Configuration for capability indication for artificial intelligence / machine learning

Abstract

Exemplary embodiments of the present disclosure relate to configuration for capability indication for artificial intelligence / machine learning (AI / ML). In an aspect, a terminal device obtains at least one reporting criterion for capability information. Then, the terminal device reports, based on the obtained at least one reporting criterion, one or more applicable AI / ML-enabled functionalities based on the at least one reporting criterion being fulfilled.

Description

CONFIGURATION FOR CAPABILITY INDICATION FOR ARTIFICIAL INTELLIGENCE / MACHINE LEARNINGCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from, and the benefit of, US Provisional Application No. 63 / 736312, filed December 19, 2024, which is hereby incorporated by reference in its entirety.FIELD

[0002] Exemplary embodiments of the present disclosure generally relate to the field of communications, and in particular, to apparatuses, methods and a computer-readable storage medium for configuration for capability indication for artificial intelligence / machine learning (AI / ML).BACKGROUND

[0003] A communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network. A mobile or wireless communication network is one example of a communication network.

[0004] Such communication networks operate in according with standards such as those provided by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of standards are the so-called 5G (5th Generation) standards provided by 3GPP.SUMMARY

[0005] In general, exemplary embodiments of the present disclosure provide a solution for configuration for capability indication for AI / ML.

[0006] In a first aspect, there is provided a terminal device. The terminal device comprises: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: obtain at least one reporting criterion for capability information; and report, based on the obtained at least one reporting criterion, one or more applicable Artificial I ntelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0007] In a second aspect, there is provided a network device. The network device comprises: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to: transmit, to a terminal device, at least one reporting criterion for capability information; and receive, from the terminal device, one or more applicable Artificial I ntelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0008] In a third aspect, there is provided a method performed by a terminal device. The method comprises: obtaining at least one reporting criterion for capability information; and reporting, based on theobtained at least one reporting criterion, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled

[0009] In a fourth aspect, there is provided a method performed by a network device. The method comprises: transmitting, to a terminal device, at least one reporting criterion for capability information; and receiving, from the terminal device, one or more applicable Artificial Intelligence / Machine Learning (AI / ML)- enabled functionalities based on the at least one reporting criterion being fulfilled

[0010] In a fifth aspect, there is provided an apparatus. The apparatus comprises: means for obtaining at least one reporting criterion for capability information; and means for reporting, based on the obtained at least one reporting criterion, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0011] In a sixth aspect, there is provided an apparatus. The apparatus comprises: means for transmitting, to a terminal device, at least one reporting criterion for capability information; and means for receiving, from the terminal device, one or more applicable Artificial Intelligence / Machine Learning (AI / ML)- enabled functionalities based on the at least one reporting criterion being fulfilled

[0012] In a seventh aspect, there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method according to third or fourth aspect.

[0013] In an eighth aspect, there is provided a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to perform at least the method according to third or fourth aspect.

[0014] In a ninth aspect, there is provided a terminal device. The terminal device comprises: obtaining circuitry configured to obtain at least one reporting criterion for capability information; and reporting circuitry configured to report, based on the obtained at least one reporting criterion, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0015] In a tenth aspect, there is provided a network device. The network device comprises: transmitting circuitry configured to transmit, to a terminal device, at least one reporting criterion for capability information; and receiving circuitry configured to receive, from the terminal device, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0016] 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

[0017] Some exemplary embodiments will now be described with reference to the accompanying drawings, in which:

[0018] Fig. 1 illustrates an example of a network environment in which some exemplary embodiments of the present disclosure may be implemented;

[0019] Fig. 2 illustrates an example signaling process in accordance with some embodiments of the present disclosure;

[0020] Fig. 3 illustrates an example signaling process for terminal device-sided model for Al positioning in accordance with some embodiments of the present disclosure;

[0021] Fig. 4 illustrates a flowchart of an example method implemented at terminal device in accordance with some exemplary embodiments of the present disclosure;

[0022] Fig. 5 illustrates a flowchart of an example method implemented at network device in accordance with some exemplary embodiments of the present disclosure;

[0023] Fig. 6 illustrates a simplified block diagram of a device that is suitable for implementing some exemplary embodiments of the present disclosure; and

[0024] Fig. 7 illustrates a block diagram of an example of a computer-readable medium in accordance with some exemplary embodiments of the present disclosure.

[0025] Throughout the drawings, the same or similar reference numerals represent the same or similar elements.DETAILED DESCRIPTION

[0026] Principles of the present disclosure will now be described with reference to some exemplary embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein may be implemented in various manners other than the ones described below.

[0027] 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.

[0028] 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.

[0029] It shall be understood that although the terms “first” and “second” etc. 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. 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 exemplary embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. As used herein, “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.

[0031] 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 (for example, firmware) for operation, but the software may not be present when it is not needed for operation.

[0032] 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.

[0033] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as 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-loT) 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 fourth generation (4G), 4.5G, the future fifth generation (5G) communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will 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.

[0034] As used herein, the term “network device” (also referred to as “network node”) 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), a NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology.

[0035] 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, vehiclemounted 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 (for example, remote surgery), an industrial device and applications (for example, a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0036] 3GPP is now actively pursuing AI / ML for Air Interface. For AI / ML for positioning, case 1 , it has been recently agreed in 3GPP that: item 1) UE reports its supported functionalities to location management function (LMF) via LTE positioning protocol (LPP) Provide Capabilities message to LMF, which LMF canrequest its reporting via LPP request capabilities message; and item 2) then, at a subsequent step (see Step 4 of procedure for LCM for UE sided model for Al positioning case 1 in the agreement of RAN2#127bis ), UE reports its applicable functionalities to LMF via LPP Provide Capabilities message. For item 2), it is still open whether and how LMF controls the UE sending unsolicited LPP provide capabilities, for example, whether LMF controls the UE sending unsolicited LPP provide capabilities and signaling details are for further study.

[0037] LMF control of UE’s sending unsolicited capabilities is crucial depending on the interpretation of applicable functionality. For example, if applicable functionalities refer to (a part of) UE capabilities directly, which could be also referred to as “dynamic capability”, UE may not (frequently) update its capability information since capability information is expected to be (semi-)static by NW in order to be able to configure necessary procedures and behaviors at the UE. Further, for example, if applicable functionalities refer to functionalities that the UE is ready to apply for inference, e.g., when it has AI / ML model(s) available to support NW-side (additional) conditions, e.g., those provided in Step 3 of procedure for LCM for UE sided model for Al positioning case 1 in the agreement in RAN2#127bis, then NW would again expect that this may remain static at least, e.g., within the following conditions: i) during the positioning session to perform inference; ii) as long as the UE remains within the validity area of positioning reference signal (PRS) (or assistance data provided by NW); ill) for a certain duration if UE is requested to provide location information periodically; iv) as long as UE doesn’t change its serving cell, etc.

[0038] However, currently there are no guarantees whether or how long applicable functionalities indicated by UE are valid or stay constant. Further, with unsolicited LPP Provide Capabilities message, UE would be free to send any updates to its applicable functionalities at any time and as many times as it wants, which challenges NW to ensure stable operation of AI / ML-based positioning. In particular, NW will need to configure the UE again each time UE sends an update, e.g., signal a new / different assistance information and inference configuration, e.g., location information request, which results in unnecessary signaling overhead and inefficient operation.

[0039] For illustrative purposes, principles and example embodiments of the present disclosure for configuration for capability indication for AI / ML will be described below with reference to Figs. 1-7. However, it is to be noted that these embodiments are given to enable the skilled in the art to understand concepts of the present disclosure and implement the solution as proposed herein, and not intended to limit scope of the present disclosure in any way.

[0040] Fig. 1 illustrates an example of a network environment 100 in which some exemplary embodiments of the present disclosure may be implemented. In the descriptions of the exemplary embodiments of the present disclosure, the network environment 100 may also be referred to as a communication system 100 (for example, a portion of a communication network). For illustrative purposes only, various aspects of exemplary embodiments will be described in the context of one or more terminal devices and network devicesthat communicate with one another. It should be appreciated, however, that the description herein may be applicable to other types of apparatus or other similar apparatuses that are referenced using other terminology.

[0041] The network device 102 may provide services to the terminal device 101 , and the network device 102 and the terminal device 101 may communicate data and control information with each other. In some exemplary embodiments, the network device 102 and the terminal device 101 may communicate with direct links / channels.

[0042] In the communication system 100, a link from the network device 102 to the terminal device 101 is referred to as a downlink (DL), while a link from the terminal device 101 to the network device 102 is referred to as an uplink (UL). In downlink, the network device 102 is a transmitting (TX) device (or a transmitter) and the terminal device 101 is a receiving (RX) device (or a receiver). In uplink, the terminal device 101 is a transmitting (TX) device (or a transmitter) and the network device 102 is a RX device (or a receiver).

[0043] Communications in the network environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the fourth generation (4G) and the fifth generation (5G) and on 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.

[0044] It is to be understood that the number of devices and their connection relationships and types shown in Fig. 1 are for illustrative purposes only without suggesting any limitation. The communication system 100 may comprise any suitable number of devices adapted for implementing embodiments of the present disclosure.

[0045] The application of AI / ML to wireless communications has been limited to implementation-based approaches, both, at the network and the UE sides. However, augmenting the air-interface with features enabling improved support of AI / ML based algorithms may potentially offer enhanced performance e.g., improved throughput, robustness, accuracy or reliability, etc. depending on the use cases as well as reduced complexity / overhead. To this end, 3GPP is now actively pursuing AI / ML for Air Interface. The AI / ML for air interface study in Rel. 18 [RP-213599] has defined the functionality framework for various AI / ML use cases, including positioning, beam management and CSI compression / feedback, which is captured in the resulting TR 38.843.

[0046] For example, for UE-side models and UE-part of two-sided models, regarding AI / ML functionalityidentification, the UE indicates supported functionalities / functionality for a given sub-use-case, and the UE capability reporting is taken as starting point.

[0047] For AI / ML model identification, models are identified by model ID at the Network. UE indicates supported AI / ML models. In functionality-based life cycle management (LCM), network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signalling (e.g., radio resource control (RRC), media access control control element (MAC-CE), downlink control information (DO)). Models may not be identified at the Network, and UE may perform model-level LCM. Whether and how much awareness / interaction NW may have about model-level LCM requires further study. For functionality identification, there may be either one or more than one Functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled Feature refers to a Feature where AI / ML may be used. It should be noted that UE may have one AI / ML model for the functionality, or UE may have multiple AI / ML models for the functionality.

[0048] For AI / ML functionality identification and functionality-based LCM of UE-side models and / or UE- part of two-sided models, functionality refers to an AI / ML-enabled Feature / feature group (FG) enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of AI / ML-enabled Feature / FG or specific configurations of an AI / ML-enabled Feature / FG.

[0049] After functionality identification, necessity, mechanisms, for UE to report updates on applicable functionality(es) among functionality(es) are studied, where the applicable functionalities may be a subset of all functionalities. Applicable functionalities can be reported by the UE.

[0050] For functionality / model-ID based LCM, once functionalities / models are identified, the same or similar procedures may be used for their activation, deactivation, switching, fallback, and monitoring.

[0051] For UE capability reporting, the UE capability framework serves as the baseline to report UE’s supported AI / ML-enabled Feature / FG. Therefore, for CSI and beam management use cases, this information is indicated in UE AS capability in RRC (e.g., UECapabilityEnquiry / UECapabilitylnformation). While for positioning use cases, it is indicated by the positioning capability as defined in LPP.

[0052] For reporting applicability-related information, AI / ML models for a given use case may be tailored towards and applicable to specific scenarios, locations, configuration, deployments, among other factors. In this regard, it is acknowledged that AI / ML models may undergo updates, such as model changes, as an inherent part of their development. Therefore, to ensure efficient network control and management, especially associated to what concerns the UE-side, UEs might have the ability to indicate relevant information about their supported AI / ML models and concerning AI / ML functionalities to the network. This can allow the network to perform decisions regarding, e.g., the (de)activation, or switching of AI / ML functionalities and AI / ML models. The previously mentioned information could in principle be understood as “applicability-related information” in which the UE could, for example, report, to the network, conditionsunder which a model / functionality is applicable / suitable, or whether model(s) / functionality(es) are (non)applicable under the current context. Note, however, that the existing UE capability reporting framework cannot be used for such purposes.

[0053] Two UE reporting types, that enable UEs to report applicability-related information, are identified to convey this additional information, i.e. , “reactive” reporting, and “proactive” reporting. A reactive reporting would involve the UE to provide information to the network upon receiving an action from it. While a proactive reporting would involve the UE to provide information to the network without necessarily receiving an action from it. For example, the UE might proactively inform the RAN of updates / changes to its supported model(s) or functionality(es).

[0054] During the Rel-19 Wl on AI / ML for NR Air Interface [RP-234039], in earlier RAN2 meetings, it has been discussed to support standardizing features / FGs and functionalities enabled by AI / ML, their management, e.g., activation of a functionality by network, indication by UE regarding its supported functionalities and applicable functionalities, as well as their definitions. The following agreements have been reached.

[0055] In RAN2#125bis, it has the agreement that which AI / ML-enabled Features / FGs and functionalities are supported should be standardized, and supported AI / ML-enabled Features / FGs and supported functionalities are included in UE capability, “supported” means that the UE is capable of supporting the functionality and doesn’t mean necessarily that the UE has the model available. It has further agreed that support proactive reporting of UE-sided applicable functionality, e.g., the UE reports its applicable AI / ML functionalities via UAI message / LPP message; and support reactive reporting of UE-sided applicable functionality. The NW configures AI / ML functionalities via RRC / LPP message

[0056] For UE-sided model, for the functionality management, the “network decision, network-initiated” AI / ML management is supported as a baseline. The following can be considered further “UE autonomous, decision reported to the network”, “Network decision, UE-initiated” (i.e. proactive approach).

[0057] In RAN2#126, it has the agreement that the UE will indicate the gNB / LMF whether the AI / ML functionality is available / applicable. For a functionality to be applicable at least there may be at least one model available within it. It has also agreed that the LPP Capability Transfer procedures (RequestCapabilities / ProvideCapabilities messages) are used to indicate supported AI / ML positioning capabilities.

[0058] In more recent meetings, RAN2 has progressed to define supported, applicable, and activated, functionalities, and how these are communicated between UE and NW as part of LCM, with the following agreements in RAN2#127 that (1) applicable functionalities refer to functionalities that the UE is ready to apply for inference.

[0059] In RAN2#127bis, it has also agreed the following procedures for LCM for UE sided model for Al positioning case 1 is the baseline. The procedures comprise step 1 : location management function (LMF)may request the UE to report the supported functionalities at the UE side by LPP request capabilities message; step 2: UE sends LPP provide capabilities message to LMF with the supported functionalities at the UE side; step 3: LMF sends the LPP provide assistance data message (which may contain network side additional condition); step 4: UE reports the applicable functionality to the LMF by the LPP provide capabilities messages; step 5: The LMF requests the inferred location information using the LPP request location information message; and step 6: UE reports the inferred location using LPP provide location information message. In RAN2#127bis, whether LMF controls the UE sending unsolicited LPP provide capabilities (i.e. whether step4 is sent reactively or proactively) and the signaling details are for further study.

[0060] On RAN2#128 meeting, companies have discussed regarding the items for further study in the latest RAN2#127bis agreement. For example, in RAN2#128, it has agreed that for positioning Case 1 , RAN2 confirm that the existing unsolicited UE capability report mechanism in LPP can support UE to report the applicable functionality in both “proactive” and “reactive” as a baseline. In a proactive case, when the applicability change, UE can send an unsolicited LPP ProvideCapabilities message to LMF. In a reactive case, if the applicability changes based on the configuration in LPP ProvideAssistanceData message in step 3, UE can send an unsolicited LPP ProvideCapabilities message to LMF. However, the configuration details are for further study.

[0061] Therefore, for AI / ML for positioning, case 1 , it is still open whether and how LMF controls the UE sending unsolicited LPP provide capabilities, for example, whether LMF controls the UE sending unsolicited LPP provide capabilities and signaling details are for further study.

[0062] LMF control of UE’s sending unsolicited capabilities is crucial depending on the interpretation of applicable functionality. For example, if applicable functionalities refer to (a part of) UE capabilities directly, which could be also referred to as “dynamic capability”, UE may not (frequently) update its capability information since capability information is expected to be (semi-)static by NW in order to be able to configure necessary procedures and behaviors at the UE. Further, for example, if applicable functionalities refer to functionalities that the UE is ready to apply for inference, e.g., when it has AI / ML model(s) available to support NW-side (additional) conditions, e.g., those provided in Step 3 above, then NW would again expect that this may remain static at least, e.g., within the following conditions: i) during the positioning session to perform inference; ii) as long as the UE remains within the validity area of PRS (or assistance data provided by NW); iii) for a certain duration if UE is requested to provide location information periodically; iv) as long as UE doesn’t change its serving cell, etc.

[0063] However, currently, there are no guarantees whether or how long applicable functionalities indicated by UE are valid or stay constant. Further, with unsolicited LPP Provide Capabilities message, UE would be free to send any updates to its applicable functionalities at any time and as many times as it wants, which challenges NW to ensure stable operation of AI / ML-based positioning. In particular, NW will need to configure the UE again each time UE sends an update, e.g., signal a new / different assistance informationand inference configuration, e.g., location information request, which results in unnecessary signaling overhead and inefficient operation.

[0064] In view of the foregoing, an example signaling process 200 in accordance with some embodiments of the present disclosure will be described with referent to Fig. 2. For the purpose of discussion, the communication process 200 will be described with reference to Fig. 1 . It would be appreciated that although the communication process 200 has been described referring to the network environment 100 of Fig. 1 , this communication process 200 may be likewise applied to other similar communication scenarios. It should be appreciated that the terminal device 201 is an example of the terminal device 101 , and the network device 202 is an example of the network device 102.

[0065] As shown in Fig. 2, the network device 202 transmits (205) at least one reporting criterion for capability information to the terminal device 201. In this way, the terminal device 201 obtains (210) the reporting criterion for capability information from the message containing the reporting criterion received from the network device 202.

[0066] In some embodiments, the reporting criterion for capability information may be predefined in the specification, and the terminal device 201 may obtain the reporting criterion for capability information as predefined in the specification.

[0067] Then, the terminal device 201 transmits (215), based on the obtained at least one reporting criterion, one or more applicable AI / ML-enabled functionalities based on the at least one reporting being fulfilled. The network device 202 receives (220) the one or more applicable AI / ML-enabled functionalities.

[0068] By the process 200, with such criteria provided by network device to the terminal device or by the predefined criteria, the terminal device may then apply (i.e., select, generate, or download) suitable model(s) matching to network device needs, e.g., which can generalize well. In other words, by providing the reporting criterion (for example, in the reporting configuration) for capability information (for example, unsolicited capability information), the network is able indicate to the UE in advance the scenarios under which the network is willing to (not-) receive the capability information. This has an impact on model(s) generation / update, model availability (e.g., requiring downloading from UE’s own server) and selection at the UE. Then, the UE may report the applicable AI / ML-enabled functionalities to the NW based on the reporting criterion. This process does not cause frequent applicability reporting, and the network device will not need to configure the terminal device frequently, and will not result in unnecessary signaling overhead and inefficient operation.

[0069] In some embodiments, the one or more applicable AI / ML-enabled functionalities are a subset of the capability information. For example, the applicable AI / ML-enabled functionalities comprise capabilities of the terminal device; part of capabilities of the terminal device; one or more functionalities that the terminal device is ready to apply AI / ML inference; or any combination thereof.

[0070] In some embodiments, the at least one reporting criterion for capability information comprises amaximum number of capability reports that the terminal device is allowed to send within a positioning session, across a number of consecutive sessions, within an area including a service area, or within a time window; a minimum time interval between two consecutive capability reports; a reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities; an expected performance change of a functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities; or any combination thereof.

[0071] In some embodiments, the positioning session comprises a predetermined positioning session; and the number of consecutive sessions comprises a predetermined number of consecutive sessions. In some embodiments, the area including the service area is within the positioning session or within the number of consecutive sessions; and the timing window is a predetermined timing window. In some embodiments, the timing window is within the positioning session or within the number of consecutive sessions.

[0072] In some embodiments, before transmitting the at least one reporting criterion for capability information to the terminal device, the network device 202 may determine a reporting configuration comprising the at least one reporting criterion for capability information.

[0073] In some embodiments, before reporting the one or more applicable AI / ML-enabled functionalities, the terminal device 201 may further receive, from the network device, assistance information comprising at least one network -side condition; and determine, based on the received assistance information and the at least one reporting criterion, the one or more applicable AI / ML-enabled functionalities to be reported. For example, the assistance information comprising at least one network-side condition may be received before receiving the at least one reporting criterion. For example, the network-side conditions may comprise the network device-side conditions as well as the conditions between the network device and the terminal device (such as, channel conditions).

[0074] In some embodiments, before reporting one or more applicable AI / ML-enabled functionalities to the network device, the terminal device 201 may determine whether the at least one reporting criterion is fulfilled. Based on determining that the at least one reporting criterion is fulfilled, the terminal device 201 may report the one or more applicable AI / ML-enabled functionalities.

[0075] In some embodiments, the terminal device 201 may further report the one or more applicable AI / ML-enabled functionalities together with the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities. In some embodiments, the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities comprises one or more pre-defined reasons. In some embodiments, the pre-defined reasons comprise a change in model availability, a change in network-side condition, a change in terminal deviceside condition, a rare event occurrence that is observed by the terminal device, or any combination thereof.

[0076] In some embodiments, the terminal device 201 may further report the one or more applicable AI / ML-enabled functionalities together with the expected performance change of the functionality upon whichthe terminal device is required to update and report applicable AI / ML-enabled functionalities. In some embodiments, the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities comprises estimation accuracy below an accuracy threshold.

[0077] Fig. 3 illustrates an example signaling process for LCM for UE sided model for Al positioning in accordance with some embodiments of the present disclosure. It should be appreciated that the UE 301 is an example of the terminal device 101 , and the NW (for example, LMF) 302 is an example of the network device 102.

[0078] At 305, the NW 302 requests the UE 301 capability information relating to support of a feature / FG / functionality enabled by AI / ML, e.g., for positioning use case, LMF may request UE capabilities regarding support for certain types of measurements, PRS configurations, etc., via LPP Requestcapabilities message. At 310, the UE 301 reports the requested capability information, via LPP ProvideCapabilities message, e.g., for positioning use case. For example, the UE reports the types of measurements it supports, supported PRS bandwidth, etc. to LMF, as requested by LMF.

[0079] At 315, the NW 302 provides assistance data to UE 301 . The assistance data may contain: (1 ) reference signal configurations, e.g., DL PRS parameters, TRP information and the like; (2) any further NW- side (additional) conditions; or (3) any measurement and / or reporting configuration.

[0080] In some embodiments, any further NW-side condition may include geographical coordinates of TRPs, spatial direction information (e.g. azimuth, elevation etc.) of the DL-PRS Resources of the TRPs served by the gNB, Validity Area of the Assistance Data, TRP beam / antenna information (including azimuth angle, zenith angle and relative power between PRS resources per angle per TRP), SSB information of the TRPs (the time / frequency occupancy of SSBs), etc.

[0081] In some embodiments, any further NW-side condition may further include network synchronization information, e.g., in terms of error range / margin, TRP timing error information, e.g., error range / group ID, channel estimation error range, etc. In some embodiments, NW-side (additional) conditions may also be provided by means of an identifier (so-called “associated ID”) where the identifier may be mapped to different network-side conditions.

[0082] In some embodiments, the measurement and / or reporting configuration may be e.g., channel measurement parameters, e.g., measurement type (timing, phase, angle, frequency information to be measured such as PDP, DP, CIR), measurement window, starting time, number of paths, samples, etc.), reporting periodicity / event, etc.; or the combination thereof.

[0083] At 320, the NW 302 provides reporting configuration for unsolicited capability information, which could contain applicable functionalities (might be also referred to as dynamic capability indication).

[0084] In some embodiments, the reporting configuration may contain maximum number of unsolicited capability reports UE can send within any positioning session or across certain number of consecutivesessions, or within an area including service area, e.g., cell / gNB / RNA, (which could be also within any / certain session or across certain number of consecutive sessions) or within a certain time window (which could be also within any / certain session or across certain number of consecutive sessions).

[0085] In some embodiments, the reporting configuration may contain minimum time interval between two consecutive unsolicited capability reports.

[0086] In some embodiments, the reporting configuration may contain certain reason upon which UE is allowed to (or not allowed to) update and report its applicable functionality, e.g., according to pre-defined reasons, e.g., “change in model availability”, “change in NW-side additional condition”, “change in UE-side condition”, or “rare event occurrence”, and the like. In some embodiments, NW may further configure UE to report the underlying reason together with its applicability report.

[0087] In some scenarios, the NW, for a period of time, may not be willing to change functionality in case of certain cause(s). In this case, the network indicates this in advance. For example, when the network indicates in advance the disablement of unsolicited capability reporting due to “change in model availability”, the UE is compelled to choose a model which generalizes well.

[0088] In some other scenarios, the NW, for a period of time, may be willing to change functionality only in case of certain cause(s). In this case, the network indicates this in advance. For example, when the network indicates in advance the enablement of unsolicited capability reporting due to “rare event occurrence”, the UE can send the unsolicited capability report when it observes a rare event, e.g., emergency, natural disaster, hardware or software malfunction, etc. Upon rare event occurrence, the UE may need to frequently change the model during the rare event or switch to a rare-event specific model. Furthermore, UE may be provided with a list or type of rare events that is acceptable at the network.

[0089] In some embodiments, the reporting configuration may contain expected performance change of a functionality upon which UE is required to update and report its applicable functionality, e.g., if estimation accuracy below a certain threshold.

[0090] By providing the configuration for unsolicited capability information, the network is able indicate to the UE in advance the scenarios under which the network is willing to (not-)receive the unsolicited capability information. This has an impact on model(s) generation / update, model availability (e.g., requiring downloading from UE’s own server) and selection at the UE. In scenarios where the unsolicited reporting is disabled, the UE makes sure that it has the suitable models (e.g., models that generalize well across multiple scenarios) for the scenarios readily available without requiring to update the corresponding capability information.

[0091] At 325, the UE 301 determines applicable functionalities based on the assistance information received at 315 and also considering the reporting configuration received in previous step 320. For example, the UE finds a suitable AI / ML model that can support the PRS configuration received in assistance information, and also can satisfy the reporting configuration, e.g., by expecting this model / functionality to beapplicable for at least the indicated minimum time interval between functionality updates.

[0092] At 330, the UE 301 reports its applicable functionalities to the NW 302 according to the reporting configuration. At 335, based on the indicated supported functionality, the NW 302 determines LCM decision(s) for one or more functionality, e.g., to activate a functionality reported as applicable by the UE, or switching of AI / ML functionalities and AI / ML models, or fallback to non-AI / ML scenario.

[0093] At 340, the NW 302 requests UE to perform inference, by providing any further necessary configuration, e.g., measurement and reporting configuration, thus activates the functionality it determines. For example, LMF sends LPP Request Location Information for UE to report UE location using AI / ML with certain PRS configuration. At 345, the UE 301 provides requested inference outcome to NW, e.g., UE reports its location it estimated using the AI / ML model it has selected.

[0094] Fig. 4 illustrates a flowchart of an example method 400 implemented at a terminal device in accordance with some other embodiments of the present disclosure. For the purpose of discussion, the method 400 will be described from the perspective of the terminal device 101 with reference to Fig. 1.

[0095] As shown in Fig. 4, at block 410, the terminal device obtains at least one reporting criterion for capability information. At block 420, the terminal device reports, based on the obtained at least one reporting criterion, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0096] In some embodiments, the at least one reporting criterion for capability information comprises at least one of the following: a maximum number of capability reports that the terminal device is allowed to send within a positioning session, across a number of consecutive sessions, within an area including a service area, or within a time window; a minimum time interval between two consecutive capability reports; a reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities; or an expected performance change of a functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

[0097] In some embodiments, the positioning session comprises a predetermined positioning session; and the number of consecutive sessions comprises a predetermined number of consecutive sessions. In some embodiments, the area including the service area is within the positioning session or within the number of consecutive sessions; the timing window is a predetermined timing window; or the timing window is within the positioning session or within the number of consecutive sessions.

[0098] In some embodiments, the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities comprises one or more pre-defined reasons comprising at least one of the following: a change in model availability; a change in network-side condition; a change in terminal device-side condition; or a rare event occurrence that is observed by the terminal device. In some embodiments, the terminal device further reports the one or more applicable AI / ML-enabled functionalities together with the reason for which the terminal device is allowed to or not allowed to updateand report applicable AI / ML-enabled functionalities.

[0099] In some embodiments, the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities comprises estimation accuracy below an accuracy threshold. In some embodiments, the terminal device further reports the one or more applicable AI / ML-enabled functionalities together with the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML- enabled functionalities.

[0100] In some embodiments, the terminal device reports the one or more applicable AI / ML-enabled functionalities by determining whether the at least one reporting criterion is fulfilled; and based on determining that the at least one reporting criterion is fulfilled, reporting the one or more applicable AI / ML-enabled functionalities.

[0101] In some embodiments, the terminal device further receives, from the network device, assistance information comprising at least one network-side condition; and determines, based on the received assistance information and the at least one reporting criterion, the one or more applicable AI / ML-enabled functionalities.

[0102] In some embodiments, the capability information comprises the one or more applicable AI / ML- enabled functionalities, and the applicable AI / ML-enabled functionalities comprise at least one of the following: capabilities of the terminal device; part of capabilities of the terminal device; or one or more functionalities that the terminal device is ready to apply AI / ML inference.

[0103] In some embodiments, the terminal device obtains at least one reporting criterion for capability information by receiving, from the network device, a reporting configuration for capability information comprising the at least one reporting criterion for capability information. In some embodiments, the at least one reporting criterion for capability information is predefined.

[0104] In some embodiments, the capability information comprises unsolicited capability information.

[0105] Fig. 5 illustrates another flowchart of an example method 500 implemented at a network device in accordance with some other embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the network device 102 with reference to Fig. 1.

[0106] As shown in Fig. 5, at block 510, the network device transmits, to a terminal device, at least one reporting criterion for capability information. At block 520, the network device receives, from the terminal device, one or more applicable Artificial I ntelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0107] In some embodiments, the at least one reporting criterion for capability information comprises at least one of the following: a maximum number of capability reports that the terminal device is allowed to send within a positioning session, across a number of consecutive sessions, within an area including a service area, or within a time window; a minimum time interval between two consecutive capability reports; a reasonfor which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities; or an expected performance change of a functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

[0108] In some embodiments, the positioning session comprises a predetermined positioning session; or the number of consecutive sessions comprises a predetermined number of consecutive sessions. In some embodiments, the area including the service area is within the positioning session or within the number of consecutive sessions; the timing window is a predetermined timing window; or the timing window is within the positioning session or within the number of consecutive sessions.

[0109] In some embodiments, the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities comprises one or more pre-defined reasons comprising at least one of the following: a change in model availability; a change in network-side condition; a change in terminal device-side condition; or a rare event occurrence that is observed by the terminal device.

[0110] In some embodiments, the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities comprises estimation accuracy below an accuracy threshold. In some embodiments, capability information comprises the one or more applicable AI / ML-enabled functionalities; and the applicable AI / ML-enabled functionalities comprise at least one of the following: capabilities of the terminal device; part of capabilities of the terminal device; or one or more functionalities that the terminal device is ready to apply AI / ML inference.

[0111] In some embodiments, the capability information comprises unsolicited capability information. In some embodiments, the network device further determines a reporting configuration comprising the at least one reporting criterion for capability information.

[0112] In some embodiments, an apparatus (for example, the terminal device 101) capable of performing the method 400 may comprise means for performing the respective steps 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.

[0113] In some embodiments, the apparatus comprises means for obtaining at least one reporting criterion for capability information; and means for reporting, based on the obtained at least one reporting criterion, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0114] In some embodiments, the means for reporting the one or more applicable AI / ML-enabled functionalities reports the one or more applicable AI / ML-enabled functionalities together with the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities. In some embodiments, the means for reporting the one or more applicable AI / ML-enabled functionalities reports the one or more applicable AI / ML-enabled functionalities together with the expectedperformance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

[0115] In some embodiments, the means for reporting the one or more applicable AI / ML-enabled functionalities comprises means for determining whether the at least one reporting criterion is fulfilled; and means for based on determining that the at least one reporting criterion is fulfilled, reporting the one or more applicable AI / ML-enabled functionalities.

[0116] In some embodiments, the apparatus further comprises means for receiving, from the network device, assistance information comprising at least one network-side condition; and means for determining, based on the received assistance information and the at least one reporting criterion, the one or more applicable AI / ML-enabled functionalities.

[0117] In some embodiments, the means for obtaining the at least one reporting criterion obtains at least one reporting criterion for capability information by receiving, from the network device, a reporting configuration for capability information comprising the at least one reporting criterion for capability information.

[0118] In some embodiments, the apparatus further comprises means for performing other steps in some embodiments of the method 400. In some embodiments, the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.

[0119] In some embodiments, an apparatus (for example, the network device 102) capable of performing the method 500 may comprise means for performing the respective steps 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.

[0120] In some embodiments, the apparatus comprises means for transmitting, to a terminal device, at least one reporting criterion for capability information; and means for receiving, from the terminal device, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

[0121] In some embodiments, the apparatus further comprises means for determining a reporting configuration comprising the at least one reporting criterion for capability information.

[0122] In some embodiments, the apparatus further comprises means for performing other steps in some embodiments of the method 500. In some embodiments, the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.

[0123] Fig. 6 illustrates a simplified block diagram of a device 600 that is suitable for implementing some exemplary embodiments of the present disclosure. The device 600 may be provided to implement a communication device, for example, the network device 102 or the terminal device 101 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.

[0124] The communication module 640 is for bidirectional communications. The communication module 640 has at least one antenna to facilitate communication. The communication interface may represent any interface that is necessary for communication with other network elements.

[0125] 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.

[0126] 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), 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.

[0127] A computer program 630 includes computer executable instructions that are executed by the associated processor 610. The program 630 may be stored in the ROM 624. The processor 610 may perform any suitable actions and processing by loading the program 630 into the RAM 622.

[0128] The 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 above. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0129] In some exemplary 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. The computer-readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like.

[0130] Fig. 7 illustrates a block diagram of an example of a computer-readable medium 700 in accordance with some exemplary embodiments of the present disclosure. The computer-readable medium 700 has the program 630 stored thereon. It is noted that although the computer-readable medium 700 is depicted in form of CD or DVD, the computer-readable medium 700 may be in any other form suitable for carry or hold the program 630.

[0131] 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 implementedin hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While 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.

[0132] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computerexecutable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the method 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.

[0133] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, 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.

[0134] In the context of the present disclosure, the computer program codes 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.

[0135] 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. 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).

[0136] Further, while 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, while 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. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination.

[0137] 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.

Claims

WHAT IS CLAIMED IS:1 . A terminal device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: obtain at least one reporting criterion for capability information; and report, based on the obtained at least one reporting criterion, one or more applicable Artificial I ntelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

2. The terminal device of claim 1 , wherein the at least one reporting criterion for capability information comprises at least one of the following: a maximum number of capability reports that the terminal device is allowed to send within a positioning session, across a number of consecutive sessions, within an area including a service area, or within a time window; a minimum time interval between two consecutive capability reports; a reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities; or an expected performance change of a functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

3. The terminal device of claim 2, wherein the positioning session comprises a predetermined positioning session; and the number of consecutive sessions comprises a predetermined number of consecutive sessions.

4. The terminal device of claim 2 or 3, wherein the area including the service area is within the positioning session or within the number of consecutive sessions; the timing window is a predetermined timing window; or the timing window is within the positioning session or within the number of consecutive sessions.

5. The terminal device of any of claims 2 to 4, wherein the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities comprises one or more pre-defined reasons comprising at least one of the following: a change in model availability;a change in network-side condition; a change in terminal device-side condition; or a rare event occurrence that is observed by the terminal device.

6. The terminal device of any of claims 2 to 5, wherein the terminal device is further caused to: report the one or more applicable AI / ML-enabled functionalities together with the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities.

7. The terminal device of any of claims 2 to 6, wherein the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities comprises estimation accuracy below an accuracy threshold.

8. The terminal device of any of claims 2 to 7, wherein the terminal device is further caused to: report the one or more applicable AI / ML-enabled functionalities together with the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

9. The terminal device of any of claims 1 to 8, wherein the terminal device is caused to report the one or more applicable AI / ML-enabled functionalities by: determining whether the at least one reporting criterion is fulfilled; and based on determining that the at least one reporting criterion is fulfilled, reporting the one or more applicable AI / ML-enabled functionalities.

10. The terminal device of any of claims 1 to 9, wherein the terminal device is further caused to: receive, from the network device, assistance information comprising at least one network-side condition; and determine, based on the received assistance information and the at least one reporting criterion, the one or more applicable AI / ML-enabled functionalities.

11. The terminal device of any of claims 1 to 10, wherein the capability information comprises the one or more applicable AI / ML-enabled functionalities, and the applicable AI / ML-enabled functionalities comprise at least one of the following: capabilities of the terminal device; part of capabilities of the terminal device; or one or more functionalities that the terminal device is ready to apply AI / ML inference.

12. The terminal device of any of claims 1 to 11 , wherein the terminal device is caused to obtain at least one reporting criterion for capability information by: receiving, from the network device, a reporting configuration for capability information comprising the at least one reporting criterion for capability information.

13. The terminal device of any of claims 1 to 12, wherein the at least one reporting criterion for capability information is predefined.

14. The terminal device of any of claims 1 to 13, wherein the capability information comprises unsolicited capability information.

15. A network device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to: transmit, to a terminal device, at least one reporting criterion for capability information; and receive, from the terminal device, one or more applicable Artificial Intelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

16. The network device of claim 15, wherein the at least one reporting criterion for capability information comprises at least one of the following: a maximum number of capability reports that the terminal device is allowed to send within a positioning session, across a number of consecutive sessions, within an area including a service area, or within a time window; a minimum time interval between two consecutive capability reports; a reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities; or an expected performance change of a functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities.

17. The network device of claim 16, wherein the positioning session comprises a predetermined positioning session; or the number of consecutive sessions comprises a predetermined number of consecutive sessions.

18. The network device of claim 16 or 17, wherein the area including the service area is within the positioning session or within the number of consecutive sessions; the timing window is a predetermined timing window; or the timing window is within the positioning session or within the number of consecutive sessions.

19. The network device of any of claims 16 to 18, wherein the reason for which the terminal device is allowed to or not allowed to update and report applicable AI / ML-enabled functionalities comprises one or more pre-defined reasons comprising at least one of the following: a change in model availability; a change in network-side condition; a change in terminal device-side condition; or a rare event occurrence that is observed by the terminal device.

20. The network device of any of claims 16 to 19, wherein the expected performance change of the functionality upon which the terminal device is required to update and report applicable AI / ML-enabled functionalities comprises estimation accuracy below an accuracy threshold.

21. The network device of any of claims 15 to 20, wherein the capability information comprises the one or more applicable AI / ML-enabled functionalities; and the applicable AI / ML-enabled functionalities comprise at least one of the following: capabilities of the terminal device; part of capabilities of the terminal device; or one or more functionalities that the terminal device is ready to apply AI / ML inference.

22. The network device of any of claims 15 to 21 , wherein the capability information comprises unsolicited capability information.

23. The network device of any of claims 15 to 22, wherein the network device is further caused to: determine a reporting configuration comprising the at least one reporting criterion for capability information.

24. A method at a terminal device comprising: obtaining at least one reporting criterion for capability information; andreporting, based on the obtained at least one reporting criterion, one or more applicable Artificial I ntelligence / Machine Learning (AIZML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

25. A method at a network device comprising: transmitting, to a terminal device, at least one reporting criterion for capability information; and receiving, from the terminal device, one or more applicable Artificial I ntelligence / Machine Learning (AI / ML)-enabled functionalities based on the at least one reporting criterion being fulfilled.

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