Usage of network additional conditions for positioning

Abstract

The present disclosure relates to identification and usage of network additional conditions for positioning. In particular, a method includes a terminal receiving, from a location management function LMF, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the terminal and at least one further terminal; or second information indicating a PRS parameter used for determining PRS resources specific to the terminal by applying the PRS parameter to second PRS resources; and receiving, from a plurality of gNB / TRPs. a plurality of PRSs based on the PRS configuration.

Description

I7TITT INC Jr LILIjUo

[0001] Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for usage of network additional conditions for positioning. BACKGROUND

[0002] With developments in the integration of Artificial Intelligence (AI) 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 3rd generation 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

[0003] 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 positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and receive, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

[0004] In a second aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to: determine, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and transmit the PRS configuration to the first apparatus.

[0005] In a third aspect of the present disclosure, there is provided a method. The method comprises: receiving, from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and receiving, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

[0006] In a fourth aspect of the present disclosure, there is provided a method. The method comprises: determining, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and transmitting the PRS configuration to the first apparatus.

[0007] 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 positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and means for receiving, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

[0008] In a sixth aspect of the present disclosure, there is provided a second apparatus. The second apparatus comprises means for determining, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and means for transmitting the PRS configuration to the first apparatus.

[0009] 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.

[0010] 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.

[0011] 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

[0012] Some example embodiments will now be described with reference to the accompanying drawings, where:

[0013] FIG. I illustrates an example communication environment in which example embodiments of the present disclosure can be implemented;

[0014] FIG. 2 illustrates a signaling flow of PRS configuration procedure according to some example embodiments of the present disclosure;

[0015] FIG. 3 A and FIG. 3B illustrate signaling flows of PRS configuration procedure according to some example embodiments of the present disclosure;

[0016] FIG. 4 illustrates a flowchart of a method implemented at a first apparatus according to some example embodiments of the present disclosure;

[0017] FIG. 5 illustrates a flowchart of a method implemented at a second apparatus according to some example embodiments of the present disclosure;

[0018] FIG. 6 illustrates a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure; and

[0019] FIG. 7 illustrates a block diagram of an example computer readable medium in accordance with some example embodiments of the present disclosure.

[0020] Throughout the drawings, the same or similar reference numerals represent the same or similar element. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] As used herein, unless stated explicitly, performing a step “in response to A” does not indicate that the step is performed immediately after “A” occurs and one or more intervening steps may be included.

[0027] 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.

[0028] As used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0029] 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.

[0030] As used herein, the term “communication network” refers to a network following any suitable communication standards, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), Narrow Band Internet of Things (NB-IoT) and so on. Furthermore, the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G), the sixth generation (6G) communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0031] 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.

[0032] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. The terminal device may also correspond to a Mobile Termination (MT) part of an IAB node (e.g., a relay node). In the following description, the terms “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0033] 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.

[0034] The term “transmission reception point (TRP)” used herein may be defined as an antenna array, with one or more antenna elements, available to the network located at a specific geographical location for a specific area. In some embodiments, the TRP may be implemented at a network device. The term “condition registered in a network side” used herein may refer to a condition at network side which include additional information to assist terminal units to improve consistency between training and inference model. The term “condition registered in a network side”, the term “condition registered in a second apparatus” and the term “network (NW) side additional condition” may be used interchangeable. The process of model identification pinpoints what are known as “additional conditions”, which may include, for example, training dataset category, site-related information, timestamps, implicit identification information (such as, labels for specific gNB / UE implementation details), statistical information (e.g., delay spread, angular spread, line of sight (LOS) / None-LOS data and so on), and other factors. The term “condition registered in a UE side” used herein may refer to a condition at UE side which include additional information to improve consistency between training and inference model. The term “condition registered in a UE side”, the term “condition registered in a first apparatus” and the term “UE side additional condition” may be used interchangeable.

[0035] 3GPP has started to provide supports for AI / ML positioning with the following objectives. - Positioning accuracy enhancements, encompassing: o Direct AI / ML positioning: ■ (1st priority) Case 1: UE-based positioning with UE-side model, direct AI / ML positioning ■ (2nd priority) Case 2b: UE-assisted / location management function (LMF)-based positioning with LMF-side model, direct AI / ML positioning ■ (1st priority) Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning o AI / ML assisted positioning ■ (2nd priority) Case 2a: UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning ■ (1st priority) Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning o Specify necessary measurements, signalling / mechanism(s) to facilitate life circle management (LCM) operations specific to the Positioning accuracy enhancements use cases, if any o Investigate and specify the necessary signalling of necessary measurement enhancements (if any) o Enabling method(s) to ensure consistency between training and inference regarding NW-side additional conditions (if identified) for inference at UE for relevant positioning sub use cases

[0036] Further, network (NW)-side additional conditions are being prioritized if compared to UE-side additional conditions. For NW-side additional conditions, it has investigated a variety of scenarios for model generalization considering the following scenarios: Model generalizations To investigate the model generalization capability, at least the following aspect(s) are considered for the evaluation for AI / ML based positioning: - Different drops: Training dataset from drops {Ao, Ai,..., An-i}, test dataset from unseen drop(s) (i.e., different drop(s) than any in {Ao, Ai,..., An-i}). Here N>1. - Clutter parameters, e.g., training dataset from one clutter parameter (e.g., {40%, 2m, 2m{), test dataset from a different clutter parameter (e.g., {60%, 6m, 2m{); - Network synchronization error, e.g., training dataset without network synchronization error, test dataset with network synchronization error; - UE / gNB RX and TX timing error; - The baseline non-AI / ML method may enable the Rel-17 enhancement features (e.g., UE Rx TEG, UE RxTx TEG). - InF scenarios, e.g., training dataset from one indoor factory scenario (e.g., indoor factory^dense high, InF-DH), test dataset from a different InF scenario (e.g., Indoor Factory with High Tx and High Rx, InF-HH) - The following issues related to measurements on the positioning accuracy of the AI / ML model. The simulation assumptions reflecting these issues are up to companies. - SNR mismatch (i.e., SNR when training data are collected is different from SNR when model inference is performed). - Time varying changes (e.g., mobility of clutter objects in the environment) - Channel estimation error For both direct AI / ML approach and AI / ML assisted approach, for a given AI / ML model design (e.g., input, output, single-TRP vs multi-TRP), identify the generalization aspects where model fine-tuning / mixed training dataset / model switching is necessary.

[0037] Most of these scenarios are related to impairments such as SNR mismatch, time varying changes, scenarios where the clutter characteristics change, or different radio channel conditions. However, most of these scenarios are in the scope of simulation setups and can be categorized as characteristics of particular training scenarios.

[0038] To facilitate the discussion, it studies the model identification type A with more details related to use cases. To facilitate the discussion, it also studies the following options as starting point for model identification type B with more details related to all use cases: • Option 1: Model identification with data collection related configuration(s) and / or indication(s); • Option 2: Model identification with dataset transfer; • Option 3: Model identification in model transfer from NW to UE; • Option 4. Model identification via standardization of reference models, (for CSI compression); • Option 5. Model identification via model monitoring.

[0039] Regarding Option 1 (i.e., model identification with data collection related configuration(s) and / or indication(s)) of model identification type B, RANI further studies the following aspects: relationship between model ID and data collection related configuration(s) and / or indication(s); information transmitted from NW to UE (if any); information transmitted from UE to NW (if any); the associated procedure; usage / applicable use case(s) of Option 1.

[0040] From RANI perspective, for UE-sided model(s) developed (e.g., trained, updated) at UE side, following procedure is an example (noted as AI-Examplel) of MI-Optionl for further study (including the feasibility / necessity): • A: For data collection, NW signals the data collection related configuration(s) and it / their associated ID(s), o Associated IDs for each sub use case in relation with NW-sided additional conditions, • B: UE(s) collects the data corresponding to the associated ID(s), • C: AI / ML models are developed (e.g., trained, updated) at UE side based on the collected data corresponding to the associated ID(s). • D: UE reports information of its AI / ML models corresponding to associated IDs to the NW. Model ID is determined / assigned for each AI / ML model, o relationship between model ID(s) and the associated ID(s), o How model ID(s) is determined / assigned, e.g., ■ Alt. 1: NW assigns Model ID, ■ Alt.2: UE assigns / reports Model ID, ■ Alt.3: Associated ID(s) is assumed as model ID(s), • “Model ID is determined / assigned for each AI / ML model” in D is not needed, ■ Alt.4: Model ID is determined by pre-defined rule(s) in the specification, o Note: D is to facilitate AI / ML model inference • Note: Step A / B / C and additional interaction of associated IDs between UE and NW can be considered as a different solution for resolving the consistency without model identification.

[0041] In the context of the present disclosure, for an AI / ML-enabled feature / feature group (FG), additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. One interpretation for NW-side additional conditions is that they represent a set of settings or parameters defined by the NW entity, which has direct impact on the measurements realized in the UE entity.

[0042] Example embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Example Environment

[0043] FIG. 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In the communication environment 100, the first apparatus 110-1 and one or more third apparatus 130-1, ..., 130-N (collectedly referred to as the third apparatus 130, and N is an integer number) can communication with each other. The communication environment 100 also may comprise the first apparatus 110-2 and the first apparatus 110-3. The first apparatus 110-1, the first apparatus 110-2 and the first apparatus 110-3 also may be collectedly referred to as the first apparatus 110.

[0044] The communication environment 100 may also include a second apparatus 120, and the second apparatus 120 may communicate with the first apparatus 110 via one or more of the third apparatuses 130.

[0045] In the example of FIG. 1, in some embodiments, the first apparatus 110 may include a terminal device, the second apparatus 120 may include a core network (CN) apparatus, such as, a location management function (LMF) entity. Further, the second apparatus 120 may include a network device, such as, a gNB or a TRP.

[0046] In some example embodiments, if the first apparatus 110 is a terminal device, a link from the third apparatus 130 to the first apparatus 110 is referred to as a downlink (DL), and a link from the first apparatus 110 to the third apparatus 130 is referred to as an uplink (UL). In DL, the third apparatus 130 is a transmitting (TX) device (or a transmitter) and the first apparatus 110 is a receiving (RX) device (or a receiver). In UL, the first apparatus 110 is a TX device (or a transmitter) and the third apparatus 130 is a RX device (or a receiver).

[0047] In some example embodiments, a positioning procedure may be implemented in the communication environment 100. In some example embodiments, the field dl-PRS-ID may be used for configuring PRS. Specifically, this field is used along with a DL-PRS Resource Set ID and a DL-PRS Resources ID to uniquely identify a DL-PRS Resource. Further, dl-PRS-ID may be associated with multiple DL-PRS Resource Sets associated with a single TRP.

[0048] Communications in the communication environment 100 may be implemented according to any proper communication protocol(s), comprising, but not limited to, cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G), the fifth generation (5G), the sixth generation (6G), and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple-Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future. Work Principle and Example Signaling for Communication

[0049] Generally speaking, PRS transmission / configuration is not UE-specific, and many target UEs may be localized with a receiving range of the same set of PRS. This is purposely done so, since PRS are full bandwidth (BW), and sent by many TRPs, thus, having a UE-specific / unique PRS transmission / configuration is spectrally inefficient and introduces high latency (since UEs should wait their turns in receiving their UE-specific PRS).

[0050] Since the positioning session using standard PRS transmission / configuration may be suboptimal with respect to the positioning outcome (e.g., low position accuracy), the LMF should be given a chance to reconfigure the session. Hence, the concept of on-demand PRS i.e., on-demand PRS was born. The on-demand PRS transmission procedure allows the LMF to control and decide whether to transmit the PRSs and / or whether to change the characteristics of an ongoing PRS transmission. The on-demand PRS transmission procedure may be initiated either by the UE or LMF. The actual PRS changes are requested by the LMF irrespective of whether the procedure is UE-initiated or LMF-initiated.

[0051] The on-demand PRS procedure may be triggered upon some indication that the localization session is suboptimal e.g., low SNR, low LOS, etc, and may or may not be approved by the LMF or by the gNB configuring the TRPs. This behaviour is purposeful, to prevent the situation in which multiple UEs are requesting at the same time conflicting on-demand PRS changes.

[0052] Thus, on-demand PRS should not be the default procedure used to configure signal transmission for AI / ML positioning. The on-demand PRS should be a fallback and the standard PRS should be used instead. In the current discussion on, the proposed solutions for solving the consistency between training and inference mainly rely on on-demand PRS features instead of the standard PRS.

[0053] In real deployments, the following scenarios may be faced. • Scenario #1: All UEs have the same UE-capabilities (conditions). Thus, no need to use on-demand PRS. One unique PRS configuration may be used for AIML positioning data collection or inference. • Scenario #2: All UEs have different capabilities (conditions). Thus, setting specific PRS transmission / configuration makes the solution impractical. Even, if the on-demand PRS is used, the usage of this procedure is limited and could not be considered as a default solution for AI / ML positioning.

[0054] According to some example embodiments of the present disclosure, a solution of usage of network additional conditions for positioning is proposed.

[0055] Reference is made to FIG. 2, which illustrates a signaling flow 200 of communication in accordance with some embodiments of the present disclosure. For the purposes of discussion, signaling flow 200 will be discussed with reference to FIG. 1, for example, by using the first apparatus 110-1, the third apparatus 130 and the second apparatus 120.

[0056] It is to be understood that the operations at the first apparatus 110-1 and the second apparatus 120 (and / or the third apparatus 130) should be coordinated. In other words, the first apparatus 110-1, the second apparatus 120 (and / or the third apparatus 130) should have common understanding about configurations, parameters and so on. Such common understanding may be implemented by any suitable interactions or applying the same rule / policy.

[0057] In the following, although some operations are described from a perspective of the first apparatus 110-1, it is to be understood that the corresponding operations should be performed by the second apparatus 120. Similarly, although some operations are described from a perspective of the second apparatus 120, it is to be understood that the corresponding operations should be performed by the first apparatus 110-1. Merely for brevity, some of the same or similar contents are omitted here.

[0058] Merely for a better understanding, in the following example embodiments, the first apparatus 110-1 may function as a terminal apparatus, the second apparatus 120 may function as an LMF, and the third apparatuses 130 may function as TRPs / gNBs. In this event, LMF may communicate with the terminal device via LPP messages are LMF-UE, while the LMF may communicate with the TRP(s) via NRPPa message.

[0059] As illustrated in FIG. 2, in operation, the second apparatus 120 transmits (220-1) a PRS configuration for ML data processing to the first apparatus 110-1. Accordingly, the first apparatus 110-1 receives (220-2) the PRS configuration from the second apparatus 120.

[0060] Further, according to some example embodiments of the present disclosure, the PRS configuration indicates first information indicating first common PRS resources configured for the first apparatus 110-1 and at least one further first apparatus (such as, the first apparatus 110-2 and the first apparatus 110-3). Alternatively, according to some other example embodiments of the present disclosure, the PRS configuration indicates second information indicating a PRS parameter. In particular, when the PRS parameter is applied to second PRS resources, PRS resources specific to the first apparatus 110-1 may be obtained.

[0061] In addition, the second apparatus 120 also needs to provide PRS configuration to the third apparatuses 130 (i.e., TRPs), via such as NRPPa message. As illustrated in the FIG. 2, the second apparatus 120 trasnmits (230-1) the PRS configuration (indicating the first common PRS resources or the second PRS resources) to the third apparatuses 130, and the third apparatus 130 receives (230-2) the PRS configuration accordingly.

[0062] Then, the first apparatus 110-1 receives (240) a plurality of PRSs from a plurality of third apparatuses 130 based on the PRS configuration.

[0063] In some example embodiments, the first apparatus 110-1 may receive the plurality of PRSs based on the PRS configuration during a data collection phase, a model training phase and / or a model inference phase.

[0064] In the following, example embodiments related to the first information and the second information will be discussed separately. EXAMPLE EMBODIMENTS RELATED TO THE FIRST INFORMATION

[0065] Reference is now made to FIG. 3A, which illustrates a signaling chart 300A of PRS configuration procedure according to some example embodiments of the present disclosure.

[0066] In a nutshell, for the data collection and inference purposes, a set of PRS transmission / configurations may be specified as default configurations for the purpose of having an efficient usage of radio resources. These PRS configurations may be standardized based on a list of mandatory UE capabilities to support PRS. For example: for data collection and inference purposes, the variety of UEs deployed in the field may not use efficiently the PRS configuration resources when a variety of UEs (e.g., different vendors) supporting different UE-capabilities must be set for data collection and / or inference purposes.

[0067] In the example of FIG. 3 A, the variety of UEs may support mandatory conditions and the LMF set a unique set of resources for all UEs. In other words, the first apparatus 110-1 may be expected to support the first information.

[0068] As one example embodiment, UE1 and UE2 may have similar PRS configuration parameters. Thus, UE1 and UE2 do not have the necessity to request a new PRS configuration. In this event, the on-demand PRS procedure is unnecessary.

[0069] In operation, for data collection and inference purposes, if UE1 and UE2 have similar PRS configuration parameters, the combination of these PRS configuration parameters may generate a set of nrMaxResourcelDs-r 16 and it may derive on a list of these nrMaxResourcelDs-r 16. For this purpose. Below is an example of DL-PRS-ID-Info.

[0070] In operation, a parameter (represented as nrMaxResourcelDs-r 19) may be introduced for AI / ML purposes by the LMF as discussed below with the example signalling chart in FIG. 3 A.

[0071] In some example embodiments, in order to ensure the second apparatus 120 may determine the first information properly, the first apparatus 110-1 (and other first apparatus 110) may transmit, to the second apparatus 120, a condition of the first apparatus 110-1 for ML-based positioning. As one example, the LMF may receive conditions (also may called as UE capabilities sometimes) of N different UEs (which may be provided by different vendors and may have different capabilities) for data collection and inference purposes.

[0072] As illustrated in FIG. 3A, the first apparatus 110-1 may transmit (310-1) UE capability for data collection (i.e., a condition) to the second apparatus 120. Similarly, the first apparatus 110-2 may transmit (311-1) UE capability for data collection (i.e., a condition) the second apparatus 120. As a result, the second apparatus 120 may receive (310-2, 315-2) UE capabilities for data collection from the first apparatus 110-1 and the first apparatus 110-2, respectively.

[0073] Then, the second apparatus 120 may determine the first information at least based on the condition and a further condition for ML-based positioning received from a further first apparatus. Specifically, by using these conditions, the LMF may identify the common / mandatory PRS configuration settings (or PRS resource ID) represented with variable C, the combinatorial of C will determine the ResourceSetID for AIML purposes. In this case, a new IE may be introduced with the name nrMaxResourcelDs-r 19. In other words, LMF determines a common dimension (across UEs that involved in data collection) for max number of PRS Resources for the list of PRS resources configured under DL-PRS-1D-lnfo, via introducing nrMaxResourcelDs-r 19.

[0074] As illustrated in FIG. 3A, the second apparatus 120 may determine (320) a common PRS configuration with a size of nrMaxResourcelDs-r 19. Then, for data collection, the LMF may select (325), from all nr-DL-PRS-ResourcelD-List-r 16, one NR-DL-PRS-ResourceID-rl6 to be broadcasted to all available UEs (it is noted that the suitable Resource ID may be identified by any suitable manner).

[0075] As illustrated in FIG. 3A, the second apparatus 120 transmits (330-1, 335-1) the NR-DL-PRS-ResourcelD-r 16 for data collection to the first apparatus 110-1 and the first apparatus 110-2. As a result, the first apparatus 110-1 receives (330-2) / the first apparatus 110-2 receives (335-2) the NR-DL-PRS-ResourcelD-r 16 accordingly.

[0076] Further, PRS transmission can be for training and inference, and LMF selects PRS Resource ID(s) as needed for transmitting PRS where the NW-additional conditions assumptions under each PRS resource ID is the same.

[0077] An additional operation for data collection is when the LMF may use all available PRS resources listed in nr-DL-PRS-ResourcelD-List-r 16 for data collection. In other words, every NR-DL-PRS-ResourcelD may have its own dataset to be used for training and later for inference.

[0078] Next, for inference, if there are generated datasets for all NR-DL-PRS-ResourcelDs, the LMF may have flexibility to select the most suitable NR-DL-PRS-ResourcelD for inference purposes.

[0079] As illustrated in FIG. 3A, the second apparatus 120 transmits (340-1, 345-1) the NR-DL-PRS-ResourcelD-r 16 for data inference to the first apparatus 110-1 and the first apparatus 110-2. As a result, the first apparatus 110-1 receives (340-2) / the first apparatus 110-2 receives (345-2) the NR-DL-PRS-ResourcelD-r 16 accordingly. EXAMPLE EMBODIMENTS RELATED TO THE SECOND INFORMATION

[0080] In some example embodiments, the PRS configuration indicating the second information may be received during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

[0081] Reference is now made to FIG. 3B, which illustrates a signaling chart 300B of PRS configuration procedure according to some example embodiments of the present disclosure.

[0082] In the example of FIG. 3B, there is no previous arrangement of PRS configuration determined by the LMF. In this event, from the variety of UEs, some of them may request on-demand RPS configuration. As one example embodiment, UE1 and UE2 have different PRS configurations and as a consequence they have the necessity to request a new PRS configuration. In this event, the on-demand PRS procedure is needed.

[0083] In the example of FIG. 3B, UE1 and UE2 do not have similar PRS configurations, and the LMF may configure an overarching PRS configuration set and a corresponding set of masks Ml and M2, which, when applied to the overarching set generate the PRS configurations (or PRS resource ID) which are specific to the UE1 and respectively UE2.

[0084] In operation, if UEk (k=l, 2 ... K, K is an integer) requests a PRS configuration set Pk, the LMF configures an overarching set of PRS U.

[0085] The LMF also may configure a mask set Mk for K UEs. When applied Mk to set U, it may yield the configuration Pk which the UEk requested PRS configuration.

[0086] In a first example embodiment, the set U covers the largest bandwidth requested by all K UEs, the finest comb requested by all K UEs, the largest repetition size and the smallest periodicity. In this event, the mask Mk may remove some of the entries in set U.

[0087] Specifically, in some example embodiments, the PRS parameter may be a first mask and the second PRS resources may be associated with at least one of the following: • a range of carrier frequencies supported by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a largest bandwidth requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a finest comb requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a largest repetition size requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a smallest periodicity requested by a plurality of first apparatuses 1 lOcomprising the first apparatus.

[0088] In this event, the PRS resources specific to the first apparatus 110-1 may be determined by removing at least one resource element from the second PRS resources based on the first mask.

[0089] In a second example embodiment, the set U covers the smallest bandwidth requested by all K UEs, the coarsest comb requested by all K UEs, the smallest repetition size and the largest periodicity. In this event, the mask Mk may interpolate the missing entries in set U to yield Pk.

[0090] Specifically in some example embodiments, the PRS parameter may be a second mask and the second PRS resources may be associated with at least one of the following: • a range of carrier frequencies supported by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a smallest bandwidth requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a coarsest comb requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a smallest repetition size requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • a largest periodicity requested by a plurality of first apparatuses 1 lOcomprising the first apparatus.

[0091] In this event, the PRS resources specific to the first apparatus 110-1 may be determined by interpolating at least one resource element to the second PRS resources based on the second mask.

[0092] In a third example embodiment, the set U covers an average PRS configuration corresponding to the average of Pl, ... PK, such as, the average bandwidth requested by all K UEs, the average comb requested by all K UEs, the average repetition size and the average periodicity. In this event, the mask Mk may filter the missing entries in U with a filter indicative of the difference between the PRS entries in U and PRS entries in Pk.

[0093] Specifically, in some example embodiments, the PRS parameter may be a third mask and the second PRS resources may be associated with at least one of the following: • a range of carrier frequencies supported by a plurality of first apparatuses 1 lOcomprising the first apparatus, • an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • an average comb of a plurality of combs requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses 1 lOcomprising the first apparatus, • an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses 1 lOcomprising the first apparatus.

[0094] In this event, PRS resources specific to the first apparatus 110-1 may be determined by adjusting the second PRS resources based on the third mask.

[0095] In summary, the first apparatus may receive the PRS configuration via LPP. In addition to the PRS configuration, the mask Mk is also provided to the first apparatus 110. As for the third apparatuses 130. The third apparatus trasnmits a ‘global’ PRSs. As a result, it is up to the first apparatus 110 to obtain suitable signals that it can work with, by applying the respective mask Mk. Especially, if the PRS is sparser than the UE needs, the UE needs to interpolate and extrapolate the missing entries using the mask Mk. Alternatively, if the PRS is denser than the UE needs, the UE needs to remove sone additional entries using the mask Mk.

[0096] In some example embodiments, the second information may be comprised in one of the following: a long term evolution positioning protocol (LPP) provide assistance data message, or a mobile original-location request (MO-LR) response.

[0097] Still refer to FIG. 3B, at Step 0, the LMF may receive information on the possible On-Demand PRS configurations that the gNB can support during the TRP Information Exchange procedure.

[0098] At Step 1, in case of UE-initiated On-Demand PRS, the LMF may configure the UE with pre-defined PRS configurations Nia LPP Provide Assistance Data message or via posSI.

[0099] At Step 2a. in case of UE-initiated On-Demand PRS, the UE sends an On-Demand PRS request to the LMF via LPP Request Assistance Data message. The On-Demand PRS request can be a request for a pre-defined PRS configuration indicated with pre-defined PRS configuration ID or explicit parameter for PRS configuration and may be a request for PRS transmission or change to the PRS transmission characteristics for positioning measurements.

[0100] It is noted that the LPP Request Assistance Data message for On-Demand PRS may also be sent in an MO-LR location service request message.

[0101] It is also noted that if the NW has provided the pre-defined On-Demand PRS configurations to the UE, the UE is allowed to request On-Demand PRS parameters based on pre-defined PRS configuration ID (index-based request) or explicit parameter requests that is within the scope of the received pre-defined On-Demand PRS configurations. Otherwise, the UE may blindly request On-Demand PRS parameters via an explicit request within the scope of the allowed parameter list.

[0102] At Step 2b, in case of LMF-initiated On-Demand PRS, the LMF and the UE may exchange LPP messages e g., to obtain UE measurements or the DL-PRS positioning capabilities of the UE, etc.

[0103] At Step 3, the LMF determines the need for PRS transmission or change to the transmission characteristics of an ongoing PRS transmission.

[0104] At Step 4, the LMF requests the serving and non-serving gNBs / TRPs for new PRS transmission or PRS transmission with changes to the PRS configuration via NRPPa PRS CONFIGURATION REQUEST message. The overarching set of PRS may be defined in NRPPa PRS CONFIGURATION REQUEST.

[0105] At Step 5, the gNBs / TRPs provide the successfully configured or updated PRS transmission in the NRPPa PRS CONFIGURATION RESPONSE message accordingly.

[0106] At Step 6, LMF may provide the PRS configuration used for PRS transmission or error cause via LPP Provide Assistance Data message to the UE. The masks Mk may be supported via a new IE in LPP Provide Assistance Data (i.e., On-Demand PRS response)

[0107] It is noted that if the LPP Request Assistance Data for On-Demand DL-PRS at Step 2a was sent in an MO-LR location service request message, the LMF provides a MO-LR response.

[0108] It is noted that it is up to Network (LMF) implementation on the steps to follow (accept / reject / ignore) on receiving UE-initiated On-Demand PRS request.

[0109] Further, it is up to Network (TRP) implementation on the steps to follow (accept / reject / ignore) on receiving LMF-initiated On-Demand PRS requests.

[0110] In addition, the UE may utilize the UE-initiated on-demand DL-PRS procedure to request a DL-PRS configuration which aligns with any configured (e)DRX, e.g., by setting the explicit parameter for the DL-PRS configuration accordingly.

[0111] The on-demand DL-PRS procedure may also be used to request a DL-PRS configuration which supports bandwidth aggregation across DL-PRS positioning frequency layers. Example Methods

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

[0113] At block 410, the first apparatus receives, from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus, or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources.

[0114] At block 420, the first apparatus receives, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

[0115] In some example embodiments, the first apparatus may receive the plurality of PRSs based on the PRS configuration during a data collection phase, a model training phase and / or a model inference phase.

[0116] In some example embodiments, first apparatus may be expected to support the first information.

[0117] In some example embodiments, the first apparatus may transmit to the second apparatus, a condition of the first apparatus for ML-based positioning.

[0118] In some example embodiments, the PRS configuration indicating the second information is received during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

[0119] In some example embodiments, the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

[0120] In some example embodiments, the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a coarsest comb requested by a plurality of first apparatuses comprising the first apparatus, a smallest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a largest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

[0121] In some example embodiments, the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus, an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus, an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus, an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

[0122] In some example embodiments, the second information is comprised in one of the following: a long term evolution positioning protocol (LPP) provide assistance data message, or a mobile original-location request (MO-LR) response.

[0123] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity.

[0124] FIG. 5 shows a flowchart of an example method 500 implemented at a second apparatus in accordance with some example embodiments of the present disclosure. For the purpose of discussion, the method 500 will be described from the perspective of the second apparatus 120 in FIG. 1.

[0125] At block 510, the second apparatus determines, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus, or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources.

[0126] At block 520, the second apparatus transmits the PRS configuration to the first apparatus.

[0127] In some example embodiments, the second apparatus may receive, from the first apparatus, a condition of the first apparatus for ML-based positioning; and determining the first information at least based on the condition and a further condition for ML-based positioning received from a further first apparatus.

[0128] In some example embodiments, the second apparatus may transmit the first information to at least one third apparatus providing PRSs to the first apparatus.

[0129] In some example embodiments, t the second apparatus may determine a PRS parameter set requested by the first apparatus, the PRS parameter set comprising at least one of the following: a bandwidth, a comb, a repetition size or a periodicity; and determine the second PRS resources at least based on the PRS parameter set and a further PRS parameter set requested by a further first apparatus; and transmit third information indicating the second PRS resources to at least one third apparatus providing PRSs to the first apparatus.

[0130] In some example embodiments, the third information is comprised in a PRS configuration request.

[0131] In some example embodiments, the third information is transmitted during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

[0132] In some example embodiments, the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

[0133] In some example embodiments, the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

[0134] In some example embodiments, the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus, an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus, an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus, an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

[0135] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity. Example Apparatus, Device and Medium

[0136] In some example embodiments, a first apparatus capable of performing any of the method 400 (for example, the first apparatus 110 in FIG. 1) may comprise means for performing the respective operations of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The first apparatus may be implemented as or included in the first apparatus 110 in FIG. 1. [01371In some example embodiments, the first apparatus comprises means for receiving, from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and means for receiving, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

[0138] In some example embodiments, the first apparatus further comprises: means for receiving the plurality of PRSs based on the PRS configuration during a data collection phase, a model training phase and / or a model inference phase. [01391In some example embodiments, the first apparatus is expected to support the first information.

[0140] In some example embodiments, the first apparatus further comprises: means for transmitting, to the second apparatus, a condition of the first apparatus for ML-based positioning. [0141 ]In some example embodiments, the PRS configuration indicating the second information is received during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

[0142] In some example embodiments, the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

[0143] In some example embodiments, the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a coarsest comb requested by a plurality of first apparatuses comprising the first apparatus, a smallest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a largest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

[0144] In some example embodiments, the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus, an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus, an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus, an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

[0145] In some example embodiments, the second information is comprised in one of the following: a long term evolution positioning protocol (LPP) provide assistance data message, or a mobile original-location request (MO-LR) response.

[0146] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity.

[0147] In some example embodiments, a second apparatus capable of performing any of the method 500 (for example, the second apparatus 120 in FIG. 1 may comprise means for performing the respective operations of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The second apparatus may be implemented as or included in the second apparatus 120 in FIG. 1.

[0148] In some example embodiments, the second apparatus comprises means for determining, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following: first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; or second information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; and means for transmitting the PRS configuration to the first apparatus. [() 1491In some example embodiments, the second apparatus further comprises: means for receiving, from the first apparatus, a condition of the first apparatus for ML-based positioning; and means for determining the first information at least based on the condition and a further condition for ML-based positioning received from a further first apparatus. [() 1501In some example embodiments, the second apparatus further comprises: means for transmitting the first information to at least one third apparatus providing PRSs to the first apparatus. [015l]In some example embodiments, the second apparatus further comprises: means for determining a PRS parameter set requested by the first apparatus, the PRS parameter set comprising at least one of the following: a bandwidth, a comb, a repetition size or a periodicity; and means for determining the second PRS resources at least based on the PRS parameter set and a further PRS parameter set requested by a further first apparatus; and means for transmitting third information indicating the second PRS resources to at least one third apparatus providing PRSs to the first apparatus.

[0152] In some example embodiments, the third information is comprised in a PRS configuration request.

[0153] In some example embodiments, the third information is transmitted during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

[0154] In some example embodiments, the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

[0155] In some example embodiments, the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus, a finest comb requested by a plurality of first apparatuses comprising the first apparatus, a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus, a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus, and wherein PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

[0156] In some example embodiments, the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following: a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus, an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus, an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus, an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus, an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus, and wherein the PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

[0157] In some example embodiments, the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity.

[0158] FIG. 4 is a simplified block diagram of a device 400 that is suitable for implementing example embodiments of the present disclosure. The device 400 may be provided to implement a communication device, for example, the first apparatus 110 or the second apparatus 120 as shown in FIG. 1. As shown, the device 400 includes one or more processors 410, one or more memories 420 coupled to the processor 410, and one or more communication modules 440 coupled to the processor 410.

[0159] The communication module 440 is for bidirectional communications. The communication module 440 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interfaces may represent any interface that is necessary for communication with other network elements. In some example embodiments, the communication module 440 may include at least one antenna.

[0160] The processor 410 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 400 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0161] The memory 420 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) 424, 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) 422 and other volatile memories that will not last in the power-down duration.

[0162] A computer program 430 includes computer executable instructions that are executed by the associated processor 410. The instructions of the program 430 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 430 may be stored in the memory, e.g., the ROM 424. The processor 410 may perform any suitable actions and processing by loading the program 430 into the RAM 422.

[0163] The example embodiments of the present disclosure may be implemented by means of the program 430 so that the device 400 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.

[0164] In some example embodiments, the program 430 may be tangibly contained in a computer readable medium which may be included in the device 400 (such as in the memory 420) or other storage devices that are accessible by the device 400. The device 400 may load the program 430 from the computer readable medium to the RAM 422 for execution. In some example embodiments, the computer readable medium may include any types of non-transitory storage medium, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like. The term “non-transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0165] FIG. 5 shows an example of the computer readable medium 500 which may be in form of CD, DVD or other optical storage disk. The computer readable medium 500 has the program 430 stored thereon.

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

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

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

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

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

[0171] Further, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be 5 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. 10 Conversely, unless explicitly stated, various features that are described in the context of a single embodiment may also be implemented in a plurality of embodiments separately or in any suitable sub-combination.

[0172] Although the present disclosure has been described in languages specific to structural features and / or methodological acts, it is to be understood that the present 15 disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A first apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to:receive, from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus; orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andreceive, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

2. The first apparatus of claim 1, wherein the first apparatus is caused to:receive the plurality of PRSs based on the PRS configuration during a data collection phase, a model training phase and / or a model inference phase.

3. The first apparatus of claim 1 or 2, wherein the first apparatus is caused to: the first apparatus is expected to support the first information.

4. The first apparatus of claim 1, wherein the first apparatus is caused to:transmit, to the second apparatus, a condition of the first apparatus for ML-based positioning.

5. The first apparatus of claim 4, wherein the PRS configuration indicating the second information is received during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

6. The first apparatus of claim 4 or 5, wherein the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus,a finest comb requested by a plurality of first apparatuses comprising the first apparatus,a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus,a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus,and wherein the PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

7. The first apparatus of claim 4 or 5, wherein the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus,a coarsest comb requested by a plurality of first apparatuses comprising the first apparatus,a smallest repetition size requested by a plurality of first apparatuses comprising the first apparatus,a largest periodicity requested by a plurality of first apparatuses comprising the first apparatus,and wherein the PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

8. The first apparatus of claim 4 or 5, wherein the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus,an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus,an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus,an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus,and wherein PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

9. The first apparatus of any of claims 4 to 8, wherein the second information is comprised in one of the following:a long term evolution positioning protocol (LPP) provide assistance data message, or a mobile original-location request (MO-LR) response.

10. The first apparatus of any of claims 1-9, wherein the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity.

11. A second apparatus comprising:at least one processor; andat least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to:determine, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andtransmit the PRS configuration to the first apparatus.

12. The second apparatus of claim 11, wherein the second apparatus is caused to:receive, from the first apparatus, a condition of the first apparatus for ML-based positioning; anddetermine the first information at least based on the condition and a further condition for ML-based positioning received from a further first apparatus.

13. The second apparatus of claim 11 or 12, wherein the second apparatus is caused to: transmit the first information to at least one third apparatus providing PRSs to the first apparatus.

14. The second apparatus of claim 11, wherein the second apparatus is caused to:determine a PRS parameter set requested by the first apparatus, the PRS parameter set comprising at least one of the following: a bandwidth, a comb, a repetition size or a periodicity; anddetermine the second PRS resources at least based on the PRS parameter set and a further PRS parameter set requested by a further first apparatus; andtransmit third information indicating the second PRS resources to at least one third apparatus providing PRSs to the first apparatus.

15. The second apparatus of claim 14, wherein the third information is comprised in a PRS configuration request.

16. The second apparatus of claim 14 or 15, wherein the third information is transmitted during a user equipment (UE)-initiated PRS configuration procedure or a network-initiated PRS configuration procedure.

17. The second apparatus of any of claims 14 to 16, wherein the PRS parameter is a first mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,a largest bandwidth requested by a plurality of first apparatuses comprising the first apparatus,a finest comb requested by a plurality of first apparatuses comprising the first apparatus,a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus,a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus,and wherein PRS resources specific to the first apparatus is determined by removing at least one resource element from the second PRS resources based on the first mask.

18. The second apparatus of any of claims 14 to 16, wherein the PRS parameter is a second mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,a smallest bandwidth requested by a plurality of first apparatuses comprising the first apparatus,a finest comb requested by a plurality of first apparatuses comprising the first apparatus,a largest repetition size requested by a plurality of first apparatuses comprising the first apparatus,a smallest periodicity requested by a plurality of first apparatuses comprising the first apparatus,and wherein PRS resources specific to the first apparatus is determined by interpolating at least one resource element to the second PRS resources based on the second mask.

19. The second apparatus of any of claims 14 to 16, wherein the PRS parameter is a third mask and the second PRS resources is associated with at least one of the following:a range of carrier frequencies supported by a plurality of first apparatuses comprising the first apparatus,an average bandwidth of a plurality of bandwidths requested by a plurality of first apparatuses comprising the first apparatus,an average comb of a plurality of combs requested by a plurality of first apparatuses comprising the first apparatus,an average repetition size of a plurality of repetition sizes requested by a plurality of first apparatuses comprising the first apparatus,an average periodicity of a plurality of periodicities requested by a plurality of first apparatuses comprising the first apparatus,and wherein the PRS resources specific to the first apparatus is determined by adjusting the second PRS resources based on the third mask.

20. The second apparatus of any of claims 11-19, wherein the first apparatus is a terminal device, and the second apparatus is a location management function (LMF) entity.

21. A method comprising:receiving, at a first apparatus and from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for the first apparatus and at least one further first apparatus, orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andreceiving, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

22. A method comprising:determining, at a second apparatus and a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus, orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andtransmitting the PRS configuration to the first apparatus.

23. A first apparatus comprising:means for receiving, from a second apparatus, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for thefirst apparatus and at least one further first apparatus; orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andmeans for receiving, from a plurality of third apparatuses, a plurality of PRSs based on the PRS configuration.

24. A second apparatus comprising:means for determining, a positioning reference signal (PRS) configuration for a machine learning (ML) data processing, the PRS configuration indicating one of the following:first information indicating first common PRS resources configured for a first apparatus and at least one further first apparatus; orsecond information indicating a PRS parameter used for determining PRS resources specific to the first apparatus by applying the PRS parameter to second PRS resources; andmeans for transmitting the PRS configuration to the first apparatus.

25. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 21 or 22.42

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