AI / ML for beam management

Abstract

The present disclosure is related to artificial intelligence / machine learning (AI / ML) for beam management. AI / ML-based beam predication is applied at the terminal device. This involves predicting the best transmit beam or transmit-receive beam pairs using a subset of measurements at the input of the AI / ML model and reporting the predicted top-K best beams to a network device. The terminal device receives 330 from the network device N1 channel state information (CSI) report configurations out of N CSI report configurations for AI / ML beam prediction. The N1 CSI report configurations are sufficient to provide basic functionalities of the AI / ML beam prediction model. The terminal device determines, 335, N2 CSI report configurations to be used for “advanced functionalities” of the AI / ML model. Preferably, N2=N-N1. The terminal device determines 340 whether the advance functionalities are applicable, e.g. based on inference performances with the N2 CSI report configurations. The terminal device transmits 345 to the network device indication information indicating whether the N2 CSI reports are applicable.

Description

FIELD

[0001] Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for artificial intelligence / machine learning (AI / ML) for beam management. BACKGROUND

[0002] In the telecommunication industry, artificial intelligence / machine learning (AI / ML) models have been employed in telecommunication systems to improve the performance. For AI / ML enhancements related to beam management, two sub-use cases have been identified in RANI, including beam prediction in the spatial domain (BM-Casel), and beam prediction in the time domain (BM-Case2).

[0003] The primary motivation is to support a reduced overhead and lower beam measurements and reporting latency. Based on the evaluation, the benefits and gains were verified based on given metrics, and they could be supported by single-side models and consider supporting the necessary / recommended life-cycle management (LCM) components for selected sub use cases.

[0004] For an AI / ML-based beam prediction applied at the terminal device, it is possible to use the AI / ML model to predict the best transmit (Tx) beam or transmit-receive (Tx-Rx) beam pair(s) by using a subset of measurements at the input of the AI / ML model and reporting the predicted top-K best beam to the network device. SUMMARY

[0005] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to: receive, from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; determine, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction; determine applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; and transmit, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

[0006] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to: transmit, to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; and receive, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

[0007] In a third aspect of the present disclosure, there is provided a method. The method comprises: receiving, by a first apparatus and from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; determining, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction; determining applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; and transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

[0008] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: transmitting, by a second apparatus and to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; and receiving, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

[0009] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for receiving, from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; means for determining, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction; means for determining applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; and means for transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

[0010] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for transmitting, to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; and means for receiving, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

[0011] In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect.

[0012] In an eighth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the fourth aspect.

[0013] It is to be understood that the Summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0015] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0016] FIG. 2 illustrates example CSI report and measurement hierarchy;

[0017] FIG. 3 illustrates a signaling flow of AI / ML-based beam prediction in accordance with some example embodiments of the present disclosure;

[0018] FIG. 4 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0019] FIG. 5 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0020] FIG. 6 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0021] FIG. 7 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0022] Throughout the drawings, the same or similar reference numerals represent the same or similar element. DETAILED DESCRIPTION

[0023] Principle of the present disclosure will now be described with reference to some example 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. Embodiments described herein can be implemented in various manners other than the ones described below.

[0024] 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.

[0025] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment 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 are not necessarily referring to the same embodiment. 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.

[0026] It shall be understood that although the terms “first,” “second,”..., etc. in front of noun(s) and 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 and they do not limit the order of the noun(s). For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0027] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0028] As used herein, unless stated explicitly, performing a step “in response to A” 5 does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included.

[0029] 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 10 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. 15

[0030] As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0031] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0032] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (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. Furthermore, 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), the sixth generation (6G) 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 of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0033] As used herein, the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives 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), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low power node such as a femto, a pico, a non-terrestrial network (NTN) or non-ground network device such as a satellite network device, a low earth orbit (LEO) satellite and a geosynchronous earth orbit (GEO) satellite, an aircraft network device, and so forth, depending on the applied terminology and technology. In some example embodiments, radio access network (RAN) split architecture comprises a Centralized Unit (CU) and a Distributed Unit (DU) at an IAB donor node. An IAB node comprises a Mobile Terminal (IAB-MT) part that behaves like a UE toward the parent node, and a DU part of an IAB node behaves like a base station toward the next-hop IAB node. In some example, a network device may include a core network (CN) device. The core network includes one more core network devices or devices that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network devices, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network device. Example core network devices include functions of one or more of Location Management Function (LMF), Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or User Plane Function (UPF).

[0034] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, 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 and applications (e.g., remote surgery), an industrial device and applications (e.g., 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. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0035] As used herein, the term “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” may refer to any resource for performing a communication, for example, a communication between a terminal device and a network device, such as a resource in time domain, a resource in frequency domain, a resource in space domain, a resource in code domain, or any other combination of the time, frequency, space and / or code domain resource enabling a communication, and the like. In the following, unless explicitly stated, a resource in both frequency domain and time domain will be used as an example of a transmission resource for describing some example embodiments of the present disclosure. It is noted that example embodiments of the present disclosure are equally applicable to other resources in other domains.

[0036] As used herein, the term “model” is referred to as an association between an input and an output learned from training data, and thus a corresponding output may be generated for a given input after the training. The generation of the model may be based on machine learning (ML) techniques. The machine learning techniques may also be referred to as artificial intelligence (AI) techniques. In general, a machine learning model can be built, which receives input information and makes predictions based on the input information. For example, a classification model may predict a class of the input information among a predetermined set of classes. As used herein, “model” may also be referred to as “machine learning model”, “learning model”, “machine learning network”, or “learning network,” which are used interchangeably herein.

[0037] To facilitate understanding of the terminologies, some definitions of the list of terminologies used for AI / ML are provided below.

[0038] AI / ML Model: A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0039] AI / ML model delivery: A generic term referring to delivery of an AI / ML model from one entity to another entity in any manner. Note: An entity could mean a network node / function (e.g., gNB, an LMF, etc.), UE, proprietary server, etc.

[0040] AI / ML model Inference: A process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0041] AI / ML model testing: A subprocess of training, to evaluate the performance of a final AI / ML model using a dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0042] AI / ML model training: A process to train an AI / ML Model [by learning the input / output relationship] in a data driven manner and obtain the trained AI / ML Model for inference.

[0043] Data collection: A process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference.

[0044] Functionality identification: A process / method of identifying an AI / ML functionality for the common understanding between the network and the UE. Note: Information regarding the AI / ML functionality may be shared during functionality identification. Where AI / ML functionality resides depends on the specific use cases and sub use cases.

[0045] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0046] Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data.

[0047] Supervised learning: A process of training a model from input and its corresponding labels.

[0048] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the terminal device or UE.

[0049] Unsupervised learning: A process of training a model without labelled data.

[0050] FIG. 1 illustrates a schematic diagram of an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, a plurality of communication devices, including a terminal device 110 and a network device 120, can communicate with each other.

[0051] In the example of FIG. 1, the terminal device 110 may be a UE and the network device 120 may be a base station serving the UE. The serving area of the network device 120 may be called a cell 102.

[0052] It is to be understood that the number of devices and their connections shown in FIG. 1 are only for the purpose of illustration without suggesting any limitation. The communication environment 100 may include any suitable number of devices configured to implementing example embodiments of the present disclosure. Although not shown, it would be appreciated that one or more additional devices may be located in the cell 102, and one or more additional cells may be deployed in the communication environment 100. It is noted that although illustrated as a network device, the network device 120 may be another device than a network device. Although illustrated as a terminal device, the terminal device 110 may be another device than a terminal device.

[0053] In the following, for the purpose of illustration, some example embodiments are described with the terminal device 110 operating as a UE and the network device 120 operating as abase station. However, in some example embodiments, operations described in connection with a terminal device may be implemented at a network device or other device, and operations described in connection with a network device may be implemented at a terminal device or other device.

[0054] In some example embodiments, a link from the network device 120 to the terminal device 110 is referred to as a downlink (DL), while a link from the terminal device 110 to the network device 120 is referred to as an uplink (UL). In DL, the network device 120 is a transmitting (TX) device (or a transmitter) and the terminal device 110 is a receiving (RX) device (or a receiver). In UL, the terminal device 110 is a TX device (or a transmitter) and the network device 120 is a RX device (or a receiver).

[0055] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0056] In some example embodiments, one or more AI / ML models 105 may be configured, e.g., as a functionality. An AI / ML model 105 may sometimes be referred to as an AI model or an ML model for short. Different AI / ML models 105 may be configured to implement the same different algorithms in the communication environment 100. For an AI / ML-based beam prediction applied at the terminal device, it is possible to use the AI / ML model to predict the best transmit (Tx) beam or transmit-receive (Tx-Rx) beam pair(s) by using a subset of measurements at the input of the AI / ML model and reporting the predicted top-K best beam to the network device.

[0057] Inference, testing, training, and / or validation of an AI / ML model 105 may be performed at the terminal device 110, the network device 120, and / or other entities. An AI / ML model 105 may be delivered from one entity to another entity in any manner. Delivery of an AI / ML model 105 over the air interface in a manner that is not transparent to 3 GPP signalling, either parameters of a model structure known at the receiving end or a new model with parameters. Delivery may contain a full model or a partial model.

[0058] In communication specification meeting, the following was agreed to provide specification support applying AI / ML techniques to NR air interface on beam management: - Beam management - DL Tx beam prediction for both UE-side model and network-side model, encompassing [RAN1 / RAN2]: o Spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams (“BM-Casel”); o Temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams (“BM-Case2”); o Specify necessary signalling / mechanism(s) to facilitate LCM operations specific to the Beam Management use cases, if any; o Enabling method(s) to ensure consistency between training and inference regarding network-side additional conditions (if identified) for inference at UE.

[0059] It is noted that a common framework may be design to support both BM-Casel and BM-Case2

[0060] Rei-19 will focus on one-side model regarding beam management use case during normative work limiting the scope in assuming off-line training only, UE-side or network-side model only, based on selective sub use cases that demonstrate sufficient benefit vs complexity / cost during the Rel-18 Air Interface AI / ML Study Item (SI).

[0061] In general framework for AI / ML (models and functionalities), in order to distinguish AI / ML models and functionalities supported by the AI / ML models, RANI #111 introduced two different ML-related identification types (functionality identification, model-identification) for later discussions, where the model identification was assumed to use a “model-ID” in the identification process and functionality identification was assumed to use a “functionality” in the identification process.

[0062] Model identification is a process / method of identifying an AI / ML model for the common understanding between the network device and the UE. It is noted that the process / method of model identification may or may not be applicable. It is also noted that information regarding the AI / ML model may be shared during model identification.

[0063] Functionality identification is a process / method of identifying an AI / ML functionality for the common understanding between the network device and the UE. It is noted information regarding the AI / ML functionality may be shared during functionality identification.

[0064] In RANI #114bis meeting, RANI agreed on four approaches related to handling consistency between training and inference when model is operated at UE side, as in following Table 1. Table 1 Agreement - For inference for UE-side models, to ensure consistency between training and inference regarding network-side additional conditions (if identified), the following options can be taken as potential approaches (when feasible and necessary): o Model identification to achieve alignment on the network-side additional condition between network-side and UE-side o Model training at the network device and transfer to UE, where the model has been trained under the additional condition o Information and / or indication on network-side additional conditions is provided to UE o Consistency assisted by monitoring (by UE and / or NW, the performance of UE-side candidate models / functionalities to select a model / functionality) o Other approaches are not precluded o Note: it does not deny the possibility that different approaches can achieve the same function.

[0065] Apart from above agreement, discussions around the necessity of addressing additional conditions were initiated in RAN1&112bis-e AI 9.2.1 and captured in the Technical Report (TR). Considering both functionality-based LCM and model-ID based LCM, the need on whether / how to address additional conditions aiding UE-side model were proposed but not discussed during study phase in Rel-18. Focusing on BM-Casel and BM-Case2 with the UE-side model, no further discussions were made to detail the addressing mechanism. From above agreement made in RANl#112bis, it is understandable that, additional conditions of a model can be interpreted into two following categories: network-side additional conditions, and UE-side additional conditions (e.g., UE speed, UE-side beam pattern).

[0066] To ensure compatibility between UE-side models and network-side additional conditions, the following approaches have been identified: 1. Network-side additional conditions are provided to UE as assistance information, or 2. Model identification to achieve offline / online alignment on the network-side additional condition between network-side and UE-side, 3. Model training at the network device and transfer to UE, 4. Assessing the performance of UE-side candidate models to select a model for inference.

[0067] A CSI-RS is transmitted from a network device to a terminal device, e.g., UE. The terminal device may measure the channel state information-reference signal (CSI-RS) and transmit a CSI report to the network device.

[0068] The CSI-RS reporting framework includes the procedure of indicating particular CSI-RS resources to be measured by the network device and corresponding feedback information from the UE.

[0069] UE uses physical uplink shared channel (PUSCH) or physical uplink control channel (PUCCH) for CSI-RS reporting to the network device to support its DL transmissions. A CSI report includes the followings: channel quality indicator (CQI), CSI-RS resource indicator. Rank indicator, Layer indication (LI), Precoding Matrix Indicator (PMI), Layer 1-Reference Signal Received Power (Ll-RSRP), and SS / PBCH Block resource indicator (SSBRI).

[0070] FIG. 2 illustrates a CSI report and measurement hierarchy / structure 200 concerning CSI report parameters. A CSI report configuration is used configure a CSI resource configuration ID (ConfigID), e.g., none-zero-power (NZP) or interference management (IM), and a report configuration type(ReportConfigType), e.g., periodic, etc. A CSI measurement configuration specifies on what type of reference signal (e.g., NZP-CSI-RS, CSI-IM, etc.) is to be transmitted. A CSI resource set defines all related physical resources for individual UE.

[0071] For the beam reporting framework, Other background information includes various technical aspects of the CSI framework, configuration, Ll-RSRP (Layer 1 Reference Signal Received Power) reporting, and UCI (Uplink Control Information) bit sequence generation in technical specifications like TS 38.306, TR 38.831, TS 38.214, and TS 38.212 and are summarized as follows.

[0072] CSI reporting framework capability (TS 38.306 clause 4.2.7) describes the capability of the UE to support CSI reporting. It includes parameters defining the maximum number of periodic / aperiodic CSI reports that can be configured per CC (Component Carrier), per BWP (Bandwidth Part) and per beam. Moreover, it specifies the concurrent CSI reports per CC that the UE can measure and process, including periodic, semi-persistent and aperiodic CSI, including beam reports.

[0073] CSI report configuration (TR 38.831 and TS 38.331 clause 6.2.1) describes the configuration parameters used to set up periodic, aperiodic or semi-persistent CSI reports sent on the PUCCH or PUSCH for a particular cell or triggered by downlink control information (DCI). It includes fields such as report quantity, frequency domain configuration, time domain behavior and channel measurement resource allocation that affect how the UE perform reports based on different configurations.

[0074] CSI measurement configuration defines the use of two CSI resource configurations including channel measurements interference measurements. CSI-RS configuration of type NZP-CSI-RS enables resources dedicated for channel measurements (known as channel measurement resources (CMR)), whereas CSI-RS configuration of the type zero-power (ZP)-CSI-RS enables resources dedicated for interference measurements (known as information measurement (IM)).

[0075] There has been no use case specific or any agreement on using any of given approaches into AI / ML-based beam prediction use case.

[0076] A related solution is to assess the performance of UE-side candidate models under the network device additional condition. This proposal is about how to ensure the compatibility between UE-side model additional condition and the network device side additional condition.

[0077] Another related solution proposes consistency without model identification, and reporting of applicable functionalities (based on assessing the performance of UE-side of configured functionalities). The network device implementation methods are to enable functionality performance monitoring procedures prior to inference, start ML inference always with a basic functionality prior activating inference on advanced functionalities. In the UE implementation methods, UE handles the model selections transparently by assessing the performance of UE-side candidate models.

[0078] A further related solution also proposes consistency without model identification and reporting of applicable functionalities (e.g., based on assessing the performance of configured functionalities at UE-side). In the network device implementation methods, it is proposed to use performance monitoring procedures to determine if configured functionalities are applicable. In the UE implementation methods, UE handles the additional conditions by training multiple models, and perform model selections transparently by assessing the performance of such candidate models.

[0079] However, for the AI / ML-based beam prediction use case, there are no discussion or solutions on training and inference consistency between UE and the network side. There is no prior art consistency assisted by monitoring in which UE can handle the model selections transparently to enable consistency by assessing the performance of UE-side model and enabling the network device to initiate functionalities based on UE-side performance monitoring procedures.

[0080] To enable alignment between training and inference in BM uses cases, consistency assisted by monitoring is sufficient. As an example, the terminal device can handle the model selections transparently to enable consistency by assessing the performance of UE-side candidate models and configure the terminal device inference always with a basic functionality (e.g., Set B being subset of Set A where Set B size=32 and Set A size=64) prior to activating inference on advanced functionalities.

[0081] In example embodiments of the present disclosure, basic beam prediction functionality with more generic and operable inference feature at the terminal device is referred to as basic functionality, and beam prediction functionality with more specific inference feature at the terminal device is referred to as advanced functionality. In the following, issues have to be taken into account enabling consistency between training and inference in case of applying BM use cases: • Enhancements on the existing CSI Reporting framework where the network device configures and the terminal device determines basic functionalities have not been specified or even discussed yet in 3GPP. • Specifications and methods how to define reporting of specific set of applicable functionalities evaluated based on assessing the performance of UE-side of more advanced functionalities have not been discussed yet in 3GPP. • The mechanism for consistency of inference at the UE on CSI configurations with minimum specification impact should be prioritized. • The mechanism for consistency of inference at the UE on CSI configurations should be applicable for different variants of BM-Casel and BM-Case2.

[0082] Example embodiments of the present disclosure will cover these issues, focusing on both re-using the current CSI report configuration and introducing a new CSI report(s) dedicated for reporting advanced applicable functionalities to enable consistency between inference and training at the network device and UE assisted by UE-side performance monitoring.

[0083] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0084] FIG. 3 illustrates a flowchart of a signaling flow 300 for AI / ML-based beam prediction in accordance with some example embodiments of the present disclosure. As shown in FIG. 3, the signaling flow 300 involves the terminal device 110 and the network device 120 in FIG. 1.

[0085] The signaling flow 300 enables to indicate assessment from UE-side model for consistency between the terminal device 110 and the network device 120 using the CSI reporting framework.

[0086] In the signaling flow 300, the network device 120 transmits (325), to the terminal device 110, configuration information indicating a second number (represented as “A7”) of channel state information (CSI) report configurations amongst a first number (represented as “A”) of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction.

[0087] In some example embodiments, the network device 120 may configure the terminal device 110 with the AI / ML functionality for spatial beam prediction (BM-Casel) or AI / ML functionality for temporal beam prediction (BM-Case2) with Set B beams (corresponding to measurement resources).

[0088] The second number of CSI report configurations are configured as corresponding to basic functionalities for the AI / ML-based beam prediction. For AI / ML-based beam prediction, an AI / ML model is used to predict the best transmit (Tx) beam or transmit-receive (Tx-Rx) beam pair(s) by using a subset of measurements at the input of the AI / ML model and reporting the predicted top-K best beam to the network device. An example basic functionality is that Set B size of 32 beams are used to predict a whole set of Set A which includes all 64 beams.

[0089] In some example embodiments, the terminal device 110 may transmit (305) and the network device 120 may receive (310) capability information of the terminal device 110. The capability information may indicate basic functionalities for the AI / ML-based beam prediction that are supported by the terminal device 110. The supported basic functionalities may be reported to the network device 120 via the UE capability framework.

[0090] In some example embodiments, the network device 120 may transmit (315) and the terminal device 110 may receive (320) further configuration information indicating the first number (N) of CSI report configurations. The network device 120 may configure the terminal device 110 with M CSI report configurations for legacy CSI reporting and N CSI report configurations for the AI / ML-based beam prediction. The N CSI report configurations for the AI / ML-based beam prediction may sometimes be referred to as AI / ML-enabled CSI report configurations.

[0091] In some example embodiments, the TV CSI report configurations (referred to as ML-CSI-reportconfigToaddlist) may be configured independently or included within the M CSI report configurations (referred to as CSI-reportconfigToaddlist which may be included in a CSI configuration message, CSI-MeasConfig). The AML-enabled CSI report configuration list may consist of N1 CSI reports which are considered as the terminal device 110 basic functionalities, where the terminal device 110 is expected to support these functionalities without any additional feedback on feasibility or applicability of functionality.

[0092] In some example embodiments, the further configuration information indicating the first number N of CSI report configurations is separate from the configuration information comprising the second number N1 of CSI report configurations. For example, the terminal device 110 receives N1 CSI report configurations as a new CSI configuration set (referred to as ML-Set X-CSI-reportconfigToaddlist'), which is separate from the N CSI report configurations, ML-CSI-reportconfigToaddlis.

[0093] In some example embodiments, the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations. For example, the terminal device 110 may receive an indicator in a CSI report, wherein the indicator categorizing the CSI report may be considered within the Nl CSI reports or not.

[0094] The terminal device 110 receives (330) the configuration information indicating the second number of CSI report configurations. Then the terminal device 110 determines (335), based on the configuration information, a third number (represented as “A2”) of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction.

[0095] In some example embodiments, the terminal device 110 may determine remaining CSI report configurations amongst the first number of CSI report configurations other than the second number of CSI report configurations, to be the third number of CSI report configurations. For example, the terminal device 110 determines N2 out of N (e.g., N-NJ) CSI report configurations as advanced functionalities, wherein the terminal device 110 may consider for any additional feedback on feasibility or applicability of functionality.

[0096] That is, the second number N1 of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback, while the third number N2 of CSI report configurations corresponding to the advanced functionalities are determined as applicable based on additional feedback.

[0097] Functionality definition associated with the BM-Casel and BM-case2 with DL Tx beam prediction which basic or / and advanced functionalities can be configured with respect to the following modes.

[0098] On functionality definition associated with the BM-Casel or BM-Case2 assuming DL Tx beam prediction, it should be possible for the NW to consider different reporting modes of operation. For example, the following modes can be assumed:

[0099] Mode 1-1: Report of only “predicted CRI” for Top-K beams for BM-Casel. This reporting mode is intended to be used with BM-Casel Alt. i): Set A and Set B are different (Set B is NOT a subset of Set A) and with Alt. ii): Set B is a subset of Set A. The UE shall measure the RSRP on CSI-RS or SSB resources corresponding to Set B beams and use the LI RSRP measurement and eventually the corresponding DL Tx beam ID as input to the AI / ML model. The output of the AI / ML model shall be, for example, the probability of each beam in Set A to be the Top-1 beam. The number K may be configured. By default, the UE can report the Top-1 beam ID.

[0100] Mode 1-2: Report of both “predicted CRI” and “predicted RSRP” for Top-K beams for BM-Case 1. This reporting mode is an extension to the above Mode 1-1, where ML model used at the UE can also predict RSRP. Predicted RSRP can be reported together with the corresponding predicted CRI.

[0101] Mode 2-1: Report only “predicted CRI” for Top-K beams for BM-Case2. This reporting mode is intended to be used with BM-Case2 with Alt. i): Set A and Set B are different (Set B is NOT a subset of Set A), with Alt. ii): Set B is a subset of Set A (Set A and Set B are not the same), and with Alt. iii): Set A and Set B are the same. Here, the UE predicts time domain beam(s) prediction of N future time instance(s). The UE shall measure the RSRP on CSI-RS or SSB resources corresponding to Set B beams for multiple time instances. Then the UE shall use the LI RSRP measurements from historic time instance(s) and eventually the corresponding DL Tx beam ID as model input. The output of the AI / ML model shall be, for example, the probability of each beam in Set A to be the Top-1 beam for N future time instance(s). The UE reports the predicted Top-K beam IDs for N future time instance(s), where the number K may be configured. By default, the UE can report the Top-1 beam IDs for N future time instance(s).

[0102] Mode 2-2: Report of both “predicted CRI” and “predicted RSRP” for Top-K beam for BM-Case2. This reporting mode is an extension to the above Model 2-1, where ML model used at the UE can also predict corresponding RSRP of predicted beams for future time instances. The UE reports the predicted Top-K beam IDs in addition to the corresponding predicted RSRP for N future time instance(s).

[0103] For the advanced functionalities corresponding to any mentioned reporting modes, the terminal device 110 determines (340) applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations.

[0104] In some example embodiments, the terminal device 110 may measure respective downlink reference signal (RS) from the network device 120. The terminal device 110 may monitor available RSs (if available for measurements) corresponding to different RS sets associated with N2 CSI report configurations. Further, the terminal device 110 may determine inference performances by running inactive inference of AI / ML-based beam prediction based on measurement results of the respective downlink reference signals and the third number of CSI report configurations belonging to the same reporting mode. Then the terminal device 110 may determine applicability of at least one of the third number of CSI reports based on the inference performances. Based on internal performance monitoring (e.g., over certain time period by running inactive ML model inference for the N2 CSI reports), the terminal device 110 may determine an applicability or not applicability for one or more CSI reports within the N2 CSI reports from the same reporting mode.

[0105] Based on the determined applicability of one or more CSI reports within the N2 CSI reports, the terminal device 110 transmits (345), to the network device 120, indication information indicating whether at least one of the third number of CSI reports belonging to the same reporting mode is applicable or is inapplicable. Upon receiving (350) the indication information from the terminal device 110, the network device 120 can determine which one(s) of the third number of CSI reports is applicable and / or which one(s) of the third number of CSI reports is inapplicable.

[0106] In some example embodiments, the indication information may be communicated via a media access control (MAC) control element (CE). In some example embodiments, based on a determination of the applicability, the terminal device 110 may transmit indication information indicating at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is applicable. For example, if any of N2 CSI reports are determined as applicable (based on the performance monitoring at the terminal device 110), the terminal device 110 may trigger an MAC-CE transmission where at least one identity of at least one CSI report configuration (represented as CSIreportConfigIDs) is reported as an applicable CSI report.

[0107] In some example embodiments, the indication information may alternatively or additionally indicate at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is inapplicable. For example, if any of N2 CSI reports are determined as not applicable (based on performance monitoring at the terminal device 110), the terminal device 110 may trigger an MAC-CE transmission where the corresponding CSIreportConfigIDs is reported as an not applicable CSI report.

[0108] In summary, if any of N2 CSI reports (belonging to the same reporting mode)are determined as advanced applicable (based on performance monitoring at the terminal device 110), the terminal device 110 may trigger a MAC-CE transmission where corresponding CSIreportConfigIDs can be indicated using the MAC-CE bit field. The network device 120 receives CSIreportConfigIDs and will not send an activation / selection / switch command asking the terminal device 110 to enable the CSI reporting corresponding to that inapplicable CSI report. If the network device 120 determine from the received indication information that at least one of the third number of CSI reports is inapplicable, the network device 120 may disable a transmission of an activation command to the first apparatus to activate CSI reporting corresponding to the at least one inapplicable CSI report.

[0109] After any indication of not applicability of a CSI report within the N2 CSI reports, the terminal device 110 is not expected to receive an activation / selection / switch command that asking to enable the CSI reporting corresponding to that inapplicable CSI report.

[0110] In some example embodiments, if a CSI report amongst the third number of CSI reports is determined as inapplicable and is used in inference of AI / ML-based beam prediction, the terminal device 110 may switch, according to a predefined rule, to one of the second number of CSI reports corresponding to the second number of CSI report configurations for use in the inference of AI / ML-based beam prediction. For example, if the inapplicable CSI report is already used in the inference operation, the terminal device 110 may be defined with a rule to switch into a CSI report within N1 CSI reports within the same reporting mode.

[0111] The example embodiments of the present disclosure provides an enhanced CSI Reporting framework where the network device 120 configures, and the terminal device determines basic functionalities. Further, the terminal device can report a specific set of applicable functionalities evaluated based on assessing the performance of UE side of more advanced functionalities. This provides an approach for consistency of inference at the terminal device on CSI configurations with minimum specification impact has been proposed. The mechanism for consistency of inference at the terminal device on CSI configurations can be applicable for different variants, e.g., BM-Casel and BM-Case2.

[0112] FIG. 4 shows a flowchart of an example method 400 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 400 will be described from the perspective of the first apparatus. The first apparatus may be or may be comprised in a terminal device, e.g., the terminal device 110 in FIG. 1.

[0113] At block 410, the first apparatus receives, from a second apparatus (which may be or may be comprised in the network device 120), configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (Al / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction.

[0114] At block 420, the first apparatus determines, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction.

[0115] At block 430, the first apparatus determines applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations.

[0116] At block 440, the first apparatus transmits, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

[0117] In some example embodiments, the method 400 further comprises: receiving, from the second apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

[0118] In some example embodiments, the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

[0119] In some example embodiments, the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; and wherein the third number of CSI report configurations corresponding to the advanced functionalities are determined as applicable based on additional feedback.

[0120] In some example embodiments, determining the third number of CSI report configurations comprises: determining remaining CSI report configurations amongst the first number of CSI report configurations other than the second number of CSI report configurations, to be the third number of CSI report configurations.

[0121] In some example embodiments, determining applicability of at least one of the third number of CSI reports comprises: measuring respective downlink reference signal from the second apparatus; determining inference performances by running inactive inference of AI / ML-based beam prediction based on measurement results of the respective downlink reference signals and the third number of CSI report configurations; and determining applicability of at least one of the third number of CSI reports based on the inference performances.

[0122] In some example embodiments, transmitting indication information comprises: transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating at least one of the following: at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is applicable, or at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is inapplicable.

[0123] In some example embodiments, the indication information is transmitted via a media access control (MAC) control element (CE).

[0124] In some example embodiments, the method 400 further comprises: in accordance with a determination that a CSI report amongst the third number of CSI reports is determined as inapplicable and is used in inference of AI / ML-based beam prediction, switching, according to a predefined rule, to one of the second number of CSI reports corresponding to the second number of CSI report configurations for use in the inference of AI / ML-based beam prediction.

[0125] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

[0126] FIG. 5 shows a flowchart of an example method 500 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the second apparatus, which may be or may be comprised in the network device 120 FIG. 1.

[0127] At block 510, the second apparatus transmits, to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction.

[0128] At block 520, the second apparatus receives, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

[0129] In some example embodiments, the method 500 further comprises: in accordance with a determination, based on the indication information, that at least one of the third number of CSI reports is inapplicable, disable a transmission of an activation command to the first apparatus to activate CSI reporting corresponding to the at least one inapplicable CSI report.

[0130] In some example embodiments, the method 500 further comprises: transmitting, to the first apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

[0131] In some example embodiments, the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

[0132] In some example embodiments, the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; and wherein the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable based on additional feedback.

[0133] In some example embodiments, receiving indication information comprises: receiving, from the first apparatus, indication information indicating at least one of the following: at least one identity of at least one CSI report configuration for which the at least one CSI report is applicable out of the third number of CSI report configurations, or at least one identity of at least one CSI report configuration for which the at least one CSI report is inapplicable out of the third number of CSI report configurations.

[0134] In some example embodiments, the indication information is transmitted via a media access control (MAC) control element (CE).

[0135] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

[0136] In some example embodiments, a first apparatus capable of performing any of the method 400 (for example, the first apparatus or the terminal device 110 in FIG. 1) may comprise means for performing the respective operations of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first apparatus or the terminal device 110 in FIG. 1.

[0137] In some example embodiments, the first apparatus comprises means for receiving, from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; means for determining, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction; means for determining applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; and means for transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

[0138] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

[0139] In some example embodiments, the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

[0140] In some example embodiments, the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; and wherein the third number of CSI report configurations corresponding to the advanced functionalities are determined as applicable based on additional feedback.

[0141] In some example embodiments, the means for determining the third number of CSI report configurations comprises: means for determining remaining CSI report configurations amongst the first number of CSI report configurations other than the second number of CSI report configurations, to be the third number of CSI report configurations.

[0142] In some example embodiments, the means for determining applicability of at least one of the third number of CSI reports comprises: means for measuring respective downlink reference signal from the second apparatus; means for determining inference performances by running inactive inference of AI / ML-based beam prediction based on measurement results of the respective downlink reference signals and the third number of CSI report configurations; and means for determining applicability of at least one of the third number of CSI reports based on the inference performances.

[0143] In some example embodiments, the means for transmitting indication information comprises: means for transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating at least one of the following: at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is applicable, or at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is inapplicable.

[0144] In some example embodiments, the indication information is transmitted via a media access control (MAC) control element (CE).

[0145] In some example embodiments, the first apparatus further comprises: in accordance with a determination that a CSI report amongst the third number of CSI reports is determined as inapplicable and is used in inference of AI / ML-based beam prediction, means for switching, according to a predefined rule, to one of the second number of CSI reports corresponding to the second number of CSI report configurations for use in the inference of AI / ML-based beam prediction.

[0146] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

[0147] In some example embodiments, the first apparatus further comprises means for performing other operations in some example embodiments of the method 400 or the first apparatus or the terminal device 110. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the first apparatus.

[0148] In some example embodiments, a second apparatus capable of performing any of the method 500 (for example, the second apparatus or the network device 120 in FIG. 1) may comprise means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The second apparatus may be implemented as or included in the second apparatus or the network device 120 in FIG. 1.

[0149] In some example embodiments, the second apparatus comprises means for transmitting, to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations (of the same reporting mode) for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; and means for receiving, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

[0150] In some example embodiments, the second apparatus further comprises: means for in accordance with a determination, based on the indication information, that at least one of the third number of CSI reports belonging to the same reporting mode of operation is inapplicable, disable a transmission of an activation command to the first apparatus to activate CSI reporting corresponding to the at least one inapplicable CSI report.

[0151] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

[0152] In some example embodiments, the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

[0153] In some example embodiments, the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; and wherein the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable based on additional feedback.

[0154] In some example embodiments, the means for receiving indication information comprises: means for receiving, from the first apparatus, indication information indicating at least one of the following: at least one identity of at least one CSI report configuration for which the at least one CSI report is applicable out of the third number of CSI report configurations, or at least one identity of at least one CSI report configuration for which the at least one CSI report is inapplicable out of the third number of CSI report configurations.

[0155] In some example embodiments, the indication information is transmitted via a media access control (MAC) control element (CE).

[0156] In some example embodiments, the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

[0157] In some example embodiments, the second apparatus further comprises means for performing other operations in some example embodiments of the method 500 or the second apparatus or the network device 120. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the second apparatus.

[0158] FIG. 6 is a simplified block diagram of a device 600 that is suitable for implementing example embodiments of the present disclosure. The device 600 may be provided to implement a communication device, for example, the first apparatus or the second apparatus, or the terminal device 110 or the network device 120 as shown in FIG. 1. As shown, the device 600 includes one or more processors 610, one or more memories 620 coupled to the processor 610, and one or more communication modules 640 coupled to the processor 610.

[0159] The communication module 640 is for bidirectional communications. The communication module 640 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 640 may include at least one antenna.

[0160] The processor 610 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 600 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0161] The memory 620 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 624, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), an optical disk, a laser disk, and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 622 and other volatile memories that will not last in the power-down duration.

[0162] A computer program 630 includes computer executable instructions that are executed by the associated processor 610. The instructions of the program 630 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 630 may be stored in the memory, e.g., the ROM 624. The processor 610 may perform any suitable actions and processing by loading the program 630 into the RAM 622.

[0163] The example embodiments of the present disclosure may be implemented by means of the program 630 so that the device 600 may perform any process of the disclosure as discussed with reference to FIG. 3 to FIG. 6. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0164] In some example embodiments, the program 630 may be tangibly contained in a computer readable medium which may be included in the device 600 (such as in the memory 620) or other storage devices that are accessible by the device 600. The device 600 may load the program 630 from the computer readable medium to the RAM 622 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0165] FIG. 7 shows an example of the computer readable medium 700 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 700 has the program 630 stored thereon.

[0166] Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, and other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. Although various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0167] Some example embodiments of the present disclosure also provide at least one computer program product tangibly stored on a computer readable medium, such as a non-transitory computer readable medium. The computer program product includes computerexecutable instructions, such as those included in program modules, being executed in a device on a target physical or virtual processor, to carry out any of the methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[0168] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0169] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0170] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, although several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated, certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable sub-combination.

[0172] Although the present disclosure has been described in languages specific to structural features and / or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0173] A list of all used abbreviations together with their representation in unshortened form. Widely established and unique abbreviations can be assumed as known. All others should be listed here. AI- Artificial Intelligence BM- Beam management BM-CASE1- beam management case 1 BM-CASE2- beam management case 2 BP- beam prediction CSI - Channel State Information CSI-RS - channel state information reference signal DCI - Down Link Control Information DL - Down Link gNB - 5G / NR base station ML - Machine Learning LCM - life cycle management NR New Radio the network device 120 - Network NZP-CSI-RS - nonzero power channel state information reference signal RAN - Radio Access Network SSB - Synchronization Signal Block UE - User Equipment UL - Uplink RRC - Radio Access Control RSRP - Reference Signal Received Signal Tx - Transmitter ZP-CSI-RS - zero-power channel state information reference signal

Claims

1. A first apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to:receive, from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction;determine, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction;determine applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; andtransmit, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

2. The first apparatus of claim 1, wherein the first apparatus is caused to:receive, from the second apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

3. The first apparatus of claim 1, wherein the configuration information indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

4. The first apparatus of any of claims 1 to 3, wherein the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; andwherein the third number of CSI report configurations corresponding to the advanced functionalities are determined as applicable based on additional feedback.

5. The first apparatus of any of claims 1 to 4, wherein the first apparatus is caused to:determine remaining CSI report configurations amongst the first number of CSI report configurations other than the second number of CSI report configurations, to be the third number of CSI report configurations.

6. The first apparatus of any of claims 1 to 5, wherein the first apparatus is caused to: measure respective downlink reference signal from the second apparatus;determine inference performances by running inactive inference of AI / ML-based beam prediction based on measurement results of the respective downlink reference signals and the third number of CSI report configurations; anddetermine applicability of at least one of the third number of CSI reports based on the inference performances.

7. The first apparatus of any of claims 1 to 6, wherein the first apparatus is caused to: transmit, to the second apparatus and based on a determination of the applicability, indication information indicating at least one of the following:at least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is applicable, orat least one identity of at least one CSI report configuration out of the third number of CSI report configurations for which the at least one CSI report is inapplicable.

8. The first apparatus of any of claims 1 to 7, wherein the indication information is transmitted via a media access control (MAC) control element (CE).

9. The first apparatus of any of claims 1 to 8, wherein the first apparatus is further caused to:in accordance with a determination that a CSI report amongst the third number of CSI reports is determined as inapplicable and is used in inference of AI / ML-based beam prediction, switch, according to a predefined rule, to one of the second number of CSI reports corresponding to the second number of CSI report configurations for use in theinference of AI / ML-based beam prediction.

10. The first apparatus of any of claims 1 to 9, wherein the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

11. A second apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus at least to:transmit, to a first apparatus, configuration information indicating a second number of channel state information (C SI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; andreceive, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

12. The second apparatus of claim 11, wherein the second apparatus is further caused to:in accordance with a determination, based on the indication information, that at least one of the third number of CSI reports is inapplicable, disable a transmission of an activation command to the first apparatus to activate CSI reporting corresponding to the at least one inapplicable CSI report.

13. The second apparatus of claim 11 or 12, wherein the second apparatus is caused to: transmit, to the first apparatus, further configuration information indicating the first number of CSI report configurations, the further configuration information being separate from the configuration information comprising the second number of CSI report configurations.

14. The second apparatus of any of claims 11 to 13, wherein the configurationinformation indicates the first number of CSI report configurations, each of the first number of CSI report configurations comprising an indicator to indicate whether the CSI report configuration is one of the second number of CSI report configurations.

15. The second apparatus of any of claims 11 to 14, wherein the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable without additional feedback; andwherein the second number of CSI report configurations corresponding to the basic functionalities are determined as applicable based on additional feedback.

16. The second apparatus of any of claims 11 to 15, wherein the second apparatus is caused to:receive, from the first apparatus, indication information indicating at least one of the following:at least one identity of at least one CSI report configuration for which the at least one CSI report is applicable out of the third number of CSI report configurations, orat least one identity of at least one CSI report configuration for which the at least one CSI report is inapplicable out of the third number of CSI report configurations.

17. The second apparatus of any of claims 11 to 16, wherein the indication information is transmitted via a media access control (MAC) control element (CE).

18. The second apparatus of any of claims 11 to 17, wherein the first apparatus is or is comprised in a terminal device, and the second apparatus is or is comprised in a network device.

19. A method comprising:receiving, by a first apparatus and from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction;determining, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction;determining applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; andtransmitting, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

20. A method comprising:transmitting, by a second apparatus and to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction; andreceiving, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration corresponding to advanced functionalities for AI / ML-based beam prediction.

21. A first apparatus comprising:means for receiving, from a second apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic functionalities for the AI / ML-based beam prediction;means for determining, based on the configuration information, a third number of CSI report configurations out of the first number of CSI report configurations to be corresponding to advanced functionalities for AI / ML-based beam prediction;means for determining applicability of at least one of the third number of CSI reports based on the third number of CSI report configurations; andmeans for transmitting, to the second apparatus and based on a determination of the applicability, indication information indicating whether at least one of the third number of CSI reports is applicable or is inapplicable.

22. A second apparatus comprising:means for transmitting, to a first apparatus, configuration information indicating a second number of channel state information (CSI) report configurations amongst a first number of CSI report configurations for artificial intelligence / machine learning (AI / ML)-based beam prediction, the second number of CSI report configurations corresponding to basic 5 functionalities for the AI / ML-based beam prediction; andmeans for receiving, from the first apparatus, indication information indicating whether at least one of a third number of CSI reports is applicable or is inapplicable, the third number of CSI reports being based on the third number of CSI report configurations out of the first number of CSI report configurations, the third number of CSI report configuration 10 corresponding to advanced functionalities for AI / ML-based beam prediction.

23. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 19 or the method of claim 20.41

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