Identification and use of nw additional conditions for positioning

Abstract

The present disclosure relates to identification and use of network additional conditions for positioning. In particular, a method comprises receiving, at a first apparatus 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 related to one or more network-side registration conditions for positioning; receiving, from the second apparatus, a plurality of positioning reference signals based on the first positioning reference signal configuration; and performing 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

Cross-references to related applications

[0001] This application claims priority and interest in Finnish application No. 20245423, filed on April 5, 2024, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] Various exemplary embodiments of this disclosure generally relate to the telecommunications field, and in particular to methods, apparatuses, devices, and computer-readable storage media for identifying and using additional network (NW) conditions for location. Background Technology

[0003] With the integration of artificial intelligence (AI) and machine learning (ML) into the air interfaces of 5G and the emerging 6G New Radio (NR), new frontiers in network adaptability and efficiency are being explored. The 3GPP Release-18 research project and 3GPP Release-19 work project emphasize the importance of model generalization across various network scenarios to address the need for AI / ML models to maintain robust performance under diverse conditions. This includes strategically incorporating additional conditions to refine model training, ensuring that the model adapts well to the requirements of both the network and user equipment sides. Therefore, capturing these additional conditions warrants in-depth research, as understanding and utilizing these factors is crucial for AI / ML applications in 5G and its subsequent evolution, and can significantly impact network performance and user experience. Summary of the Invention

[0004] In a first aspect of this disclosure, a first apparatus is provided. The first apparatus includes: 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 a first positioning reference signal configuration for data collection from a second apparatus, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; receive a plurality of positioning reference signals from the second apparatus 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 identifier or the first network-side identifier.

[0005] In a second aspect of this disclosure, a second apparatus is provided. The second apparatus includes: 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 identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; and transmit a plurality of positioning reference signals to the first apparatus based on the first positioning reference signal configuration.

[0006] In a third aspect of this disclosure, a method is provided. The method includes: receiving from a second device 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 identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; receiving from the second device 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 identifier or the first network-side identifier.

[0007] In a fourth aspect of this disclosure, a method is provided. The method includes: transmitting to a first device 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 identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; and transmitting a plurality of positioning reference signals to the first device based on the first positioning reference signal configuration.

[0008] In a fifth aspect of this disclosure, a first apparatus is provided. The first apparatus includes: means for receiving a first positioning reference signal configuration for data collection from a second apparatus, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; means for receiving a plurality of positioning reference signals from the second apparatus based on the first positioning reference signal configuration; and means for performing data collection on the plurality of positioning reference signals based on at least one of the first cell identifier or the first network-side identifier.

[0009] In a sixth aspect of this disclosure, a second apparatus is provided. The second apparatus includes: components for transmitting a first positioning reference signal configuration for data collection to a first apparatus, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; and components for transmitting a plurality of positioning reference signals to the first apparatus based on the first positioning reference signal configuration.

[0010] In a seventh aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to a third aspect.

[0011] In an eighth aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to the fourth aspect.

[0012] It should be understood that the summary portion is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] Some exemplary embodiments will now be described with reference to the accompanying drawings, in which: Figure 1 An example communication environment in which example embodiments of this disclosure may be implemented is shown; Figure 2 The following is illustrated: a signaling flow for improving consistency between training and inference for localization, according to some example embodiments of the present disclosure; Figure 3A and Figure 3B Signaling flows for data collection and model identification according to some example embodiments of this disclosure are shown respectively; Figure 4 A flowchart is shown illustrating a method implemented at a first device according to some exemplary embodiments of the present disclosure; Figure 5 A flowchart is shown illustrating a method implemented at a second device according to some example embodiments of the present disclosure; Figure 6 A simplified block diagram of a device suitable for implementing example embodiments of the present disclosure is shown; and Figure 7 A block diagram of an example computer-readable medium according to some example embodiments of the present disclosure is shown.

[0014] Throughout all the accompanying figures, the same or similar reference numerals indicate the same or similar elements. Detailed Implementation

[0015] The principles of this disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are described for illustrative purposes only and to assist those skilled in the art in understanding and implementing this disclosure, and do not imply any limitation on the scope of this disclosure. The embodiments described herein can be implemented in various ways other than those described below.

[0016] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0017] References to "an embodiment," "embodiment," "example embodiment," etc., in this disclosure indicate that the described embodiments may include specific features, structures, or characteristics, but it is not necessary for every embodiment to include that specific feature, structure, or characteristic. Furthermore, these phrases do not necessarily refer to the same embodiment. Further, when a specific feature, structure, or characteristic is described in connection with an embodiment, it is to be noted that those skilled in the art will recognize, whether explicitly described or not, that such features, structures, or characteristics apply in conjunction with other embodiments.

[0018] It should be understood that although terms such as "first," "second," etc., preceding nouns in this document may be used to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish one element from another and they do not restrict the order of nouns. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. As used herein, the term "and / or" includes any or all combinations of one or more of the listed terms.

[0019] As used herein, “at least one of the following: ” and “at least one of ” and similar expressions, wherein the list of two or more elements is connected by “and” or “or”, means at least any one of these elements, or at least any two or more of these elements, or at least all of these elements.

[0020] As used herein, unless explicitly stated otherwise, the “responding to A” action does not indicate that the action is performed immediately after “A” occurs, and may include one or more intermediate steps.

[0021] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” “having,” “possessing,” “containing,” and / or “covering,” as used herein, specify the presence of the stated features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0022] As used in this application, the term "circuit" may refer to one or more or all of the following: (a) Hardware circuit implementation only (such as implementation in analog and / or digital circuits only) and (b) A combination of hardware circuitry and software, such as (if applicable): (i) A combination of analog and / or digital hardware circuitry with software / firmware, and (ii) Any part of a hardware processor (including (multiple) digital signal processors), software, and memory that work together to enable a device (such as a mobile phone or server) to perform various functions; and (c) (Multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or portions of (multiple) microprocessors, which require software (e.g., firmware) to operate, but may not exist when the software is not required to operate.

[0023] This definition of "circuit" applies to all uses of the term in this application (including in any claim). As another example, as used herein, the term "circuit" also encompasses only hardware circuitry or a processor (or multiple processors) or portions thereof, and their accompanying software and / or firmware implementations. The term "circuit" also encompasses, for example and if applicable to a particular claim element, baseband integrated circuits or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

[0024] As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard, such as New Radio (NR), LTE (Long Term Evolution), LTE-A Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. Furthermore, communication between terminal devices and network devices in a communication network can be performed according to any suitable generation of communication protocol, including but not limited to first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, fifth-generation (5G), sixth-generation (6G) communication protocols, and / or any other currently known or to be developed in the future. Embodiments of this disclosure can be applied to various communication systems. Given the rapid development of communications, there will naturally be future types of communication technologies and systems that can implement this disclosure. The scope of this disclosure should not be considered limited to the systems described above.

[0025] As used herein, the term "network device" refers to a node in a communication network through which terminal devices access the network and receive services. Depending on the terminology and technology applied, a network device can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), an NR NB (also known as a gNB), a Remote Radio Unit (RRU), a Radio Head (RH), a Remote Radio Head (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low-power node (such as a femto or pico), a non-terrestrial network (NTN) or non-terrestrial network equipment (such as satellite network equipment, low Earth orbit (LEO) satellites, and geostationary Earth orbit (GEO) satellites), an aircraft network equipment, etc. In some example embodiments, the Radio Access Network (RAN) split architecture includes a central unit (CU) and a distributed unit (DU) at the IAB host node. An IAB node includes a mobile terminal (IAB-MT) portion similar to the UE facing the parent node, and the DU portion of the IAB node is similar to the base station facing the next-hop IAB node.

[0026] The term "terminal device" refers to any end device with wireless communication capabilities. As an example and not a limitation, a terminal device can refer to communication equipment, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices can include, but are not limited to, mobile phones, cellular phones, smartphones, VoIP phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image acquisition terminal devices (such as digital cameras), gaming terminal devices, music storage and playback devices, in-vehicle wireless terminal devices, wireless endpoints, mobile stations, laptop embedded devices (LEE), laptop mounted devices (LME), USB dongles, smart devices, wireless client devices (CPE), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in the context of industrial and / or automated processing chains), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. The terminal device may also correspond to the mobile terminal MT portion of an IAB node (e.g., a relay node). In the following description, the terms "terminal device," "communication device," "terminal," "user equipment," and "UE" are used interchangeably.

[0027] As used herein, the terms “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” can refer to any resource used to perform communication, such as communication between a terminal device and a network device, including resources in the time domain, frequency domain, spatial domain, code domain, or any other combination of time-domain, frequency-domain, spatial-domain, and / or code-domain resources that enable communication. In the following, unless explicitly stated otherwise, resources in the frequency and time domains will be used as examples of transmission resources used to describe some exemplary embodiments of this disclosure. Note that the exemplary embodiments of this disclosure are equally applicable to other resources in other domains.

[0028] As used herein, the term "transmitter-receiver point (TRP)" can be defined as an antenna array having one or more antenna elements, which can be used in a network located at a specific geographic location in a particular area. In some embodiments, the TRP can be implemented at a network device.

[0029] In some embodiments, for AI / ML-enabled features / FGs, additional conditions refer to any aspect of the training assumptions of the model, but not part of the UE capability of the AI / ML-enabled features / FGs. This does not mean that additional conditions must be specified. Additional conditions can be categorized into two types: NW-side additional conditions and UE-side additional conditions. The term “conditions registered on the network side” as used herein can refer to a set of settings or parameters defined by the NW entity that have a direct impact on the measurements implemented in the UE entity. The terms “conditions registered on the network side,” “conditions registered in a second device,” and “network (NW)-side additional conditions” are used interchangeably. The process of model identification accurately interprets the so-called “additional conditions,” which can include, for example, training dataset categories, site-related information, timestamps, implicit identification information (such as labels for specific gNB / UE implementation details), statistical information (e.g., latency spread, angle spread, LOS / NLOS data, etc.), and other factors. The term “conditions registered on the UE side” as used herein can refer to UE-side conditions that include additional information to improve consistency between the training and inference models. The terms "registration conditions on the UE side," "registration conditions in the first device," and "additional conditions on the UE side" are used interchangeably. The term "cell identifier" as used herein can refer to information used to identify a cell (e.g., an index or bit string). The term "network-side identifier" as used herein can refer to information used to distinguish network-side registration conditions. The terms "network-side identifier" and "NW identifier" are used interchangeably.

[0030] As used herein, a machine learning (ML) entity can be an ML model or can contain ML models and related metadata. ML entities can be managed as a single composite entity. In some example implementations, an ML entity can be implemented as an ML application (MLApp).

[0031] To facilitate understanding of the terminology, the RAN1 convention for the list of terms used in AI / ML is provided below.

[0032] AI / ML Models: Data-driven algorithms that apply AI / ML techniques to generate a set of outputs based on a set of inputs.

[0033] AI / ML Model Delivery: The general term refers to delivering an AI / ML model from one entity to another in any way. Note: An entity can refer to a network node / function (e.g., gNB, LMF, etc.), UE, dedicated server, etc.

[0034] AI / ML model inference: The process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0035] AI / ML Model Testing: A sub-process of training used to evaluate the performance of the final AI / ML model using a different dataset than that used for model training and validation. Unlike AI / ML model validation, testing does not assume subsequent adjustments to the model.

[0036] AI / ML model training: The process of training an AI / ML model in a data-driven manner [by learning input / output relationships] and obtaining the trained AI / ML model for inference.

[0037] AI / ML model transmission: AI / ML models are delivered over the air interface in a manner opaque to 3GPP signaling. These models may contain parameters of a model structure known to the receiving end, or they may be new models with parameters. The transmission may include a complete model or a partial model.

[0038] AI / ML Model Validation: A sub-process of training that evaluates the quality of an AI / ML model using a dataset different from the dataset used for model training, and helps in selecting model parameters that generalize to datasets other than those used for model training.

[0039] Data collection: The process by which network nodes, management entities, or UEs collect data for use in AI / ML model training, data analysis, and inference.

[0040] Federated learning / federated training: A machine learning technique that trains AI / ML models across multiple distributed edge nodes (e.g., UE, gNB), with each node performing local model training using local data samples. This technique requires multiple interactions between models but does not exchange local data samples.

[0041] Function Identification: Identifies the process / method for mutual understanding between the NW and UE regarding AI / ML functions. Note: Information about AI / ML functions can be shared during function identification. The residency of AI / ML functions depends on specific use cases and sub-use cases.

[0042] Model Activation: Enables AI / ML models for specific functions.

[0043] Model deactivation: Disables AI / ML models for specific functions.

[0044] Model download: The transfer of the model from the network to the UE.

[0045] Model Identification: The process / method for identifying the AI / ML model used for mutual understanding between the NW and UE. Note: The process / method for model identification may or may not be applicable. Note: Information about the AI / ML model may be shared during model identification.

[0046] Model monitoring: The process of monitoring the inference performance of AI / ML models.

[0047] Model parameter update: The process of updating the model parameters.

[0048] Model selection: The process of selecting the AI / ML model to be activated from among multiple models that share the same AI / ML-supporting features. Note: Model selection may or may not be performed concurrently with model activation.

[0049] Model switching: Deactivate the currently active AI / ML model and activate different AI / ML models for specific functions.

[0050] Model update: The process of updating the model parameters and / or model structure.

[0051] Model upload: The transfer of the model from the UE to the network.

[0052] Network-side (AI / ML) model: An AI / ML model in which inference is performed entirely at the network.

[0053] Offline field data: Data collected from the field and used for offline training of AI / ML models.

[0054] Offline training: The AI / ML training process in which a model is trained based on a collected dataset, and the trained model is later used or delivered for inference. Note: This definition is for guidance only. There may be cases that do not perfectly fit this definition but can still be classified as offline training through generally accepted conventions.

[0055] Online field data: Data collected from the field and used for online training of AI / ML models.

[0056] Online training: The AI / ML training process in which the model used for inference is trained (usually continuously) (nearly) real-time as new training samples arrive. Note: The concepts of (near) real-time and non-real-time are context-dependent and relative to the inference timescale. Note: This definition is for guidance only. There may be cases that do not perfectly fit this definition but can still be classified as online training by generally accepted conventions. Note: Fine-tuning / retraining can be done via online or offline training.

[0057] Reinforcement learning (RL): The process of training an AI / ML model based on inputs (also called states) and feedback signals (also called rewards) generated by the model's outputs (also called actions) in an environment where the model interacts with itself.

[0058] Semi-supervised learning: The process of training a model using a mixture of labeled and unlabeled data.

[0059] Supervised learning: The process of training a model based on inputs and their corresponding labels.

[0060] Two-sided (AI / ML) model: A pair of AI / ML models that perform joint inference, where joint inference includes AI / ML inference jointly performed across the UE and the network, i.e., the first part of the inference is first performed by the UE, and then the remaining part is performed by the gNB, and vice versa.

[0061] UE-side (AI / ML) model: An AI / ML model where inference is performed entirely at the UE.

[0062] Unsupervised learning: The process of training a model without labeled data.

[0063] Proprietary format models: From a 3GPP perspective, these are ML models using vendor / device-specific proprietary formats. Different vendors cannot recognize these models, and sharing them hides model design information from other vendors. Note: An example is a device-specific binary executable format.

[0064] Open format models: From a 3GPP perspective, these are specified format ML models that are mutually identifiable across suppliers and allow interoperability. They are mutually identifiable between suppliers and do not hide model design information from other suppliers when shared.

[0065] 3GPP has begun to support AI / ML positioning, with the following objectives.

[0066]

[0067] Furthermore, network-side conditions are prioritized over UE-side conditions. For network-side conditions, various scenarios considering the following scenarios have been studied for model generalization:

[0068] During the AIML localization study, multiple evaluations were conducted to validate the performance of the AIML model in generalized scenarios. In most of these scenarios, the evaluations indicated poor generalization performance. For example, performance significantly decreased when the model was trained on a dataset generated in a scenario with 60% clutter density and tested on a dataset generated in a scenario with 40% clutter density. When generalization fails to provide good performance, a potential solution is based on fine-tuning, model retraining, or model switching. Model switching represents an optimal edge case solution; however, the methodology for selecting a suitable model remains to be developed. There is no functional framework-based signaling to guide the UE in making optimal decisions to ensure consistency between training and inference based on model switching. Without such signaling, model identification may not be applicable to real-world scenarios.

[0069] According to some example embodiments of this disclosure, a solution is provided for identifying and defining the process of using model identifiers and NW-side appending conditions in AIML localization use cases. In this way, the performance of AI / ML models can be improved. Furthermore, the consistency between training and inference for AIML localization can be enhanced.

[0070] Figure 1 An example communication environment 100 in which exemplary embodiments of the present disclosure can be implemented is shown. In communication environment 100, a plurality of communication devices, including a first device 110 and a second device 120, can communicate with each other. Figure 1 In the example, the first device 110 can be a UE, and the second device 120 can be a base station serving the UE. The service area of ​​the second device 120 can be referred to as cell 102.

[0071] It should be understood that Figure 1 The number of devices and their connections shown are for illustrative purposes only and do not imply any limitation. Communication environment 100 may include any suitable number of devices configured to implement the exemplary embodiments of this disclosure. Although not shown, it should be understood that one or more additional devices may be located in cell 102, and one or more additional cells may be deployed in communication environment 100. Note that although shown as a network device, the second device 120 may be another device besides a network device. Although shown as a terminal device, the first device 110 may be another device besides a terminal device.

[0072] In the following description, for illustrative purposes, some example embodiments are described in which the first device 110 operates as a UE and the second device 120 operates as a base station. However, in some example embodiments, the operations described in connection with the terminal device can be implemented at the network device or other device, and the operations described in connection with the network device can be implemented at the terminal device or other device.

[0073] In some example embodiments, the link from the second device 120 to the first device 110 is referred to as a downlink (DL), and the link from the first device 110 to the second device 120 is referred to as an uplink (UL). In the DL, the second device 120 is a transmitting (TX) device (or transmitter), and the first device 110 is a receiving (RX) device (or receiver). In the UL, the first device 110 is a TX device (or transmitter), and the second device 120 is an RX device (or receiver).

[0074] Communication in communication environment 100 can be implemented according to any suitable communication protocol, including but not limited to cellular communication protocols such as first-generation (1G), second-generation (2G), third-generation (3G), fourth-generation (4G), fifth-generation (5G), 5.5G, and sixth-generation (6G), wireless local network communication protocols such as IEEE 802.11, and / or any other currently known or future-developed protocols. Furthermore, communication can utilize any suitable wireless communication technology, including 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 Multiplexing (OFDM), Discrete Fourier Transform Extended OFDM (DFT-s-OFDM), and / or any other currently known or future-developed technologies.

[0075] Example embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. For illustrative purposes, the example embodiments are described with reference to a positioning scenario. Note that the example embodiments of the present disclosure can also be implemented in other scenarios, such as channel state information (CSI).

[0076] refer to Figure 2 This illustrates a signaling flow 200 using a Global Identifier (GI) for AI / ML localization according to some embodiments of this disclosure. For discussion purposes, reference will be made to... Figure 1 For example, signaling flow 200 can be discussed by using first device 110 and second device 120.

[0077] 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) 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 processing NW additional conditions.

[0078] The second device 120 transmits (2010) a Location Reference Signal (PRS) configuration for data collection (hereinafter referred to as the "first PRS configuration") to the first device 110. In other words, the first device 110 receives (2010) a first PRS configuration for data collection from the second device 120. The first PRS configuration for data collection is associated with at least one of the following: a cell identifier (referred to as the "first cell identifier") or a network-side identifier (referred to as the "first network-side identifier") associated with one or more network-side registration conditions for location (referred to as the "first network-side identifier"). For example, the first PRS configuration may be associated with a Global Identifier (GI) for data collection (referred to as the "first GI"), which includes the cell identifier and the first network-side identifier. In some example embodiments, the first GI may be determined by a network entity (e.g., a Location Management Function (LMF)). The first GI can provide a unique identifier between operators (independent of geographic location).

[0079] In some example embodiments, network-side registration conditions (also referred to as "NW-side additional conditions") may include one or more NW-side considerations when transmitting PRS resources. In this case, in some example embodiments, one or more NW-side considerations may not be explicitly known to the first device 110.

[0080] In some example embodiments, a network-side identifier can provide an identifier between different network settings associated with a set of reference signal settings used for data collection purposes. For example, the network-side identifier can be used to distinguish NW-side additional conditions associated with reference signal settings. For instance, the network-side identifier can implicitly distinguish between a first set of relevant network-side registration conditions and a second set of relevant network-side registration conditions, wherein the first and second sets of relevant network-side registration conditions may be considered using one or more different NW sides when transmitting PRS resources.

[0081] Alternatively or additionally, a network-side identifier can be used to implicitly identify a set of NW additional conditions from the perspective of the NW provider. In some example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is specific to the network provider. For example, the network-side identifier can be defined based on NW provider preferences. In some other example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is common to the network provider. For example, collaboration can be carried out among NW providers to agree on the definition of NW identifier numbers and the interpretation of NW identifiers to NW additional conditions.

[0082] In some example embodiments, a new field named "NR-DL-AIML-ADDITIONAL-CONDITION-R19", which carries the identifier for the PRS resource set list and has the parameter "nr-DL_NW-Identifier-r19", can be included in the information element (IE) NR-DL-PRS-Info that defines the DLPRS configuration. Table 1 shows the IE. NR-DL-PRS-Info Examples are provided. Note that Table 1 is merely an example and not a limitation.

[0083] Table 1

[0084] In some other example embodiments, a new field named "nr-DL_NW-Identifier-r19" associated with the PRS resource set can be included in the IE defining the DLPRS configuration. NR-DL-PRS-Info Table 2 shows the IE... NR-DL- PRS-Info Another example. Note that Table 2 is merely an example and not a limitation.

[0085] Table 2

[0086] In some example implementations, the cell identifier can be a Global Cell Identifier (GCI), a unique identifier assigned to a cellular base station. The GCI can be used to locate and communicate with mobile devices within the base station's range. For example, the GCI can be a combination of several elements, including a Mobile Country Code (MCC), a Mobile Network Code (MNC), and a Location Area Code (LAC). Note that the cell identifier can include other types of identifiers assigned to the base station.

[0087] In some example embodiments, the first cell identifier and the first network-side identifier may be included in the first PRS configuration. Alternatively, the first network-side identifier may be included in the first PRS configuration, and another configuration (referred to as the "first configuration") received by the first device 110 may include the first cell identifier. For example, the first cell identifier may be included in the System Information Block (SIB) received from the second device 120. As an example, the GCI may be retrieved from the SIB1 received during initial access, for example from a lower-layer IE-based... NGCI-r15 of nr-CellGlobalId-r17 Searching within the database. Table 3 shows examples of IEs that include cell identifiers. Note that Table 3 is merely an example and not a limitation.

[0088] Table 3

[0089] In some example embodiments, when the first device 110 is performing NW access, it may (e.g., via SIB1) receive a cell identifier and use the cell identifier to identify the model associated with the cell identifier and the available network-side identifier that matches the specific cell identifier. In some other example embodiments, when the first device 110 is performing NW access, the first device 110 may receive a GI that includes both the cell identifier and a cell identifier associated with the cell identifier, and may use the GI to identify the model associated with the GI.

[0090] The second device 120 transmits (2015) multiple location reference signals to the first device 110 based on the first PRS configuration. In other words, the first device 110 receives (2015) multiple location reference signals from the second device 120 based on the first PRS configuration. For example, a network identifier may represent a list of PRS resources or configurations associated with an NW provider.

[0091] The first device 110 performs (2020) data collection on multiple positioning reference signals based on at least one of a first cell identifier or a first network-side identifier. For example, the first device 110 may perform measurements on the multiple positioning reference signals and collect measurement data. The first device 110 may perform classification on the measurement data obtained from the multiple positioning reference signals based on the first cell identifier and the first network-side identifier. In some example embodiments, data collection for training is performed under a PRS resource previously identified by GI.

[0092] The first device 110 may train (2025) one or more models based on collected data associated with a first cell identifier and a first network-side identifier. For example, the first device 110 may obtain a dataset after data collection and then use the dataset to train one or more models. In some example embodiments, one or more models may be associated with each specific GI (i.e., cell identifier and network-side identifier).

[0093] The second device 120 may transmit (2030) a PRS configuration for inference (hereinafter referred to as the "second PRS configuration") to the first device 110. The second PRS configuration for inference may be associated with at least one of the following: another cell identifier (referred to as the "second cell identifier") or another network-side identifier (referred to as the "second network-side identifier").

[0094] In some example embodiments, the first device 110 may select (2035) a model for inference based on at least one of a second cell identifier or a second network-side identifier. For example, the first device 110 may select a matching model based on multiple applicable network-side identifiers of multiple PRS resource sets when supporting inference operations.

[0095] In some example embodiments, the first device 110 may select a model for inference by checking whether the cell identifier and network-side identifier associated with the model also include a second cell identifier and a second network-side identifier. Alternatively or additionally, the first device 110 may receive a model for inference determined based on at least one of the second cell identifier or the second network-side identifier from a server (such as a UE server). For example, one or more cell identifiers associated with the model for inference include the second cell identifier. Furthermore, one or more network-side identifiers associated with the model for inference include the second network-side identifier.

[0096] In some example embodiments, the first device 110 may transmit (2040) a report indicating one or more applicable network-side identifiers 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, within the scope of a functional framework (e.g., a lower-layer protocol (LLP) procedure), in addition to reporting all supported conditions (e.g., UE capabilities), the first device 110 may also report applicable network-side identifiers (e.g., via UE capability updates or via a separate reporting mode). In this case, in some example embodiments, the reported applicable network-side identifier is determined based on a model identified by the received GCI. In some other example embodiments, if the first device 110 receives a network-side identifier via GI, it may not always be necessary to report the applicable network-side identifier (e.g., as long as the first device 110 can support all indicated network-side identifiers).

[0097] In some example embodiments, the second device 120 may determine (2043) the model for inference based on one or more reported applicable network-side identifiers by checking whether one or more cell identifiers associated with the model for inference include a second cell identifier and whether one or more network-side identifiers associated with the model for inference include a second network-side identifier. The second device 120 may then transmit (2045) a second configuration indicating the model for inference, determined based on the one or more reported applicable network-side identifiers, to the first device 110. In other words, the first device 110 may receive (2045) a second configuration indicating the model for inference from the second device 120.

[0098] In some example embodiments, the network (e.g., LMF) may configure functions or function groups, including a list of PRS resource sets. In this case, each PRS resource set may be associated with a corresponding network-side identifier. In some other example embodiments, the network may configure each PRS resource set associated with a corresponding GI (where the network-side identifier is part of the GI).

[0099] In some example embodiments, the collected data obtained from the data collection is used to develop one or more models associated with a first cell identifier and a first network-side identifier. For example, the first device 110 may train / update one or more models based on the collected data. Alternatively or additionally, the collected data may be used to access one or more pre-trained models and associate one or more pre-trained models with the first cell identifier and the first network-side identifier.

[0100] 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 a first cell identifier or a first network-side identifier. 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 the following: a model identifier for the model for model identification, a first cell identifier, or a first network-side identifier.

[0101] First device 110 can transmit a (2050) model identifier report to second device 120. In other words, second device 120 can receive a (2050) model identifier report from first device 110. For example, after selecting a model associated with GI, first device 110 can report a model identifier to NW. The model identifier can refer to one or more network-side identifiers. The reported model identifier can be called a model ID, and second device 120 can associate a set of NW additional conditions with the model ID based on the network-side identifier.

[0102] In some example embodiments, the first device 110 may report a model identifier to the second device 120. The model identifier may refer to one or more GCIs. The reported model identifier may be referred to as a model ID, and the second device 120 may associate multiple GCIs and multiple NW identifiers based on the report. The second device 120 may then deduce a set of NW additional conditions and cells to which the model ID applies. In some other example embodiments, without directly referring to the reported model identifier as a model ID, the second device 120 may assign a model ID based on the reported model identifier.

[0103] 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.

[0104] refer to Figure 3AThis illustrates a signaling flow 300 for data collection according to some embodiments of the present disclosure. For the purposes of discussion, the signaling flow 300 relates to a UE 310 and a network (NW) entity 320. In some example embodiments, a first device 110 may act as the UE 310, and a second device 120 may act as the NW entity 320.

[0105] When UE310 connects to NW entity 320, UE310 can report (3010) a capability report. The capability report may carry capabilities related to NW additional conditions (related to training and inference consistency) for processing a given location use case that supports ML. In an example embodiment, the capabilities related to processing NW additional conditions may also be related to capabilities associated with UE-side data collection.

[0106] NW entity 320 can configure UE 310 (3020) via LPP using a data collection configuration. The data collection configuration may include a PRS configuration for data collection. Each PRS configuration may be associated with a Global Identifier (GI), where the GI is determined by two elements: (1) a Global Cell Identifier (GCI) and (2) a specific NW identifier. In one example embodiment, the PRS resource set within the PRS configuration may have a configuration IE that provides the GCI and NW identifier associated with the PRS resource set. In another example embodiment, the PRS resource set within the PRS configuration may have a configuration IE that provides the NW identifier associated with the PRS resource set. Here, the GCI associated with the PRS resource set may be provided separately. In one example, the GCI may be provided in SIB#1, where UE 310 may consider all PRS resource sets associated with the connected cell to be associated with the same GCI.

[0107] In one example, an NW vendor can use multiple NW identifiers to implicitly represent a set of NW additional conditions (the identifiers can be defined according to NW vendor preferences). In another example, collaboration can also occur between NW vendors to agree on the definition of NW identifier numbering and the interpretation of NW identifiers to NW additional conditions. The purpose of NW identifiers is to provide a means of classifying data collection at the UE. For a given vendor, the same NW identifiers are expected to be used to maintain consistency when the same NW assumptions (corresponding to the implementation assumptions of NW additional conditions) are used in different cells.

[0108] NW entity 320 can transmit (3030) a PRS for data collection according to the data collection configuration. UE 310 can collect (3040) data. When UE 310 collects location measurements (data samples) based on PRS reception corresponding to the PRS resource set, UE 310 has a means to classify the data according to GCI and NW identifiers. Typically, the data collection process can occur on multiple UEs, multiple cells, and at any point in time. However, since the UE can classify the data at least based on GCI and NW identifiers, the UE server developing the model can also be associated with GCI and NW identifiers. For example, the model can be trained based on data collection (3050).

[0109] During the inference phase, UE 310 may consider the same PRS configuration (as with data collection) or be reconfigured to use a different PRS configuration for inference. For example, NW entity 320 may configure (3060) a PRS configuration for inference operations, which includes (multiple) GCIs and (multiple) NW identifiers for PRS configuration.

[0110] Based on the received PRS configuration for inference, UE 310 can select (3070) matching ML models for inference. The selection at UE 310 may consider the PRS configuration associated with (multiple) GCIs and (multiple) NW identifiers. In one example, UE 310 may support inference in cells different from the location where it collects data samples. In such cases, the UE server (such as an over-the-top (OTT) server) intended to train the model sends a matching model to UE 310 to perform inference. Here, the matching model is determined based on the GCI and (multiple) NW identifiers associated with the PRS configuration received during inference.

[0111] Additionally, there may be additional steps to coordinate with NW entity 320 for multiple NW identifiers applicable to the NW, especially when UE 310 does not support all possible variations of the NW identifier. For example, UE 310 may transmit (3080) a UE report indicating the applicable NW identifier for inference operation. NW entity 320 may configure (3090) UE 310 to select or activate inference operation based on the NW identifier(s) reported by the UE.

[0112] Referring to 3B, a signaling flow 300' for model identification is illustrated according to some embodiments of this disclosure. For discussion purposes, signaling flow 300' relates to UE 310 and network (NW) entity 320. In some example embodiments, a first device 110 may act as UE 310 and a second device 120 may act as NW entity 320.

[0113] When UE 310 connects to NW, UE 310 can report a (3010') capability report. The capability report can carry capabilities related to the support model identifier for a given location use case that supports ML.

[0114] NW entity 320 can configure UE 310 (3020') with a data collection configuration via LPP. The data collection configuration may include a PRS configuration for data collection. Each PRS configuration may be associated with a global identifier (GI), where the GI is determined by two elements: (1) a global cell identifier (GCI) and (2) a specific NW identifier. In one example embodiment, the PRS resource set within the PRS configuration may have a configuration IE that provides the GCI and NW identifier associated with the PRS resource set. In another example embodiment, the PRS resource set within the PRS configuration may have a configuration IE that provides the NW identifier associated with the PRS resource set. Here, the GCI associated with the PRS resource set may be provided separately. In one example, the GCI may be provided in SIB#1, where UE 310 may consider all PRS resource sets associated with the connected cell to be associated with the same GCI.

[0115] In one example, an NW vendor can use an NW identifier to implicitly represent a set of NW additional conditions (the identifier can be defined according to NW vendor preferences). In another example, collaboration can also occur between NW vendors to agree on the definition of NW identifier numbering and the interpretation of NW identifiers to NW additional conditions. The purpose of the NW identifier is to provide a means of classifying data collection at the UE. For a given vendor, the same NW identifier is expected to be used to maintain consistency when the same NW assumptions (corresponding to the implementation assumptions of NW additional conditions) are used in different cells.

[0116] NW entity 320 can transmit (3030') a PRS for data collection according to the data collection configuration. UE 310 can collect (3040') data. When UE 310 collects location measurements (data samples) based on PRS reception corresponding to the PRS resource set, UE 310 has a means to classify the data according to GCI and NW identifier. Typically, the data collection process can occur on multiple UEs, multiple cells, and at any point in time.

[0117] Following data collection and classification, the data collected in (3050') can be used to develop (train / update / ) models, where the models can be associated with GCI and NW identifiers. Alternatively or additionally, the data collected in (3050') can be used to evaluate pre-trained models, which will also be associated with GCI and NW identifiers. Here, the evaluation can be performed by using different datasets classified by GCI and NW identifiers to assess the performance of the pre-trained models. After this step, UE 310 can be expected to determine one or more ML models for model identification, where one or more models are associated with NW identifiers and GCI.

[0118] UE 310 can generate a model identifier report. UE 310 can then send (3060') a model identifier assigned by UE 310 or NW entity 320, along with the associated GCI(x) and NW(x) identifier(s). In an example embodiment, the NW identifier can be sent as a GCI, which can be determined by the NW based on the cell to which it is connected when UE 310 sends the (3060') report.

[0119] NW entity 320 can determine whether the reported model matches the NW identifier and GCI applicable to inference. Based on the received model identifier report, NW entity 320 can configure (3080') inference operation for UE 310, and also consider model-level selection, activation, and other signaling procedures. For example, NW entity 320 can configure (3090') UE 310 to select / activate / deactivate / switch models using the model identifier.

[0120] Figure 4 A flowchart illustrating an example method 400 implemented at a first device according to some example embodiments of the present disclosure is shown. For example, method 400 will... Figure 1 The first device in the process is implemented at point 110.

[0121] At frame 410, the first device receives a first positioning reference signal configuration for data collection from the second device. The first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning.

[0122] At frame 420, the first device is configured to receive multiple positioning reference signals from the second device based on the first positioning reference signal.

[0123] In frame 430, the first device performs data collection on the plurality of positioning reference signals based on at least one of a first cell identifier or a first network-side identifier.

[0124] In some example embodiments, the first positioning reference signal configuration includes a first cell identifier and a first network-side identifier.

[0125] In some example embodiments, method 400 further includes receiving a first configuration including a first cell identifier from a second device.

[0126] In some example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is specific to the network vendor, or the mapping between the network-side identifier and one or more network-side registration conditions is common to the network vendor.

[0127] In some example embodiments, method 400 further includes: classifying measurement data obtained from multiple positioning reference signals based on a first cell identifier and a first network-side identifier.

[0128] In some example embodiments, method 400 further includes training one or more models based on collected data associated with a first cell identifier and a first network-side identifier.

[0129] In some example embodiments, method 400 further includes: receiving a second positioning reference signal configuration for inference from a second device, wherein the second positioning reference signal configuration for inference is associated with at least one of the following: a second cell identifier or a second network-side identifier.

[0130] In some example embodiments, method 400 further includes selecting a model for inference based on at least one of a second cell identifier or a second network-side identifier.

[0131] In some example embodiments, method 400 further includes: receiving a model for inference from a server, the model for inference being determined based on at least one of a second cell identifier or a second network-side identifier.

[0132] In some example embodiments, one or more cell identifiers associated with the model used for inference include a second cell identifier, and one or more network-side identifiers associated with the model used for inference include a second network-side identifier.

[0133] In some example embodiments, method 400 further includes: transmitting to a second device a report indicating one or more applicable network-side identifiers for inference; and receiving from the second device a second configuration indicating a model for inference, the model for inference being determined based on the reported one or more applicable network-side identifiers.

[0134] In some example embodiments, the collected data obtained from the data collection is used to develop one or more models associated with a first cell identifier and a first network side identifier, or the collected data is used to access one or more pre-trained models and associate one or more pre-trained models with the first cell identifier and the first network side identifier.

[0135] In some example embodiments, method 400 further includes: determining a model for model identification, wherein the model for model identification is associated with at least one of a first cell identifier or a first network-side identifier; determining a model identification report indicating the model for model identification, wherein the model identification report includes at least one of: a model identifier of the model for model identification, a first cell identifier, or a first network-side identifier; and transmitting the model identification report to a second device.

[0136] In some example embodiments, method 400 further includes receiving from the second device a reasoning operation configuration including a positioning reference signal configuration for reasoning.

[0137] In some example embodiments, one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

[0138] In some example embodiments, the first device is a terminal device, and the second device is a network device.

[0139] Figure 5 A flowchart illustrating an example method 500 implemented at a second device according to some example embodiments of the present disclosure is shown. For example, method 500 will... Figure 1 The second device is implemented at point 120.

[0140] At frame 510, the second device transmits a first positioning reference signal configuration for data collection to the first device. The first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning.

[0141] At frame 520, the second device transmits multiple positioning reference signals to the first device based on the first positioning reference signal.

[0142] In some example embodiments, the first positioning reference signal configuration includes a first cell identifier and a first network-side identifier.

[0143] In some example embodiments, the first positioning reference signal configuration includes a first network-side identifier, and wherein the first device is caused to: transmit a first configuration including a first cell identifier to the first device.

[0144] In some example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is specific to the network vendor, or the mapping between the network-side identifier and one or more network-side registration conditions is common to the network vendor.

[0145] In some example embodiments, method 500 further includes transmitting a second positioning reference signal configuration for inference to a first device, wherein the second positioning reference signal configuration for inference is associated with at least one of the following: a second cell identifier or a second network-side identifier.

[0146] In some example embodiments, method 500 further includes: receiving from a first device a report indicating one or more applicable network-side identifiers for inference; determining a model for inference based on the reported one or more applicable network-side identifiers by checking whether one or more cell identifiers associated with the model for inference include a second cell identifier and whether one or more network-side identifiers associated with the model for inference include a second network-side identifier; and transmitting a second configuration indicating the model for inference to the first device.

[0147] In some example embodiments, method 500 further includes: receiving from a first device a model identification report indicating a model for model identification, wherein the model identification report includes: a model identifier for the model for model identification, a first cell identifier, and a first network-side identifier; and determining whether the model for model identification matches the cell identifier and network-side identifier used for inference.

[0148] In some example embodiments, method 500 further includes transmitting to the first device an inference operation configuration including a positioning reference signal configuration for inference.

[0149] In some example embodiments, one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

[0150] In some example embodiments, the first device is a terminal device, and the second device is a network device.

[0151] In some example embodiments, a first device capable of performing any method 400 (e.g., Figure 1 The first device 110 may include components for performing the corresponding operations of method 400. These components may be implemented in any suitable form. For example, the components may be implemented in a circuit system or a software module. The first device may be implemented as or included in... Figure 1 The first device 110 in the middle.

[0152] In some example embodiments, the first device includes: a component for receiving a first positioning reference signal configuration for data collection from a second device, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; a component for receiving a plurality of positioning reference signals from the second device based on the first positioning reference signal configuration; and a component for performing data collection on the plurality of positioning reference signals based on at least one of the first cell identifier or the first network-side identifier.

[0153] In some example embodiments, the first positioning reference signal configuration includes a first cell identifier and a first network-side identifier.

[0154] In some example embodiments, the first device further includes a component for receiving a first configuration including a first cell identifier from the second device.

[0155] In some example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is specific to the network vendor, or the mapping between the network-side identifier and one or more network-side registration conditions is common to the network vendor.

[0156] In some example embodiments, the first apparatus further includes a component for classifying measurement data obtained from a plurality of positioning reference signals based on a first cell identifier and a first network-side identifier.

[0157] In some example embodiments, the first apparatus further includes a component for training one or more models based on collected data associated with a first cell identifier and a first network-side identifier.

[0158] In some example embodiments, the first device further includes: a component for receiving a second positioning reference signal configuration for inference from the second device, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identifier or a second network-side identifier.

[0159] In some example embodiments, the first device further includes a component for selecting a model for inference based on at least one of a second cell identifier or a second network-side identifier.

[0160] In some example embodiments, the first apparatus further includes a component for receiving a model for inference from a server, the model for inference being determined based on at least one of a second cell identifier or a second network-side identifier.

[0161] In some example embodiments, one or more cell identifiers associated with the model used for inference include a second cell identifier, and one or more network-side identifiers associated with the model used for inference include a second network-side identifier.

[0162] In some example embodiments, the first device further includes: components for transmitting to the second device a report indicating one or more applicable network-side identifiers for inference; and components for receiving from the second device a second configuration indicating a model for inference, the model for inference being determined based on the reported one or more applicable network-side identifiers.

[0163] In some example embodiments, the collected data obtained from the data collection is used to develop one or more models associated with a first cell identifier and a first network side identifier, or the collected data is used to access one or more pre-trained models and associate one or more pre-trained models with the first cell identifier and the first network side identifier.

[0164] In some example embodiments, the first apparatus further includes: components for determining a model for model identification, wherein the model for model identification is associated with at least one of a first cell identifier or a first network-side identifier; components for determining a model identification report indicating the model for model identification, wherein the model identification report includes at least one of: a model identifier of the model for model identification, a first cell identifier, or a first network-side identifier; and components for transmitting the model identification report to the second apparatus.

[0165] In some example embodiments, the first device further includes a component for receiving from the second device an inference operation configuration including a positioning reference signal configuration for inference.

[0166] In some example embodiments, one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

[0167] In some example embodiments, the first device is a terminal device, and the second device is a network device.

[0168] In some example embodiments, a second device capable of performing any method 500 (e.g., Figure 1 The second device 120 may include components for performing the corresponding operations of method 500. These components may be implemented in any suitable form. For example, the components may be implemented in a circuit system or a software module. The second device may be implemented as or included in... Figure 1 The second device 120 in the middle.

[0169] In some example embodiments, the second device includes: a component for transmitting a first positioning reference signal configuration for data collection to the first device, wherein the first positioning reference signal configuration for data collection is associated with at least one of: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; and a component for transmitting a plurality of positioning reference signals to the first device based on the first positioning reference signal configuration.

[0170] In some example embodiments, the first positioning reference signal configuration includes a first cell identifier and a first network-side identifier.

[0171] In some example embodiments, the first positioning reference signal configuration includes a first network-side identifier, and wherein a first device is configured to transmit a first configuration including a first cell identifier to the first device.

[0172] In some example embodiments, the mapping between the network-side identifier and one or more network-side registration conditions is specific to the network vendor, or the mapping between the network-side identifier and one or more network-side registration conditions is common to the network vendor.

[0173] In some example embodiments, the second device further includes: a component for transmitting a second positioning reference signal configuration for inference to the first device, wherein the second positioning reference signal configuration for inference is associated with at least one of: a second cell identifier or a second network-side identifier.

[0174] In some example embodiments, the second apparatus further includes: components for receiving from the first apparatus a report indicating one or more applicable network-side identifiers for inference; components for determining a model for inference based on the reported one or more applicable network-side identifiers by checking whether one or more cell identifiers associated with the model for inference include a second cell identifier and whether one or more network-side identifiers associated with the model for inference include a second network-side identifier; and components for transmitting to the first apparatus a second configuration indicating the model for inference.

[0175] In some example embodiments, the second apparatus further includes: a component for receiving from the first apparatus a model identification report indicating a model for model identification, wherein the model identification report includes: a model identifier for the model for model identification, a first cell identifier, and a first network-side identifier; and a component for determining whether the model for model identification matches the cell identifier and network-side identifier used for inference.

[0176] In some example embodiments, the second device further includes a component for transmitting to the first device an inference operation configuration including a positioning reference signal configuration for inference.

[0177] In some example embodiments, one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

[0178] In some example embodiments, the first device is a terminal device, and the second device is a network device.

[0179] Figure 6 This is a simplified block diagram of a device 600 suitable for implementing exemplary embodiments of the present disclosure. Device 600 can be provided to implement a communication device, for example, Figure 1 The first device 110 or the second device 120 shown. As shown, device 600 includes one or more processors 610, one or more memories 620 coupled to processor 610, and one or more communication modules 640 coupled to processor 610.

[0180] Communication module 640 is used for bidirectional communication. Communication module 640 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interface can represent any interface necessary for communication with other network elements. In some example embodiments, communication module 640 may include at least one antenna.

[0181] Processor 610 can be any type suitable for a local technology network and may include one or more of the following as non-limiting examples: general-purpose computer, special-purpose computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. Device 600 may have multiple processors, such as application integrated circuit chips, which are time-subordinate to a clock synchronized with the main processor.

[0182] Memory 620 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory ROM 624, electrically programmable read-only memory EPROM, flash memory, hard disk, optical disc CD, digital video disc DVD, optical disc, laser disc, and other magnetic and / or optical memories. Examples of volatile memories include, but are not limited to, random access memory RAM 622 and other volatile memories that will not be retained during power loss.

[0183] Computer program 630 includes computer-executable instructions that are executed by an associated processor 610. The instructions of program 630 may include instructions for performing operations / actions of some example embodiments of this disclosure. Program 630 may be stored in memory, such as ROM 624. Processor 610 can perform any suitable actions and processes by loading program 630 into RAM 622.

[0184] Example embodiments of this disclosure can be implemented using the method of procedure 630, such that device 600 can perform as described in the reference. Figures 2 to 5 Any process discussed in this disclosure. Exemplary embodiments of this disclosure may also be implemented by hardware or a combination of software and hardware.

[0185] In some example embodiments, program 630 may be tangibly contained in a computer-readable medium, which may be included in device 600 (such as in memory 620) or other storage devices accessible by device 600. Device 600 may load program 630 from the computer-readable medium into RAM 622 for execution. In some example embodiments, the computer-readable medium may include any type of non-transitory storage medium, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. As used herein, the term "non-transitory" is a limitation of the medium itself (i.e., tangible, not tactile), and not a limitation of data storage persistence (e.g., RAM vs. ROM).

[0186] Figure 7 An example of a computer-readable medium 700 is shown, which may be in the form of a CD, DVD, or other optical storage disc. A program 630 is stored on the computer-readable medium 700.

[0187] In general, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, and others can be implemented in firmware or software executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are illustrated and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof, as examples of non-limiting examples.

[0188] Some exemplary embodiments of this 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 a program module, which are executed in a device targeting a physical or virtual processor to implement any of the methods described above. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform a particular task or implement a particular abstract data type. The functionality of the program module can be combined or split as needed among program modules in various embodiments. The machine-executable instructions for the program module can be executed within a local or distributed device. In a distributed device, the program module can reside on both local and remote storage media.

[0189] The program code used to implement the methods of this 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, a special-purpose computer, or other programmable data processing apparatus, such that, when executed by the processor or controller, the program code enables the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0190] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0191] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media will include electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0192] Furthermore, although operations are described in a specific order, this should not be construed as requiring such operations to be performed in the specific order shown or in sequential order, or to perform all shown operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the discussion above, these should not be construed as limiting the scope of this disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated otherwise, certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated otherwise, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0193] Although this disclosure has been described in language specific to structural features and / or methodological actions, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms of implementing the claims or any of the following embodiments.

Claims

1. A first device, comprising: At least one processor; as well as At least one memory, the at least one memory storing instructions, the instructions, when executed by the at least one processor, cause the first device to: The device receives 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 the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; Based on the configuration of the first positioning reference signal, multiple positioning reference signals are received from the second device; as well as The data collection is performed on the plurality of positioning reference signals based on at least one of the first cell identifier or the first network-side identifier.

2. The first device according to claim 1, wherein the first positioning reference signal configuration includes the first cell identifier and the first network side identifier.

3. The first apparatus of claim 1, wherein the first positioning reference signal configuration includes the first network-side identifier, and wherein the first apparatus is configured such that: Receive a first configuration including the first cell identifier from the second device.

4. The first apparatus according to any one of claims 1-3, wherein the mapping between the network-side identifier and the one or more network-side registration conditions is specific to a network provider, or The mapping between the network-side identifier and the one or more network-side registration conditions is common to network providers.

5. The first device according to any one of claims 1-4, wherein the first device is configured to: The measurement data obtained from the plurality of positioning reference signals are classified based on the first cell identifier and the first network side identifier.

6. The first device according to any one of claims 1-5, wherein the first device is configured to: One or more models are trained based on the collected data associated with the first cell identifier and the first network-side identifier.

7. The first device according to any one of claims 1-6, wherein the first device is configured to: The second device receives a second positioning reference signal configuration for inference, wherein the second positioning reference signal configuration for inference is associated with at least one of the following: a second cell identifier or a second network-side identifier.

8. The first device according to claim 7, wherein the first device is configured to: The model used for inference is selected based on at least one of the second cell identifier or the second network-side identifier.

9. The first device according to claim 7, wherein the first device is configured to: The system receives a model for inference from the server, the model being determined based on at least one of the second cell identifier or the second network-side identifier.

10. The first apparatus of claim 9, wherein one or more cell identifiers associated with the model for inference include the second cell identifier, and one or more network-side identifiers associated with the model for inference include the second network-side identifier.

11. The first device according to any one of claims 1-9, wherein the first device is configured to: Transmit a report to the second device indicating one or more applicable network-side identifiers for inference; and The second device receives a second configuration indicating a model for the inference, the model for the inference being determined based on the reported one or more applicable network-side identifiers.

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

13. The first device according to claim 12, wherein the first device is configured to: A model for model identification is determined, wherein the model for model identification is associated with at least one of the first cell identifier or the first network-side identifier; A model identification report indicating the model used for model identification is determined, wherein the model identification report includes at least one of the following: a model identifier for the model used for model identification, a first cell identifier, or a first network-side identifier; and The model identification report is transmitted to the second device.

14. The first device according to claim 13, wherein the first device is configured to: The second device receives an inference operation configuration including a positioning reference signal configuration for inference.

15. The first apparatus according to any one of claims 1-14, wherein one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

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

17. A second device, comprising: At least one processor; as well as At least one memory, the at least one memory storing instructions, the instructions, when executed by the at least one processor, cause the second device to: A first positioning reference signal configuration for data collection is transmitted to a first device, wherein the first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; as well as Based on the first positioning reference signal, multiple positioning reference signals are transmitted to the first device.

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

19. The second apparatus of claim 17, wherein the first positioning reference signal configuration includes the first network-side identifier, and wherein the first apparatus is configured such that: Transmit a first configuration, including the first cell identifier, to the first device.

20. The second apparatus according to any one of claims 17-19, wherein the mapping between the network-side identifier and the one or more network-side registration conditions is specific to a network provider, or The mapping between the network-side identifier and the one or more network-side registration conditions is common to network providers.

21. The second device according to any one of claims 17-20, wherein the second device is configured to: A second positioning reference signal configuration for inference is transmitted to the first device, wherein the second positioning reference signal configuration for inference is associated with at least one of the following: a second cell identifier or a second network-side identifier.

22. The second device according to any one of claims 17-21, wherein the second device is configured such that: Receive a report from the first device indicating one or more applicable network-side identifiers for inference; The model used for inference is determined based on the reported one or more applicable network side identifiers by checking whether one or more cell identifiers associated with the model used for inference include the second cell identifier and whether one or more network side identifiers associated with the model used for inference include the second network side identifier. as well as A second configuration of the model, indicating the reasoning, is transmitted to the first device.

23. The second device according to any one of claims 17-22, wherein the second device is configured such that: Receive a model identification report from the first device indicating the model for model identification, wherein the model identification report includes: The model identifier, the first cell identifier, and the first network-side identifier used for model identification; as well as Determine whether the model used for model identification matches the cell identifier and network-side identifier used for inference.

24. The second device according to claim 23, wherein the second device is configured to: The first device is transmitted a reasoning operation configuration, including a positioning reference signal configuration for reasoning.

25. The second apparatus according to any one of claims 17-24, wherein the one or more network-side registration conditions may include one or more considerations related to positioning on the network side during the transmission of positioning reference signals.

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

27. A method comprising: A first positioning reference signal configuration for data collection is received from a second device at a first device, wherein the first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; Based on the configuration of the first positioning reference signal, multiple positioning reference signals are received from the second device; as well as The data collection is performed on the plurality of positioning reference signals based on at least one of the first cell identifier or the first network-side identifier.

28. A method comprising: A first positioning reference signal configuration for data collection is transmitted from the second device to the first device, wherein the first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; as well as Based on the first positioning reference signal, multiple positioning reference signals are transmitted to the first device.

29. A first device, comprising: Components for receiving a first positioning reference signal configuration for data collection from a second device, wherein the first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; Components for configuring the receiving of multiple positioning reference signals from the second device based on the first positioning reference signal; as well as A component for performing the data collection on the plurality of positioning reference signals based on at least one of the first cell identifier or the first network side identifier.

30. A second device, comprising: Components for transmitting a first positioning reference signal configuration for data collection to a first device, wherein the first positioning reference signal configuration for data collection is associated with at least one of the following: a first cell identifier or a first network-side identifier associated with one or more network-side registration conditions for positioning; as well as A component for configuring the transmission of multiple positioning reference signals to the first device based on the first positioning reference signal.

31. A computer-readable medium comprising instructions stored thereon, the instructions being configured to cause a device to perform at least the method of claim 27 or 28.

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