Data Augmentation for Machine Learning Training in Positioning

Abstract

An example embodiment of the present disclosure relates to data augmentation for training a model. In this solution, a first device receives a configuration for data augmentation from a second device. The first device determines a data augmentation parameter set based on the configuration. The first device trains a model based on data acquired based on the data augmentation parameter set. In this way, the first device increases the amount of quality training data, enabling robust model training. Furthermore, the first device can evaluate the completeness of the augmented data set by using a probability value for each data point or data segment.

Description

[Technical Field]

[0001] Various example embodiments of the present disclosure relate generally to the field of telecommunications, and more particularly to methods, devices, apparatus, and computer-readable storage media for data augmentation for machine learning training in positioning. [Background technology]

[0002] In the telecommunications industry, techniques have been proposed to improve the performance of telecommunications systems. For example, artificial intelligence / machine learning (AI / ML) models have been adopted in telecommunications systems to improve the performance of telecommunications systems. Therefore, it is worth considering the training of AI / ML models adopted in telecommunications systems. Summary of the Invention

[0003] A first aspect of the present disclosure provides a first device comprising at least one processor and at least one memory that stores instructions that, when executed by the at least one processor, cause the first device to at least perform the following: receive from a second device a data augmentation configuration for training a positioning model in the first device; determine a data augmentation parameter set based on the data augmentation configuration; acquire data including measurement data and augmented data of the first device based on the data augmentation procedure and the data augmentation parameter set; and train the positioning model based on a combination of the measurement data and the augmented data.

[0004] A second aspect of the present disclosure provides a second device comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the second device to at least perform the following: receive wireless measurements from at least one third device; determine a data augmentation configuration for training a positioning model at the first device based on the measurements from the at least one third device; and transmit the data augmentation configuration to the first device.

[0005] A third aspect of the present disclosure provides a first device, comprising: at least one processor and at least one memory that stores instructions that, when executed by the at least one processor, cause the first device to at least: send a request for assistance for data augmentation to a second device; receive augmented data from the second device for training a positioning model; and train the positioning model based on a combination of the augmented data and the measurement data.

[0006] A fourth aspect of the present disclosure provides a second device, the second device comprising: at least one processor; and at least one memory that stores instructions that, when executed by the at least one processor, cause the second device to at least perform the following: receive an assistance request for data augmentation from the first device; acquire measurement data from a third device; generate augmented data for training a positioning model at the first device based on the measurement data; and transmit the augmented data to the first device.

[0007] A fifth aspect of the present disclosure provides a method, including: receiving from a second device a data augmentation configuration for training a positioning model at a first device; determining a data augmentation parameter set based on the data augmentation configuration; acquiring data including measurement data and augmented data of the first device based on the data augmentation procedure and the data augmentation parameter set; and training the positioning model based on a combination of the measurement data and the augmented data.

[0008] A sixth aspect of the present disclosure provides a method, the method including: receiving wireless measurements from at least one third device; determining a configuration of data augmentation for training a positioning model at a first device based on the measurements from the at least one third device; and transmitting the configuration of data augmentation to the first device.

[0009] A seventh aspect of the present disclosure provides a method, including sending a request for assistance for data augmentation to a second device, receiving augmented data from the second device for training a positioning model, and training the positioning model based on a combination of the augmented data and the measurement data.

[0010] An eighth aspect of the present disclosure provides a method, including receiving an assistance request for data augmentation from a first device, acquiring measurement data from a third device, generating augmented data for training a positioning model at the first device based on the measurement data, and transmitting the augmented data to the first device.

[0011] A ninth aspect of the present disclosure provides an apparatus, including: means for receiving from a second device a data augmentation configuration for training a positioning model in a first device; means for determining a data augmentation parameter set based on the data augmentation configuration; means for acquiring data including measurement data and augmented data of the first device based on the data augmentation procedure and the data augmentation parameter set; and means for training the positioning model based on a combination of the measurement data and the augmented data.

[0012] A tenth aspect of the present disclosure provides an apparatus, including: means for receiving wireless measurements from at least one third device; means for determining a configuration of data augmentation for training a positioning model at a first device based on the measurements from the at least one third device; and means for transmitting the configuration of data augmentation to the first device.

[0013] An eleventh aspect of the present disclosure provides an apparatus, including: means for sending an assistance request for data augmentation to a second device; means for receiving augmented data from the second device for training a positioning model; and means for training the positioning model based on a combination of the augmented data and the measurement data.

[0014] A twelfth aspect of the present disclosure provides an apparatus, including: means for receiving an assistance request for data augmentation from a first device; means for acquiring measurement data from a third device; means for determining augmentation data for training a positioning model at the first device based on the measurement data; and means for transmitting the augmentation data to the first device.

[0015] In a thirteenth aspect of the present disclosure, a computer-readable medium is provided, the computer-readable medium storing instructions for causing an apparatus to perform a method according to at least any of the fifth, sixth, seventh, or eighth aspects.

[0016] It should be understood that this summary is not intended to identify key or essential features of the embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become readily apparent through the following description.

[0017] Some example embodiments will now be described with reference to the accompanying drawings. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure may be implemented. [Figure 2] FIG. 1 is a schematic diagram of data collection according to some example embodiments of the present disclosure. [Figure 3] FIG. 1 is a schematic diagram of an architecture for model training with data augmentation, according to some example embodiments of the present disclosure. [Figure 4] FIG. 1 is a signaling diagram for communication according to some example embodiments of the present disclosure. [Figure 5] 10A-10C are schematic diagrams of further measurements during a measurement request according to some example embodiments of the present disclosure. [Figure 6A] FIG. 1 is a signaling diagram for communication according to some example embodiments of the present disclosure. [Figure 6B] FIG. 1 is a signaling diagram for communication according to some example embodiments of the present disclosure. [Figure 7] FIG. 1 is a signaling diagram for communication according to some example embodiments of the present disclosure. [Figure 8] FIG. 1 illustrates a flowchart of a method according to some example embodiments of the present disclosure. [Figure 9] FIG. 1 illustrates a flowchart of a method according to some example embodiments of the present disclosure. [Figure 10] FIG. 1 illustrates a flowchart of a method according to some example embodiments of the present disclosure. [Figure 11] FIG. 1 illustrates a flowchart of a method according to some example embodiments of the present disclosure. [Figure 12] FIG. 1 is a simplified block diagram of a device suitable for implementing example embodiments of the present disclosure. [Figure 13] 1 is a block diagram of an example computer-readable medium according to some example embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0019] Throughout the drawings, the same or similar reference numbers represent the same or similar elements.

[0020] The principles of the present disclosure will be explained below with reference to several exemplary embodiments. It should be understood that these embodiments are merely provided for illustrative purposes to assist those skilled in the art in understanding and practicing the present disclosure, and are not intended to imply any limitation on the scope of the present disclosure. The embodiments described herein may be implemented in various ways other than those described below.

[0021] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0022] References in this disclosure to "one embodiment," "an embodiment," "an example embodiment," and the like indicate that the described embodiment may include particular features, structures, or characteristics, but not all embodiments need to include these particular features, structures, or characteristics. Moreover, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with one embodiment, it is believed to be within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments, whether or not it is explicitly described.

[0023] Additionally, although terms such as "first" and "second" may be used herein to describe various elements, it should be understood that these elements are not limited by these terms. These terms are merely used to distinguish one element from another. For example, a first element could be referred to as a second element, and similarly, a second element could be referred to as a first element, without departing from the scope of the example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0024] As used herein, "at least one of the following: <list of two or more elements>" means "at least one of the following: )" and "at least one of <list of 2 or more elements>" )" and similar expressions connecting lists of two or more elements with "and" or "or" mean at least any one of those elements, or at least any two or more of those elements, or at least all of those elements.

[0025] Unless expressly stated, performing a step "in response to A," as used herein, does not imply that the step is performed immediately upon the occurrence of "A," and may include one or more intervening steps.

[0026] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit example embodiments. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, as used herein, the terms "comprises," "comprising," "has," "having," "includes," and / or "including" specify the presence of stated features, elements, and / or components, etc., but will be understood not to exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0027] As used in this application, the term "circuitry" may mean one, more than one, or all of the following: (a) purely hardware circuit implementations (e.g., purely analog and / or purely digital circuit implementations); and (b) (where applicable) any combination of hardware circuitry and software that: (i) a combination of analog and / or digital hardware circuitry and software / firmware; and (ii) any portion of a hardware processor(s) with software (including digital signal processor(s), software, and memory(s) that together cause a device such as a mobile phone or server to perform various functions); and (c) Hardware circuit(s) that require software (e.g., firmware) to operate, but the software may be absent when not necessary for operation, and / or processor(s), such as microprocessor(s) or part of microprocessor(s).

[0028] This definition of circuit applies to all uses of the term in this application, including the claims. As a further example, the term circuit as used in this application may cover a hardware circuit or processor(s) alone, or a portion of a hardware circuit or processor and its(these) accompanying software and / or firmware implementations. The term circuit may also cover, for example, a baseband or processor integrated circuit for a mobile device, or a similar integrated circuit in a server, cellular network device, or other computing or network device, where applicable to certain claim elements.

[0029] As used herein, the term "communication network" refers to a network conforming to any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), and Narrow Band Internet of Things (NB-IoT). Furthermore, communications between terminal devices and network devices in a communication network may be performed according to any suitable generation of communication protocols, 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) communication protocols, and / or any other protocols now known or developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Considering the rapid development of communications, there will naturally be future communications technologies and systems in which the present disclosure can be embodied, and the scope of the present disclosure should not be considered limited to only the systems mentioned above.

[0030] As used herein, the term "network device" refers to a node in a communication network through which a terminal device accesses the network and receives services therefrom. Depending on the terminology and technology applied, the network device may refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), evolved Node B (eNodeB or eNB), NR NB (also referred to as gNB), remote radio unit (RRU), radio header (RH), remote radio head (RRH), relay, integrated access and backhaul (IAB) node, low-power node such as femto or pico, non-terrestrial network (NTN) or non-terrestrial network device such as a satellite network device, low earth orbit (LEO) satellite, or geosynchronous earth orbit (GEO) satellite, and an airborne network device. In some example embodiments, a radio access network (RAN) split architecture includes a Centralized Unit (CU) and a Distributed Unit (DU) in an IAB donor node. The IAB node includes a Mobile Terminal (IAB-MT) portion that behaves like a UE toward a parent node, and the DU portion of the IAB node behaves like a base station toward a next-hop IAB node.

[0031] The term "terminal device" refers to any end device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices include, but are not limited to, mobile phones, cellular phones, smartphones, voice-over-IP (VoIP) phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback equipment, in-vehicle wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), Internet of Things (IoT) devices, watches or other wearables, head-mounted displays (HMD), 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 an industrial chain and / or automated processing chain), consumer electronics devices, and the like. Examples of terminal devices include a mobile termination (MT) portion of an IAB node (e.g., a relay node), a wireless communication device (e.g., a wireless LAN), and a device operating on a commercial and / or industrial wireless network. A terminal device may also correspond to a mobile termination (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" may be used interchangeably.

[0032] As used herein, the terms "resource," "transmission resource," "resource block," "physical resource block (PRB)," "uplink resource," or "downlink resource" may refer to any resource for performing communication, such as communication between a terminal device and a network device, such as a resource in the time domain, a resource in the frequency domain, a resource in the spatial domain, a resource in the code domain, or any other resource that enables communication. Hereinafter, unless otherwise specified, resources in both the frequency domain and the time domain are used as examples of transmission resources to describe some example embodiments of the present disclosure. Note that the example embodiments of the present disclosure are equally applicable to other resources in other domains.

[0033] As mentioned above, AI / ML can be employed in communication systems. For example, AI / ML can be used to improve positioning accuracy. Furthermore, discussions about data collection for training primarily focus on whether data can be collected from the entire simulation area or from a series of grids located within the simulation area. The basic assumption seems to be that positioning data is readily available within the system. However, this assumption is only valid for simulated environments and cannot be applied to real-world environments where only a limited amount of data is available.

[0034] Assuming that a supervised AI / ML model runs on the UE side, AI / ML training may require a certain amount of labeled data (typically a large amount) so that AI / ML inference can be performed with a certain accuracy. The UE alone may require a long time to collect all the necessary data. In this case, the network can support the UE by sending more labeled data, but this comes at the cost of more signaling.

[0035] Supervised learning is an important technique for extracting value from big data. However, the effectiveness of supervised learning requires large amounts of high-quality training data. Often, the size of the training data is not large enough to effectively train a supervised learning classifier. Data augmentation is a widely adopted approach to increase the amount of training data. However, the quality of the augmented data is questionable and may depend on the use case.

[0036] With the large-scale deployment of 5G cellular infrastructure, traffic prediction has become an essential part of cellular resource management systems to provide reliable and high-speed communication services that can meet increasing quality-of-service requirements. A promising approach to address this issue is to introduce intelligent methods using AI / ML models to implement highly effective and efficient radio resource management procedures. Meanwhile, the integration of multi-access edge computing frameworks into 5G cellular networks facilitates the application of intelligent traffic prediction models by enabling their implementation at the network edge. However, data scarcity and privacy issues may still be obstacles to training robust and accurate prediction models at the edge. Therefore, it is worth considering the training of AI / ML models employed in telecommunication systems.

[0037] Example environment 1 illustrates an example communication environment 100 in which example embodiments of the present disclosure can be implemented. Communication environment 100 includes devices 110-1, 110-2, 110-3, ..., and 110-N, which can be collectively referred to as "devices 110." The communication environment also includes devices 120 and 130. Device(s) 110, 120, and 130 can communicate with one another.

[0038] 1, device 110 may include a terminal device, and device 130 may include a network device that provides services to the terminal device. Device 120 may include a core network device. For example, device 120 may include a device that can implement a location management function (LMF).

[0039] It should be understood that the number of devices and their connections shown in FIG. 1 are not intended to be limiting and are for illustrative purposes only. Communication environment 100 may include any suitable number of devices configured to implement example embodiments of the present disclosure. Although not shown, it will be understood that one or more additional devices may be present within the cell of device 130, and one or more additional cells may be deployed within communication environment 100. It should be noted that while device 130 is shown as a network device, it may be a device other than a network device. While device 110 is shown as a terminal device, it may be a device other than a terminal device.

[0040] For purposes of explanation, the following describes some example embodiments in which device 110 operates as a terminal device and device 130 operates as a network device. However, in some example embodiments, operations described with respect to a terminal device may be performed in a network device or other device, and operations described with respect to a network device may be performed in a terminal device or other device.

[0041] In some example embodiments, when device 110 is a terminal device and device 130 is a network device, the link from device 130 to device 110 is referred to as a downlink (DL) and the link from device 110 to device 130 is referred to as an uplink (UL). In the DL, device 130 is the transmitting (TX) device (or transmitter) and device 110 is the receiving (RX) device (or receiver). In the UL, device 110 is the TX device (or transmitter) and device 130 is the RX device (or receiver).

[0042] Communications in communication environment 100 may be performed according to any suitable communications protocol(s), including, but not limited to, cellular communications protocols such as first generation (1G), second generation (2G), third generation (3G), fourth generation (4G), fifth generation (5G), and sixth generation (6G), wireless local network communications protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11, and / or other protocols now known or developed in the future. Further, the communications may 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 Multiple Access (OFDM), Discrete Fourier Transform spread OFDM (DFT-s-OFDM), and / or any other technology now known or developed in the future.

[0043] Operational principles and signaling examples for communication According to some example embodiments of the present disclosure, a solution for data augmentation for training an AI / ML model is provided. In this solution, a first device receives a configuration for data augmentation from a second device. The first device determines a data augmentation parameter set based on the configuration. The first device trains a model based on a combination of measurement data and augmented data obtained based on the data augmentation parameter set. In this way, the first device increases the amount of quality training data and enables robust model training. Furthermore, the first device can evaluate the completeness of the augmented data set by using a probability value for each data point or data segment.

[0044] FIG. 2 is a schematic diagram of an example data collection according to some example embodiments of the present disclosure. The device 110-1 may collect a series of measurements. For example, the device 110-1 may acquire measurement data 210 as shown in FIG. 2. The size of the collected data may not be sufficient to perform AI / ML model training. In this case, data augmentation may be applied. As used herein, the term "data augmentation" may refer to a technique for increasing the amount of data within a region of interest (ROI). Furthermore, spatial interpolation techniques may be applied to derive additional samples, representing estimated radio measurements for missing locations. In this case, the device 110-1 may use data augmentation to acquire augmented data 220 for the missing locations. Furthermore, the efficiency of this data augmentation method depends on tuning the spatial interpolation function and is related to the selection of appropriate parameters, which may be performed with network assistance. In this way, the amount of data, including the measurement data 210 and the augmented data 220, is sufficient to train a model.

[0045] FIG. 3 is a schematic diagram of an example structure 300 for model training with data augmentation, according to some example embodiments of the present disclosure. The structure 300 may be implemented in the device 110 shown in FIG. 1. As shown in FIG. 3, in some example embodiments, after data augmentation is performed using spatial interpolation to reach a target data size, a labeled dataset module 310 may add an indication of whether the data is interpolated or measured, allowing AI / ML model training to further consider this information. The spatial interpolation method may also provide a level of data augmentation and associate a probability value for each predicted value or augmented data segment. This value may indicate, for example, the likelihood that the augmented data is of comparable or lesser accuracy than the measured data. In this way, the AI / ML model may prioritize measured / real data over interpolated or augmented data when minimizing training error.

[0046] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0047] Reference is now made to signaling diagram 4 for interaction 400 in accordance with certain example embodiments of the present disclosure. As shown in FIG. 4, this signaling diagram illustrates interaction 400 between device 410, device 420, and device 430. For purposes of explanation, reference is made to FIG. 1 to describe signaling diagram 200. For example, device 410 may represent or include device 110-1 shown in FIG. 1, and device 430 may represent or include device 110-2 shown in FIG. 1. Device 420 may represent or include device 120 shown in FIG. 1. It should be noted that devices 410, 420, and 430 may represent or include any suitable devices.

[0048] The device 410 may report its capabilities to the device 420. For example, the capabilities may include one or more supported models of the device 410. Alternatively or additionally, the capabilities may include memory resources of the device 410. In other example embodiments, the capabilities may include computational capabilities of the device 410. The capabilities may also include computational resources in the device 410. In some example embodiments, the capabilities may be reported via long term positioning protocol (LPP) messages.

[0049] In some example embodiments, the device 410 can proactively initiate data collection (e.g., Layer 1 (L1) measurements) requests to train a model at the device 410. For example, the device 410 can send an assistance request for data augmentation to the device 420 (4005). In some example embodiments, the assistance request can indicate a percentage of available data relative to a target data size used in training a positioning model. As used herein, the term "positioning model" can refer to a processing model or AIML model used to position the device. In some example embodiments, the assistance request can be sent along with the capabilities of the device 410. For example, the assistance request and the reported capabilities can be included in an LPP message.

[0050] In some example embodiments, the request for assistance can include an indication of the percentage of currently available data relative to the target data size for model training. By way of example only, if the request for assistance indicates 75%, the device 420 can understand that 75% of the target data size has been collected and 25% of the target data size still needs to be collected, i.e., 25% of the data needs to be interpolated to reach the target data size for training. In this way, measurement collection and model training can be sped up.

[0051] Alternatively, the request for assistance may not include an indication of the percentage of currently available data, in which case the device 420 may determine the percentage of required data to request from the device 410. In some example embodiments, the device 420 may determine the required data based on feedback regarding data enhancement capabilities received at the device 420. Alternatively, the required data may be determined without feedback.

[0052] In some example embodiments, device 410 may send a request for assistance to device 420 when it newly joins device 420's network. Alternatively, device 410 may send a request for assistance to device 420 when it determines that the current dataset does not allow for model training with the required accuracy. In this case, in some example embodiments, the required accuracy may be based on a key performance indicator (KPI) for positioning accuracy. Alternatively or additionally, the required accuracy may be based on one or more intermediate KPIs for an intermediate model used for line-of-sight (LOS) or non-LOS (NLOS) classification. In other example embodiments, the required accuracy may be based on the positioning latency of device 410.

[0053] One or more devices may transmit their radio measurements to device 420. As shown in FIG. 4, device 430 transmits 4010 its radio measurements to device 420. One or more devices may be present within a region specified by device 410. For example, device 430 may be present within a surrounding area of ​​device 410. In other words, device 430 may be a neighboring UE of device 410.

[0054] In some example embodiments, the radio measurements may indicate reference signal received power (RSRP) measured by the device 430. Alternatively or additionally, the radio measurements may indicate channel state information. In other example embodiments, the radio measurements may indicate a channel response, such as a channel impulse response (CIR). Alternatively or additionally, the radio measurements may indicate one or more of an angle of arrival (AoA), an angle of departure (AoD), a time difference of arrival (TDoA), or a round trip time (RTT).

[0055] The device 420 determines 4015 a configuration of data augmentation for training the positioning model based on the radio measurements. In some example embodiments, the device 420 can determine a data augmentation parameter set based on the radio measurements. For example, the radio measurements or feedback from other devices can be used to estimate an optimal list of data augmentation parameters. By way of example only, the data augmentation parameter set can include one or more parameters related to an interpolation function used to predict the data.

[0056] The device 420 transmits (4020) a data augmentation configuration to the device 410. In some example embodiments, the configuration may include a data augmentation parameter set determined by the device 420. Alternatively, the configuration may include radio measurements from the device 430. For example, if the device 420 determines that the device 430 is present within the surrounding area of ​​the device 410 based on the coarse location of the device 410, the data augmentation configuration may include radio measurements from the device 430. By way of example only, as shown in FIG. 5 , neighborhood measurement data 530 from the device 430 may be provided to the device 410. In some other example embodiments, the data augmentation configuration may include an indication of whether the data for training the positioning model is interpolated or measured. The data augmentation configuration may also indicate the dimensions (e.g., temporal or spatial) of the data for training the positioning model.

[0057] In some example embodiments, device 430 may transmit 4025 the radio measurements directly to device 410. For example, the radio measurements may be transmitted over a sidelink between device 410 and device 430.

[0058] In some example embodiments, the device 420 may proactively transmit the configuration of the data extension, in other words, the transmission of the configuration of the data extension may not be based on a request for assistance.

[0059] In some example embodiments, the data extension configuration may be transmitted aperiodically. FIG. 6A illustrates a signaling diagram for an interaction 600 according to some example embodiments of the present disclosure. As shown in FIG. 6A, the signaling diagram illustrates the interaction 600 between the device 410 and the device 420. The device 420 may monitor performance at the device 410 (6010). If the device 420 detects or observes a performance degradation of the device 410, the device 420 may refine or update the data extension parameter set (6020). For example, the device 420 may refine the data extension parameter set if it observes that the device 410's position uncertainty is systematically high. The device 420 may transmit the data extension configuration to the device 410 (6030). In this case, the data extension configuration may further indicate a validity period for the data extension configuration. For example, this new parameterization may be accompanied by a validity period, i.e., a maximum duration for which the device 420 considers the configuration valid. This validity period can be expressed as the number of subframes for which the configuration is valid, starting from the time the message is received at device 410 .

[0060] In some example embodiments, the data extension configuration may be transmitted periodically. FIG. 6B illustrates a signaling diagram for interaction 601 according to some example embodiments of the present disclosure. As shown in FIG. 6B, the signaling diagram illustrates interaction 601 between device 410 and device 420. Device 420 may refine or update the data extension parameter set (6120). Device 420 may transmit the data extension configuration to device 410 (6130). In this case, the data extension configuration may further indicate a validity period for the data extension configuration. For example, this new parameterization may be accompanied by a validity period, i.e., a maximum duration for which device 420 considers the configuration valid. This validity period may be expressed as the number of subframes for which the configuration is valid, starting from the time the message is received at device 410.

[0061] 4, the device 410 determines 4030 a data extension parameter set based on the configuration of the data extension. For example, in some example embodiments, if the configuration of the data extension includes a data extension parameter set, the device 410 may extract the data extension parameter set from the configuration of the data extension.

[0062] Alternatively, if the data augmentation configuration includes radio measurements from the device 430, the device 410 may determine the data augmentation parameter set based on the radio measurements of the device 430. For example, the device 410 may adjust the data augmentation parameter set based on the radio measurements. In some example embodiments, the data augmentation parameter set (e.g., spatial interpolation parameters) may be adjusted by minimizing an error between the interpolated data and the data reported from the device 430.

[0063] In some example embodiments, as described above, radio measurements may be transmitted over a sidelink between devices 410 and 430. In this case, device 410 may determine a data extension parameter set based on the radio measurements from device 430.

[0064] The device 410 acquires (4035) data for training the positioning model based on the data augmentation procedure and the data augmentation parameter set. In other words, the device 410 can perform data augmentation using the data augmentation configuration from the device 420. For example, the device 410 can select a kriging procedure as the data augmentation procedure. The data augmentation procedure can be any appropriate procedure capable of increasing the amount of data for training. The data for training the positioning model includes measurement data from the device 410 and augmented data generated based on the data augmentation parameter set. Alternatively, the data for training the positioning model can include radio measurement data from the device 430. For example, as shown in FIG. 5, the data for training the positioning model can include measurement data 510, augmented data 520, and radio measurement data 530. The augmented data 520 can be used to estimate a location not measured by the device 410. In this way, the amount of quality training data can be increased.

[0065] The device 410 trains (4040) a positioning model based on a combination of the measured data and the augmented data. For example, the combination of the measured data and the augmented data may indicate that data at a particular location is measured and data at another particular location is interpolated (i.e., augmented). In some example embodiments, the combination of the measured data and the augmented data may be based on weights of the data. For example, the measured data may have a higher weight than the augmented data. In this manner, robust model training is possible. Furthermore, a method for assessing the completeness of the augmented data set by using a probability value for each data point or data segment is also possible. For example, if a large data segment is only predicted, the position estimate may not be used for critical applications based on the associated probability value.

[0066] As just one example of spatial interpolation, the spatial interpolation method Kriging can be used. Kriging assumes that missing values ​​can be estimated using weighted linear combinations of available neighboring values. The calculation of Kriging weights can be based on the relationship between observations expressed through a spatial autocovariance function.

[0067] In some example embodiments, the device 410 may transmit (4045) feedback information to the device 420 indicating its current data enhancement performance. For example, the feedback information may indicate the interpolation accuracy achieved at the device 410. In this case, the device 420 may update (4050) its data enhancement configuration based on the feedback information. For example, the device 420 may expand the area over which it collects radio measurements of neighboring devices. By way of example only, if the achieved interpolation accuracy indicated in the feedback information is below a threshold, the device 420 may expand the area over which it collects radio measurements.

[0068] According to example embodiments of the present disclosure, methods and procedures are proposed targeting data augmentation for AI / ML-based positioning using AI / ML inference performed on the UE side. For example, the present disclosure proposes a method that enables an increase in the amount of raw labeled data: radio measurements and corresponding geographic locations. Additional signaling can ensure efficient data augmentation operation at the UE with network assistance. Regarding data augmentation methods, cases of supervised learning methods performed on the UE side can be considered, either to estimate features useful for estimating localization, such as LOS / NLOS classification, or to directly estimate localization.

[0069] Reference is now made to FIG. 7, which illustrates a signaling diagram for interactions 700 in accordance with certain example embodiments of the present disclosure. As shown in FIG. 7, the signaling diagram illustrates interactions 700 between devices 710, 720, and 730. For illustrative purposes, reference is made to FIG. 1 to describe signaling diagram 700. For example, device 710 may represent or include device 110-1 shown in FIG. 1, and device 730 may represent or include device 110-2 or device 130 shown in FIG. 1. Device 720 may represent or include device 120 shown in FIG. 1. It should be noted that devices 710, 720, and 730 may represent or include any suitable devices.

[0070] The device 710 may report its capabilities to the device 720. For example, the capabilities may include one or more supported models of the device 710. Alternatively or additionally, the capabilities may include memory resources of the device 710. In other example embodiments, the capabilities may include computational capabilities of the device 710. The capabilities may also include computational resources of the device 710. In some example embodiments, the capabilities may be reported via a Long Term Positioning Protocol (LPP) message.

[0071] In some example embodiments, the device 710 can proactively initiate data collection (e.g., Layer 1 (L1) measurements) requests to train a model at the device 710. For example, the device 710 can send a request for assistance for data augmentation to the device 720 (7005). In some example embodiments, the request for assistance can indicate a ratio of available data to a target data size used in training the positioning model. As used herein, the term "positioning model" can refer to a processing model or AIML model used to position the device. In some example embodiments, the request for assistance can be sent along with the capabilities of the device 710. For example, the request for assistance and the reported capabilities can be included in an LPP message.

[0072] In some example embodiments, the request for assistance may include an indication of the percentage of currently available data relative to the target data size for model training. By way of example only, if the request for assistance indicates 75%, the device 420 may understand that 75% of the target data size has been collected and 25% of the target data size still needs to be collected. In this manner, measurement collection and model training may be sped up.

[0073] Alternatively, the request for assistance may not include an indication of the percentage of currently available data, in which case the device 720 may determine the percentage of required data requested from the device 410 based on feedback received at the device 720 regarding data expansion capabilities.

[0074] In some example embodiments, device 710 may send a request for assistance to device 720 when it newly joins device 720's network. Alternatively, device 710 may send a request for assistance to device 720 when it determines that the current dataset does not allow for model training with the required accuracy. In this case, in some example embodiments, the required accuracy may be based on key performance indicators (KPIs) for positioning accuracy. Alternatively or additionally, the required accuracy may be based on one or more intermediate KPIs for an intermediate model used for line-of-sight (LOS) or non-line-of-sight (NLOS) classification. In other example embodiments, the required accuracy may be based on the positioning latency of device 410.

[0075] One or more devices may transmit their radio measurements to the device 720. As shown in FIG. 7, the device 730 transmits (7010) its radio measurements to the device 720. In some example embodiments, the device 430 may be within a surrounding area of ​​the device 410. In other words, the device 430 may be a neighboring UE of the device 410. In some example embodiments, the radio measurements may indicate a reference signal received power (RSRP) measured by the device 730. Additionally or alternatively, the radio measurements may indicate channel state information. In other example embodiments, the radio measurements may indicate a channel response, such as a channel impulse response (CIR). Alternatively or additionally, the radio measurements may indicate one or more of the angle of arrival (AoA), angle of departure (AoD), time difference of arrival (TDoA), or round trip time (RTT).

[0076] Alternatively, device 730 can be a network device associated with device 710. In this case, data collection is performed by network devices (i.e., device 730) participating in the positioning process of device 710, i.e., the serving gNB and neighboring gNBs. The gNBs can specifically collect measurements of an UL sounding reference signal (SRS) (or UL SRS for positioning: SRS-P) emitted by device 710 while device 710 is present at a specified location. The gNBs can then provide such measurements to device 720, and other devices (e.g., device 110-2) can provide device 730 with the location where the SRS was transmitted. In this way, SRS can be applied to DL positioning, UL positioning, and UL+DL positioning.

[0077] The device 720 determines (7015) data for training the positioning model based on the radio measurements. In other words, the device 720 can perform data augmentation. For example, the device 720 can select a Kriging procedure as the data augmentation procedure. Note that the data augmentation procedure can be any appropriate procedure that can increase the amount of data for training. The data for training the positioning model includes measurement data and augmented data of the device 730. Alternatively, the data for training the positioning model can include radio measurement data from the device 730.

[0078] The device 720 transmits 7020 data to the device 710 to train the positioning model. For example, the data can be transmitted in an LPP message.

[0079] The device 710 trains (7025) a positioning model based on a combination of the measured data and the augmented data. For example, the combination of the measured data and the augmented data may indicate that data at a particular location is measured and data at another particular location is interpolated (i.e., augmented). In some example embodiments, the combination of the measured data and the augmented data may be based on weights of the data. For example, the measured data may have a higher weight than the augmented data. In this manner, robust model training is possible. Furthermore, a method for assessing the completeness of the augmented data set by using a probability value for each data point or data segment is also possible. For example, if a large data segment is only predicted, the position estimate may not be used for critical applications based on the associated probability value.

[0080] Example method 8 illustrates a flowchart of an example method 800 that may be implemented in or performed by a first device, according to some example embodiments of the present disclosure. For purposes of explanation, the method 800 will be described from the perspective of the device 110 of FIG.

[0081] At block 810, the first device receives from the second device a data augmentation configuration for training a positioning model at the first device. In some example embodiments, the first device may send an assistance request for data augmentation to the second device. For example, the assistance request may further indicate a ratio of available data to a target data size to be used in training the positioning model. In some example embodiments, the data augmentation configuration may include an indication of whether the data for training the positioning model is interpolated or measured.

[0082] At block 820, the first device determines a data extension parameter set based on the configuration of data extension. In some example embodiments, the configuration of data extension may include a data extension parameter set based on radio measurements of a third device. In this case, the first device may extract the data extension parameter set from the configuration of data extension.

[0083] Alternatively, the data extension configuration may include radio measurements of a third device, in which case the first device may determine the data extension parameter set based on the radio measurements of the third device.

[0084] In another example embodiment, the first device may receive radio measurements of the third device via a sidelink between the first device and the third device, in which case the first device may determine the data extension parameter set based on the radio measurements of the third device.

[0085] At block 830, the first device acquires data based on the data enhancement procedure and the data enhancement parameter set, the data including measurement data and enhanced data of the first device.

[0086] At block 840, the first device trains a positioning model based on a combination of the measured data and the augmented data. For example, the combination of the measured data and the augmented data may indicate that data at a particular location is measured and data at another particular location is interpolated (i.e., augmented). In some example embodiments, the combination of the measured data and the augmented data may be based on weights of the data. For example, the measured data may have a higher weight than the augmented data. In this manner, robust model training is possible. In some example embodiments, the first device may send feedback information to the second device indicating the current data augmentation performance at the first device.

[0087] 9 illustrates a flowchart of an example method 900 that may be implemented in or performed by a second device, according to some example embodiments of the present disclosure. For purposes of explanation, the method 900 will be described from the perspective of the device 120 of FIG.

[0088] At block 910, the second device receives radio measurements from a third device.

[0089] In block 920, the second device determines a data augmentation configuration for training a positioning model at the first device based on the measurements from the third device. In some example embodiments, the data augmentation configuration may further indicate a validity period for the data augmentation configuration.

[0090] At block 930, the second device transmits the data extension configuration to the first device. In some example embodiments, the second device can determine the data extension parameter set based on radio measurements. In this case, the data extension configuration can include the data extension parameter set. Alternatively, the data extension configuration can include radio measurements of a third device.

[0091] In some example embodiments, the second device can receive a request for assistance for data augmentation from the first device. For example, the request for assistance can further indicate a ratio of available data to a target data size to be used in training the positioning model.

[0092] In some example embodiments, the second device can monitor the performance of the first device, and if a degradation in the performance of the first device is detected, the second device can send a configuration for data extension to the first device. Alternatively, the second device can send a configuration for data extension periodically.

[0093] In some example embodiments, the second device may receive feedback information from the first device indicating the current data extension performance of the first device, in which case the second device may update the data extension configuration based on the feedback information.

[0094] 10 illustrates a flowchart of an example method 1000 implemented in or performed by a first device, according to some example embodiments of the present disclosure. For purposes of explanation, the method 1000 will be described from the perspective of the device 110 of FIG.

[0095] The first device sends a request for assistance for data augmentation to the second device at block 1010. In some example embodiments, the request for assistance may further indicate a ratio of available data to a target data size to be used in training the positioning model.

[0096] At block 1020, the first device receives augmented data from the second device for training the positioning model.

[0097] At block 1030, the first device trains a positioning model based on a combination of the augmented data and the measured data. For example, the combination of the measured data and the augmented data may indicate that data at a particular location is measured and data at another particular location is interpolated (i.e., augmented). In some example embodiments, the combination of the measured data and the augmented data may be based on weights of the data. For example, the measured data may have a higher weight than the augmented data. In this manner, robust model training is possible.

[0098] 11 illustrates a flowchart of an example method 1100 that may be implemented in or performed by a second device, according to some example embodiments of the present disclosure. For illustrative purposes, the method 1100 will be described from the perspective of the device 120 of FIG.

[0099] At block 1110, the second device receives a request for assistance for data augmentation from the first device. In some example embodiments, the request for assistance may further indicate a ratio of available data to a target data size to be used in training the positioning model.

[0100] The second device obtains measurement data from the third device at block 1120. In some example embodiments, the measurement data may include at least one of uplink measurement data or downlink measurement data.

[0101] At block 1130, the second device generates augmented data for training a positioning model at the first device based on the measurement data.

[0102] In block 1140, the second device transmits the extended data to the first device.

[0103] Device, equipment and media examples In some example embodiments, a first apparatus capable of performing any of the methods 800 (e.g., device 110 of FIG. 1 ) may include means for performing each operation of the method 800. The means may be implemented in any suitable form. For example, the means may be implemented in a circuit or a software module.

[0104] In one embodiment, a first apparatus includes means for receiving from a second device a data augmentation configuration for training a positioning model in the first device, means for determining a data augmentation parameter set based on the data augmentation configuration, means for acquiring data including measurement data and augmented data of the first device based on the data augmentation procedure and the data augmentation parameter set, and means for training a positioning model based on a combination of the measurement data and the augmented data.

[0105] In some example embodiments, the first apparatus includes means for transmitting a request for assistance for data enhancement to the second device.

[0106] In some example embodiments, the request for assistance further indicates the ratio of available data to a target data size to be used in training the positioning model.

[0107] In some example embodiments, the configuration of data extension includes a data extension parameter set based on radio measurements of the third device. In some example embodiments, the means for determining the data extension parameter set includes means for extracting the data extension parameter set from the configuration of data extension.

[0108] In some example embodiments, the configuration of the data extension includes radio measurements of a third device.

[0109] In some example embodiments, the means for determining the data extension parameter set includes means for determining the data extension parameter set based on radio measurements of the third device.

[0110] In some example embodiments, the first apparatus includes means for receiving radio measurements of the third device from the third device via a sidelink between the first device and the third device, and means for determining a data extension parameter set based on the radio measurements of the third device.

[0111] In some example embodiments, the data augmentation configuration includes an indication as to whether the data for training the positioning model is interpolated or measured.

[0112] In some example embodiments, the first apparatus includes means for transmitting feedback information to the second device indicating current data enhancement capabilities at the first device.

[0113] In some example embodiments, the first device comprises a terminal device and the second device comprises a network device.

[0114] In some example embodiments, a second apparatus (e.g., device 120 of FIG. 1) capable of performing any of the methods 900 may include means for performing each operation of the method 900. The means may be implemented in any suitable form. For example, the means may be implemented in a circuit or a software module.

[0115] In some embodiments, the second apparatus includes means for receiving wireless measurements from a third device; means for determining, based on the measurements from the third device, a configuration of data augmentation for training a positioning model at the first device; and means for transmitting the configuration of data augmentation to the first device.

[0116] In some example embodiments, the second apparatus includes means for determining a data extension parameter set based on radio measurements, and means for transmitting a data extension configuration including the data extension parameter set to the first device.

[0117] In some example embodiments, the configuration of the data extension includes radio measurements of a third device.

[0118] In some example embodiments, the second apparatus includes means for receiving a request for assistance for data enhancement from the first device.

[0119] In some example embodiments, the request for assistance further indicates the ratio of available data to a target data size to be used in training the positioning model.

[0120] In some example embodiments, the means for transmitting the configuration of the data extension includes means for monitoring performance of the first device and means for transmitting the configuration of the data extension to the first device according to a determination that a performance degradation of the first device has been detected.

[0121] In some example embodiments, the means for transmitting the configuration of the data extension includes means for periodically transmitting the configuration of the data extension.

[0122] In some example embodiments, the data extension configuration further indicates a validity period for the data extension configuration.

[0123] In some example embodiments, the second apparatus includes means for receiving feedback information from the first device indicating current data extension performance at the first device, and means for updating the data extension configuration based on the feedback information.

[0124] In some example embodiments, the first device comprises a terminal device, the second device comprises a network device, and the third device comprises another terminal device.

[0125] In some example embodiments, a first apparatus capable of performing any of the methods 1000 (e.g., device 110 of FIG. 1 ) may include means for performing each operation of the method 1000. The means may be implemented in any suitable form. For example, the means may be implemented in a circuit or a software module.

[0126] In some example embodiments, the first apparatus includes means for sending a request for assistance for data augmentation to the second device, means for receiving augmented data from the second device for training the positioning model, and means for training the positioning model based on a combination of the augmented data and the measurement data.

[0127] In some example embodiments, the request for assistance further indicates the ratio of available data to a target data size to be used in training the positioning model.

[0128] In some example embodiments, the first device comprises a terminal device and the second device comprises a network device.

[0129] In some example embodiments, a second apparatus (e.g., device 120 of FIG. 1) capable of performing any of methods 1100 may include means for performing each operation of method 1100. The means may be implemented in any suitable form. For example, the means may be implemented in a circuit or a software module.

[0130] In some embodiments, the second apparatus includes means for receiving an assistance request for data augmentation from the first device, means for acquiring measurement data from the third device, means for generating augmented data for training a positioning model at the first device based on the measurement data, and means for transmitting the augmented data to the first device.

[0131] In some example embodiments, the request for assistance further indicates the ratio of available data to a target data size to be used in training the positioning model.

[0132] In some example embodiments, the measurement data includes at least one of uplink measurement data or downlink measurement data.

[0133] In some example embodiments, the first device comprises a terminal device, the second device comprises a network device, and the third device comprises another terminal device or another network device.

[0134] 12 is a simplified block diagram of a device 1200 suitable for practicing example embodiments of the present disclosure. Device 1200 may be provided to implement a communications device such as device 110 or device 120 shown in FIG. 1. As shown, device 1200 includes one or more processors 1210, one or more memories 1220 coupled to processor 1210, and one or more communications modules 1240 coupled to processor 1210.

[0135] The communications module 1240 is for two-way communication. The communications module 1240 has one or more communications interfaces to facilitate communication with one or more other modules or devices. The communications interface may represent any interface necessary for communication with other network elements. In some example embodiments, the communications module 1240 may include at least one antenna.

[0136] Processor 1210 can be of any type suitable for a local technology network and can include, by way of non-limiting example, one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Device 1200 can include multiple processors, such as application-specific integrated circuit chips that follow a clock that synchronizes the main processor.

[0137] Memory 1220 may include one or more nonvolatile memories and one or more volatile memories. Examples of nonvolatile memory include, but are not limited to, read-only memory (ROM) 1224, electrically programmable read-only memory (EPROM), flash memory, hard disks, compact disks (CDs), digital video disks (DVDs), optical disks, laser disks, and other magnetic and / or optical storage. Examples of volatile memory include, but are not limited to, random access memory (RAM) 1222 and other volatile memory that does not persist during power-down periods.

[0138] The computer program 1230 includes computer-executable instructions that are executed by an associated processor 1210. The instructions of the program 1230 may include instructions for performing operations / acts of some example embodiments of the present disclosure. The program 1230 may be stored in a memory, such as the ROM 1224. The processor 1210 may perform any suitable operations and processes by loading the program 1230 into the RAM 1222.

[0139] The exemplary embodiments of the present disclosure may be implemented by a program 1230 that enables the device 1200 to execute any of the processes of the present disclosure as described with reference to Figures 4 to 11. The exemplary embodiments of the present disclosure may also be implemented by hardware or a combination of software and hardware.

[0140] In some example embodiments, the program 1230 may be tangibly included in a computer-readable medium, which may be included in the device 1200 (such as in memory 1220) or other storage device accessible by the device 1200. The device 1200 may load the program 1230 from the computer-readable medium into RAM 1222 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. The term "non-transitory" as used herein is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation of data storage permanence (e.g., RAM vs. ROM).

[0141] 13 shows an example of a computer readable medium 1300, which may be in the form of a CD, DVD or other optical storage disc. The computer readable medium 1300 stores the program 1230.

[0142] In general, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software executable by a controller, microprocessor, or other computing device. While various aspects of some embodiments of the present disclosure are illustrated and described using block diagrams, flowcharts, or other graphical representations, it should be understood that the blocks, apparatus, systems, techniques, or methods described herein may be implemented in, by way of non-limiting example, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller, or other computing device, or any combination thereof.

[0143] Some example embodiments of the present disclosure also provide at least one computer program product tangibly stored on a computer-readable medium, such as a non-transitory computer-readable medium. The computer program product includes computer-executable instructions for performing any of the methods described above, as included in program modules and executed on a target physical or virtual processor within a device. Generally, program modules include routines, programs, libraries, objects, classes, components, or data structures that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of the program modules may be combined or divided as desired among program modules. The machine-executable instructions for the program modules may be executed in local or distributed devices. In distributed devices, program modules may be located in both local and remote storage media.

[0144] Program code for carrying out the methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that, when executed by the processor or controller, the functions / acts specified in the flowcharts and / or block diagrams are performed. The program code can run 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.

[0145] In the context of the present disclosure, any suitable carrier may carry computer program code or associated data to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals and computer-readable media.

[0146] The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Examples of computer-readable storage media include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0147] Furthermore, while operations are shown in a particular order, it should be understood that such operations do not require that they be performed in the particular order or sequentially shown, or that all of the operations shown be performed, to achieve desirable results. In some situations, multitasking and parallel processing may be advantageous. Similarly, while the above description includes details of specific implementations, these should not be construed as limiting the scope of the disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless otherwise specified, certain features described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, unless otherwise specified, various features described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination.

[0148] Although the present disclosure has been described in terms of particular structural features and / or methodological acts, it is to be understood that the present disclosure as defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. [Explanation of symbols]

[0149] 400 interactions 410~430 devices 4005 Submit a request for assistance 4010 Send radio measurements 4015 Configuration decided 4020 Send configuration 4025 Send radio measurements 4030 Determine data expansion parameter set 4035 Data Augmentation Retrieves data based on a parameter set 4040 Train a positioning model 4045 Send feedback information Update 4050 configuration

Claims

1. a first device, at least one processor; at least one memory for storing instructions; wherein the instructions, when executed by the at least one processor, receiving a data augmentation configuration from a second device for training a positioning model at the first device; determining a data expansion parameter set based on the data expansion configuration; obtaining data including measurement data and augmented data of the first device based on a data augmentation procedure and the data augmentation parameter set; training the positioning model based on a combination of the measurement data and the augmented data; causing the first device to at least execute A first device comprising:

2. The first device sending a request for assistance for the data enhancement to the second device; The first device of claim 1 , wherein the first device executes:

3. the request for assistance further indicates a ratio of available data to a target data size to be used in training the positioning model. The first device of claim 2 .

4. the data expansion configuration includes the data expansion parameter set based on radio measurements of a third device; Determining the data extension parameter set comprises: extracting the data extension parameter set from the data extension configuration; 4. The first device according to claim 1, comprising:

5. the data extension configuration includes radio measurements of a third device; A first device according to any one of claims 1 to 3.

6. Determining the data extension parameter set comprises: determining the data extension parameter set based on the radio measurements of the third device; The first device of claim 5 , comprising:

7. The first device receiving radio measurements of the third device from the third device via a sidelink between the first device and the third device; determining the data extension parameter set based on the radio measurements of the third device; The first device according to claim 1 , further comprising:

8. The data extension configuration is: an indication as to whether the data for training the positioning model is interpolated data or measured data; 8. The first device according to claim 1, comprising:

9. The first device transmitting feedback information to the second device indicating a current data expansion capability at the first device; The first device according to claim 1 , further comprising:

10. the first device comprises a terminal device and the second device comprises a network device; A first device according to any one of claims 1 to 9.

11. a second device, at least one processor; at least one memory for storing instructions; wherein the instructions, when executed by the at least one processor, receiving radio measurements from a third device; determining a configuration of data augmentation for training a positioning model at the first device based on the measurements from the third device; and transmitting the data extension configuration to the first device; causing the second device to at least execute A second device comprising:

12. The second device determining a data extension parameter set based on the radio measurements; transmitting the data extension configuration including the data extension parameter set to the first device; The second device of claim 11 further comprising:

13. the data extension configuration includes the radio measurements of the third device; A second device according to claim 11 or 12.

14. The second device receiving a request for assistance for the data enhancement from the first device; The second device according to claim 11 , further comprising:

15. the request for assistance further indicates a ratio of available data to a target data size to be used in training the positioning model. The second device of claim 14.

16. transmitting the configuration of the data extension, monitoring the performance of the first device; transmitting the data extension configuration to the first device in accordance with a determination that a performance degradation of the first device has been detected; 14. The second device according to claim 11, comprising:

17. transmitting the configuration of the data extension, periodically transmitting said configuration of said data extensions; 14. The second device according to claim 11, comprising:

18. the data extension configuration further indicates a validity period of the data extension configuration; 18. A second device according to claim 16 or 17.

19. The second device receiving feedback information from the first device indicating a current data expansion capability at the first device; updating the data extension configuration based on the feedback information; 19. The second device of claim 11, further comprising:

20. the first device comprises a terminal device, the second device comprises a network device, and the third device comprises another terminal device; A second device according to any one of claims 11 to 19.

21. a first device, at least one processor; at least one memory for storing instructions; wherein the instructions, when executed by the at least one processor, Sending a request for assistance for data enhancement to a second device; receiving augmentation data from the second device for training a positioning model; training the positioning model based on a combination of the augmented data and measurement data; causing the first device to at least execute A first device comprising:

22. the request for assistance further indicates a ratio of available data to a target data size to be used in training the positioning model.

22. The first device of claim 21.

23. the first device comprises a terminal device and the second device comprises a network device; 23. A first device according to claim 21 or 22.

24. a second device, at least one processor; at least one memory for storing instructions; wherein the instructions, when executed by the at least one processor, receiving a request for assistance for data enhancement from a first device; acquiring measurement data from a third device; generating augmented data for training a positioning model at the first device based on the measurement data; transmitting the enhanced data to the first device; causing the second device to at least execute A second device comprising:

25. the request for assistance further indicates a ratio of available data to a target data size to be used in training the positioning model.

25. The second device of claim 24.

26. the measurement data includes at least one of uplink measurement data or downlink measurement data; 26. A second device according to claim 24 or 25.

27. the first device includes a terminal device, the second device includes a network device, and the third device includes another terminal device or another network device; A second device according to any one of claims 24 to 26.

28. receiving, at a first device, from a second device, a configuration of data augmentation for training a positioning model at the first device; determining a data expansion parameter set based on the data expansion configuration; obtaining data including measurement data and augmented data of the first device based on a data augmentation procedure and the data augmentation parameter set; training the positioning model based on a combination of the measurement data and the augmented data; A method comprising:

29. receiving, at the second device, radio measurements from a third device; determining a configuration of data augmentation for training a positioning model at the first device based on the measurements from the third device; and transmitting the data extension configuration to the first device; A method comprising:

30. sending, at the first device, a request for assistance for data enhancement to the second device; receiving augmentation data from the second device for training a positioning model; training the positioning model based on a combination of the augmented data and measurement data; A method comprising:

31. receiving a request for assistance for data enhancement from a first device; acquiring measurement data from a third device; determining augmentation data for training a positioning model at the first device based on the measurement data; and transmitting the enhanced data to the first device; A method comprising:

32. means for receiving from the second device a configuration of data augmentation for training a positioning model at the first device; means for determining a data expansion parameter set based on the data expansion configuration; means for acquiring data including measurement data and extended data of the first device based on a data extension procedure and the data extension parameter set; means for training the positioning model based on a combination of the measurement data and the augmented data; An apparatus comprising:

33. means for receiving radio measurements from a third device; means for determining a configuration of data augmentation for training a positioning model at the first device based on the measurements from the third device; means for transmitting the data extension configuration to the first device; An apparatus comprising:

34. means for transmitting a request for assistance for data enhancement to a second device; means for receiving augmentation data from the second device for training a positioning model; means for training the positioning model based on a combination of the augmented data and measurement data; An apparatus comprising:

35. means for receiving a request for assistance for data enhancement from the first device; means for acquiring measurement data from a third device; means for determining augmentation data for training a positioning model at the first device based on the measurement data; means for transmitting the extended data to the first device; An apparatus comprising:

36. receiving a data augmentation configuration from the second device for training a positioning model at the first device; determining a data expansion parameter set based on the data expansion configuration; obtaining data including measurement data and augmented data of the first device based on a data augmentation procedure and the data augmentation parameter set; training the positioning model based on a combination of the measurement data and the augmented data; 20. A computer-readable medium storing instructions for causing the first device to perform at least the following:

37. receiving radio measurements from a third device; determining a configuration of data augmentation for training a positioning model at the first device based on the measurements from the third device; and transmitting the data extension configuration to the first device; 20. A computer-readable medium storing instructions for causing a second device to execute at least the following:

38. Sending a request for assistance for data enhancement to a second device; receiving augmentation data from the second device for training a positioning model; training the positioning model based on a combination of the augmented data and measurement data; 10. A computer-readable medium storing instructions for causing a first device to at least:

39. receiving a request for assistance for data enhancement from a first device; acquiring measurement data from a third device; generating augmented data for training a positioning model at the first device based on the measurement data; transmitting the enhanced data to the first device; 20. A computer-readable medium storing instructions for causing a second device to execute at least the following:

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