Identification and usage of NW-additional conditions for positioning

Abstract

The present disclosure relates to identification and usage of network (NW)- additional conditions for positioning. In particular, a method includes transmitting, at a first apparatus and to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; receiving, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points; determining correlation information between metadata containing conditions that are registered in the set of models for positioning in the first apparatus and the set of conditions registered in the second apparatus; and determining, from the set of models, a target model based on correlation information.

Description

IDENTIFICATION AND USAGE OF NW-ADDITIONAL CONDITIONS FOR POSITIONINGCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to, and the benefit of, GB Application No. 2402159.4, filed February 16, 2024, the contents of which are hereby incorporated by reference in their 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 identification and usage of network (NW)- additional conditions for positioning.BACKGROUND

[0003] With developments in the integration of Artificial Intelligence (Al) and Machine Learning (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. Therefore, it is worth delving into the aspects of capturing additional conditions, as understanding and leveraging these factors are crucial for the evolution of AI / ML applications in 5G and beyond, with the potential to significantly impact network performance and user experience.SUMMARY

[0004] In a first aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to: transmit, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; receive, from the second apparatus, second information comprising a set of conditions registered in a network side that is selected based on the set of transmission reception points; determine correlation information between metadata of a set of models for positioning and the set of conditions registered in the network side; and determine, from the set of models, a target model based on correlation information.

[0005] In a second aspect of the present disclosure, there is provided a secondapparatus. 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: receive, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; determine a set of conditions registered in a network side based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and transmit, to the first apparatus, second information comprising the set of conditions registered in the network side.

[0006] In a third aspect of the present disclosure, there is provided a method. The method comprises: transmitting, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; receiving, from the second apparatus, second information comprising a set of conditions registered in a network side that is selected based on the set of transmission reception points; determining correlation information between metadata of a set of models for positioning and the set of conditions registered in the network side; and determining, from the set of models, a target model based on correlation information.

[0007] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: receiving, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; determining a set of conditions registered in a network side based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and transmitting, to the first apparatus, second information comprising the set of conditions registered in the network side.

[0008] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for transmitting, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; means for receiving, from the second apparatus, second information comprising a set of conditions registered in a network side that is selected based on the set of transmission reception points; means for determining correlation information between metadata of a set of models for positioning and the set of conditions registered in the network side; and means for determining, from the set of models, a target model based on correlation information.

[0009] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for receiving, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; means for determining a set of conditions registered in a network side based onthe set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and means for transmitting, to the first apparatus, second information comprising the set of conditions registered in the network side.

[0010] In a seventh aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the third aspect.

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

[0012] 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

[0013] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0014] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0015] FIG. 2 illustrates a schematic diagram of an area with a plurality of datasets;

[0016] FIG. 3 illustrates a schematic diagram of mapping transmission reception points (TRPs) that are inside every cluster;

[0017] FIG. 4A and FIG. 4B illustrates a signaling flow of LMF assisting UE for model selection and model update / fine-tuning according to some example embodiments of the present disclosure;

[0018] FIG. 5A and FIG. 5B illustrates a signaling flow of selection model according to some example embodiments of the present disclosure;

[0019] FIG. 6 illustrates a flowchart of a method implemented at a first device according to some example embodiments of the present disclosure;

[0020] FIG. 7 illustrates a flowchart of a method implemented at a second device according to some example embodiments of the present disclosure;

[0021] FIG. 8 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0022] FIG. 9 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0023] Throughout the drawings, the same or similar reference numerals represent thesame or similar element.DETAILED DESCRIPTION

[0024] 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.

[0025] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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 moreintervening steps may be included.

[0030] 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.

[0031] As used in this application, the term “circuitry” may refer to one or more or all of the following:(a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(i) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and(c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0032] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0033] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as 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 (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G), the sixth generation (6G) communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0034] As used herein, the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), an NR NB (also referred to as a gNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low power node such as a femto, a pico, a non-terrestrial network (NTN) or non-ground network device such as a satellite network device, a low earth orbit (LEO) satellite and a geosynchronous earth orbit (GEO) satellite, an aircraft network device, and so forth, depending on the applied terminology and technology. In some example embodiments, radio access network (RAN) split architecture comprises a Centralized Unit (CU) and a Distributed Unit (DU) at an IAB donor node. An IAB node comprises a Mobile Terminal (IAB-MT) part that behaves like a UE toward the parent node, and a DU part of an IAB node behaves like a base station toward the next-hop IAB node.

[0035] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices,wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0036] 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.

[0037] 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, LOS / NLOS 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 afirst apparatus” and the term “UE side additional condition” may be used interchangeable.

[0038] 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 a MLApp.

[0039] To facilitate understanding of the terminologies, RAN1 agreements on the list of terminologies used for AI / ML are provided below.

[0040] AI / ML Model: A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] Federated learning I 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.

[0049] Functionality identification: A process / method of identifying an AI / MLfunctionality 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.

[0050] Model activation: enable an AI / ML model for a specific function.

[0051] Model deactivation: disable an AI / ML model for a specific function.

[0052] Model download: Model transfer from the network to UE.

[0053] 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 model identification.

[0054] Model monitoring: A procedure that monitors the inference performance of the AI / ML model.

[0055] Model parameter update: Process of updating the model parameters of a model.

[0056] 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.

[0057] Model switching: Deactivating a currently active AI / ML model and activating a different AI / ML model for a specific function.

[0058] Model update: Process of updating the model parameters and / or model structure of a model.

[0059] Model upload: Model transfer from UE to the network.

[0060] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0061] Offline field data: The data collected from field and used for offline training of the AI / ML model.

[0062] 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.

[0063] Online field data: The data collected from field and used for online training of the AI / ML model.

[0064] 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 time-scale. 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. (This note could be removed when we define the term fine-tuning.)

[0065] 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.

[0066] Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data.

[0067] Supervised learning: A process of training a model from input and its corresponding labels.

[0068] 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.

[0069] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the UE.

[0070] Unsupervised learning: A process of training a model without labelled data.

[0071] Proprietary-format models: ML models of vendor- / device-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.

[0072] 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.

[0073] 3GPP has started to provide support for AI / ML positioning with the following objectives.

[0074] Further, 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:

[0075] Some solution aim to set some criteria to identify additional conditions. In the solutions, there is proposed a scheme to encode potential NW-additional conditions. However, no identification aspect for NW-additional condition is disclosed. In addition, the solutions aim to create a dependency between UE-additional conditions and NW- additional conditions, which is out of the scope of the current requirement set. The solutions are lack of details for positioning AIML use cases.

[0076] According to the above summation, the identification and usability of NW-side additional conditions is still an open topic with high chances to be standardized.

[0077] During the studying of AIML positioning, several evaluations were conducted to verify the performance of AIML models on generalization scenarios. In most of these scenarios, evaluations indicated a very poor generalization performance. For instance, training a model with a dataset generated in a scenario with clutter density 60% and testing the same model using dataset generated in a scenario with clutter density 40%, the performance is substantially degraded. One potential solution when generalization aspects are not providing good performance is based on fine-tuning, model re-training, or model switching. However, there is not signalization based on the functionality framework to guide the UE on taking the best decision to guarantee the consistency between training and inference. For fine-tuning or retraining the target is to select the best matched / suitable dataset. For model switching is to select the model with the best match between channel characteristics of the dataset used for training and the actual inference scenario channel conditions.

[0078] According to some example embodiments of the present disclosure, there is provided a solution for identifying and defining the procedure to use NW-side additional conditions for AIML positioning use cases, both are described in the following. In this way, it can improve the performance of the AI / ML model.

[0079] Regarding identification of NW-side additional conditions, the main assumption before doing the identification of NW-side additional conditions is that LMF has a map of datasets on specific geographical areas, which is agnostic to the entity doing data collection. Based on this assumption, the following steps may be done to identify NW- side additional conditions. Ground truth and measurements may be mapped in the full Atotai area which may be composed by Ndatasets. The full Atotai area may be split based on a grid of squares, every square may be labeled with a numerical value based on the specific channel characteristic. The set of channel characteristics SH may be identified in full Atotai area. Clustering techniques may be applied to identify local clusters in every individual dataset, each cluster may represent each element of the set SH. Every cluster identified in every dataset may represent one specific NW-side additional condition.

[0080] Regarding usability of NW-side additional conditions, using the NW-side additional conditions may be summarized in the following steps. The usability of the already identified NW-side additional condition in the previous step may be used as additional information that the LMF indicates to the UE to assist on the proper selection of model to be used in their respective functionality. The information decoded from the model’s metadata and the NW-side additional condition may indicate a level of correlation, this correlation will support UE to select between switching or a certain level of model retraining / fine-tuning.

[0081] FIG. 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, the first apparatus 110 and the TRPs 130-1 , ... , 130- N (collectedly referred to as “TRP 130”, and N is an integer number) can communication with each other. The communication environment 100 may also include a second apparatus 120 which may be a core network device. For example, the second apparatus 120 may be a location management function (LMF) entity.

[0082] In some example embodiments, if the first apparatus 110 is a terminal device, a link from the TRP 130 to the first apparatus 110 is referred to as a downlink (DL), and a link from the first apparatus 110 to the TRP 130 is referred to as an uplink (UL). In DL, the TRP 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 TRP 130 is a RX device (or a receiver).

[0083] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s- OFDM) and / or any other technologies currently known or to be developed in the future.

[0084] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0085] The second apparatus 120 may identify NW-side additional conditions. In some example embodiments, before doing the identification of NW-side additional conditions, the second apparatus 120 may have a map of datasets on specific geographical areas, which is agnostic to the entity doing data collection. For example, the second apparatus 120 may determine an area that is associated with a plurality of datasets. In this case, each dataset may include measurement and ground truth labels. The second apparatus 120 may also determine a number of channel characteristics in the area and a predominant set of channel characteristics values in the area. In some embodiments, the second apparatus 120 may determining a plurality of clusters by performing an evaluation on each of the plurality of datasets based on the predominant set of channel characteristics values. In this case, each cluster identified in each dataset represents a group of spaces of one channel characteristic and corresponds to a set of conditions registered in the network side.

[0086] The second apparatus 120 may obtain a model that is trained based on a subset of datasets from the plurality of datasets. In some embodiments, the second apparatus 120 may determine the subset of datasets from the plurality of datasets. The second apparatus 120 may perform a model training on a model based on the subset of datasets. In this case, metadata of the subset of datasets are included as metadata information in the trained model, which correspond to conditions registered in the model side. Alternatively, another entity may perform the model training on the model based on the subset of datasets and deliver the trained model to the second apparatus 120. In some embodiments, a subset or a full set of channel characteristics identified in the dataset is used for training on the model.

[0087] For example, the second apparatus 120 may perform the following steps to identify NW-side additional conditions.1 . Considering that Atotai represents a full area that is composed by Ndatasets datasets (for example, the dataset 01 , the dataset 02, the dataset 03, the dataset 04, and the dataset 05 in FIG. 2). Every dataset may include measurements and ground truth labels. The ground truth as used to map the geographical location which are mapped on a 2D dimension and measurements may be used as source to identify the channel characteristics.2. Identifying the number of Htypechannel characteristics in the area Atotai. It is done defining a grid on area Atotai, and the size of the side square may be defined with the parameter Ssquare as indicated in F I G . 2. In every square of the grid scheme is identified for example using any of the following channel characteristic with a numerical value from all available TRPs:a) Htype A: line of sight (LOS) / non-line of sight (NLOS) rates in every square (e.g., 60% of NLOS links). b) Htype B: Time of arrival (ToA) estimation using heuristic methods vs True ToA (obtained using the ground truth) as another complementary scheme to identify LOS / NLOS rates in every square (e.g., 45% accuracy of ToA estimation). c) Htype C: Multipath root mean square (RMS) delay spread using all measurements to calculate the empirical cumulative distribution function (CDF)@90%. If the RMS CDF@90% value is high, then the scenario may be characterized by a high clutter density. If the RMS CDF@90% value is low, then the scenario may be characterized by a low clutter density (30% of clutter density).3. The numerical value of every channel characteristic (HtypeA, HtypeB, HtypeC, and so on) may be merged (e.g., weighted average) to get a master numerical value of the channel characteristic one very square. However, for the purpose of simplicity, only one channel characteristic is discussed in the present disclosure, but it is not limited to it.4. Clustering techniques may be applied (e.g., clustering based on neural network (NN)) to identify the predominant set of channel characteristic (SH) values in all area Atotai.5. Every dataset (i.e., the dataset 01 , the dataset 02, the dataset 03, the dataset 04, and the dataset 05) in the list of Ndatasets may be evaluated individually considering the set of SH already calculated in step 4. The evaluation aims to apply clustering techniques to identify local clusters in every square based on predominant numerical values in every square.6. Every cluster identified in every dataset (for example, cluster 301 , cluster 302 and cluster 303 in the dataset 01 shown in FIG. 3) may represent a group of squares of one specific channel characteristic, which will indicate the specific NW-side additional condition mapped on the 2D dimension of Atotai area, one illustration is shown in FIG. 3.

[0088] In some example embodiments, every cluster may cover more than 1 TRP in the geographical area. Thus, the TRPs that are covered by every specific cluster are mapped on a look-up table illustrated in Table 1. Every TRP may be mapped on every specific cluster, this mapping information may be included as metadata in the specific dataset.Table 1 - Illustration of the look-up table to map different channel characteristics with setof TRPs.0089] Considering that NW-additional conditions are already identified, it may consider the signalization to enable the consistency between training and inference for AIML positioning use cases. Here, embodiments of the present disclosure may split the steps on two subsections, one focusing on training stage and other in the inference.

[0090] Regarding the training stage, from the set of Ndatasets, one or more datasets may be considered / selected for model training purposes. The metadata of these selected datasets may be included as metadata information in the target trained model. These metadata information may be correlated in the inference stage with the metadata representing the NW-side additional condition.

[0091] In some example embodiments, for considered metadata, the set of channel characteristics identified in the previous step (SH) may be shared to the entity doing the model training. From this set of channel characteristics (SH), a subset or the full set may be identified in the dataset used for training on the specific model. These channel features identified in the training stage may be included / encoded on the trained model, for example, it may be encoded to the model-ID.

[0092] Regarding the inference stage, reference is made to FIG. 4A and 4B, which illustrates a signalling flow 400 of usage of NW additional conditions for AI / ML positioning in accordance with some embodiments of the present disclosure. For the purpose of discussion, the signalling flow 400 will be discussed with reference to FIG. 1 , for example, by using the first apparatus 110 and the second apparatus 120.

[0093] In some example embodiments, the first apparatus 110 may transmit (4005) capability information to the second apparatus 120. In otherwords, the second apparatus 120 may receive (4005) the capability information from the first apparatus 110. The capability information may indicate a set of capabilities supported by the first apparatus 110.

[0094] In some embodiments, the second device 120 may transmit (4010) a set of functionalities associated with positioning to the first device 110. In other words, the first device 110 may receive (4010), from the second device 120, the set of functionalities associated with positioning. The set of functionalities may be implemented based onAI / ML approaches.

[0095] In some embodiments, the second apparatus 120 may transmit (4015) an indication indicating a selected functionality by the second apparatus 120. In other words, the first apparatus 110 may receive (4015), from the second apparatus 120, an indication indicating a selected functionality by the second apparatus 120. The selected functionality may be associated with the positioning.

[0096] In some embodiments, the first apparatus 110 may transmit (4020), to the second apparatus 120, a request for conditions registered in the network side. In other words, the second apparatus 120 may receive (4020) a request for conditions registered in the network side from the first apparatus 110.

[0097] In some embodiments, the second apparatus 120 may transmit (4025), to the first apparatus 110, a request for a set of transmission reception points with positioning reference signal. In other words, the first apparatus 110 may receive (4025), from the second apparatus 120, a request for the set of transmission reception points with positioning reference signal.

[0098] The first apparatus 110 transmits (4030) to the second apparatus 120, first information indicating a set of transmission reception points with positioning reference signal. For example, the first information may indicate the set of TRs 130 which can transmit the positioning reference signal to the first apparatus 110. In other words, the apparatus 120 receives the first information from the first apparatus 110.

[0099] The second apparatus 120 determines (4035) a set of conditions registered in a network side based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics. For example, if the first information indicates the set of TRPs including TRPs 0, 1 , 2, 3 in FIG. 3, the second apparatus 120 may determine the set of conditions registered in the network side (related to the cluster 301) based on the set of TRPs included in the first information and the mapping information in table 1. By way of example, the second apparatus 120 may use the look-up table (i.e., table 1) between TRPs and the metadata representing the channel characteristics to select the NW side additional conditions.

[0100] The second apparatus 120 transmits (4040) second information including the set of conditions registered in the network side to the first apparatus 110. In other words, the first apparatus 110 receives the second information from the second apparatus 120.

[0101] The first apparatus 110 determines (4042) correlation information between metadata of a set of models for positioning and the set of conditions registered in the network side. For example, the first apparatus 110 may determine, for each model in theset of models, a correlation value between the metadata that includes conditions that are registered in the model side and mapped with an identity of the model and the set of conditions registered in the network side.

[0102] In some other example embodiments, the second apparatus 120 may determine the correlation information between metadata of the set of models for positioning and the set of conditions registered in the network side. For example, the second apparatus 120 may determine, for each model in the set of models, a correlation value between the metadata that includes conditions that are registered in the model side and mapped with an identity of the model and the set of conditions registered in the network side. In this case, the second apparatus 120 may transmit the correlation information to the first apparatus 110.

[0103] The first apparatus 110 determines (410-1 and 410-2) a target model from the set of models based on the correlation information. Example embodiments of the determination of the target model are described below.

[0104] In some embodiments, the first apparatus 110 may determine (4050), from the set of models, a first candidate model. A correlation value of the first candidate model may be a correlation threshold. In some embodiments, the correlation threshold may be a predetermined percentage. In this case, the first apparatus 110 may transmit (4050) third information indicating the first candidate model to the second apparatus 120. In some example embodiments, after receiving (4050) the third information, the second apparatus 120 may transmit (4055) a trigger indication for a monitoring to assess the first candidate model to the first apparatus 110. In this case, the first apparatus 110 may perform (4060) an assessment on the first candidate model by applying the monitoring to the first candidate model after the reception (4055) of the trigger indication. Alternatively, after receiving (4050) the third information, the second apparatus 120 may monitor the first candidate model and then transmit the monitoring result of the first candidate model to the first apparatus 110. In this case, the first apparatus 110 may perform (4060) the assessment on the first candidate model based on the monitoring result of the first candidate model from the second apparatus 120. In some example embodiments, the monitoring may include monitoring one or more specific statics of the first candidate model.

[0105] In some embodiments, the first apparatus 110 may determine (4065) whether a performance monitoring of the first candidate model is satisfying based on the assessment. For example, the assessment may indicate a difference or similarity between the one or more monitored statics and ground truth. In some example embodiments, if the difference is below a difference threshold or the similarity is abovea similarity threshold, the first apparatus 110 may determine that the performance monitoring of the first candidate model is satisfying. Alternatively, if the difference is above a difference threshold or the similarity is below a similarity threshold, the first apparatus 110 may determine that the performance monitoring of the first candidate model is unsatisfying.

[0106] In some embodiments, the first apparatus 110 may determine the first candidate model as the target model, if the performance monitoring of the first candidate model is satisfying. In this case, the first apparatus 110 may transmit (4070) to the second apparatus 120, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus 110. The first apparatus may activate (4090) the target model (i.e., the first model) or a functionality related to the target model.

[0107] In some other example embodiments, if the performance monitoring of the first candidate model is unsatisfying, the first apparatus 110 may determine (4070’), from the set of models, a second candidate model of which a correlation value exceeds the correlation threshold for further assessment, if the performance monitoring of the first candidate model is unsatisfying. In this case, the first apparatus 110 may transmit information indicating the second candidate model to the second apparatus 120. In some example embodiments, after receiving the information, the second apparatus 120 may transmit a trigger indication for a monitoring to assess the second candidate model to the first apparatus 110. In this case, the first apparatus 110 may perform an assessment on the second candidate model by applying the monitoring to the first candidate model after the reception of the trigger indication. Alternatively, after receiving the information, the second apparatus 120 may monitor the second candidate model and then transmit the monitoring result of the first candidate model to the first apparatus 110. In this case, the first apparatus 110 may perform the assessment on the second candidate model based on the monitoring result of the second candidate model from the second apparatus 120. In some embodiments, the first apparatus 110 may determine whether a performance monitoring of the second candidate model is satisfying based on the assessment. In some embodiments, the first apparatus 110 may determine the second candidate model as the target model, if the performance monitoring of the first candidate model is satisfying. In this case, the first apparatus 110 may transmit to the second apparatus 120, information indicating that the target model is ready to perform a positioning inference of the first apparatus 110. The first apparatus may activate (4090) the target model (i.e., the second model) or a functionality related to the target model.

[0108] In some embodiments, at 4045’, the first apparatus 110 may determine (4045’)a third candidate model from the set of models, if none of correlation values exceeds a correlation threshold. In some embodiments, the first apparatus 110 may transmit (4050’), to the second apparatus 120, a request for a dataset that is mapped to a condition registered in the network side. In other words, the second apparatus 120 may receive (4050’) the request for the dataset from the first apparatus 110.

[0109] In some embodiments, the second apparatus 120 may transmit (4055’) a response including at least a portion of the dataset to the first apparatus 110. Alternatively, an external entity (for example, an over the top (OTT) server) may transmit at least a portion of the dataset to the first apparatus 110. In other words, the first apparatus 110 may receive the at least a portion of the dataset from the second apparatus 120 or the external entity.

[0110] In some embodiments, the first apparatus 110 may update (4060’) the third candidate model by performing a retraining or fine-tuning on the third candidate model based on the at least a portion of the dataset. In some embodiments, the first apparatus 110 may perform (4065’) an assessment on the updated third model by applying a monitoring to the updated third candidate model or based on a monitoring result of the updated third candidate. For example, the second apparatus 120 may monitor the updated third candidate and send the monitoring result of the updated third candidate to the first apparatus 110. In some embodiments, the first apparatus 110 may determine (4070’) whether performance monitoring of the updated third candidate model is satisfying based on the assessment. The assessment (4065’) and the determination (4070’) are similar to the assessment (4060) and the determination (4065), which are omitted here.

[0111] In some embodiments, the first apparatus 110 may determine the updated third candidate as the target model, if the performance monitoring of the updated third candidate model is satisfying. In this case, the first apparatus 110 may transmit (4075’), to the second apparatus 120, fifth information indicating that the target model (i.e., the updated third model) is ready to perform a positioning inference of the first apparatus 110. The first apparatus 110 may activate (4090) at least one of: the target model (i.e., the updated third model) or a functionality related to the target model.

[0112] In some embodiments, the first apparatus 110 may determine (4080), from the set of models, a fourth candidate model for further retraining or fine-tuning, based on a determination that the performance monitoring of the updated third candidate model is unsatisfying. In this case, if the updated fourth candidate model is determined as the target model, the first apparatus 110 may activate (4090) at least one of: the target model (i.e., the updated fourth model) or a functionality related to the target model.

[0113] According to example embodiments described with reference to FIG. 4A and FIG. 4B, the model can be selected properly, thereby achieving better communication performance. Further, the model can be fine-tuned to improve its accuracy.

[0114] Reference is made to FIG. 5A and 5B, which illustrates a signalling flow 500 and 500’ of usage of NW additional conditions for AI / ML positioning in accordance with some embodiments of the present disclosure. For the purpose of discussion, the signalling flow 500 involves a UE 510 and an LMF 520. In some example embodiments, the first apparatus 110 may act as the UE 510 and the second apparatus 120 may act as the LMF 520. The signalling flow 500 and 500’ may be applied in a scenario where the UE 510 is moving to a new scenario / space / cell-ID.

[0115] As shown in FIG. 5A, the UE 510 may report (5005) all set of conditions to the LMF 520. Based on the UE reported conditions, the LMF 520 may set a list of functionalities using the combination of such conditions. The LMF 520 may assist the UE 510 sending the set of functionalities, and the LMF 520 may indicate (5010) the preferred Functionality.

[0116] Under the selected functionality, the UE 510 may request (5015) the NW-side additional condition to the LMF 520. The LMF 520 may request (5020) the set of TRPs with PRS on the target UE 510. The UE 510 may report (5025) the set of TRPs with PRS.

[0117] The LMF 520 using as a reference the TRPs linked to the target UE may use (5030) a look-up table to map the specific TRPs with the metadata of the specific NW- side additional condition, as illustrated in Table 1. The NW-side additional condition may be encoded or not. The LMF 520 may deliver (5035) the NW-side additional condition to the UE 510.

[0118] The UE 510 may use (5040) the NW-side additional condition metadata and check in the set of models the best match between the metadata of the model and the metadata received from the LMF 520 (NW-side additional condition). In other words, the correlation between the metadata recovered from one specific target model and the metadata received from LMF 520 (NW-side additional condition) is indicated using metric C.

[0119] In some example embodiments, the target model is included on the set of models with fully correlation. In some example embodiments, the target model may be activated (5045) by model switching on the specific area or scenario. In this case, the criteria to identity if a model is suitable for switching may include that the set of models is with fully correlation.

[0120] In some example embodiments, between all models with fully correlation, only one model may be selected (5050). The UE 510 may report (5055) to LMF 520 that thecandidate model is ready for assessment. The LMF 520 may trigger (5060) a monitoring method to assess the candidate model. The UE 510 may determine (5065) whether the model inference of the candidate model is acceptable.

[0121] If monitoring decision indicates that the model inference is acceptable, the UE 510 report (5070) that the selected candidate model is ready to do UE positioning inference. If the monitoring decision is not acceptable (i.e. , negative), the UE 510 may select another candidate model, and the steps 5050 to 5070 may be repeated.

[0122] In some other example embodiments, the target model is included on the set of models with not fully correlation. Between all models with not fully correlation, only one model is selected (5145). In this case, the criteria to identity if a model is suitable for retraining / fine-tunning may include that the set of models is not with fully correlation.

[0123] In some example embodiments, the dataset mapped to the specific additional condition may be requested from the UE 510. In this case, the dataset or subset of the dataset may be delivered (5152) to the UE 510. The delivery of the dataset may be done by the air interface or other interfaces. The UE 510 may retrain (5150) or perform (5150) fine-tuning the selected candidate model using the recently received dataset.

[0124] After the candidate model is retrained, the UE 510 may report (5155) to LMF 520 that the retrained candidate model is ready for assessment. The LMF 520 may trigger (5160) a monitoring method to assess the retrained candidate model. The UE 510 may determine (5165) whether the model inference of the candidate model is acceptable.

[0125] If monitoring decision indicates that the model inference is acceptable, the UE 510 report (5170) that the retrained candidate model is ready to do UE positioning inference. If the monitoring decision is not acceptable (i.e., negative), the UE 510 may select another candidate model for retraining or fine-tuning, and the steps 5145 to 5170 may be repeated. In some embodiments, for the case of retraining / fine-tunning, the level of correlation (C) may define the size of the dataset to be used in retraining / fine-tunning.

[0126] FIG. 6 shows a flowchart of an example method 600 implemented at a first device 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 first apparatus 110 in FIG. 1 .

[0127] At block 610, the first apparatus 110 transmits, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal.

[0128] At block 620, the first apparatus 110 receives, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points.

[0129] At block 630, the first apparatus 110 determines correlation information between metadata of a set of models for positioning and the set of conditions registered in the second apparatus.

[0130] At block 640, the first apparatus 110 determines, from the set of models, a target model based on correlation information.

[0131] In some example embodiments, the method 600 further comprises: determining, for each model in the set of models, a correlation value between the metadata that comprises conditions that are registered in the first apparatus and mapped with an identity of the model and the set of conditions registered in the second apparatus; or receiving, from the second apparatus, the correlation information, wherein for each model in the set of models, the correlation information comprises the correlation value between the metadata and the set of conditions registered in the second apparatus.

[0132] In some example embodiments, the method 600 further comprises: determining, from the set of models, a first candidate model of which a correlation value exceeds a correlation threshold; transmitting, to the second apparatus, third information indicating the first candidate model; performing an assessment on the first candidate model by applying the monitoring to the first candidate model after a reception of a trigger indication for a monitoring to assess the first candidate model from the second apparatus, or perform the assessment based on a monitoring result of the first candidate model from the second apparatus; and determining whether a performance monitoring of the first candidate model is satisfying based on the assessment.

[0133] In some example embodiments, the method 600 further comprises: based on a determination that the performance monitoring of the first candidate model is satisfying, determining the first candidate model as the target model; transmitting, to the second apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and activating at least one of: the target model or a functionality related to the target model.

[0134] In some example embodiments, the method 600 further comprises: based on a determination that the performance monitoring of the first candidate model is unsatisfying, determining, from the set of models, a second candidate model of which a correlation value exceeds the correlation threshold for further assessment.

[0135] In some example embodiments, the method 600 further comprises: based on a determination that none of correlation values exceeds a correlation threshold, determining a third candidate model from the set of models; transmitting, to the second apparatus, a request for a dataset that is mapped to a condition registered in the second apparatus; receiving a response including at least a portion of the dataset from thesecond apparatus or an external entity; and updating the third candidate model by performing a retraining or fine-tuning on the third candidate model based on the at least a portion of the dataset.

[0136] In some example embodiments, the method 600 further comprises: performing an assessment on the updated third model by applying a monitoring to the updated third candidate model or based on a monitoring result of the updated third candidate; and determining whether performance monitoring of the updated third candidate model is satisfying based on the assessment.

[0137] In some example embodiments, the method 600 further comprises: based on a determination of the performance monitoring of the updated third candidate model is satisfying, determining the updated third candidate as the target model; transmitting, to the second apparatus, fifth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and activating at least one of: the target model or a functionality related to the target model.

[0138] In some example embodiments, the method 600 further comprises: based on a determination that the performance monitoring of the updated third candidate model is unsatisfying, determining, from the set of models, a third candidate model for further retraining or fine-tuning.

[0139] In some example embodiments, the method 600 further comprises: transmitting, to the second apparatus, capability information indicating a set of capabilities supported by the first apparatus.

[0140] In some example embodiments, the method 600 further comprises: receiving, from the second apparatus, a set of functionalities associated with positioning; receiving, from the second apparatus, an indication indicating a selected functionality by the second apparatus; and transmitting, to the second apparatus, a request for conditions registered in the second apparatus.

[0141] In some example embodiments, the method 600 further comprises: receiving, from the second apparatus, a request for the set of transmission reception points with positioning reference signal.

[0142] In some example embodiments, the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

[0143] FIG. 7 shows a flowchart of an example method 700 implemented at a second device 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 second apparatus 120 in FIG. 1.

[0144] At block 710, the second apparatus 120 receives, from a first apparatus, firstinformation indicating a set of transmission reception points with positioning reference signal.

[0145] At block 720, the second apparatus 120 determines a set of conditions registered in a second apparatus based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics.

[0146] At block 730, the second apparatus 120 transmits, to the first apparatus, second information comprising the set of conditions registered in the second apparatus.

[0147] In some example embodiments, the method 700 further includes determining, for each model in the set of models, a correlation value between the metadata containing conditions that are registered in the first apparatus and the set of conditions registered in the second apparatus; and transmitting, to the first apparatus, correlation information including the determined correlation values for each model in the set of models.

[0148] In some example embodiments, the method 700 further comprises: receiving, from the first apparatus, third information indicating a candidate model.

[0149] In some example embodiments, the method 700 further comprises: transmitting, to the first apparatus, a trigger indication for a monitoring to assess the candidate model; or wherein the second apparatus is caused to: monitoring the candidate model; and transmitting a monitoring result of the candidate model.

[0150] In some example embodiments, the method 700 further comprises: receiving, from the first apparatus, a request or indication for a dataset that is mapped to a condition registered in the second apparatus.

[0151] In some example embodiments the method 700 further comprises: transmitting, to the first apparatus, a response including at least a portion of the dataset.

[0152] In some example embodiments, the method 700 further comprises: receiving, from the first apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus.

[0153] In some example embodiments, the method 700 further comprises: receiving, from the first apparatus, capability information indicating a set of capabilities supported by the first apparatus.

[0154] In some example embodiments, the method 700 further comprises: transmitting, to the first apparatus, a set of functionalities associated with positioning; transmitting, to the first apparatus, an indication indicating a selected functionality by the second apparatus; and receiving, from the first apparatus, a request for condition registered in the second apparatus.

[0155] In some example embodiments, the method 700 further comprises: transmitting,to the first apparatus, a request for the set of transmission reception points with positioning reference signal.

[0156] In some example embodiments, the method 700 further comprises: determining an area that is associated with a plurality of datasets, wherein each dataset comprises measurement and ground truth labels; determining a number of channel characteristics in the area; determining a predominant set of channel characteristics values in the area; and determining a plurality of clusters by performing an evaluation on each of the plurality of datasets based on the predominant set of channel characteristics values, wherein each cluster identified in each dataset represents a group of spaces of one channel characteristic and corresponds to a set of conditions registered in the second apparatus.

[0157] In some example embodiments, the method 700 further comprises: determining a subset of datasets from the plurality of datasets; and performing a model training on a model based on the subset of datasets, wherein metadata of the subset of datasets are included as metadata information in the trained model, which correspond to conditions registered in the first apparatus.

[0158] In some example embodiments, a subset or a full set of channel characteristics identified in the dataset is used for training on the model.

[0159] In some example embodiments, the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

[0160] In some example embodiments, a first apparatus capable of performing any of the method 600 (for example, the first apparatus 110 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 first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1 .

[0161] In some example embodiments, the first apparatus comprises means for transmitting, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; means for receiving, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points; means for determining correlation information between metadata of a set of models for positioning and the set of conditions registered in the second apparatus; and means for determining, from the set of models, a target model based on correlation information.

[0162] In some example embodiments, the first apparatus further comprises: means for determining, for each model in the set of models, a correlation value between the metadata that comprises conditions that are registered in the first apparatus and mappedwith an identity of the model and the set of conditions registered in the second apparatus; or means for receiving, from the second apparatus, the correlation information, wherein for each model in the set of models, the correlation information comprises the correlation value between the metadata and the set of conditions registered in the second apparatus.

[0163] In some example embodiments, the first apparatus further comprises: means for determining, from the set of models, a first candidate model of which a correlation value exceeds a correlation threshold; means for transmitting, to the second apparatus, third information indicating the first candidate model; means for performing an assessment on the first candidate model by applying the monitoring to the first candidate model after a reception of a trigger indication for a monitoring to assess the first candidate model from the second apparatus, or perform the assessment based on a monitoring result of the first candidate model from the second apparatus; and means for determining whether a performance monitoring of the first candidate model is satisfying based on the assessment.

[0164] In some example embodiments, the first apparatus further comprises: means for based on a determination that the performance monitoring of the first candidate model is satisfying, determining the first candidate model as the target model; means for transmitting, to the second apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and means for activating at least one of: the target model or a functionality related to the target model.

[0165] In some example embodiments, the first apparatus further comprises: means for based on a determination that the performance monitoring of the first candidate model is unsatisfying, determining, from the set of models, a second candidate model of which a correlation value exceeds the correlation threshold for further assessment.

[0166] In some example embodiments, the first apparatus further comprises: means for based on a determination that none of correlation values exceeds a correlation threshold, determining a third candidate model from the set of models; means for transmitting, to the second apparatus, a request for a dataset that is mapped to a condition registered in the second apparatus; means for receiving a response including at least a portion of the dataset from the second apparatus or an external entity; and means for updating the third candidate model by performing a retraining or fine-tuning on the third candidate model based on the at least a portion of the dataset.

[0167] In some example embodiments, the first apparatus further comprises: means for performing an assessment on the updated third model by applying a monitoring to the updated third candidate model or based on a monitoring result of the updated third candidate; and means for determining whether performance monitoring of the updatedthird candidate model is satisfying based on the assessment.

[0168] In some example embodiments, the first apparatus further comprises: means for based on a determination of the performance monitoring of the updated third candidate model is satisfying, determining the updated third candidate as the target model; means for transmitting, to the second apparatus, fifth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and means for activating at least one of: the target model or a functionality related to the target model.

[0169] In some example embodiments, the first apparatus further comprises: means for based on a determination that the performance monitoring of the updated third candidate model is unsatisfying, determining, from the set of models, a third candidate model for further retraining or fine-tuning.

[0170] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, capability information indicating a set of capabilities supported by the first apparatus.

[0171] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a set of functionalities associated with positioning; means for receiving, from the second apparatus, an indication indicating a selected functionality by the second apparatus; and means for transmitting, to the second apparatus, a request for conditions registered in the second apparatus.

[0172] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a request for the set of transmission reception points with positioning reference signal.

[0173] In some example embodiments, the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

[0174] In some example embodiments, the first apparatus further comprises means for performing other operations in some example embodiments of the method 600 or the first apparatus 110. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the first apparatus.

[0175] In some example embodiments, a second apparatus capable of performing any of the method 700 (for example, the second apparatus 120 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 second apparatus may be implemented as or included in the second apparatus 120 in FIG. 1.

[0176] In some example embodiments, the second apparatus comprises means for receiving, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; means for determining a set of conditions registered in a second apparatus based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and means for transmitting, to the first apparatus, second information comprising the set of conditions registered in the second apparatus.

[0177] In some example embodiments, the second apparatus comprises means for determining, for each model in the set of models, a correlation value between the metadata containing conditions that are registered in the first apparatus and the set of conditions registered in the second apparatus; and means for transmitting, to the first apparatus, correlation information including the determined correlation values for each model in the set of models.

[0178] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, third information indicating a candidate model.

[0179] In some example embodiments, the second apparatus comprises means for transmitting, to the first apparatus, a trigger indication for a monitoring to assess the candidate model; or wherein the second apparatus is caused to: means for monitoring the candidate model; and means for transmitting a monitoring result of the candidate model.

[0180] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, a request or indication for a dataset that is mapped to a condition registered in the second apparatus.

[0181] In some example embodiments, the second apparatus comprises means for transmitting, to the first apparatus, a response including at least a portion of the dataset.

[0182] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus.

[0183] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, capability information indicating a set of capabilities supported by the first apparatus.

[0184] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a set of functionalities associated with positioning; means for transmitting, to the first apparatus, an indication indicating a selected functionality by the second apparatus; and means for receiving, from the firstapparatus, a request for condition registered in the second apparatus.

[0185] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a request for the set of transmission reception points with positioning reference signal.

[0186] In some example embodiments, the second apparatus further comprises: means for determining an area that is associated with a plurality of datasets, wherein each dataset comprises measurement and ground truth labels; means for determining a number of channel characteristics in the area; means for determining a predominant set of channel characteristics values in the area; and determining a plurality of clusters by performing an evaluation on each of the plurality of datasets based on the predominant set of channel characteristics values, wherein each cluster identified in each dataset represents a group of spaces of one channel characteristic and corresponds to a set of conditions registered in the second apparatus.

[0187] In some example embodiments, the second apparatus further comprises: means for determining a subset of datasets from the plurality of datasets; and means for performing a model training on a model based on the subset of datasets, wherein metadata of the subset of datasets are included as metadata information in the trained model, which correspond to conditions registered in the first apparatus.

[0188] In some example embodiments, a subset or a full set of channel characteristics identified in the dataset is used for training on the model.

[0189] In some example embodiments, the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

[0190] In some example embodiments, the second apparatus further comprises means for performing other operations in some example embodiments of the method 700 or the second apparatus 120. In some example embodiments, the means comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the performance of the second apparatus.

[0191] FIG. 8 is a simplified block diagram of a device 800 that is suitable for implementing example embodiments of the present disclosure. The device 800 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown in FIG. 1. As shown, the device 800 includes one or more processors 810, one or more memories 820 coupled to the processor 810, and one or more communication modules 840 coupled to the processor 810.

[0192] The communication module 840 is for bidirectional communications. The communication module 840 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communicationinterfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 840 may include at least one antenna.

[0193] The processor 810 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 800 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.

[0194] The memory 820 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) 824, 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) 822 and other volatile memories that will not last in the power-down duration.

[0195] A computer program 830 includes computer executable instructions that are executed by the associated processor 810. The instructions of the program 830 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 830 may be stored in the memory, e.g., the ROM 824. The processor 810 may perform any suitable actions and processing by loading the program 830 into the RAM 822.

[0196] The example embodiments of the present disclosure may be implemented by means of the program 830 so that the device 800 may perform any process of the disclosure as discussed with reference to FIG. 2 to FIG. 7. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0197] In some example embodiments, the program 830 may be tangibly contained in a computer readable medium which may be included in the device 800 (such as in the memory 820) or other storage devices that are accessible by the device 800. The device 800 may load the program 830 from the computer readable medium to the RAM 822 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storagepersistency (e.g., RAM vs. ROM).

[0198] FIG. 9 shows an example of the computer readable medium 900 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 900 has the program 830 stored thereon.

[0199] 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.

[0200] 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. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[0201] 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.

[0202] In the context of the present disclosure, the computer program code or relateddata may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0203] 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.

[0204] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, although several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated, certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable sub-combination.

[0205] 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

CLAIMS: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: transmit, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; receive, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points; determine correlation information between metadata containing conditions that are registered in the set of models for positioning in the first apparatus and the set of conditions registered in the second apparatus; and determine, from the set of models, a target model based on correlation information.

2. The first apparatus of claim 1 , wherein the first apparatus is caused to: determine, for each model in the set of models, a correlation value between the metadata that comprises conditions that are registered in the first apparatus and mapped with an identity of the model and the set of conditions registered in the second apparatus; or receive, from the second apparatus, the correlation information, wherein for each model in the set of models, the correlation information comprises the correlation value between the metadata and the set of conditions registered in the second apparatus.

3. The first apparatus of claim 1 or 2, wherein the first apparatus is caused to: determine, from the set of models, a first candidate model of which a correlation value exceeds a correlation threshold; transmit, to the second apparatus, third information indicating the first candidate model; perform an assessment on the first candidate model by applying the monitoring to the first candidate model after a reception of a trigger indication for a monitoring to assess the first candidate model from the second apparatus, or perform the assessment based on a monitoring result of the first candidate model from the second apparatus; and determine whether a performance monitoring of the first candidate model is satisfying based on the assessment.

4. The first apparatus of claim 3, wherein the first apparatus is caused to: based on a determination that the performance monitoring of the first candidate model is satisfying, determine the first candidate model as the target model; transmit, to the second apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and activate at least one of: the target model or a functionality related to the target model.

5. The first apparatus of claim 2, wherein the first apparatus is caused to: based on a determination that none of correlation values exceeds a correlation threshold, determine a third candidate model from the set of models; transmit, to the second apparatus, a request for a dataset that is mapped to a condition registered in the second apparatus; receive a response including at least a portion of the dataset from the second apparatus or an external entity; and update the third candidate model by performing a retraining or fine-tuning on the third candidate model based on the at least a portion of the dataset.

6. The first apparatus of claim 5, wherein the first apparatus is caused to: perform an assessment on the updated third model by applying a monitoring to the updated third candidate model or based on a monitoring result of the updated third candidate; and determine whether performance monitoring of the updated third candidate model is satisfying based on the assessment.

7. The first apparatus of claim 6, wherein the first apparatus is caused to: based on a determination of the performance monitoring of the updated third candidate model is satisfying, determine the updated third candidate as the target model; transmit, to the second apparatus, fifth information indicating that the target model is ready to perform a positioning inference of the first apparatus; and activate at least one of: the target model or a functionality related to the target model.

8. The first apparatus of any of claims 1-7, wherein the first apparatus is caused to: transmit, to the second apparatus, capability information indicating a set of capabilities supported by the first apparatus.

9. The first apparatus of any of claims 1-8, wherein the first apparatus is caused to: receive, from the second apparatus, a set of functionalities associated with positioning; receive, from the second apparatus, an indication indicating a selected functionality by the second apparatus; and transmit, to the second apparatus, a request for conditions registered in the second apparatus.

10. The first apparatus of any of claims 1-9, wherein the first apparatus is caused to: receive, from the second apparatus, a request for the set of transmission reception points with positioning reference signal.

11. The first apparatus of any of claims 1-10, wherein the first apparatus comprises a terminal device, and the second apparatus comprises a network device.

12. 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: receive, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; determine a set of conditions registered in a second apparatus based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and transmit, to the first apparatus, second information comprising the set of conditions registered in the second apparatus.

13. The second apparatus of claim 12, wherein the second apparatus is caused to: determine, for each model in the set of models, a correlation value between the metadata containing conditions that are registered in the first apparatus and the set of conditions registered in the second apparatus; and transmit, to the first apparatus, correlation information including the determined correlation values for each model in the set of models.

14. The second apparatus of claim 12 or 13, wherein the second apparatus is caused to: receive, from the first apparatus, third information indicating a candidate model.

15. The second apparatus of claim 14, wherien the second apparatus is caused to: transmit, to the first apparatus, a trigger indication for a monitoring to assess the candidate model; or wherein the second apparatus is caused to: monitor the candidate model; and transmit a monitoring result of the candidate model.

16. The second apparatus of claim 14, wherein the second apparatus is caused to: receive, from the first apparatus, a request or indication for at least a portion of the dataset that is mapped to a condition registered in the second apparatus.

17. The second apparatus of any of claims 12-16, wherein the second apparatus is caused to: receive, from the first apparatus, fourth information indicating that the target model is ready to perform a positioning inference of the first apparatus.

18. The second apparatus of any of claims 12-17, wherein the second apparatus is caused to: determine an area that is associated with a plurality of datasets, wherein each dataset comprises measurement and ground truth labels; determine a number of channel characteristics in the area; determine a predominant set of channel characteristics values in the area; and determining a plurality of clusters by performing an evaluation on each of the plurality of datasets based on the predominant set of channel characteristics values, wherein each cluster identified in each dataset represents a group of spaces of one channel characteristic and corresponds to a set of conditions registered in the second apparatus.

19. The second apparatus of claim 18, wherein the second apparatus is caused to: obtain a model that is trained based on a subset of datasets from the plurality of datasets, wherein metadata of the subset of datasets are included as metadata information in the trained model, which correspond to conditions registered in the first apparatus.

20. The second apparatus of claim 19, wherein a subset or a full set of channel characteristics identified in the dataset is used for training on the model.

21. A method implemented at a first apparatus, comprising: transmitting, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; receiving, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points; determining correlation information between metadata of a set of models for positioning registered in the first apparatus and the set of conditions registered in the second apparatus; and determining, from the set of models, a target model based on correlation information.

22. A method implemented at a second apparatus, comprising: receiving, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal; determining a set of conditions registered in a second apparatus based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and transmitting, to the first apparatus, second information comprising the set of conditions registered in the second apparatus.

23. A first apparatus comprising: means for transmitting, to a second apparatus, first information indicating a set of transmission reception points with positioning reference signal; means for receiving, from the second apparatus, second information comprising a set of conditions registered in a second apparatus that is selected based on the set of transmission reception points; means for determining correlation information between metadata of a set of models for positioning and the set of conditions registered in the second apparatus; and means for determining, from the set of models, a target model based on correlation information.

24. A second apparatus comprising: means for receiving, from a first apparatus, first information indicating a set of transmission reception points with positioning reference signal;means for determining a set of conditions registered in a second apparatus based on the set of transmission reception points and mapping information between transmission reception points and metadata representing channel characteristics; and means for transmitting, to the first apparatus, second information comprising the set of conditions registered in the second apparatus.

25. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method claim 21 or 22.

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