processor 802 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some implementations, the memory 804 may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.

The I/O controller 808 may manage input and output signals for the device 800. The I/O controller 808 may also manage peripherals not integrated into the device M02. In some implementations, the I/O controller 808 may represent a physical connection or port to an external peripheral. In some implementations, the I/O controller 808 may utilize an operating system such as or another known operating system. In some implementations, the I/O controller 808 may be implemented as part of a processor, such as the processor 806. In some implementations, a user may interact with the device 800 via the I/O controller 808 or via hardware components controlled by the I/O controller 808.

In some implementations, the device 800 may include a single antenna 810. However, in some other implementations, the device 800 may have more than one antenna 810 (i.e., multiple antennas) , including multiple antenna panels or antenna arrays, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 806 may communicate bi-directionally, via the one or more antennas 810, wired, or wireless links as described herein. For example, the transceiver 806 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 806 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 810 for transmission, and to demodulate packets received from the one or more antennas 810. The transceiver 806 may include one or more transmit chains, one or more receive chains, or a combination thereof.

A transmit chain may be configured to generate and transmit signals (e.g., control information, data, packets) . The transmit chain may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM) , frequency modulation (FM) , or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM) . The transmit chain may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmit chain may also include one or more antennas 810 for transmitting the amplified signal into the air or wireless medium.

A receive chain may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receive chain may include one or more antennas 810 for receive the signal over the air or wireless medium. The receive chain may include at least one amplifier (e.g., a low-noise amplifier (LNA) ) configured to amplify the received signal. The receive chain may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receive chain may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

FIG. 9 illustrates an example of a device that supports AI/ML inference for communication in accordance with aspects of the present disclosure. The device 900 may be an example of the base station 102 as described herein. The device 900 may support wireless communication with one or more network entities 102, UEs 104, or any combination thereof. The device 900 may include components for bi-directional communications including components for transmitting and receiving communications, such as a processor 902, a memory 904, a transceiver 906, and, optionally, an I/O controller 908. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 902, the memory 904, the transceiver 906, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. For example, the processor 902, the memory 904, the transceiver 906, or various combinations or components thereof may support a method for performing one or more of the operations described herein.

In some implementations, the processor 902, the memory 904, the transceiver 906, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry) . The hardware may include a processor, a digital signal processor (DSP) , an application-specific integrated circuit (ASIC) , a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure. In some implementations, the processor 902 and the memory 904 coupled with the processor 902 may be configured to perform one or more of the functions described herein (e.g., executing, by the processor 902, instructions stored in the memory 904) .

For example, the processor 902 may support wireless communication at the device 900 in accordance with examples as disclosed herein. The processor 902 may be configured to operable to support a means for transmitting, via the transceiver, a configuration comprising a first indication for a network side additional condition; and means for receiving, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

The processor 902 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof) . In some implementations, the processor 902 may be configured to operate a memory array using a memory controller. In some other implementations, a memory controller may be integrated into the processor 902. The processor 902 may be configured to execute computer-readable instructions stored in a memory (e.g., the memory 904) to cause the device 900 to perform various functions of the present disclosure.

The memory 904 may include random access memory (RAM) and read-only memory (ROM) . The memory 904 may store computer-readable, computer-executable code including instructions that, when executed by the processor 902 cause the device 900 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some implementations, the code may not be directly executable by the processor 902 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some implementations, the memory 904 may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.

The I/O controller 908 may manage input and output signals for the device 900. The I/O controller 908 may also manage peripherals not integrated into the device M02. In some implementations, the I/O controller 908 may represent a physical connection or port to an external peripheral. In some implementations, the I/O controller 908 may utilize an operating system such as or another known operating system. In some implementations, the I/O controller 908 may be implemented as part of a processor, such as the processor 906. In some implementations, a user may interact with the device 900 via the I/O controller 908 or via hardware components controlled by the I/O controller 908.

In some implementations, the device 900 may include a single antenna 910. However, in some other implementations, the device 900 may have more than one antenna 910 (i.e., multiple antennas) , including multiple antenna panels or antenna arrays, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 906 may communicate bi-directionally, via the one or more antennas 910, wired, or wireless links as described herein. For example, the transceiver 906 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 906 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 910 for transmission, and to demodulate packets received from the one or more antennas 910. The transceiver 906 may include one or more transmit chains, one or more receive chains, or a combination thereof.

A transmit chain may be configured to generate and transmit signals (e.g., control information, data, packets) . The transmit chain may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM) , frequency modulation (FM) , or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM) . The transmit chain may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmit chain may also include one or more antennas 910 for transmitting the amplified signal into the air or wireless medium.

A receive chain may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receive chain may include one or more antennas 910 for receive the signal over the air or wireless medium. The receive chain may include at least one amplifier (e.g., a low-noise amplifier (LNA) ) configured to amplify the received signal. The receive chain may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receive chain may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

FIG. 10 illustrates an example of a processor that supports consistency of NW-side additional condition in accordance with aspects of the present disclosure. The processor 1000 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 1000 may include a controller 1002 configured to perform various operations in accordance with examples as described herein. The processor 1000 may optionally include at least one memory 1004. Additionally, or alternatively, the processor 1000 may optionally include one or more arithmetic-logic units (ALUs) 1000. One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 1000 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory local to or included in the processor chipset (e.g., the processor 1000) or other memory (e.g., random access memory (RAM) , read-only memory (ROM) , dynamic RAM (DRAM) , synchronous dynamic RAM (SDRAM) , static RAM (SRAM) , ferroelectric RAM (FeRAM) , magnetic RAM (MRAM) , resistive RAM (RRAM) , flash memory, phase change memory (PCM) , and others) .

The controller 1002 may be configured to manage and coordinate various operations (e.g., signaling, receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) of the processor 1000 to cause the processor 1000 to support various operations in accordance with examples as described herein. For example, the controller 1002 may operate as a control unit of the processor 1000, generating control signals that manage the operation of various components of the processor 1000. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

The controller 1002 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 1004 and determine subsequent instruction (s) to be executed to cause the processor 1000 to support various operations in accordance with examples as described herein. The controller 1002 may be configured to track memory address of instructions associated with the memory 1004. The controller 1002 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 1002 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 1000 to cause the processor 1000 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 1002 may be configured to manage flow of data within the processor 1000. The controller 1002 may be configured to control transfer of data between registers, arithmetic logic units (ALUs) , and other functional units of the processor 1000.

The memory 1004 may include one or more caches (e.g., memory local to or included in the processor 1000 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementation, the memory 1004 may reside within or on a processor chipset (e.g., local to the processor 1000) . In some other implementations, the memory 1004 may reside external to the processor chipset (e.g., remote to the processor 1000) .

The memory 1004 may store computer-readable, computer-executable code including instructions that, when executed by the processor 1000, cause the processor 1000 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. The controller 1002 and/or the processor 1000 may be configured to execute computer-readable instructions stored in the memory 1004 to cause the processor 1000 to perform various functions (e.g., functions or tasks supporting transmit power prioritization) . For example, the processor 1000 and/or the controller 1002 may be coupled with or to the memory 1004, the processor 1000, the controller 1002, and the memory 1004 may be configured to perform various functions described herein. In some examples, the processor 1000 may include multiple processors and the memory 1004 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.

The one or more ALUs 1000 may be configured to support various operations in accordance with examples as described herein. In some implementation, the one or more ALUs 1000 may reside within or on a processor chipset (e.g., the processor 1000) . In some other implementations, the one or more ALUs 1000 may reside external to the processor chipset (e.g., the processor 1000) . One or more ALUs 1000 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 1000 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 1000 be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, or alternatively, the one or more ALUs 1000 may support logical operations such as AND, OR, exclusive-OR (XOR) , not-OR (NOR) , and not-AND (NAND) , enabling the one or more ALUs 1000 to handle conditional operations, comparisons, and bitwise operations.

The processor 1000 may support wireless communication in accordance with examples as disclosed herein. The processor 1002 may be configured to or operable to support a means for receiving, via the transceiver, a configuration comprising a first indication for a network side additional condition; and means for transmitting, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

FIG. 11 illustrates an example of a processor that supports AI/ML inference for communication in accordance with aspects of the present disclosure. The processor 1100 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 1100 may include a controller 1102 configured to perform various operations in accordance with examples as described herein. The processor 1100 may optionally include at least one memory 1104. Additionally, or alternatively, the processor 1100 may optionally include one or more arithmetic-logic units (ALUs) 1100. One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses) .

The processor 1100 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory local to or included in the processor chipset (e.g., the processor 1100) or other memory (e.g., random access memory (RAM) , read-only memory (ROM) , dynamic RAM (DRAM) , synchronous dynamic RAM (SDRAM) , static RAM (SRAM) , ferroelectric RAM (FeRAM) , magnetic RAM (MRAM) , resistive RAM (RRAM) , flash memory, phase change memory (PCM) , and others) .

The controller 1102 may be configured to manage and coordinate various operations (e.g., signaling, receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) of the processor 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. For example, the controller 1102 may operate as a control unit of the processor 1100, generating control signals that manage the operation of various components of the processor 1100. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

The controller 1102 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 1104 and determine subsequent instruction (s) to be executed to cause the processor 1100 to support various operations in accordance with examples as described herein. The controller 1102 may be configured to track memory address of instructions associated with the memory 1104. The controller 1102 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 1102 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 1102 may be configured to manage flow of data within the processor 1100. The controller 1102 may be configured to control transfer of data between registers, arithmetic logic units (ALUs) , and other functional units of the processor 1100.

The memory 1104 may include one or more caches (e.g., memory local to or included in the processor 1100 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementation, the memory 1104 may reside within or on a processor chipset (e.g., local to the processor 1100) . In some other implementations, the memory 1104 may reside external to the processor chipset (e.g., remote to the processor 1100) .

The memory 1104 may store computer-readable, computer-executable code including instructions that, when executed by the processor 1100, cause the processor 1100 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. The controller 1102 and/or the processor 1100 may be configured to execute computer-readable instructions stored in the memory 1104 to cause the processor 1100 to perform various functions (e.g., functions or tasks supporting transmit power prioritization) . For example, the processor 1100 and/or the controller 1102 may be coupled with or to the memory 1104, the processor 1100, the controller 1102, and the memory 1104 may be configured to perform various functions described herein. In some examples, the processor 1100 may include multiple processors and the memory 1104 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.

The one or more ALUs 1100 may be configured to support various operations in accordance with examples as described herein. In some implementation, the one or more ALUs 1100 may reside within or on a processor chipset (e.g., the processor 1100) . In some other implementations, the one or more ALUs 1100 may reside external to the processor chipset (e.g., the processor 1100) . One or more ALUs 1100 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 1100 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 1100 be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, or alternatively, the one or more ALUs 1100 may support logical operations such as AND, OR, exclusive-OR (XOR) , not-OR (NOR) , and not-AND (NAND) , enabling the one or more ALUs 1100 to handle conditional operations, comparisons, and bitwise operations.

The processor 1100 may support wireless communication in accordance with examples as disclosed herein. The processor 1102 may be configured to or operable to support a means for transmitting, via the transceiver, a configuration comprising a first indication for a network side additional condition; and means for receiving, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.

FIG. 12 illustrates flowchart of method that supports AI/ML inference for communication in accordance with aspects of the present disclosure. The operations of the method 1200 may be implemented by a device or its components as described herein. For example, the operations of the method 1200 may be performed by a UE 104 as described herein. In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware.

At 1205, the method may include receiving, via the transceiver, a configuration comprising a first indication for a network side additional condition. The operations of 1205 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1205 may be performed by a device as described with reference to FIG. 1.

At 1210, the method may include transmitting, transmit, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication. The operations of 1210 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1210 may be performed by a device as described with reference to FIG. 1.

FIG. 13 illustrates flowchart of method that supports AI/ML inference for communication in accordance with aspects of the present disclosure. The operations of the method 1300 may be implemented by a device or its components as described herein. For example, the operations of the method 1300 may be performed by a base station 102 as described herein. In some implementations, the device may execute a set of instructions to control the function elements of the device to perform the described functions. Additionally, or alternatively, the device may perform aspects of the described functions using special-purpose hardware.

At 1305, the method may include transmitting, via the transceiver, a configuration comprising a first indication for a network side additional condition. The operations of 1305 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1305 may be performed by a device as described with reference to FIG. 1.

At 1310, the method may include receiving, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication. The operations of 1310 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1310 may be performed by a device as described with reference to FIG. 1.

It should be noted that the methods described herein describes possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible. Further, aspects from two or more of the methods may be combined.

The various illustrative blocks and components described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a DSP, an ASIC, a CPU, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer. By way of example, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM) , flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor.

As used herein, including in the claims, an article “a” before an element is unrestricted and understood to refer to “at least one” of those elements or “one or more” of those elements. The terms “a, ” “at least one, ” “one or more, ” and “at least one of one or more” may be interchangeable. As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of” or “one or both of” ) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C) . Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. Further, as used herein, including in the claims, a “set” may include one or more elements.

The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

A user equipment (UE) comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to:

receive, via the transceiver, a configuration comprising a first indication for a network side additional condition; and

transmit, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.
The UE of claim 1, wherein the first indication is associated with at least one functionality or model of an artificial intelligence (AI) /machine learning (ML) .
The UE of claim 1, wherein the first indication identifies at least one of following network side additional conditions:

a downlink (DL) transmit (Tx) beam codebook;

at least one DL spatial domain transmission filter corresponding to beams in a first beam set and a second beam set for an AI/ML-enabled feature or FG;

a relationship of the first beam set and the second beam set;

an order of resources for the first beam set;

an order of resources for the second beam set;

DL transmission power;

an antenna height of a base station; or

network side beam shape information.
The UE of claim 1, wherein the processor is further configured to:

receive, via the transceiver, an indication of reporting at least one applicable functionality or model of the UE associated with the first indication; and

transmit, via the transceiver, information of the at least one applicable functionality or model based on the configuration.
The UE of claim 1, wherein the configuration comprises a second indication which is determined from a set of first indications supported by the UE.
The UE of claim 5, wherein the processor is further configured to:

transmit, via the transceiver, a report of the set of first indications supported by the UE and at least one functionality or model related to the set of first indications.
The UE of claim 1, wherein:

the network side additional condition is applied to a configuration level; and

a network side additional condition for the configuration level is consistent with the network side additional condition associated with the first indication.
The UE of claim 7, wherein the configuration level comprises one of the following:

a level of channel state information (CSI) report setting,

a level of CSI resource setting,

a level of reference signal (RS) resource set, or

a level of RS resource.
The UE of claim 2, wherein:

the beam report is determined based on a functionality or model among the at least one functionality or model, and

the functionality or model is determined based on performance monitoring on the at least one functionality or model.
The UE of claim 9, wherein the performance monitoring is performed based on a performance monitoring configuration comprising at least one of the following:

a type of performance metric;

a target performance; or

a duration for monitoring in which one or more pairs of RS resources for a first beam set and RS resources for a second beam set are transmitted, wherein the first beam set and the second beam set are associated with the AI/ML-enabled feature or FG.
The UE of claim 9, wherein the processor is further configured to:

report, via the transceiver, a result of the performance monitoring.
The UE of claim 11, wherein the reporting of the result is based on a CSI report configuration associated with the first indication.
The UE of claim 12, wherein a report quantity for monitoring is set such that the UE reports the result of the performance monitoring in a CSI report.
The UE of claim 11, wherein the reporting of the result of the performance monitoring is based on at least one resource configured by a base station.
The UE of claim 14, wherein the at least one resource comprises at least one scheduling request (SR) resource, and the processor is further configured to:

based on detecting at least one functionality or model is valid based on the performance monitoring, transmit, via the transceiver, a positive SR on the at least one scheduling request SR resource, and

receive, via the transceiver, scheduling information of at least one physical uplink shared channel (PUSCH) resource, wherein the result of the performance monitoring is reported in the scheduled at least one PUSCH resource.
The UE of claim 15, wherein the processor is further configured to:

based on detecting no functionality or model is valid based on the performance monitoring, transmit a negative SR on the at least one scheduling request SR resource; and

determine to fall back to non-AI/ML beam management.
The UE of claim 14, wherein:

the at least one resource comprises at least one physical uplink control channel (PUCCH) resources which correspond to at least one functionalities or models; wherein the result of the performance monitoring is reported on one PUCCH resource corresponding to the at least one functionalities or models; or

the result of performance monitoring is reported on a plurality of PUCCH resources corresponding to a plurality of functionalities or models, respectively.
A base station comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to:

transmit, via the transceiver, a configuration comprising a first indication for a network side additional condition; and

receive, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.
A method performed by a user equipment (UE) , the method comprising:

receiving, a configuration comprising a first indication for a network side additional condition; and

transmitting, a beam report based on the configuration, wherein the beam report is determined based on the first indication.
A processor for wireless communication, comprising:

at least one memory; and

a controller coupled with the at least one memory and configured to cause the controller to:

receive, via a transceiver, a configuration comprising a first indication for a network side additional condition; and

transmit, via the transceiver, a beam report based on the configuration, wherein the beam report is determined based on the first indication.