Identification and usage of NW-additional conditions for positioning

Abstract

The present disclosure relates to identification and usage of network-additional conditions for positioning. In particular, a method includes receiving, at a first apparatus and from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

Description

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

[0001] This application claims priority from, and the benefit of, Finland Application No. 20245423, filed on April 5, 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: receive, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; receive, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and perform the datacollection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

[0005] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to: transmit, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and transmit, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[0006] In a third aspect of the present disclosure, there is provided a method. The method comprises: receiving, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

[0007] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: transmitting, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and transmitting, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[0008] In a fifth aspect of the present disclosure, there is provided a first apparatus. The first apparatus comprises means for receiving, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; means for receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and means for performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

[0009] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for transmitting, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and means for transmitting, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[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 signaling flow for improving consistency between training and inference for positioning according to some example embodiments of the present disclosure;

[0016] FIG. 3A and FIG. 3B illustrate signaling flows for data collection and model identification according to some example embodiments of the present disclosure, respectively;

[0017] FIG. 4 illustrates a flowchart of a method implemented at a first apparatus in accordance with some example embodiments of the present disclosure;

[0018] FIG. 5 illustrates a flowchart of a method implemented at a second apparatus in accordance with some example embodiments of the present disclosure;

[0019] FIG. 6 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0020] FIG. 7 illustrates a block diagram of an example computer readable medium in accordancewith some example embodiments of the present disclosure.

[0021] Throughout the drawings, the same or similar reference numerals represent the same or similar element.DETAILED DESCRIPTION

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening stepsmay be included.

[0028] 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.

[0029] 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 ci rcuit(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.

[0030] 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.

[0031] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), Narrow Band Internet of Things (NB-loT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1 G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourthgeneration (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.

[0032] 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.

[0033] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehiclemounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an I nternet 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. Theterminal 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.

[0034] 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.

[0035] 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.

[0036] In some embodiments, for an AI / ML-enabled feature / FG, additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE- side additional conditions. The term “condition registered in a network side” used herein may refer a set of settings or parameters defined by the NW entity, which has direct impact on the measurements realized in the UE entity. 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 a first apparatus” and the term “UE side additional condition” may be used interchangeable. The term “cell identity” used herein may refer to information (such as, an indexor a bit string) that is used to identify a cell. The term “network side identity” used herein may refer to information that is used to differentiate network side registered conditions. The terms “network side identity” and “NW-identifier” may be used interchangeably.

[0037] 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.

[0038] To facilitate understanding of the terminologies, RAN1 agreements on the list of terminologies used for AI / ML are provided below.

[0039] AI / ML Model: A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] Federated learning / federated training: A machine learning technique that trains an AI / ML model across multiple decentralized edge nodes (e.g., UEs, gNBs) each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.

[0048] Functionality identification: A process / method of identifying an AI / ML functionality for the common understanding between the NW and the UE. Note: Information regarding the AI / MLfunctionality may be shared during functionality identification. Where AI / ML functionality resides depends on the specific use cases and sub use cases.

[0049] Model activation: enable an AI / ML model for a specific function.

[0050] Model deactivation: disable an AI / ML model for a specific function.

[0051] Model download: Model transfer from the network to UE.

[0052] 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.

[0053] Model monitoring: A procedure that monitors the inference performance of the AI / ML model.

[0054] Model parameter update: Process of updating the model parameters of a model.

[0055] 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.

[0056] Model switching: Deactivating a currently active AI / ML model and activating a different AI / ML model for a specific function.

[0057] Model update: Process of updating the model parameters and / or model structure of a model.

[0058] Model upload: Model transfer from UE to the network.

[0059] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0060] Offline field data: The data collected from field and used for offline training of the AI / ML model.

[0061] 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.

[0062] Online field data: The data collected from field and used for online training of the AI / ML model.

[0063] 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 notexactly 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.

[0064] 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.

[0065] Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data.

[0066] Supervised learning: A process of training a model from input and its corresponding labels.

[0067] 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.

[0068] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the UE.

[0069] Unsupervised learning: A process of training a model without labelled data.

[0070] Proprietary-format models: ML models of vendor-Zdevice-specific proprietary format, from 3GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared. Note: An example is a device-specific binary executable format.

[0071] 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.

[0072] 3GPP has started to provide support for AI / ML positioning with the following objectives.0073] 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:0074] 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 onfine-tuning, model re-training, or model switching. Model switching representing the best corner case solution, however, the method to select suitable models is still open. 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 based on model switching. Without this signalization, the model identification may not be possible to apply on practical and realistic scenarios.

[0075] According to some example embodiments of the present disclosure, there is provided a solution for identifying and defining the procedure to use model identification and NW-side additional conditions for AIML positioning use cases. In this way, it can improve the performance of the AI / ML model. Further, it can improve consistency between training and inference for AIML positioning.

[0076] FIG. 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, a plurality of communication devices, including a first device 110 and a second device 120, can communicate with each other. In the example of FIG. 1 , the first device 110 may be a UE and the second device 120 may be a base station serving the UE. The serving area of the second device 120 may be called a cell 102.

[0077] It is to be understood that the number of devices and their connections shown in FIG. 1 are only for the purpose of illustration without suggesting any limitation. The communication environment 100 may include any suitable number of devices configured to implementing example embodiments of the present disclosure. Although not shown, it would be appreciated that one or more additional devices may be located in the cell 102, and one or more additional cells may be deployed in the communication environment 100. It is noted that although illustrated as a network device, the second device 120 may be another device than a network device. Although illustrated as a terminal device, the first device 110 may be another device than a terminal device.

[0078] In the following, for the purpose of illustration, some example embodiments are described with the first device 110 operating as a UE and the second device 120 operating as a base station. However, in some example embodiments, operations described in connection with a terminal device may be implemented at a network device or other device, and operations described in connection with a network device may be implemented at a terminal device or other device.

[0079] In some example embodiments, a link from the second device 120 to the first device 110 is referred to as a downlink (DL), while a link from the first device 110 to the second device 120 is referred to as an uplink (UL). In DL, the second device 120 is a transmitting (TX) device (or a transmitter) and the first device 110 is a receiving (RX) device (or a receiver). In UL, the first device 110 is a TX device (or a transmitter) and the second device 120 is a RX device (or a receiver).

[0080] 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 (1 G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), 5.5G, 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.

[0081] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. For the purpose of illustrations, example embodiments are described with reference to positioning scenario. It is noted that example embodiments of the present disclosure can also be implemented in other scenarios, for example, channel state information (CSI).

[0082] Reference is made to FIG. 2, which illustrates a signalling flow 200 of usage of global identification (Gl) for AI / ML positioning in accordance with some embodiments of the present disclosure. For the purpose of discussion, the signalling flow 200 will be discussed with reference to FIG. 1 , for example, by using the first device 110 and the second device 120.

[0083] In some example embodiments, the first device 110 may transmit (2005) capability information to the second device 120. In other words, the second device 120 may receive (2005) the capability information from the first device 110. The capability information may indicate a set of capabilities supported by the first device 110. For example, the capability information may include capabilities related to handling NW-additional conditions.

[0084] The second device 120 transmits (2010) a positioning reference signal (PRS) configuration (referred to as “a first PRS configuration” hereinafter) for data collection to the first device 110. In other words, the first device 110 receives (2010) the first positioning reference signal configuration for data collection from the second device 120. The first PRS configuration for data collection is associated with at least one of: a cell identity (referred to as “first cell identity”) or a network side identity (referred to as “first network side identity”) that is related to one or more network side registered conditions for positioning. For example, the first PRSC configuration may be associated with a global identification (Gl) (referred to as “a first Gl”) for data collection which includes thecell identity and the first network side identity. In some example embodiments, the first Gl may be determined by a network entity (for example, location management function (LMF)). The first Gl may provide a unique identification between operators (independent of geographical location).

[0085] In some example embodiments, the network side registered condition (also referred to as “NW-side additional condition”) may include one or more NW-side considerations when transmitting PRS resources. In this case, in some example embodiments, the one or more NW- side considerations may not be explicitly known to the first device 110.

[0086] In some example embodiments, the network side identity may provide an identification between different network settings related to a set of reference signal settings for data collection purposes. By way of example, the network side identity may be used to differentiate NW-sided additional conditions associated with reference signal settings. For example, NW-side identity can implicitly differentiate a first set of related network side registered conditions from a second set of related network side registered conditions, where the first and second sets of related network side registered conditions may use different one or more NW-side considerations when transmitting PRS resources.

[0087] Alternatively, or in addition, the network side identity may be used to implicitly identifying a set of NW-additional conditions from a NW-vendor perspective. In some example embodiments, a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor. For example, the network side identity may be defined according to the NW-vendor preference. In some other example embodiments, the mapping between the network side identity and the one or more network side registered conditions is common to network vendors. For example, NW-vendors may be collaborated to align on defining NW-identifier numbering and interpretations of NW-identifiers to NW-additional conditions.

[0088] In some example embodiments, a new field named as “NR-DL-AIML-ADDITIONAL- CONDITION-R19” with parameter “nr-DL_NW-ldentifier-r19” to carry the identification of the list of PRS resource set may be included in an information element (IE) NR-DL-PRS-Info which defines DL PRS configuration. Table 1 shows an example of the IE NR-DL-PRS-Info. It is noted that Table 1 is only an example not limitation.Table 10089] In some other example embodiments, a new field named as “nr-DL_NW-ldentifier-r19” associated to a PRS resource set may be included in an IE NR-DL-PRS-Info which defines DL PRS configuration. Table 2 shows another example of the IE NR-DL-PRS-Info. It is noted that Table 2 is only an example not limitation. Table 20090] In some example embodiments, the cell identity may be a global cell identity (GCI) which is a unique identifier assigned to a cellular base station. The GCI may be used to locate and communicate with mobile devices that are within the range of the base station. For example, GCI may be a combination of several elements, including the Mobile Country Code (MCC), Mobile Network Code (MNC), and Location Area Code (LAC). It is noted that the cell identity may include other types of identifiers assigned to a base station.

[0091] In some example embodiments, the first cell identity and the first network side identity may be included in the first PRS configuration. Alternatively, the first network side identity may be included in the first PRS configuration and another configuration (referred to as “first configuration”)received by the first device 110 may include the first cell identity. For example, the first cell identity may be included in a system information block (SIB) received from the second device 120. By way of example, the GCI may be retrieved from the SIB1 received in the initial access, for example from nr-CellGloballd-r17 which is based on the low-level IE NGCI-r15. Table 3 shows an example of IE including the cell identity. It is noted that Table 3 is only an example not limitation.Table 3- ASN1 STARTNR-DL-PRS-TRP-TEG-lnfo-r17 ::= SEQUENCE (SIZE (1..nrMaxFreqLayers-r16)) OFNR-DL-PRS-TRP-TEG-lnfoPerFreqLayer-r17NR-DL-PRS-TRP-TEG-lnfoPerFreqLayer-r17 ::= SEQUENCE (SIZE (1..nrMaxTRPsPerFreq-r16)) OFNR-DL-PRS-TRP-TEG-lnfoPerTRP-r17NR-DL-PRS-TRP-TEG-lnfoPerTRP-r17 SEQUENCE ! dl-PRS-ID-r17 INTEGER (0..255), nr-PhysCelllD-r17 NR-PhysCelllD-r16 OPTIONAL, - Need ON nr-CellGloballD-r17 NGCI-r15 OPTIONAL, - Need ON nr-ARFCN-r17 ARFCN-ValueNR-r15 OPTIONAL, - Need ON dl-PRS-TEG-lnfoSet-r17 SEQUENCE (SIZE(1..nrMaxSetsPerTrpPerFreqLayer-r16)) OFDL-PRS-TEG-lnfoPerResourceSet-r17,[[ ’ nr-TRP-TxTEG-TimingErrorMargin-r17 TEG-TimingErrorMargin-r17 OPTIONAL - Need ON ]]receive the cell identity (e.g., via SIB1) and uses it to identify models associated with the cell identity, and the available network side identity that match with the specific cell identity. In some other example embodiments, when the first device 110 is doing the NW-entry, the first device 110 may receive the Gl that includes both cell identity and cell identity associated with the cell identity and may use it to identify models associated with the Gl.

[0093] The second device 120 transmits (2015), to the first device 110, a plurality of positioning reference signals based on the first PRS configuration. I n other words, the first device 110 receives (2015) from the second device 120 the plurality of positioning reference signals based on the firstPRS configuration. For example, the network identity may represent a list of PRS recourse or configuration associated to one NW-vendor.

[0094] The first device 110 performs (2020) the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity. For example, the first device 110 may perform measurements on the plurality of positioning reference signals and collect the measurement data. The first device 110 may perform a categorization on measurement data obtained from the plurality of positioning reference signals based on the first cell identity and the first network side identity. In some example embodiments, the data collection for training is done under the PRS resources, which are previously identified with the Gl.

[0095] The first device 110 may train (2025) one or more models based on collected data that is associated with the first cell identity and the first network side identity. For example, the first device 110 may obtain a dataset after the data collection and then train the one or more models using the dataset. In some example embodiments, the one or more models may be associated with every specific Gl (i.e., cell identity and network side identity).

[0096] The second device 120 may transmit (2030) a PRS configuration for inference (referred to as “a second PRS configuration” hereinafter) to the first device 110. The second PRS configuration for inference may be associated with at least one of: another cell identity (referred to as “a second cell identity”) or another network side identity (referred to as “a second network side identity”).

[0097] In some example embodiments, the first device 110 may select (2035) a model for inference based on at least one of the second cell identity or the second network side identity. For example, the first device 110 may select a matching model based on the applicable network side identity(ies) of the PRS resource set(s) when supporting the inference operation.

[0098] In some example embodiments, the first device 110 may select the model for inference by checking whether cell identities and network side identities associated with the model also contain the second cell identity and the second network side identity. Alternatively, or in addition, the first device 110 may receive, from a server (such as, UE server), a model for inference which is determined based on at least one of the second cell identity or the second network side identity. For example, one or more cell identities associated with the model for inference include the second cell identity. In addition, one or more network side identities associated with the model for inference include t the second network side identity.

[0099] In some example embodiments, the first device 110 may transmit (2040) a report indicating one or more applicable network side identities for inference to the second device 120. In other words, the second device 120 may receive (2040) the report from the first device. In some example embodiments, in a scope of the functionality framework (e.g., lower layer protocol (LLP) procedures), in addition to reporting all supported conditions (e.g., UE capabilities), the first device110 may additionally report applicable network side identities (e.g., via UE-capability updates or via a separate reporting mode). In this case, in some example embodiments, reported applicable network side identities are determined based on identifying models for the received GCI. In some other example embodiments, if the first device 110 receives the network side identities via the Gl, the reporting of applicable network side identities may not be always needed (for example, as long as the first device 110 can support all indicted network side identities).

[0100] In some example embodiments, the second device 120 may determine (2043) a model for the inference based on the reported one or more applicable network side identities by checking whether one or more cell identities associated with the model for inference include the second cell identity and one or more network side identities associated with the model for inference include t the second network side identity. The second device 120 may then transmit (2045), to the first device 110, a second configuration indicating a model for the inference which is determined based on the reported one or more applicable network side identities. In other words, the first device 110 may receive (2045) the second configuration indicating the model for the inference from the second device 120.

[0101] In some example embodiments, the network (e.g. LMF) may set the functionality or group of functionalities, including the list of PRS resources sets. In this case, each PRS resource set may be associated with the respective network side identity. In some other example embodiments, the network may configure each PRS resource set associated to a respective Gl (where the network side identity is a part of Gl).

[0102] In some example embodiments, collected data obtained from the data collection is used to develop one or more models associated with the first cell identity and the first network side identity. For example, the first device 110 may train / update the one or more models based on the collected data. Alternatively, or in addition, the collected data may be used to access one or more pretrained models and relate the one or more pre-trained models with the first cell identity and the first network side identity.

[0103] In some example embodiments, the first device 110 may determine a model for model identification. In this case, the model for model identification may be associated with at least one of the first cell identity or the first network side identity. The first device 110 may then determine a model identification report indicating the model for model identification. The model identification report may include at least one of: a model identifier of the model for model identification, the first cell identity, or the first network side identity.

[0104] The first device 110 may transmit (2050) the model identification report to the second device 120. In other words, the second device 120 may receive (2050) the model identification report fromthe first device 110. For example, after selecting models that are associated with the Gl, the first device 110 may report the model identifier to the NW. The model identifier may refer to one or more network side identities. The reported model identifier may be referred as the model-ID and the second device 120 may associate set of NW-additional conditions to the model-ID based on the network side identities.

[0105] In some example embodiments, the first device 110 may reports the model identifier to the second device 120. The model identifier may refer to one or more GIs. The reported model identifier may be referred as the model-ID and the second device 120 can associate GCIs and NW-identifiers based on the report. The second device 120 may then derive set of NW-additional conditions and cells that the model-ID is applicable. In some other example embodiments, without directly referring reported model identifier refers as model-ID, the second device 120 may assign model-ID based on the reported model identifiers.

[0106] In some example embodiments, the second device 120 may transmit an inference operation configuration including a positioning reference signal configuration for inference to the first device 110. In other words, the first device 110 may receive the inference operation configuration from the second device 120.

[0107] Reference is made to FIG. 3A, which illustrates a signalling flow 300 for data collection in accordance with some embodiments of the present disclosure. For the purpose of discussion, the signalling flow 300 involves a UE 310 and a network (NW) entity 320. In some example embodiments, the first device 110 may act as the UE 310 and the second device 120 may act as the NW entity 320.

[0108] When the UE 310 connects to the NW entity 320, the UE 310 may report (3010) the capability report. The capability report may carry capabilities related to handling of NW-additional conditions (related to training and inference consistency) for a given ML-enabled Positioning usecase. In an example embodiment, the capabilities related to handling NW-additional conditions may also be related to the capabilities associated with the UE-side data collection.

[0109] The NW entity 320 may configure (3020) the UE 310 with a data collection configuration via LPP. The data collection configuration may include PRS configurations for the data collection. Each PRS configuration may be associated with a global identification (Gl), where Gl is determined by two elements: (1) a global cell identity (GCI) and (2) a specific NW-identifier. In one example embodiment, PRS resource set within a PRS configuration can have a configuration lEs that provides a GCI and a NW-identifier associated with the PRS resource set. In another example embodiment, PRS resource set within a PRS configuration can have a configuration IE that provides a NW-identifier associated with the PRS resource set. Here, GCI associated with the PRSresource set may be provided separately. In one example, GCI may be provided in the SIB#1, where the UE 310 may consider all PRS resource sets associated with the connected cell to be associated with the same GCI.

[0110] In one example, a NW vendor may use NW-identifiers to implicitly represent a set of NW- additional conditions (identifiers may be defined according to the NW-vendor preference). In another example, it may be also possible to collaborate among NW-vendors to align on defining NW-identifier numbering and interpretations of NW-identifiers to NW-additional conditions. The purpose of the NW identifier is to provide means to categorize data collection at the UEs. For a given vendor, when the same NW assumptions (implementation assumptions that corresponding to the NW additional conditions) are used in a different cell, it is expected that same NW-identifier is used to keep the consistency.

[0111] The NW entity 320 may transmit (3030) PRS for data collection according to data collection configuration. The UE 310 may collect (3040) the data. When the UE 310 collects positioning measurements (data samples) based on PRS reception corresponding to a PRS resource set, the UE 310 has the means to categorize data according to the GCI and NW-identifier. In general, data collection process may happen over multiple UEs, multiple cells, and at any point in the time. However, as UEs can categorize the data at least based on GCI and NW-identifier, the UE server who develop models can further be associated with the GCIs and NW-identifiers. For example, the model may be trained (3050) based on the data collection.

[0112] During the inference stage, the UE 310 may either consider the same PRS configuration (as the data collection) or reconfigured to use different PRS configuration for inference. For example, the NW entity 320 may configure (3060) a PRS configuration for an inference operation that includes GCI(s) and NW-identifier(s) for PRS configuration.

[0113] Based on the received PRS configuration for inference, the UE 310 may select (3070) a matching ML model for inference. The selection at the UE 310 may consider PRS configuration associated GCI(s) and NW-identifier(s). In one example, the UE 310 may be supporting inference in a different cell than where it collected data samples. In such cases, it is expected that a UE server (such as, over the top (OTT) server), which does the model training, to send a matching model to the UE 310 to run the inference. Here, matching model is determined based on GCIs and NW-identifier(s) associated with the PRS configuration received in the inference.

[0114] In addition, there may be additional steps to coordinate with the NW entity 320 on applicable NW-identifiers with the NW, especially when the UE 310 does not support all possible variants of NW-identifiers. For example, the UE 310 may transmit (3080) a UE report indicating applicable NW-identifiers for inference operation. The NW entity 320 may configure (3090) the UE 310 toselect or activate inference operation according to the UE-reported NW-identifier(s).

[0115] Reference is made to 3B, which illustrates a signalling flow 300’ for model identification in accordance with some embodiments of the present disclosure. For the purpose of discussion, the signalling flow 300’ involves a UE 310 and a network (NW) entity 320. In some example embodiments, the first device 110 may act as the UE 310 and the second device 120 may act as the NW entity 320.

[0116] When the UE 310 connects to the NW, the UE 310 may report (3010’) the capability report. The capability report may carry capabilities related to supporting model identification for a given ML-enabled Positioning use-case.

[0117] The NW entity 320 may configure (3020’) the UE 310 with a data collection configuration via LPP. The data collection configuration may include PRS configurations for the data collection. Each PRS configuration may be associated with a global identification (Gl), where Gl is determined by two elements: (1) a global cell identity (GCI) and (2) a specific NW-identifier. In one example embodiment, PRS resource set within a PRS configuration can have a configuration lEs that provides a GCI and a NW-identifier associated with the PRS resource set. In another example embodiment, PRS resource set within a PRS configuration can have a configuration IE that provides a NW-identifier associated with the PRS resource set. Here, GCI associated with the PRS resource set may be provided separately. In one example, GCI may be provided in the SIB#1, where the UE 310 may consider all PRS resource sets associated with the connected cell to be associated with the same GCI.

[0118] In one example, a NW vendor may use NW-identifiers to implicitly represent a set of NW- additional conditions (identifiers may be defined according to the NW-vendor preference). In another example, it may be also possible to collaborate among NW-vendors to align on defining NW-identifier numbering and interpretations of NW-identifiers to NW-additional conditions. The purpose of the NW identifier is to provide means to categorize data collection at the UEs. For a given vendor, when the same NW assumptions (implementation assumptions that corresponding to the NW additional conditions) are used in a different cell, it is expected that same NW-identifier is used to keep the consistency.

[0119] The NW entity 320 may transmit (3030’) PRS for data collection according to data collection configuration. The UE 310 may collect (3040’) the data. When the UE 310 collects positioning measurements (data samples) based on PRS reception corresponding to a PRS resource set, the UE 310 has the means to categorize data according to the GCI and NW-identifier. In general, data collection process may happen over multiple UEs, multiple cells, and at any point in the time.

[0120] After data collection and categorization, collected data may be used (3050’) to develop (trainthe model / update / ) models where models can be associated with GCIs and NW-identifiers. Alternatively, or in addition, the collected data may be used (3050’) to assess pre-trained models, and further relate pre-trained models with GCIs and NW-identifiers. Here assessing may be done by evaluating the performance of pre-trained models with different data sets categorized with GCIs and NW-identifiers. After this step, the UE 310 may be expected to determine one or more ML models for model identification, where one or more models are associated with NW-identifiers and GCIs.

[0121] The UE 310 may generate a model identification report. The UE 310 may then send (3060’) a model identifier, assigned by the UE 310 or NW entity 320, and associated GCI(s) and NW- Identifier(s). In an example embodiment, NW-identifiers may be sent as GCI that can be determined by the NW based on the connected cell that the UE 310 sends (3060’) the report.

[0122] The NW entity 320 may determine whether reported models are matching with NW- identifiers and GCIs applicable for inference. Based on the received model identification report, the NW entity 320 can configure (3080’) an inference operation towards the UE 310 and also consider model level selection, activation, and other signaling procedures. For example, the NW entity 320 may configure (3090’) the UE 310 to select / activate / deactivate / switch models with model identifier.

[0123] FIG. 4 shows a flowchart of an example method 400 implemented at a first apparatus in accordance with some example embodiments of the present disclosure. For example, the method 400 will be implemented at the first device 110 in FIG. 1 .

[0124] At block 410, the first apparatus receives, from a second apparatus, a first positioning reference signal configuration for data collection. The first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning.

[0125] At block 420, the first apparatus receives, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[0126] At block 430, the first apparatus performs the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

[0127] In some example embodiments, the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

[0128] In some example embodiments, the method 400 further comprises: receiving, from the second apparatus, a first configuration comprising the first cell identity.

[0129] In some example embodiments, a mapping between the network side identity and the oneor more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.

[0130] In some example embodiments, the method 400 further comprises: performing a categorization on measurement data obtained from the plurality of positioning reference signals based on the first cell identity and the first network side identity.

[0131] In some example embodiments, the method 400 further comprises: training one or more models based on collected data that is associated with the first cell identity and the first network side identity.

[0132] In some example embodiments, the method 400 further comprises: receiving, from the second apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

[0133] In some example embodiments, the method 400 further comprises: selecting a model for inference based on at least one of the second cell identity or the second network side identity.

[0134] In some example embodiments, the method 400 further comprises: receiving, from a server, a model for inference which is determined based on at least one of the second cell identity or the second network side identity.

[0135] In some example embodiments, one or more cell identities associated with the model for inference include the second cell identity, and one or more network side identities associated with the model for inference include t the second network side identity.

[0136] In some example embodiments, the method 400 further comprises: transmitting, to the second apparatus, a report indicating one or more applicable network side identities for inference; and receiving, from the second apparatus, a second configuration indicating a model for the inference which is determined based on the reported one or more applicable network side identities.

[0137] In some example embodiments, collected data obtained from the data collection is used to develop one or more models associated with the first cell identity and the first network side identity, or wherein the collected data is used to access one or more pre-trained models and relate the one or more pre-trained models with the first cell identity and the first network side identity.

[0138] In some example embodiments, the method 400 further comprises: determining a model for model identification, wherein the model for model identification is associated with at least one of the first cell identity or the first network side identity; determining a model identification report indicating the model for model identification, wherein the model identification report comprises at least one of: a model identifier of the model for model identification, the first cell identity, or thefirst network side identity; and transmitting, to the second apparatus, the model identification report.

[0139] In some example embodiments, the method 400 further comprises: receiving, from the second apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

[0140] In some example embodiments, one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

[0141] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a network device.

[0142] FIG. 5 shows a flowchart of an example method 500 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For example, the method 500 will be implemented at the second device 120 in FIG. 1 .

[0143] At block 510, the second apparatus transmits, to a first apparatus, a first positioning reference signal configuration for data collection. The first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning.

[0144] At block 520, the second apparatus transmits, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[0145] In some example embodiments, the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

[0146] In some example embodiments, the first positioning reference signal configuration comprises the first network side identity, and wherein the first apparatus is caused to: transmitting, to the first apparatus, a first configuration comprising the first cell identity.

[0147] In some example embodiments, a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.

[0148] In some example embodiments, the method 500 further comprises: transmitting, to the first apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

[0149] In some example embodiments, the method 500 further comprises: receiving, from the first apparatus, a report indicating one or more applicable network side identities for inference;determining a model for the inference based on the reported one or more applicable network side identities by checking whether one or more cell identities associated with the model for inference include the second cell identity and one or more network side identities associated with the model for inference include t the second network side identity; and transmitting, to the first apparatus, a second configuration indicating the model for the inference.

[0150] In some example embodiments, the method 500 further comprises: receiving, from the first apparatus, a model identification report indicating the model for model identification, wherein the model identification report comprises: a model identifier of the model for model identification, the first cell identity, and the first network side identity; and determining whether the model for model identification matches with a cell identity and network side identity for inference.

[0151] In some example embodiments, the method 500 further comprises: transmitting, to the first apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

[0152] In some example embodiments, the one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

[0153] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a network device.

[0154] In some example embodiments, a first apparatus capable of performing any of the method 400 (for example, the first device 110 in FIG. 1 ) may comprise means for performing the respective operations of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first device 110 in FIG. 1 .

[0155] In some example embodiments, the first apparatus comprises means for receiving, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; means for receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and means for performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

[0156] In some example embodiments, the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

[0157] In some example embodiments, the first apparatus further comprises: means for receiving,from the second apparatus, a first configuration comprising the first cell identity.

[0158] In some example embodiments, a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.

[0159] In some example embodiments, the first apparatus further comprises: means for performing a categorization on measurement data obtained from the plurality of positioning reference signals based on the first cell identity and the first network side identity.

[0160] In some example embodiments, the first apparatus further comprises: means for training one or more models based on collected data that is associated with the first cell identity and the first network side identity.

[0161] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

[0162] In some example embodiments, the first apparatus further comprises: means for selecting a model for inference based on at least one of the second cell identity or the second network side identity.

[0163] In some example embodiments, the first apparatus further comprises: means for receiving, from a server, a model for inference which is determined based on at least one of the second cell identity or the second network side identity.

[0164] In some example embodiments, one or more cell identities associated with the model for inference include the second cell identity, and one or more network side identities associated with the model for inference include t the second network side identity.

[0165] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, a report indicating one or more applicable network side identities for inference; and means for receiving, from the second apparatus, a second configuration indicating a model for the inference which is determined based on the reported one or more applicable network side identities.

[0166] In some example embodiments, collected data obtained from the data collection is used to develop one or more models associated with the first cell identity and the first network side identity, or wherein the collected data is used to access one or more pre-trained models and relate the one or more pre-trained models with the first cell identity and the first network side identity.

[0167] In some example embodiments, the first apparatus further comprises: means fordetermining a model for model identification, wherein the model for model identification is associated with at least one of the first cell identity or the first network side identity; means for determining a model identification report indicating the model for model identification, wherein the model identification report comprises at least one of: a model identifier of the model for model identification, the first cell identity, or the first network side identity; and means for transmitting, to the second apparatus, the model identification report.

[0168] In some example embodiments, the first apparatus further comprises: means for receiving, from the second apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

[0169] In some example embodiments, one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

[0170] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a network device.

[0171] In some example embodiments, a second apparatus capable of performing any of the method 500 (for example, the second device 120 in FIG. 1) may comprise means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The second apparatus may be implemented as or included in the second device 120 in FIG. 1.

[0172] In some example embodiments, the second apparatus comprises means for transmitting, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and means for transmitting, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

[0173] In some example embodiments, the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

[0174] In some example embodiments, the first positioning reference signal configuration comprises the first network side identity, and wherein the first apparatus is caused to: means for transmitting, to the first apparatus, a first configuration comprising the first cell identity.

[0175] In some example embodiments, a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.

[0176] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

[0177] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, a report indicating one or more applicable network side identities for inference; means for determining a model for the inference based on the reported one or more applicable network side identities by checking whether one or more cell identities associated with the model for inference include the second cell identity and one or more network side identities associated with the model for inference include t the second network side identity; and means for transmitting, to the first apparatus, a second configuration indicating the model for the inference.

[0178] In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, a model identification report indicating the model for model identification, wherein the model identification report comprises: a model identifier of the model for model identification, the first cell identity, and the first network side identity; and means for determining whether the model for model identification matches with a cell identity and network side identity for inference.

[0179] In some example embodiments, the second apparatus further comprises: means for transmitting, to the first apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

[0180] In some example embodiments, the one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

[0181] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a network device.

[0182] FIG. 6 is a simplified block diagram of a device 600 that is suitable for implementing example embodiments of the present disclosure. The device 600 may be provided to implement a communication device, for example, the first device 110 or the second device 120 as shown in FIG. 1. As shown, the device 600 includes one or more processors 610, one or more memories 620 coupled to the processor 610, and one or more communication modules 640 coupled to the processor 610.

[0183] The communication module 640 is for bidirectional communications. The communication module 640 has one or more communication interfaces to facilitate communication with one ormore other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 640 may include at least one antenna.

[0184] The processor 610 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 600 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0185] The memory 620 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 624, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), an optical disk, a laser disk, and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random-access memory (RAM) 622 and other volatile memories that will not last in the power-down duration.

[0186] A computer program 630 includes computer executable instructions that are executed by the associated processor 610. The instructions of the program 630 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 630 may be stored in the memory, e.g., the ROM 624. The processor 610 may perform any suitable actions and processing by loading the program 630 into the RAM 622.

[0187] The example embodiments of the present disclosure may be implemented by means of the program 630 so that the device 600 may perform any process of the disclosure as discussed with reference to FIG. 2 to FIG. 5. The example embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0188] In some example embodiments, the program 630 may be tangibly contained in a computer readable medium which may be included in the device 600 (such as in the memory 620) or other storage devices that are accessible by the device 600. The device 600 may load the program 630 from the computer readable medium to the RAM 622 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0189] FIG. 7 shows an example of the computer readable medium 700 which may be in form ofCD, DVD or other optical storage disk. The computer readable medium 700 has the program 630 stored thereon.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] In the context of the present disclosure, the computer program code or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable medium, and the like.

[0194] 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.

[0195] 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.

[0196] 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 or any of the below embodiments.

Claims

CLAIMS1 . A first apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to: receive, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; receive, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and perform the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

2. The first apparatus of claim 1 , wherein the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

3. The first apparatus of claim 1 , wherein the first positioning reference signal configuration comprises the first network side identity, and wherein the first apparatus is caused to: receive, from the second apparatus, a first configuration comprising the first cell identity.

4. The first apparatus of any of claims 1-3, wherein a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.

5. The first apparatus of any of claims 1-4, wherein the first apparatus is caused to: perform a categorization on measurement data obtained from the plurality of positioning reference signals based on the first cell identity and the first network side identity.

6. The first apparatus of any of claims 1-5, wherein the first apparatus is caused to: train one or more models based on collected data that is associated with the first cell identity and the first network side identity.

7. The first apparatus of any of claims 1-6, wherein the first apparatus is caused to: receive, from the second apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

8. The first apparatus of claim 7, wherein the first apparatus is caused to: select a model for inference based on at least one of the second cell identity or the second network side identity.

9. The first apparatus of claim 7, wherein the first apparatus is caused to: receive, from a server, a model for inference which is determined based on at least one of the second cell identity or the second network side identity.

10. The first apparatus of claim 9, wherein one or more cell identities associated with the model for inference include the second cell identity, and one or more network side identities associated with the model for inference include t the second network side identity.11 . The first apparatus of any of claims 1-9, wherein the first apparatus is caused to: transmit, to the second apparatus, a report indicating one or more applicable network side identities for inference; and receive, from the second apparatus, a second configuration indicating a model for the inference which is determined based on the reported one or more applicable network side identities.

12. The first apparatus of any of claim 1-11 , wherein collected data obtained from the data collection is used to develop one or more models associated with the first cell identity and the first network side identity, or wherein the collected data is used to access one or more pre-trained models and relate the one or more pre-trained models with the first cell identity and the first network side identity.

13. The first apparatus of claim 12, wherein the first apparatus is caused to: determine a model for model identification, wherein the model for model identification is associated with at least one of the first cell identity or the first network side identity; determine a model identification report indicating the model for model identification, wherein the model identification report comprises at least one of: a model identifier of the model for model identification, the first cell identity, or the first network side identity; and transmit, to the second apparatus, the model identification report.

14. The first apparatus of claim 13, wherein the first apparatus is caused to: receive, from the second apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

15. The first apparatus of any of claims 1-14, wherein one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

16. The first apparatus of any of claims 1-15, wherein the first apparatus is a terminal device, and the second apparatus is a network device.

17. 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: transmit, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and transmit, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

18. The second apparatus of claim 17, wherein the first positioning reference signal configuration comprises the first cell identity and the first network side identity.

19. The second apparatus of claim 17, wherein the first positioning reference signal configuration comprises the first network side identity, and wherein the first apparatus is caused to: transmit, to the first apparatus, a first configuration comprising the first cell identity.

20. The second apparatus of any of claims 17-19, wherein a mapping between the network side identity and the one or more network side registered conditions is specific to a network vendor, or wherein the mapping between the network side identity and the one or more network side registered conditions is common to network vendors.21 . The second apparatus of any of claims 17-20, wherein the second apparatus is caused to: transmit, to the first apparatus, a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identity or a second network side identity.

22. The second apparatus of any of claims 17-21 , wherein the second apparatus is caused to: receive, from the first apparatus, a report indicating one or more applicable network side identities for inference; determine a model for the inference based on the reported one or more applicable network side identities by checking whether one or more cell identities associated with the model for inference include the second cell identity and one or more network side identities associated with the model for inference include t the second network side identity; and transmit, to the first apparatus, a second configuration indicating the model for the inference.

23. The second apparatus of any of claims 17-22, wherein the second apparatus is caused to: receive, from the first apparatus, a model identification report indicating the model for model identification, wherein the model identification report comprises: a model identifier of the model for model identification, the first cell identity, and the first network side identity; and determine whether the model for model identification matches with a cell identity and network side identity for inference.

24. The second apparatus of claim 23, wherein the second apparatus is caused to: transmit, to the first apparatus, an inference operation configuration comprising a positioning reference signal configuration for inference.

25. The second apparatus of any of claims 17-24, wherein the one or more network side registered conditions may include one or more consideration related to positioning at network side during transmitting positioning reference signals.

26. The second apparatus of any of claims 17-25, wherein the first apparatus is a terminal device, and the second apparatus is a network device.

27. A method comprising: receiving, at a first apparatus and from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

28. A method comprising: transmitting, at a second apparatus and to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and transmitting, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

29. A first apparatus comprising: means for receiving, from a second apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; means for receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and means for performing the data collection on the plurality of positioning reference signals based on at least one of: the first cell identity or the first network side identity.

30. A second apparatus comprising: means for transmitting, to a first apparatus, a first positioning reference signal configuration for data collection, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identity or a first network side identity that is related to one or more network side registered conditions for positioning; and means for transmitting, to the first apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration.

31. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method claim 27 or 28.

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