Ensure consistency for machine learning procedure

Abstract

Example embodiments of the present disclosure are directed to a solution for ensuring consistency for artificial intelligence (AI) / machine learning (ML) procedure. A method comprises receiving, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus. [FIG. 5]

Description

ENSURE CONSISTENCY FOR MACHINE LEARNING PROCEDURECROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from US Provisional Application No. 63 / 701743, filed Oct. 1, 2024, which is hereby incorporated by reference in its entirety.FIELDS

[0002] 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 ensuring consistency for artificial intelligence (Al) / machine learning (ML) procedure.BACKGROUND

[0003] With developments in the integration of AI / ML within the 5G and emerging 6G new radio (NR) air interface, a new frontier in network adaptability and efficiency is being explored. The 3rdgeneration partner project (3GPP) Release-18 study item and 3GPP Release-19 work item emphasize the importance of model generalization across various network scenarios, addressing the need for AI / ML models to maintain robust performance under diverse conditions. This includes the strategic incorporation of additional conditions to refine model training, ensuring models are well-suited to both network-side and user equipment-side requirements. There may be a plurality of phases during a life cycle of an ML model. It is important to ensure the consistency between different phases such as model training and model inference.SUMMARY

[0004] I n 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 to: receive, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and associate the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0005] 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 to: determine, at least one respective first set of access node identities corresponding to at least one first area identity; and transmit, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least onefirst area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0006] In a third aspect of the present disclosure, there is provided a first apparatus. The third 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 to: transmit, to a second apparatus, a request for an area configuration; receive a response of the request from the second apparatus, the response indicating a first area configuration comprising at least first area identity; and determine, based at least in part on the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0007] In a fourth aspect of the present disclosure, there is provided a second apparatus. The fourth 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 to: receive, from a first apparatus, a request for an area configuration; determine a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and transmit, to the first apparatus, a response indicating the first area configuration.

[0008] In a fifth aspect of the present disclosure, there is provided a first apparatus. The fifth 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 to: transmit, to a second apparatus, at least one area configuration, each area configuration comprising at least area identity; receive, from the second device, a first area configuration determined by the second apparatus based on the at least one area configuration, wherein the first area configuration comprising at least first area identity; determine, based at least in part in the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0009] ln a sixth aspect of the present disclosure, there is provided a second apparatus. The sixth 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 to: receive, from a first apparatus, at least one area configuration, each area configuration comprising at least area identity; determine, based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and transmit the first area configuration to the first apparatus.

[0010] In a seventh aspect of the present disclosure, there is provided a method. The methodcomprises: receiving, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0011] In an eighth aspect of the present disclosure, there is provided a method. The method comprises: determining, at least one respective first set of access node identities corresponding to at least one first area identity; and transmitting, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0012] In a ninth aspect of the present disclosure, there is provided a method. The method comprises: transmitting, to a second apparatus, a request for an area configuration; receiving a response of the request from the second apparatus, the response indicating a first area configuration comprising at least first area identity; and determining, based at least in part on the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0013] In a tenth aspect of the present disclosure, there is provided a method. The method comprises: receiving, from a first apparatus, a request for an area configuration; determining a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and transmitting, to the first apparatus, a response indicating the first area configuration.

[0014] In an eleventh aspect of the present disclosure, there is provided a method. The method comprises: transmitting, to a second apparatus, at least one area configuration, each area configuration comprising at least area identity; receiving, from the second device, a first area configuration determined by the second apparatus based on the at least one area configuration, wherein the first area configuration comprising at least first area identity; determining, based at least in part in the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0015] In a twelfth aspect of the present disclosure, there is provided a method. The method comprises: receiving, from a first apparatus, at least one area configuration, each area configuration comprising at least area identity; determining, based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprisingat least one first area identity; and transmitting the first area configuration to the first apparatus.

[0016] In a thirteenth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for receiving, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and means for associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0017] In a fourteenth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for determining, at least one respective first set of access node identities corresponding to at least one first area identity; and means for transmitting, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0018] In a fifteenth aspect of the present disclosure, there is provided a first apparatus. The third apparatus comprises means for transmitting, to a second apparatus, a request for an area configuration; means for receiving a response of the request from the second apparatus, the response indicating a first area configuration comprising at least first area identity; and means for determining, based at least in part on the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0019] In a sixteenth aspect of the present disclosure, there is provided a second apparatus. The fourth apparatus comprises means for receiving, from a first apparatus, a request for an area configuration; means for determining a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and means for transmitting, to the first apparatus, a response indicating the first area configuration.

[0020] In a seventeenth aspect of the present disclosure, there is provided a first apparatus. The fifth apparatus comprises means for transmitting, to a second apparatus, at least one area configuration, each area configuration comprising at least area identity; means for receiving, from the second device, a first area configuration determined by the second apparatus based on the at least one area configuration, wherein the first area configuration comprising at least first area identity; means for determining, based at least in part in the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0021] In an eighteenth aspect of the present disclosure, there is provided a second apparatus. Thesixth apparatus comprises means for receiving, from a first apparatus, at least one area configuration, each area configuration comprising at least area identity; means for determining, based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and means for transmitting the first area configuration to the first apparatus.

[0022] In a nineteenth 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 any of seventh to twelfth aspects.

[0023] 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

[0024] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0025] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0026] FIG. 2A and FIG. 2B illustrate signaling flows of data collection / model training procedure according to some example embodiments of the present disclosure.

[0027] FIG. 3A and FIG. 3B illustrate signaling flows of model inference / performance monitoring procedure according to some example embodiments of the present disclosure.

[0028] FIG. 4A and FIG. 4B illustrate signaling flows of model inference / performance monitoring procedure according to some example embodiments of the present disclosure.

[0029] FIG. 5 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0030] FIG. 6 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0031] FIG. 7 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0032] FIG. 8 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0033] FIG. 9 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0034] FIG. 10 illustrates a flowchart of a method implemented at a second apparatus in accordancewith some example embodiments of the present disclosure;

[0035] FIG. 11 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0036] FIG. 12 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0037] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0038] 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.

[0039] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same mean as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included.

[0044] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0045] 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.

[0046] 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.

[0047] 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-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 first generation (1 G),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.

[0048] 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 (I AB) 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.

[0049] As used herein, the term “network device” also may refer to a core network (CN) entity / node / function / apparatus / device. Example core network nodes may such as include functions of one or more of a 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 de-concealing function (SIDF), unified data management (UDM), security edge protection proxy (SEPP), network exposure function (NEF), and / or a user plane function (UPF) and so on.

[0050] 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 ofThings (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.

[0051] 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.

[0052] The term “transmission reception point (TRP)” used herein may be defined as an antenna array, with one or more antenna elements, available to the network located at a specific geographical location for a specific area. In some embodiments, the TRP may be implemented at a network device. The term “condition registered in a network side” used herein may refer to a condition at network side which include additional information to assist terminal units to improve consistency between training and inference model. The term “condition registered in a network side”, the term “condition registered in a second apparatus” and the term “network (NW) side additional condition” may be used interchangeable. The process of model identification pinpoints what are known as “additional conditions”, which may include, for example, training dataset category, site-related information, timestamps, implicit identification information (such as, labels for specific gNB / UE implementation details), statistical information (e.g., delay spread, angular spread, line of sight (LOS)ZNone-LOS data and so on), and other factors. The term “condition registered in a UE side” used herein may refer to a condition at UE side which include additional information to improve consistency between training and inference model. The term “condition registered in a UE side”, the term “condition registered in a first apparatus” and the term “UE side additional condition” may be used interchangeable.

[0053] As used herein, a machine learning (ML) entity may be an ML model or may contain an ML model and ML model related metadata. The ML entity may be managed as a single composite entity. In some example embodiments, the ML entity may be implemented as an ML application.

[0054] To facilitate understanding of the terminologies, RAN1 agreements on the list of terminologiesused for AI / ML are provided below.

[0055] AI / ML Model: A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0056] 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, LMF, etc.), UE, proprietary server, etc.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] AI / ML model transfer: Delivery of an AI / ML model over the air interface in a manner that is not transparent to 3GPP 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.

[0061] AI / ML model validation: A subprocess of training, to evaluate the quality of an AI / ML model using a dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0062] 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.

[0063] Federated learning / federated training: A machine learning technique that trains an AI / ML model across multiple decentralized edge nodes (e.g., UEs, gNBs) each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0064] Functionality identification: A process / method of identifying an AI / ML functionality for the common understanding between the NW 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.

[0065] Model activation: enable an AI / ML model for a specific function.

[0066] Model deactivation: disable an AI / ML model for a specific function.

[0067] Model download: Model transfer from the network to UE.

[0068] Model identification: A process / method of identifying an AI / ML model for the common understanding between the NW and the UE. Note: The process / method of model identification may or may not be applicable. Note: Information regarding the AI / ML model may be shared during modelidentification.

[0069] Model monitoring: A procedure that monitors the inference performance of the AI / ML model.

[0070] Model parameter update: Process of updating the model parameters of a model.

[0071] Model selection: The process of selecting an AI / ML model for activation among multiple models for the same AI / ML enabled feature. Note: Model selection may or may not be carried out simultaneously with model activation.

[0072] Model switching: Deactivating a currently active AI / ML model and activating a different AI / ML model for a specific function.

[0073] Model update: Process of updating the model parameters and / or model structure of a model.

[0074] Model upload: Model transfer from UE to the network.

[0075] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0076] Offline field data: The data collected from field and used for offline training of the AI / ML model.

[0077] Offline training: An AI / ML training process where the model is trained based on collected dataset, and where the trained model is later used or delivered for inference. Note: This definition only serves as a guidance. There may be cases that may not exactly conform to this definition but could still be categorized as offline training by commonly accepted conventions.

[0078] Online field data: The data collected from field and used for online training of the AI / ML model.

[0079] Online training: An AI / ML training process where the model being used for inference) is (typically continuously) trained in (near) real-time with the arrival of new training samples. Note: the notion of (near) real-time vs. non real-time is context-dependent and is relative to the inference timescale. Note: This definition only serves as a guidance. There may be cases that may not exactly conform to this definition but could still be categorized as online training by commonly accepted conventions. Note: Fine-tuning / re-training may be done via online or offline training.

[0080] Reinforcement Learning (RL): A process of training an AI / ML model from input (a.k.a. state) and a feedback signal (a.k.a. reward) resulting from the model’s output (a.k.a. action) in an environment the model is interacting with.

[0081] Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data.

[0082] Supervised learning: A process of training a model from input and its corresponding labels.

[0083] Two-sided (AI / ML) model: A paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e. , the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0084] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the UE.

[0085] Unsupervised learning: A process of training a model without labelled data.

[0086] Proprietary-format models: ML models of vendor-Zdevice-specific proprietary format, from 3GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared. Note: An example is a device-specific binary executable format.

[0087] Open-format models: ML models of specified format that are mutually recognizable across vendors and allow interoperability, from 3GPP perspective. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

[0088] In the context of the present disclosure, for an AI / ML-enabled feature / feature group (FG), additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. One interpretation for NW-side additional conditions is that they represent a set of settings or parameters defined by the NW entity, which has direct impact on the measurements realized in the UE entity.

[0089] 3GPP has started to provide supports for AI / ML positioning with the following objectives._ _

[0090] Further, network (NW)-side additional conditions are being prioritized if compared to UE-side additional conditions. For NW-side additional conditions, it has investigated a variety of scenarios for model generalization considering the following scenarios:_0091] In current discussion, one feature list (FL) was related to validity area. Specifically, for training data collection of Case 1 (UE-based positioning with UE-side model, direct AI / ML positioning) and 2a(UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning), at least the following assistance data is provided to the UE for generating Part A: training data validity area, i.e., information for determining an area where the training dataset is collected (pending on details for providing the training data validity area, e.g., use the existing Areal D-CellList as a starting point), and DL PRS configuration, where the DL PRS configuration can reuse legacy mechanisms.

[0092] The IE Areal D-CellList provides the NR Cell-IDs of the TRPs belonging to a particular network area where the associated assistance data are valid. Each cell is included in only one area.

[0093] Similar to the existing Areal D-CellList, the validity area for AI / ML operation also may be defined as a list of TRPs, where the TRPs transmits downlink (DL) positioning reference signal (PRS) and covers the surrounding area to support positioning.

[0094] Validity area is one of the most popular schemes to enhance the generalization of models in AI / ML positioning. In this regard, area identify(ies) / ID(s) are information that may be used to ensure consistency between, such as, mode training (based on the dataset) and model inference.

[0095] I n order to solve at least part of the above problems or other potential problems, some schemesmay be adopted. One scheme is LMF centric, which means that LMF defines and selects area I D(s) by grouping specific TRPs with one specific identity. Another scheme is that using information of TRP I D(s), the UE during data collection may define and select preferred area I D(s) or set of area I D(s). According to the present discourse, by defining and selecting area information, the consistency among different phases may be ensured.

[0096] In the following, a data collection (and / or model training) and model inference (and / or performance monitoring) are used as example ML phases for describing some specific example embodiments of the present disclosure. However, the solution discussed herein may be applicable to any suitable ML phases. The present disclosure is not limited in this regard.

[0097] Further, a location management function (LMF) is used as an example of a core network entity for describing some specific example embodiments of the present disclosure. It is noted that example embodiments described with regard to the LMF are equally applicable to other core network entity, including but not limited to, access and mobility management function (AMF) and so on.

[0098] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Example Environment

[0099] FIG. 1 illustrates 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 apparatuses, including a first apparatus 110 and a second apparatus 120, may communicate with each other. In the example of FIG. 1 , the first apparatus 110 may be a UE and the second apparatus 120 may be a network device (especially a core network device, such as, LMF).

[0100] Further, the first apparatus 110 may receive signals (such as, positioning reference signals) from more than one access node (such as, gNB, TRP, relay node, access point and so on). In the example of FIG. 1 , the first apparatus 110 may receive signals from the access nodes 130-1 to 130- N. For ease of discussion, the third apparatuses 130-1 to 130-N may be collectively referred to as the access node 130.

[0101] In the example of FIG. 1 , one or more models 115 may be deployed at the first apparatus 110. Further, the first apparatus 110 may perform / complete data collection (model training) by using an entity of the first apparatus 110, or by assisting of the external server (140) (such as, an over the top, OTT, server).

[0102] In the following, for the purpose of illustration, some example embodiments are described with the first apparatus 110 operating as a UE and the second apparatus 120 operating as a network device. 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.

[0103] In some example embodiments, a transmission direction from the access node 130 to the first apparatus 110 is referred to as a downlink (DL), while a transmission direction from the first apparatus 110 to the access node 130 is referred to as an uplink (UL). In DL, the access node 130 is a transmitting (TX) device (or a transmitter) and the first apparatus 110 is a receiving (RX) device (or a receiver). In UL, the first apparatus 110 is a TX device (or a transmitter) and the access node 130 is a RX device (or a receiver).

[0104] 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 environments 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 deployed in the communication environments 100. It is noted that although illustrated as a network device, the second apparatus 120 may be another device than a network device (especially a core network device, such as, LMF). Although illustrated as a terminal device, the first apparatus 110 may be another device than a terminal device.

[0105] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols, 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. l / l / br Principle and Example Signaling for Communication

[0106] Details will be discussed with reference to FIG. 2A to FIG. 4B, which illustrate signaling flows 200A to 400B. In the following example embodiments, the first apparatus 110 may be a terminal apparatus, the second apparatus may be a core network entity and / or network apparatus, and the access node may be a TRP.

[0107] In the following discussions, more than one model may be deployed at the first apparatus 110. Further, each model may be associated with a configuration which may comprised any information related to the model. For example, as for data collection / model training, the first apparatus 110 may use utilize the configuration to collect data / perform model training to obtain / deploy the model. Further, when the model / corresponding functionality is activated, the first apparatus 110 may utilize the configuration to perform model inference / performance monitoring. The configuration also may be usedfor other LCM phases of the model.

[0108] In some example embodiments, the model (and / or functionality) deployed at the first apparatus 110 (or to be deployed at the first apparatus 110) may be associated with a positioning function. In this event, in some example embodiments, the first apparatus 110 may receive downlink positioning reference signals (DL-PRSs) from the access nodes 130.

[0109] According to some embodiments of the present disclosure, the area identity(ies) (or called as the area configuration, or area identity information) and / or the set of access node identity(ies) may be used for identifying a specific model. As one example, the area identity(ies) (or the area configuration / area identity information) may be associated with a configuration of a model. By using this configuration, a model may be deployed / training. In the following, by using the same area identity(ies) (or the area configuration / area identity information), the corresponding model may be selected / identified for model inference / performance monitoring.

[0110] Reference is now made to FIG. 2A, which illustrates a signaling flow 200A of data collection / model training procedure according to some example embodiments of the present disclosure.

[0111] In operation, as illustrated in FIG. 2A, the second apparatus 120 determines (210) at least one respective first set of access node identities (such as, TRP IDs) corresponding to at least one first area identity, and transmits (212-1 ) a first area configuration to the first apparatus 110, where the first area configuration comprises at least one first area identity and at least one respective first set of access node identities.

[0112] The first apparatus 110 receives (212-2) the first area configuration accordingly and then associates (214) the first area configuration with a first configuration of a first model (or first functionality) at the first apparatus 110. In some example embodiments, the first configuration may comprise a first dataset, and the first apparatus 110 may store the first area configuration as metadata information of the first dataset.

[0113] According to some embodiments of the present disclosure, the first apparatus 110 may define the area identity information and report the defined area identity information to the second apparatus 120, while the second apparatus 120 may made the final decision based on the reported area identity information. Such example embodiments will be discussed in the following.

[0114] In some example embodiments, the first apparatus 110 may transmit (208-1 ) first area information to the second apparatus 120, where the first area information may indicate the at least one first area identity and at least one respective list of access node identities (such as, TRP IDs) corresponding to the at least one first area identity. The second apparatus 120 may determine the first area configuration based on the first area information. That is, a list of access node identities associated with an area identity being the same as or different from a first set of access node identitiesassociated with the same area identity. In other words, the first area information reported by the first apparatus may be adjusted by the second apparatus 120, i.e., the second apparatus 120 has power on the final decision.

[0115] In some cases, as the first apparatus 110 may not know the available access node(s), the second apparatus 120 may provide assistant information to the first apparatus 110, such that the first apparatus 110 may determine the first area information. As illustrated in FIG. 2A, the second apparatus 120 may transmit (204-1) information indicating a plurality of access node identities to the first apparatus 110, where the information may optionally include validity area information. The first apparatus 110 may receive (204-2) the information indicating a plurality of access node identities from the second apparatus 120, and then may determine (206) the at least one first area identity, wherein each first area identity is associated with a list access node identities selected from the plurality of access node identities.

[0116] In some example embodiments, the first apparatus 110 may select a respective list of access nodes 130 corresponding to the area identity based on at least one quality indicator and / or geographical location information of the plurality of access nodes 130. It should be noted that, in the other example embodiments, other factors may be used for selecting the access nodes 130.

[0117] In some example embodiments, the first apparatus 110 may perform (216) data collection (or model training) based at least in part on the first configuration to obtain the first model. In some embodiments, the data collection (or model training) may be performed by an entity at the first apparatus 110. Alternatively, or in addition, in some example embodiments, the data collection (or model training) may be performed by an external server supporting the data collection or model training. One example external server is the over the top (OTT) server.

[0118] Further, the first apparatus 110 may deploy more than one model. In other words, the above procedure may be executed repeatedly. For example, the first apparatus 110 may further receive a second area configuration from a second apparatus, where the second area configuration may comprise at least one second area identity, and at least one respective second set of access node identities corresponding to the at least one second area identity. Similarly, the first apparatus 110 may associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus 110. Other processes are similar. For brevity, the same or similar contents are omitted.

[0119] According to some embodiments of the present disclosure, the first apparatus 110 may optionally provide capability information to the second apparatus 120. As illustrated in FIG. 2A, the first apparatus 110 may transmit (202-1) capability-related information to the second apparatus 120, and the second apparatus 120 may receive (202-2) the capability-related information accordingly, where the capability-related information may indicate that the first apparatus 110 supports utilizing atleast one area identity for consistence among different model phases. With this capability-related information, the second apparatus 120 may determine whether to provide the information about the available access nodes to the first apparatus 110.

[0120] Merely for a better understanding, further example embodiments will be discussed with reference to FIG. 2B.

[0121] In the example of FIG. 2B, UE(s) may optionally provide (step 1) capability-related information to LMF. Further, it is expected that the information that indicates the TRP IDs (by reusing existing IE or introducing a new IE) may be delivered from the LTM to the UE(s) as a regular assistance data. As illustrated in FIG. 2B, the generic assistance data for data collection including validity area information is provided (step 2) by LMF.

[0122] After receiving the assistance data which includes the TRP ID(s) information in the entity doing data collection (such as, for Case 1 , i.e., UE-based positioning with UE-side model), the target UE (or set of UEs) may select (step 3) the suitable TRP I D(s) to define one or more area I D(s).

[0123] In some example embodiments, the criteria used by UE to select suitable TRP I D(s) to define one or more area I D(s) is up to implementation on the UE-side. This criteria may be based for example on the quality indicator for labels and may be done by a centralized entity in UE-side data collection managing the suitable selection of different area I D(s).

[0124] After that, these area ID(s) (i.e., area information) may be suggested / reported (step 4) to LMF. Based on the suggestion reported by UE(s), the LMF may make (step 5) a final decision of TRP I D(s) that are composing the suggested area I D(s) identifiers, which may be stored for consistency purposes.

[0125] Next, the LMF assists the UE with the final definition of area I D (s) and the TRP(s) that are linked to each Area ID. As illustrated in FIG. 2B, the LMF transmits (step 6) the final decision on area I D(s) and the correspondent set of TRP ID(s) that compose each area ID (i.e., the area configuration) to the UE.

[0126] Finally, the area ID information (i.e., the area configuration) may be associated with a configuration of a model by UE, such as, may be included as metadata information of the dataset of a corresponding configuration.

[0127] As a result, this dataset may be used for training purposes (step 8) on the training entity for Case 1 (UE-based positioning with UE-side model, direct AI / ML positioning; it could be an external server as OTT or any other entity supporting UEs on doing the training).

[0128] According to the above processes, area identity information (area configuration) may be associated with a configuration / model. During model inference / performance monitoring, the LMF may assist UE with delivering assistance data relative to TRP I D(s) information. With this information, the UE may apply a similar criteria used in the data collection step to select suitable TRP I D(s) to define one or more Area I D(s). Such example embodiments will be discussed in the following.

[0129] Reference is now made to FIG. 3A to FIG. 4B, which illustrate signaling flows 300A to 400B of model inference / performance monitoring procedure according to some example embodiments of the present disclosure.

[0130] In the examples of FIG. 3A to FIG. 4B, more than one model has been deployed / trained at the first apparatus 110 (as discussed with reference to FIG. 2A and FIG. 2B). Further, there may be a plurality of configurations maintained by the first apparatus 110, where each configuration corresponds to one model.

[0131] In some example embodiments, the more than one model may comprise a first model (or a first functionality) and a second model (or a second functionality). In some example embodiments, the first model or the first functionality may be obtained via a first data collection procedure or a first model training procedure, and the second model or the second functionality is obtained via a second data collection procedure different from the first data collection procedure or a second model training procedure different from the first model training procedure.

[0132] Accordingly, the plurality of configurations maintained by the first apparatus 110 may comprise a first configuration and a second configuration. The first configuration is associated with the first model (or the first functionality), the second configuration is associated with the second model (or the second functionality).

[0133] Further, the first configuration may be associated with a first area configuration comprising the at least one first area identity, and the second configuration may be associated with a second area configuration comprising the at least one second area identity.

[0134] Additionally, in some example embodiments, the first area configuration may further comprise at least one respective first set of access node identities corresponding to the at least one first area identity, and the second area configuration may further comprise at least one respective second set of access node identities corresponding to the at least one second area identity.

[0135] With the above configurations (corresponding information is also known to the second apparatus 120) maintained at the first apparatus 110, the model may be selected properly for model inference / performance monitoring purpose.

[0136] Reference is now made to FIG. 3A. As illustrated, for purpose of model inference / performance monitoring, the first apparatus 110 transmits (304-1) a request for an area configuration to a second apparatus 120.

[0137] In response to receiving (304-2) the request, the second apparatus 120 determine (306) a first area configuration to be used by the first apparatus 110 for determining a first configuration from a plurality of configurations, where the first configuration is associated with a first area configuration comprising at least one first area identity. Then, the second apparatus 120 transmits (308-1 ) a response indicating the first area configuration to the first apparatus 110.

[0138] The first apparatus 110 receives (308-2) the response comprising first area configuration, and then determines (310), based at least in part on the first area configuration, the first configuration of a first model or first functionality from a plurality of configurations.

[0139] Then, the first apparatus 110 may perform (312) model inference or performance monitoring based at least in part on the first configuration by using the first model or first functionality.

[0140] Optionally, in the example of FIG. 3A, the first apparatus 110 also may provide capability information to the second apparatus 120.

[0141] As illustrated in FIG. 3A, the first apparatus 110 may transmit (302-1) capability-related information to the second apparatus 120, and the second apparatus 120 may receive (302-2) the capability-related information accordingly, where the capability-related information may indicate that the first apparatus 110 supports utilizing at least one area identity for consistence among different model phases.

[0142] Merely for a better understanding, further example embodiments will be discussed with reference to FIG. 3B. In the example of FIG. 3B, UE may optionally provide (step 1 ) the capability report to LMF if UE supports Areas IDs for ensuring consistence.

[0143] The UE may further request (step 2) assistance information from the LMF to enhance the selection of models, the LMF may provide (step 3) area IDs (and optionally provide correspondent set of TRP I D(s)) to the UE, i.e., area configuration is provided from the LMF to the UE.

[0144] With the area ID information (i.e., area configuration), UE may select a suitable model for model inference (performance monitoring). Next, UE may inform (step 5) the LTM that UE is ready for Model inference / performance monitoring. LTM may activate (step 6) the functionality at the UE. Then model inference / performance monitoring may be executed (step 7).

[0145] Reference is now made to FIG. 4A, which illustrate another signaling flow 400A of model inference / performance monitoring procedure according to some example embodiments of the present disclosure.

[0146] Different from transmitting a request to the second apparatus 120 in FIG. 3A, in the example of FIG. 4A, the first apparatus 110 transmit (404-1) at least one area configuration to the second apparatus 120, where each area configuration may comprise at least area identity.

[0147] Upon receiving (404-2) the at least one area configuration from the first apparatus 110, the second apparatus 120 determines (406), based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus 110. The second apparatus transmits (408- 1 ) the first area configuration to the first apparatus 110. With the first area configuration, the first apparatus 110 may determine a first configuration from a plurality of configurations, where the first configuration is associated with the first area configuration.

[0148] In some embodiments, different area configuration may be associated with different validityareas, and the second apparatus may determine the first area configuration further based on the at least validity area of the at least one area configuration.

[0149] The first apparatus 110 receives (408-2) the first area configuration from the first apparatus 110, and then determines (410), based at least in part in the first area configuration, the first configuration from a plurality of configurations, where the first configuration is associated with the first area configuration.

[0150] Similar with the example of FIG. 3A, the first apparatus 110 may perform (412) model inference or performance monitoring based at least in part on the first configuration by using the first model or first functionality.

[0151] Optionally, in the example of FIG. 4A, the first apparatus 110 also may provide capability information to the second apparatus 120. As illustrated in FIG. 4A, the first apparatus 110 may transmit (402-1 ) capability-related information to the second apparatus 120, and the second apparatus 120 may receive (402-2) the capability-related information accordingly, where the capability-related information may indicate that the first apparatus 110 supports utilizing at least one area identity for consistence among different model phases.

[0152] Merely for a better understanding, further example embodiments will be discussed with reference to FIG. 4B. In the example of FIG. 4B, UE may optionally provide (step 1 ) the capability report to LMF if UE supports Areas IDs for ensuring consistence.

[0153] UE may report (step 2) to LMF area ID information (optionally including information of correspondent set of TRP ID(s)) linked to available Models in UE-side.

[0154] Based on the area ID information, LMF may assess (step 3) the similarities of Areas ID(s) correspondent to that specific validity area. Then, the assessment outcome (such as, the selected area configuration / area ID information) may be delivered (step 4) to UE.

[0155] The assessment outcome (such as, the selected area configuration) used by UE to do a final decision on model selection. As illustrated in FIG. 4B, UE may select (step 5) a suitable model for model inference (performance monitoring). Next, UE may inform (step 6) the LTM that UE is ready for Model inference / performance monitoring. LTM may activate (step 8) the functionality at the UE. Then model inference / performance monitoring may be executed (step 8).

[0156] In the above processes of FIG. 3A to FIG. 4B, for model inference / performance monitoring, UE may request assistance from the LMF to enhance the selection of models. In the other example embodiments, UE may do the model selection for mole inference (or performance monitoring) without any assistance from the LMF. The specific selection criteria may up to UE implementation.Example Method

[0157] FIG. 5 shows a flowchart of an example method 500 implemented at a first 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 first apparatus 110 in FIG. 1.

[0158] At block 510, the first apparatus receives, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity.

[0159] At block 520, the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0160] In some example embodiments, the first apparatus may perform, based at least in part on the first configuration, data collection or model training to obtain the first model by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0161] In some example embodiments, the first apparatus may transmit, to the second apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity.

[0162] In some example embodiments, the first apparatus may receive, from the second apparatus, information indicating a plurality of access node identities; and determine the at least one first area identity, wherein each first area identity is associated with a list access node identities selected from the plurality of access node identities.

[0163] In some example embodiments, the first apparatus may select a respective list of access nodes corresponding to the area identity based on at least one of the following: at least one quality indicator, or geographical location information of the plurality of access nodes.

[0164] In some example embodiments, the first apparatus may transmit, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0165] In some example embodiments, the first apparatus may store the first area configuration as metadata information of the first dataset.

[0166] In some example embodiments, the first apparatus may receive, from a second apparatus, a second area configuration comprising: at least one second area identity, and at least one respective second set of access node identities corresponding to the at least one second area identity; and associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

[0167] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0168] In some example embodiments, the first apparatus may receive, downlink positioning referencesignals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0169] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0170] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0171] FIG. 6 shows a flowchart of an example method 600 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 600 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0172] At block 610, the second apparatus determines, at least one respective first set of access node identities corresponding to at least one first area identity.

[0173] At block 620, the second apparatus transmits, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0174] In some example embodiments, the second apparatus may receive, from the first apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity; and determine the first area configuration based on the first area information.

[0175] In some example embodiments, the second apparatus may transmit, to the first apparatus, information indicating a plurality of access node identities.

[0176] In some example embodiments, the second apparatus may receive, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0177] In some example embodiments, the second apparatus may transmit, to the first apparatus, a second area configuration comprising: at least one second area identity and at least one respective second set of access node identities corresponding to the at least one second area identity, such that the first apparatus associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

[0178] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0179] In some example embodiments, the first apparatus is a terminal apparatus, and the secondapparatus is a core network entity and / or network apparatus.

[0180] FIG. 7 shows a flowchart of an example method 700 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 700 will be described from the perspective of the first apparatus 110 in FIG. 1.

[0181] At block 710, the first apparatus transmits, to a second apparatus, a request for an area configuration.

[0182] At block 720, the first apparatus receives a response of the request from the second apparatus, the response indicating a first area configuration comprising at least first area identity.

[0183] At block 730, the first apparatus determines, based at least in part on the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0184] In some example embodiments, the first apparatus may perform, by using the first model or first functionality, model inference or performance monitoring based at least in part on the first configuration.

[0185] In some example embodiments, the first area configuration further comprises at least one respective first set of access node identities corresponding to the at least one first area identity.

[0186] In some example embodiments, the first apparatus may receive, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0187] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0188] In some example embodiments, the plurality of configurations further comprise a second configuration of a second model or second functionality, and the second configuration is associated with a second area configuration comprising the at least one second area identity.

[0189] In some example embodiments, the first model or the first functionality is obtained via a first data collection procedure or a first model training procedure; and the second model or the second functionality is obtained via a second data collection procedure different from the first data collection procedure or a second model training procedure different from the first model training procedure.

[0190] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0191] In some example embodiments, the first apparatus may receive, from the second apparatus, the first area configuration; associate the first area configuration with the first configuration; and perform, based at least in part on the first configuration, data collection or model training to obtain the first model by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0192] In some example embodiments, the first apparatus may transmit, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0193] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0194] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0195] FIG. 8 shows a flowchart of an example method 800 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 800 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0196] At block 810, the second apparatus receives, from a first apparatus, a request for an area configuration.

[0197] At block 820, the second apparatus determines a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity.

[0198] At block 830, the second apparatus transmits, to the first apparatus, a response indicating the first area configuration.

[0199] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0200] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0201] In some example embodiments, the second apparatus may transmit the first area configuration to the first apparatus, such that the first apparatus associate the first area configuration with the first configuration and performs data collection or model training based at least in part on the first configuration to obtain the first model.

[0202] In some example embodiments, the second apparatus may receive, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0203] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0204] FIG. 9 shows a flowchart of an example method 900 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 900 will be described from the perspective of the first apparatus 110 in FIG. 1.

[0205] At block 910, the first apparatus transmits, to a second apparatus, at least one area configuration, each area configuration comprising at least area identity.

[0206] At block 920, the first apparatus receives, from the second device, a first area configuration determined by the second apparatus based on the at least one area configuration, wherein the first area configuration comprising at least first area identity.

[0207] At block 930, the first apparatus determines, based at least in part in the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0208] In some example embodiments, the first apparatus may perform, by using the first model or first functionality, model inference or performance monitoring based at least in part on the first configuration.

[0209] In some example embodiments, the first area configuration further comprises at least one respective first set of access node identities corresponding to the at least one first area identity.

[0210] In some example embodiments, the first apparatus may receive, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0211] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0212] In some example embodiments, the plurality of configurations further comprise a second configuration of a second model or second functionality, and the second configuration is associated with a second area configuration comprising the at least one second area identity.

[0213] In some example embodiments, the first model or the first functionality is obtained via a first data collection procedure or a first model training procedure; and the second model or the second functionality is obtained via a second data collection procedure different from the first data collection procedure or a second model training procedure different from the first model training procedure.

[0214] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0215] In some example embodiments, the first apparatus may receive, from the second apparatus, the first area configuration; associate the first area configuration with the first configuration; and perform, based at least in part on the first configuration, data collection or model training to obtain the first mode by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0216] In some example embodiments, the first apparatus may transmit, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0217] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0218] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0219] FIG. 10 shows a flowchart of an example method 1000 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 1000 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0220] At block 1010, the second apparatus receives, from a first apparatus, at least one area configuration, each area configuration comprising at least area identity.

[0221] At block 1020, the second apparatus determines, based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity.

[0222] At block 1030, the second apparatus transmits the first area configuration to the first apparatus.

[0223] In some example embodiments, different area configuration are associated with different validity areas, and the second apparatus is further caused to: determining the first area configuration further based on the at least validity area of the at least one area configuration.

[0224] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0225] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0226] In some example embodiments, the second apparatus may transmit the first area configuration to the first apparatus, such that the first apparatus associate the first area configuration with the first configuration and performs data collection or model training based at least in part on the first configuration to obtain the first model.

[0227] In some example embodiments, the second apparatus may receive, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0228] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.Example Apparatus, Device and Medium

[0229] In some example embodiments, a first apparatus capable of performing any of the method 500 (for example, the first apparatus 110 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 first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1 .

[0230] In some example embodiments, the first apparatus comprises means for receiving, from asecond apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and means for associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0231] In some example embodiments, the first apparatus further comprises means for performing, based at least in part on the first configuration, data collection or model training to obtain the first model by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0232] In some example embodiments, the first apparatus further comprises means for transmitting, to the second apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity.

[0233] In some example embodiments, the first apparatus further comprises means for receiving, from the second apparatus, information indicating a plurality of access node identities; and determining the at least one first area identity, wherein each first area identity is associated with a list access node identities selected from the plurality of access node identities.

[0234] In some example embodiments, the first apparatus further comprises means for selecting a respective list of access nodes corresponding to the area identity based on at least one of the following: at least one quality indicator, or geographical location information of the plurality of access nodes.

[0235] In some example embodiments, the first apparatus further comprises means for transmitting, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0236] In some example embodiments, the first apparatus further comprises: means for storing the first area configuration as metadata information of the first dataset.

[0237] In some example embodiments, the first apparatus further comprises means for receiving, from a second apparatus, a second area configuration comprising: at least one second area identity, and at least one respective second set of access node identities corresponding to the at least one second area identity; and means for associating the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

[0238] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0239] In some example embodiments, the first apparatus further comprises: means for receiving, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0240] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0241] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0242] In some example embodiments, a second apparatus capable of performing any of the method 600 (for example, the second apparatus 120 in FIG. 1 ) may comprise means for performing the respective operations of the method 600. 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 120 in FIG. 1.

[0243] In some example embodiments, the second apparatus comprises means for determining, at least one respective first set of access node identities corresponding to at least one first area identity; and means for transmitting, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

[0244] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity; and means for determining the first area configuration based on the first area information.

[0245] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, information indicating a plurality of access node identities.

[0246] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0247] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a second area configuration comprising: at least one second area identity and at least one respective second set of access node identities corresponding to the at least one second area identity, such that the first apparatus associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

[0248] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0249] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0250] In some example embodiments, a first apparatus capable of performing any of the method 700 (for example, the first apparatus 110 in FIG. 1) may comprise means for performing the respective operations of the method 700. 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 110 in FIG. 1 .

[0251] In some example embodiments, the first apparatus comprises means for transmitting, to a second apparatus, a request for an area configuration; means for receiving a response of the request from the second apparatus, the response indicating a first area configuration comprising at least first area identity; and means for determining, based at least in part on the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0252] In some example embodiments, the first apparatus further comprises: means for performing, by using the first model or first functionality, model inference or performance monitoring based at least in part on the first configuration.

[0253] In some example embodiments, the first area configuration further comprises at least one respective first set of access node identities corresponding to the at least one first area identity.

[0254] In some example embodiments, the first apparatus further comprises: means for receiving, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0255] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0256] In some example embodiments, the plurality of configurations further comprise a second configuration of a second model or second functionality, and the second configuration is associated with a second area configuration comprising the at least one second area identity.

[0257] In some example embodiments, the first model or the first functionality is obtained via a first data collection procedure or a first model training procedure; and the second model or the second functionality is obtained via a second data collection procedure different from the first data collection procedure or a second model training procedure different from the first model training procedure.

[0258] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0259] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, the first area configuration; means for associating the first area configuration with the first configuration; and means for performing, based at least in part on the first configuration, data collection or model training to obtain the first model by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0260] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0261] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0262] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0263] In some example embodiments, a second apparatus capable of performing any of the method 800 (for example, the second apparatus 120 in FIG. 1 ) may comprise means for performing the respective operations of the method 800. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The fourth apparatus may be implemented as or included in the second apparatus 120 in FIG. 1.

[0264] In some example embodiments, the second apparatus comprises means for receiving, from a first apparatus, a request for an area configuration; means for determining a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and means for transmitting, to the first apparatus, a response indicating the first area configuration.

[0265] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0266] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0267] In some example embodiments, the second apparatus further comprises: means for transmitting the first area configuration to the first apparatus, such that the first apparatus associate the first area configuration with the first configuration and performs data collection or model training based at least in part on the first configuration to obtain the first model.

[0268] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0269] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0270] In some example embodiments, a first apparatus capable of performing any of the method 900 (for example, the first apparatus 110 in FIG. 1) may comprise means for performing the respective operations of the method 900. 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 implementedas or included in the first apparatus 110 in FIG. 1 .

[0271] In some example embodiments, the first apparatus comprises means for transmitting, to a second apparatus, at least one area configuration, each area configuration comprising at least area identity; means for receiving, from the second device, a first area configuration determined by the second apparatus based on the at least one area configuration, wherein the first area configuration comprising at least first area identity; means for determining, based at least in part in the first area configuration, a first configuration of a first model or first functionality from a plurality of configurations, wherein the first configuration is associated with the first area configuration.

[0272] In some example embodiments, the first apparatus further comprises means for performing, by using the first model or first functionality, model inference or performance monitoring based at least in part on the first configuration.

[0273] In some example embodiments, the first area configuration further comprises at least one respective first set of access node identities corresponding to the at least one first area identity.

[0274] In some example embodiments, the first apparatus further comprises means for receiving, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.

[0275] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0276] In some example embodiments, the plurality of configurations further comprise a second configuration of a second model or second functionality, and the second configuration is associated with a second area configuration comprising the at least one second area identity.

[0277] In some example embodiments, the first model or the first functionality is obtained via a first data collection procedure or a first model training procedure; and the second model or the second functionality is obtained via a second data collection procedure different from the first data collection procedure or a second model training procedure different from the first model training procedure.

[0278] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0279] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, the first area configuration; associate the first area configuration with the first configuration; and perform, based at least in part on the first configuration, data collection or model training to obtain the first mode by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

[0280] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0281] In some example embodiments, the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

[0282] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0283] In some example embodiments, a second apparatus capable of performing any of the method 1000 (for example, the second apparatus 110 in FIG. 1) may comprise means for performing the respective operations of the method 1000. 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 120 in FIG. 1.

[0284] In some example embodiments, the second apparatus comprises means for receiving, from a first apparatus, at least one area configuration, each area configuration comprising at least area identity; means for determining, based at least on part on the at least one area configuration, a first area configuration to be used by the first apparatus for determining a first configuration from a plurality of configurations, wherein the first configuration is associated with a first area configuration comprising at least one first area identity; and means for transmitting the first area configuration to the first apparatus.

[0285] In some example embodiments, different area configuration are associated with different validity areas, and the second apparatus is further caused to: means for determining the first area configuration further based on the at least validity area of the at least one area configuration.

[0286] In some example embodiments, the first area configuration further comprises: at least one respective first set of access node identities corresponding to the at least one first area identity.

[0287] In some example embodiments, the access node identity is a transmission reception point (TRP) identity.

[0288] In some example embodiments, the second apparatus further comprises: means for transmitting the first area configuration to the first apparatus, such that the first apparatus associate the first area configuration with the first configuration and performs data collection or model training based at least in part on the first configuration to obtain the first model.

[0289] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

[0290] In some example embodiments, the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

[0291] FIG. 11 is a simplified block diagram of a device 1100 that is suitable for implementing example embodiments of the present disclosure. The device 1100 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown inFIG. 1. As shown, the device 1100 includes one or more processors 1110, one or more memories 1120 coupled to the processor 1110, and one or more communication modules 1140 coupled to the processor 1110.

[0292] The communication module 1140 is for bidirectional communications. The communication module 1140 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 1140 may include at least one antenna.

[0293] The processor 1110 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 1100 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.

[0294] The memory 1120 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) 1124, 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) 1122 and other volatile memories that will not last in the power-down duration.

[0295] A computer program 1130 includes computer executable instructions that are executed by the associated processor 1110. The instructions of the program 1130 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 1130 may be stored in the memory, e.g., the ROM 1124. The processor 1110 may perform any suitable actions and processing by loading the program 1130 into the RAM 1122.

[0296] The example embodiments of the present disclosure may be implemented by means of the program 1130 so that the device 1100 may perform any process of the disclosure as discussed with reference to FIG. 2A to FIG. 10. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0297] In some example embodiments, the program 1130 may be tangibly contained in a computer readable medium which may be included in the device 1100 (such as in the memory 1120) or other storage devices that are accessible by the device 1100. The device 1100 may load the program 1130 from the computer readable medium to the RAM 1122 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such asROM, 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).

[0298] FIG. 12 shows an example of the computer readable medium 1200 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 1200 has the program 1130 stored thereon.

[0299] 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.

[0300] 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 computer-executable 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. Machineexecutable 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.

[0301] 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.

[0302] 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 variousprocesses and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0303] 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.

[0304] 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 subcombination.

[0305] 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

l / We Claim:1 . A first apparatus, comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to: receive, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and associate the first area configuration with a first configuration of a first model or first functionality at the first apparatus.2.The first apparatus of claim 1 , wherein the first apparatus is further cause to: perform, based at least in part on the first configuration, data collection or model training to obtain the first model by at least one of the following: an entity at the first apparatus or an external server supporting the data collection or model training.

3. The first apparatus of claim 1 or 2, wherein the first apparatus is further cause to: transmit, to the second apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity.

4. The first apparatus of claim 3, wherein the first apparatus is further cause to: receive, from the second apparatus, information indicating a plurality of access node identities; and determine the at least one first area identity, wherein each first area identity is associated with a list access node identities selected from the plurality of access node identities.

5. The first apparatus of claim 4, wherein the first apparatus is further cause to: select a respective list of access nodes corresponding to the area identity based on at least one of the following: at least one quality indicator, or geographical location information of the plurality of access nodes.6.The first apparatus of any of claims 1 to 5, wherein the first apparatus is further cause to:transmit, to the second apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.7.The first apparatus of any of claims 1 to 6, wherein the first configuration comprises a first dataset, and the first apparatus is further caused to: store the first area configuration as metadata information of the first dataset.

8. The first apparatus of any of claims 1 to 7, wherein the first apparatus is further cause to: receive, from a second apparatus, a second area configuration comprising: at least one second area identity, and at least one respective second set of access node identities corresponding to the at least one second area identity; and associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

9. The first apparatus of any of claims 1 to 8, wherein the access node identity is a transmission reception point (TRP) identity.

10. The first apparatus of any of claims 1 to 9, wherein the first apparatus is further caused to: receive, downlink positioning reference signals (DL-PRSs) from at least one first set of access nodes corresponding to the at least one respective first set of access node identities.11 . The first apparatus of any of claims 1 to 10, wherein the first model is associated with a positioning function and / or the first functionality is associated with a positioning function.

12. The first apparatus of any of claims 1 to 11 , wherein the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

13. A second apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to: determine, at least one respective first set of access node identities corresponding to at least one first area identity; andtransmit, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

14. The second apparatus of claim 13, wherein the second apparatus is further cause to: receive, from the first apparatus, first area information indicating: the at least one first area identity, and at least one respective list of access node identities corresponding to the at least one first area identity, a list of access node identities associated with an area identity being the same as or different from a first set of access node identities associated with the same area identity; and determine the first area configuration based on the first area information.

15. The second apparatus of any of claims 13 or 14, wherein the second apparatus is further cause to: transmit, to the first apparatus, information indicating a plurality of access node identities.16.The second apparatus of any of claims 13 to 15, wherein the second apparatus is further cause to: receive, from the first apparatus, capability-related information indicating that the first apparatus supports utilizing at least one area identity for consistence among different model phases.

17. The second apparatus of any of claims 13 to 16, wherein the second apparatus is further cause to: transmit, to the first apparatus, a second area configuration comprising: at least one second area identity and at least one respective second set of access node identities corresponding to the at least one second area identity, such that the first apparatus associate the second area configuration with a second configuration of a second model or second functionality at the first apparatus.

18. The second apparatus of any of claims 13 to 17, wherein the access node identity is a transmission reception point (TRP) identity.

19. The second apparatus of any of claims 13 to 18, wherein the first apparatus is a terminal apparatus, and the second apparatus is a core network entity and / or network apparatus.

20. A method comprising: receiving, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

21. A method comprising: determining, at least one respective first set of access node identities corresponding to at least one first area identity; and transmitting, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

22. A first apparatus comprising: means for receiving, from a second apparatus, a first area configuration comprising: at least one first area identity, and at least one respective first set of access node identities corresponding to the at least one area identity; and means for associating the first area configuration with a first configuration of a first model or first functionality at the first apparatus.

23. A second apparatus comprising: means for determining, at least one respective first set of access node identities corresponding to at least one first area identity; and means for transmitting, to the first apparatus, a first area configuration comprising: the at least one first area identity and the at least one respective first set of access node identities corresponding to the at least one first area identity, such that the first apparatus associates the first area configurationwith a first configuration of a first model or first functionality at the first apparatus.

24. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 20 or 21 .

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