Position label quality indicator

Abstract

Example embodiments of the present disclosure provide a solution for determining a position label quality indicator In an example method, a first device receives, from a second device, a positioning configuration and assistance data for determining a label quality indicator. The assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device. The first device determines the label quality indicator based on the positioning configuration and the assistance data. The first device transmits, to the second device, the determined label quality indicator.

Description

POSITION LABEL QUALITY INDICATORCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from, and the benefit of, US Provisional Application No. 63 / 703474, filed October 4, 2024, which is hereby incorporated by reference in its entirety.FIELD

[0002] Various example embodiments relate to the field of communication, and in particular, to devices, methods, apparatuses, and a computer readable medium for determining a position label quality indicator.BACKGROUND

[0003] A communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network. A mobile or wireless communication network is one example of a communication network.

[0004] Such communication networks operate in accordance with standards, such as those promulgated by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of such standards include the so-called 5G (5th Generation) standard or other standards promulgated by 3GPP.SUMMARY

[0005] In general, example embodiments of the present disclosure provide a solution for determining a position label quality indicator, especially used for model training and / or monitoring.

[0006] In a first aspect, there is provided a first device. The first device comprises at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the first device at least to: receive, from a second device, a positioning configuration and assistance data for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determine the label quality indicator based on the positioning configuration and the assistance data; and transmit, to the second device, the determined label quality indicator.

[0007] In a second aspect, there is provided a second device. The second device comprises at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the second device at least to: determine assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on a positioning configuration and measurements reported by the atleast one third device; transmit, to a first device, the positioning configuration and the assistance data; and receive, from the first device, the determined label quality indicator.

[0008] In a third aspect, there is provided a method. The method comprises: receiving, from a second device, a positioning configuration for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determining the label quality indicator based on the positioning configuration and the assistance data; and transmitting, to the second device, the determined label quality indicator.

[0009] In a fourth aspect, there is provided a method. The method comprises: determining assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by a second device based on a positioning configuration and measurements reported by the at least one third device; transmitting, to a first device, the positioning configuration and the assistance data; and receiving, from the first device, the determined label quality indicator.

[0010] In a fifth aspect, there is provided an apparatus. The apparatus comprises: means for receiving, from a second device, a positioning method configuration for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; means for determining the label quality indicator based on the positioning configuration and the assistance data; and means for transmitting, to the second device, the determined label quality indicator.

[0011] In a sixth aspect, there is provided an apparatus. The apparatus comprises: means for determining assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by a second device based on a positioning configuration and measurements reported by the at least one third device; means for transmitting, to a first device, the positioning configuration and the assistance data; and means for receiving, from the first device, the determined label quality indicator.

[0012] In a seventh aspect, there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least method of the above third aspect or fourth aspect.

[0013] In an eighth aspect, there is provided a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus to perform at least the method of the above third aspect or fourth aspect.

[0014] In a ninth aspect, there is provided a first device. The first device comprises: receiving circuitry configured to receive, from a second device, a positioning configuration and assistance data for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determining circuitry configured to determine the label quality indicator based on the positioning configuration and the assistance data; and transmitting circuitry configured to transmit, to the second device, the determined label quality indicator.

[0015] In a tenth aspect, there is provided a second device. The second device comprises: determining circuitry configured to determine assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on a positioning configuration and measurements reported by the at least one third device; transmitting circuitry configured to transmit, to a first device, the positioning configuration and the assistance data; and receiving circuitry configured to receive, from the first device, the determined label quality indicator.

[0016] It is to be understood that the summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0018] FIG. 1 illustrates an example deployment environment in which embodiments of the present disclosure may be implemented;

[0019] FIG. 2 illustrates an example of a process flow in accordance with some example embodiments of the present disclosure;

[0020] FIG. 3 illustrates an example of a process flow in accordance with some example embodiments of the present disclosure;

[0021] FIG. 4 illustrates an example harsh radio environment;

[0022] FIG. 5 illustrates a performance result in accordance with some example embodiments of thepresent disclosure;

[0023] FIG. 6 illustrates a flowchart of an example method implemented at a first device in accordance with some other embodiments of the present disclosure;

[0024] FIG. 7 illustrates a flowchart of an example method implemented at a second device in accordance with some other embodiments of the present disclosure;

[0025] FIG. 8 illustrates a simplified block diagram of a device that is suitable for implementing some example embodiments of the present disclosure; and

[0026] FIG. 9 illustrates a block diagram of an example of a computer-readable medium in accordance with some example embodiments of the present disclosure.

[0027] Throughout the drawings, the same or similar reference numerals represent the same or similar elements.DETAILED DESCRIPTION

[0028] Principles of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones described below.

[0029] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

[0030] References in the present disclosure to “one embodiment,” “an embodiment,” “an example embodiment,” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0031] It shall be understood that although the terms “first” and “second” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and / or” includes any and all combinations of one or more of the listed terms.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a”, “an” and “the”are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0033] As used in this application, the term “circuitry” may refer to one or more or all of the following:(a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and(b) combinations of hardware circuits and software, such as (as applicable):(i) a combination of analog and / or digital hardware circuit(s) with software / firmware and(ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and(c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example, firmware) for operation, but the software may not be present when it is not needed for operation.

[0034] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0035] As used herein, the term “network”, “communication network” or “data network” refers to a network following any suitable communication standards, such as long term evolution (LTE), LTE-advanced (LTE-A), wideband code division multiple access (WCDMA), high-speed packet access (HSPA), narrow band Internet of things (NB-loT), wireless fidelity (Wi-Fi) and so on. Furthermore, the communications between a terminal device and a network device / element in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the fourth generation (4G), 4.5G, the future fifth generation (5G), IEEE 802.11 communication protocols, and / or any other protocols either currently known or to be developed in the future. Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course alsobe future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.

[0036] As used herein, the term “network device” refers to a node in a communication network via which a terminal device receives services (e.g., positioning services) therefrom. The network device may refer to a core network device or access network device, such as base station (BS) or an access point (AP) or a transmission and reception point (TRP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a NR NB (also referred to as a gNB), a remote radio unit (RRU), a radio header (RH), a remote radio head (RRH), a WiFi device, a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. In the following description, the terms “network device”, “AP device”, “AP” and “access point” may be used interchangeably.

[0037] The term “terminal device” refers to any end device that may be capable of wireless communication. By way of example rather than limitation, a terminal device may also be referred to as a communication device, user equipment (UE), a Subscriber Station (SS), a Portable Subscriber Station, a Mobile Station (MS), a station (STA) or station device, or an Access Terminal (AT). The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA), portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), USB dongles, smart devices, wireless customer-premises equipment (CPE), an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (for example, remote surgery), an industrial device and applications (for example, a robot and / or other wireless devices operating in an industrial and / or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and / or industrial wireless networks, and the like. In the following description, the terms “station”, “station device”, “STA”, “terminal device”, “communication device”, “terminal”, “user equipment” and “UE” may be used interchangeably.

[0038] The network may be implemented according to any proper wireless or wired communication protocol (s), comprising, but not limited to, cellular communication protocols and core network communication protocols of the fourth generation (4G) and the fifth generation (5G) and the like, wireless local network communication protocols such as Institute for Electrical and Electronics Engineers (IEEE) 802.11 and the like, and / or any other protocols currently known or to be developed in the future. Moreover, the communication may utilize any proper wireless communication technology, comprising but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple-Input Multiple- Output (MIMO), Orthogonal Frequency Division Multiple (OFDM), Discrete Fourier Transform spread OFDM(DFT-s-OFDM) and / or any other technologies currently known or to be developed in the future.

[0039] Generally, for training a model for AI / ML based positioning, the collected data samples may include the following components:

[0040] Part A: channel measurement, quality indicator of channel measurement, and time stamp of channel measurement. Part B: ground truth label (or its approximation), quality indicator of label, and time stamp of label. It should be noted that “Part A” and “Part B” terminologies are only for some discussion purpose, and may not be used in general. Contents in Part A and Part B may or may not be generated by different entities. And Part A and / or Part B, and their contents may or may not apply for each case.

[0041] It can be seen that the ‘quality indicator of label’ may be included in part B. However, it is not clear how this quality indicator is generated during data collection process for the labels within ground truth samples.

[0042] Some embodiments of this disclosure propose a label quality indicator relating to data collection for machine learning based (ML-based) UE positioning framework. The goal is to provide the proposed label quality indicator along with the label (for example, it may be the UE location information applied for direct positioning, and it may also be applied for assisted positioning where intermediate feature(s), such as line of sight (LOS) / non line of sight (NLOS) flag or time of arrival (ToA), may be used as the label).

[0043] The label quality indicator may be proposed to be generated based on a positioning precision map. The positioning precision map matches the UE / TRP relative geometry (i.e., relative distance between the UE and a set of surrounding TRPs) to a statistic positioning accuracy. In other words, the label quality indicator may be determined at the UE by matching the UE relative position with TRPs and the UE / TRP relative geometry in the positioning precision map. The positioning precision map may be created and provided to the UE by the network.

[0044] The label quality indicator may also be proposed to be generated based on a non-ML positioning method used for generating the position estimation (i.e., the label).

[0045] In fact, in 5G, new radio (NR) allows positioning to leverage widely spaced antennas within the network, which may improve the geometric diversity of the reference points. A better spread of TRPs ensures more favorable geometric conditions, thereby reducing the positioning error within the positioning precision map.

[0046] In the context of ML-based UE positioning, to train an ML model in a supervised learning manner, the labelled data may be mandatory. Therefore, ground truth true positions are estimated using non-ML positioning methods. However, instead of only estimating the labels (i.e., UE positions), it may be proposed to additionally evaluate the corresponding label quality indicator (e.g., a soft value between 0 and 1 , where 0 is the lower bound quality value and 1 is the higher bound quality value, or also a hard value either 0 or 1).

[0047] FIG. 1 illustrates an example deployment environment 100 in which embodiments of the present disclosure may be implemented. In the example of FIG. 1 , the label for location 1 of UE 1 with a selectednormal non-ML positioning method may have a label quality indicator = 0.9, whereas in location 2 of UE 2, it may have a label quality indicator = 0.5. In fact, this indicator value may be derived from the pre-established positioning precision map for the considered non-ML positioning method and accounting for the relative position of UE to it surrounding TRPs.

[0048] Therefore, the proposed label quality indicator may be used to tune different life cycle management (LCM) related operations (mainly training and / or monitoring) on ML-based UE positioning as follows.

[0049] First, a requirement on data collection may be established by the LMF with an indication of minimum number of samples with label quality indicator higher than a certain preselected threshold to trigger a model training or also for model monitoring (with high reliability). This may be valid for both UE and NW side models.

[0050] Second, data pre-processing may be realized based on the label quality indicator, such as by filtering / removing samples with qualities lower than a preselected threshold before realizing model training.

[0051] In some embodiments of this disclosure, non-ML methods may be used to derive the ground truth labels, which may be the UE location information for direct positioning. The ground truth labels may be derived by LMF based on normal measurements (e.g., PRS or sounding reference signal (SRS) measurements) and employing non-ML positioning methods, such as downlink time difference of arrival (DL- TDOA). The proposal is to associate the label quality indicator with the estimated UE position.

[0052] Generally, some embodiments of the present disclosure propose a solution for determining a position label quality indicator. In this solution, a first device (e.g., a user equipment) receives, from a second device (e.g., a LMF), a positioning configuration and assistance data for determining a label quality indicator. The assistance data is determined based on at least one ground truth position of at least one third device (e.g., a PRU) reported by the at least one third device to the second device, and at least one position of the at least one third device. The at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device. In addition, the first device determines the label quality indicator based on the positioning configuration and the assistance data. Then, the first device transmits, to the second device, the determined label quality indicator. By implementing the example embodiments of the present disclosure, the label quality indicator can be determined.

[0053] For illustrative purposes, principles and example embodiments of the present disclosure will be described below with reference to FIG. 1 to FIG. 10. However, it is to be noted that these embodiments are given to enable the skilled in the art to understand inventive concepts of the present disclosure and implement the solution as proposed herein, and not intended to limit scope of the present application in any way.

[0054] FIG. 2 illustrates an example of a process flow 200 in accordance with some example embodiments of the present disclosure.

[0055] As shown in FIG. 2, at 210, a second device 204 may determine assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device. The atleast one position is estimated by the second device based on a positioning configuration and measurements reported by the at least one third device. Thereafter, at 220, the second device 204 may transmit, to a first device 202, the positioning configuration and the assistance data 222.

[0056] Accordingly, at 224, the first device 202 may receive, from the second device 204, the positioning configuration and the assistance data 222. Thereafter, at 230, the first device 202 may determine the label quality indicator 242 based on the positioning configuration and the assistance data 222. And at 240, the first device 202 may transmit, to the second device 204, the determined label quality indicator 242. Accordingly, at 244, the first device 202 may receive, from the first device 204, the determined label quality indicator 242. In some implementations, the first device 202 comprises a user equipment (UE), the second device comprises 204 a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

[0057] In some implementations, the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

[0058] In some implementations, the label quality indicator is determined based on a positioning accuracy, parameters for determining the label quality indicator from the positioning accuracy, or both of them. In some examples, the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration. Alternatively or additionally, the positioning accuracy is provided from a look-up table. In some implementations, the parameters for determining the label quality indicator comprise a maximum error value.

[0059] Alternatively, in some implementations, the label quality indicator is determined based on a percentile value of the first devices that achieve a target positioning accuracy, and the target positioning accuracy equals to a value for the positioning configuration, and the percentile value is provided from a lookup table as a positioning accuracy or estimated under pre-selected assumptions.

[0060] For example, for the fixed label quality indicator, in general, a reference positioning accuracy may be associated with a given normal positioning method with selected set of parameters. This may be for example evaluated based on 3GPP scenario assumptions. As an example, the label quality indicator is estimated using 90-percentile (90%) error using the formula below to make sure we have a value between 0 and 1 , which may be estimated based on 3GPP evaluations, such as from TR38.859 accounting for the scenario type as well as the selected positioning method and its specific parameters. As an example extracted from Section 6.3.2 “Summary of evaluations for NR carrier phase positioning” in TR38.859, it is listed as follows.For I nF-SH scenario:• (No differential) UL-CPP (Case 1): <1 .Ocm @50% and <1 .0cm @80%.• SD UL-CPP (Case 5): <1 ,0cm @50% and <1 .0cm @80%.• DD DL-CPP (Case 9): <1 .0cm @50% and <1 .0cm @80%.For InF-DH scenario:• (No differential) UL-CPP (Case 2): <1 ,0cm @50% and <1 .0cm @80%.• SD UL-CPP (Case 6): <1 .0cm @50% and 0.974m @80%.• DD DL-CPP (Case 10): <1.0cm @50% and 1.014m @80%.

[0061] Referring back to FIG. 2, in some implementations, the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located. In some implementations, the assistance data comprises a positioning precision map, and the label quality indicator is further determined based on the positioning precision map.

[0062] In some implementations, the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth. In some implementations, the label quality indicator is a quality indicator of a label, and the label is a position of the first device. In this event, the label quality indicator is used for model training and / or monitoring.

[0063] I n some implementations, the positioning precision map matches a set of relative distance between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map. That is, the label quality indicator is further determined the determined statistic positioning accuracy.

[0064] For example, for the variable label quality indicator, it is considered that the label quality indicator may not only depend on the selected positioning method and its related parameters (such as, the number of anchors and the PRS bandwidth), but may also depend on the area where the UE is located. For example, forTDOA method, it is proposed to employ the notion of the positioning precision map. In fact, the positioning precision map is a ‘rough’ measure of the positioning accuracy evaluated offline and accounting for the UE / TRP relative geometry. The label quality indicator may then be evaluated based on a matching between UE relative position with respect to serving TRP (identified through best beam id of each TRP) and pre- established positioning precision map.

[0065] In the above two cases, the fixed reference accuracy information or the variable reference accuracy information may be matched with a specific positioning method and related assumptions, such as the PRS bandwidth. Therefore, the positioning accuracy information may be mapped to the label quality indicator following an analytical formulation or a look up table.

[0066] In one embodiment, the label quality indicator may be calculated as follows:

[0067] where qkis the label quality indicator, ekis the positioning accuracy (e.g., estimated under3GPP assumptions for a given method and configuration, or provided via a pre-specified look-up table), and M is the maximum error value, which allows to bring the final value to a range between 0 and 1 . It should be noted, the above formula (1) is valid for both fixed and variable label quality indicators. For the fixed label quality indicator, ekis fixed, and for the variable label quality indicator, ekis variable.

[0068] In another embodiment, the quality label is calculated as follows.

[0069] For the fixed label quality indicator, in another embodiment, the label quality indicator is calculated as follows:Qfc—Pek= x (2)

[0070] where qkis the label quality indicator determined based on a target percentile pek=x(e.g., 90%) of the positioning accuracy ek(equals to a certain value x, e.g., 1 m as per positioning quality of service (QoS) or desired model performance) achieved by the UE out of all the UEs, and the value of the percentile may be provided from a pre-specified look-up table as estimated under pre-selected assumptions for the positioning configuration. It should be noted, the above formula (2) is also valid for both fixed and variable label quality indicators. For the fixed label quality indicator, ekis fixed, and for the variable label quality indicator, ekis variable.

[0071] For the variable label quality indicator, in one embodiment, the label quality indicator is calculated based on the provided positioning precision map (from LMF). It should be noted that the LMF may set up the positioning precision map with the help of PRU providing true position and allowing to assess the accuracy of non-ML positioning methods.

[0072] For the positioning precision map, the UE is served by an identified TRP and serving beam, and a mapping may be performed between the beam and the positioning precision map region with a given accuracy, which in this example is 2.5 meters (upper bound positioning error value). Thereafter, the label quality indicator is calculated as follows:2.5 0.75

[0073] FIG. 3 illustrates an example of a process flow in accordance with some example embodiments of the present disclosure.

[0074] In FIG. 3, different exchanges between UE 302, LMF 304, and PRUs 306 related to data collection (training data collection) are illustrated.

[0075] At 310, LMF 304 selects the reference non-ML positioning methods (with related configuration, such as PRS bandwidth and number of TRPs) to collect ground truth data (i.e., ‘true positions’).

[0076] At 312, LMF 304 requests PRS measurements for PRUs 306 based on the selected normal positioning method.

[0077] At 314, LMF 304 receives the PRS measurements sent from PRUs 306.

[0078] At 316, LMF 304 estimates the positions of PRUs 306 based on reported PRS measurements andfollowing the selected normal positioning method.

[0079] At 320, LMF 304 request PRUs 306 to report ground truth data (i.e., ‘true positions’).

[0080] At 322, LMF 304 receives the ground truth data (i.e., ‘true positions’) sent from PRUs 306.

[0081] At 330, LMF 304 establishes the positioning precision map (such as, the positioning precision map for TDOA method) based on the ground truth data (i.e., ‘true positions’ of PRUs 306 (received at 322) and corresponding positions estimated with the normal positioning method (estimated at 316).

[0082] At 332, LMF 304 triggers ground truth data collection from UE 302 (with an indication of the normal positioning method). Additionally, at 332, LMF 304 sends assistance data for label quality indicator evaluation (for example, the positioning precision map).

[0083] At 340, UE 302 estimates its position (alternatively, UE 302 sends PRS measurements towards LMF 304 to estimate its position). Based on the shared assistance data, UE 302 evaluates the label quality indicator.

[0084] At 342, UE 302 sends the UE position and related label quality indicator to LMF 304.

[0085] For the experimental setup, the measurement campaigns are performed in a real factory scenario and radio measurements (such as, channel state information (CSI) or channel impulse response (Cl R)) are collected from N locators (LMUs / TRPs). The radio measurements are from the UE (to be localized) to the gNB at multiple locators, in known M locations (i.e., reference points). Different options are available for the UE device, e.g., a signal generator transmitting 5G signals, a normal wireless communication device, and a smartphone.

[0086] FIG. 4 illustrates an example harsh radio environment, where there is proof of concept (PoC) (NLOS / multi-path propagation) due to numerous metallic surfaces (reflectors). In FIG. 4, the black dots refer to M reference points, the filled squares refer to position of the N LMUs, and the non-filled squares indicate the LMU orientation.

[0087] In the following, it is considered an experimental use case in premises which has an “L-shape”, as illustrated in FIG. 4, where the precise location of M reference points is known, the precise location of N locators (LMUs) is known, and the site is densely populated with metallic surfaces / structures, leading to a harsh radio environment (in terms of multi-path propagation). As illustrated in FIG. 4, LMU 1 and LMU 3 are most densely populated with metallic surfaces / structures, LMU 4 is less densely populated with metallic surfaces / structures, and LMU 2 is least densely populated with metallic surfaces / structures.

[0088] It should be noted that, due to the structure of the indoor factory (i.e., non-accessible areas and irregular structure), the N locators are not always in LOS with the UE device to be localized (i.e., non-friendly positioning scenario).

[0089] For the measurement process, the UE device to be localized is placed on a reference point for ~30 seconds. It makes sure that the UE device is transmitting a 5G SRS signal. The N locators receive the transmitted signal, and thus the radio measurements (e.g., CSI) per locator are collected. Then the UE devicemoves to the next reference point and the process is repeated. The measured data is used to create the machine learning datasets including: 1) CSI measurements from the N currently deployed locators, 2) UE ground truth position (i.e., reference point location), and 3) the precise location of the N currently deployed locators.

[0090] FIG. 5 illustrates a performance result in accordance with some example embodiments of the present disclosure. In FIG. 5, generalization with different label qualities is illustrated. It should be noted that curve 530 is the proposed solution (where the performance gains can be attributed to better data quality from the data collection process).

[0091] For the result, it is shown that the proposed data collection solution paves the way towards submeter accuracies in a more realistic and challenging deployment (i.e., harsh radio environment in FIG. 4). The comparison results are illustrated in FIG. 5, where the 2D positioning error (in meter) vs. the cumulative distribution function (CDF) of 2D positioning error is plotted. The achieved localization accuracy is in the submeter accuracy scale.

[0092] It is evaluated that the performance capabilities of the trained model when the data points used satisfy the “fixed label quality indicator” check, which is a way to indicate the label quality. A threshold rejection value is defined to identify potential outlier inputs and analyze the generalization performance in different label qualities.

[0093] In FIG. 5, the performance result of this generalization approach is illustrated. The performance of the case when the threshold rejection on the label quality indicator on the training data is not used provides a low CDF, which saturates around 0.81 @CDF the 2-D error is less than 1 meter (see curve 520 in FIG. 5). When the threshold rejection is applied (i.e., filtering based on the label quality indicator), at the 0.9@CDF the 2-D error is less than 1 meter (see curve 530 in FIG. 5). We approach the target accuracy in many industrial scenarios which is <100 cm error in >90% of the estimates. As a reference, the performance of the legacy (see curve 510 in FIG. 5) is plotted to indicate that AI / ML positioning always provides a better performance. It should be noted that the maximum positioning error now is 1.1 meter: the CDF tail problem is mitigated because it only considers high-quality data (i.e., better data collection process).

[0094] FIG. 6 illustrates a flowchart of an example method 600 implemented at a first device in accordance with some other embodiments of the present disclosure. For ease of understanding, the method 600 will be described from the perspective of the first device 202 with reference to FIG. 2.

[0095] At block 610, the first device 202 may receive, from a second device, a positioning configuration and assistance data for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determine the label quality indicator based on thepositioning configuration and the assistance data; and transmit, to the second device, the determined label quality indicator.

[0096] In some embodiments, the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

[0097] In some embodiments, the label quality indicator is determined based on at least one of the following: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy. In some embodiments, the parameters for determining the label quality indicator comprise a maximum error value.

[0098] Alternatively, in some embodiments, the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value of the percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

[0099] Alternatively, in some embodiments, the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located.

[0100] In some embodiments, the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

[0101] In some embodiments, the positioning precision map matches a set of relative distances between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map.

[0102] In some embodiments, the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth. In some embodiments, the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring. In some embodiments, the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

[0103] FIG. 7 illustrates a flowchart of an example method 700 implemented at a second device inaccordance with some other embodiments of the present disclosure. For ease of understanding, the method 700 will be described from the perspective of the second device 204 with reference to FIG. 2.

[0104] At block 710, the second device 204 may determine assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on a positioning configuration and measurements reported by the at least one third device; transmit, to a first device, the positioning configuration and the assistance data; and receive, from the first device, the determined label quality indicator.

[0105] In some embodiments, the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

[0106] In some embodiments, the label quality indicator is determined based on at least one of the following: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy. In some embodiments, the parameters for determining the label quality indicator comprise a maximum error value.

[0107] Alternatively, in some embodiments, the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value of the percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

[0108] Alternatively, in some embodiments, the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located.

[0109] In some embodiments, the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

[0110] In some embodiments, the positioning precision map matches a set of relative distance between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision. That is, the label quality indicator is further determined from the determined statistic positioning accuracy.

[0111] In some embodiments, the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS)bandwidth. In some embodiments, the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring. In some embodiments, the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

[0112] In some embodiments, an apparatus capable of performing any of the method 600 (for example, the first device 202) may comprise means for performing the respective steps of the method 600. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

[0113] In some embodiments, the apparatus comprises means for receiving, from a second device, a positioning configuration and assistance data for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; means for transmitting, to the terminal device and based on the request, a configuration of the uplink RS resources for transmission of uplink RS with frequency hopping at the terminal device; means for determining the label quality indicator based on the positioning configuration and the assistance data; and means for transmitting, to the second device, the determined label quality indicator.

[0114] In some embodiments, the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

[0115] In some embodiments, the label quality indicator is determined based on at least one of the following: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy. In some embodiments, the parameters for determining the label quality indicator comprise a maximum error value.

[0116] Alternatively, in some embodiments, the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value of the percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

[0117] Alternatively, in some embodiments, the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on thepositioning configuration and an area where the first device is located.

[0118] In some embodiments, the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

[0119] In some embodiments, the positioning precision map matches a set of relative distances between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map.

[0120] In some embodiments, the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth. In some embodiments, the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring. In some embodiments, the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

[0121] In some embodiments, the apparatus further comprises means for performing other steps in some embodiments of the method 600. In some embodiments, the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.

[0122] In some embodiments, an apparatus capable of performing any of the method 700 (for example, the second device 204) may comprise means for performing the respective steps of the method 700. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

[0123] In some embodiments, the apparatus comprises means for determining assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on a positioning configuration and measurements reported by the at least one third device; means for transmitting, to a first device, the positioning configuration and the assistance data; and means for receiving, from the first device, the determined label quality indicator.

[0124] In some embodiments, the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

[0125] In some embodiments, the label quality indicator is determined based on at least one of thefollowing: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy. In some embodiments, the parameters for determining the label quality indicator comprise a maximum error value.

[0126] Alternatively, in some embodiments, the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value of the percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

[0127] Alternatively, in some embodiments, the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located.

[0128] In some embodiments, the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

[0129] In some embodiments, the positioning precision map matches a set of relative distance between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map. That is, the label quality indicator is further determined from the determined statistic positioning accuracy.

[0130] In some embodiments, the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth. In some embodiments, the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring. In some embodiments, the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

[0131] In some embodiments, the apparatus further comprises means for performing other steps in some embodiments of the method 700. In some embodiments, the means comprises at least one processor; and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.

[0132] FIG. 8 illustrates a simplified block diagram of a device 800 that is suitable for implementing some example embodiments of the present disclosure. The device 800 may be provided to implement acommunication device, for example, the first device 202 or the second device 204 as shown in FIG. 2. As shown, the device 800 includes one or more processors 810, one or more memories 820 coupled to the processor 810, and one or more communication modules 840 coupled to the processor 810.

[0133] The communication module 840 is for bidirectional communications. The communication module 840 has at least one antenna to facilitate communication. The communication interface may represent any interface that is necessary for communication with other network elements.

[0134] The processor 810 may be of any type suitable to the local technical network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 800 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

[0135] The memory 820 may include one or more non-volatile memories and one or more volatile memories. Examples of the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 824, an electrically programmable read only memory (EPROM), a flash memory, a hard disk, a compact disc (CD), a digital video disk (DVD), and other magnetic storage and / or optical storage. Examples of the volatile memories include, but are not limited to, a random access memory (RAM) 822 and other volatile memories that will not last in the power-down duration.

[0136] A computer program 830 includes computer executable instructions that are executed by the associated processor 810. The program 830 may be stored in the ROM 824. The processor 810 may perform any suitable actions and processing by loading the program 830 into the RAM 822.

[0137] The embodiments of the present disclosure may be implemented by means of the program 830 so that the device 800 may perform any process of the disclosure as discussed with reference to FIGS. 6 and 7. The embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.

[0138] In some example embodiments, the program 830 may be tangibly contained in a computer- readable medium which may be included in the device 800 (such as in the memory 820) or other storage devices that are accessible by the device 800. The device 800 may load the program 830 from the computer- readable medium to the RAM 822 for execution. The computer-readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like.

[0139] FIG. 9 illustrates a block diagram of an example of a computer-readable medium 600 in accordance with some example embodiments of the present disclosure. The computer-readable medium 900 has the program 830 stored thereon. It is noted that although the computer-readable medium 900 is depicted in form of CD or DVD in FIG. 9, the computer-readable medium 900 may be in any other form suitable for carry or hold the program 830.

[0140] Generally, various embodiments of the present disclosure may be implemented in hardware orspecial 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 which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0141] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computerexecutable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the method 600 or 700 as described above with reference to FIG. 6 or 7. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

[0142] Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0143] In the context of the present disclosure, the computer program codes or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer-readable medium, and the like.

[0144] The computer-readable medium may be a computer-readable signal medium or a computer- readable storage medium. A computer-readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory(EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The term “non- transitory,” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0145] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable subcombination.

[0146] Although the present disclosure has been described in languages specific to structural features and / or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

WHAT IS CLAIMED IS:1 . A first device comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first device at least to: receive, from a second device, a positioning configuration and assistance data for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determine the label quality indicator based on the positioning configuration and the assistance data; and transmit, to the second device, the determined label quality indicator.

2. The first device of claim 1 , wherein the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

3. The first device of claim 2, wherein the label quality indicator is determined based on at least one of the following: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy.

4. The first device of claim 3, wherein the parameters for determining the label quality indicator comprise a maximum error value.

5. The first device of claim 2, wherein the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value ofthe percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

6. The first device of claim 1 , wherein the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located.

7. The first device of claim 6, wherein the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

8. The first device of claim 7, wherein the positioning precision map matches a set of relative distances between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map.

9. The first device of any of claims 2-8, wherein the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth.

10. The first device of any of claims 1 -9, wherein the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring.11 . The first device of any of claims 1-10, wherein the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

12. 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 at least to:determine assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on a positioning configuration and measurements reported by the at least one third device; transmit, to a first device, the positioning configuration and the assistance data; and receive, from the first device, the determined label quality indicator.

13. The second device of claim 12, wherein the label quality indicator comprises a fixed label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method.

14. The second device of claim 13, wherein the label quality indicator is determined based on at least one of the following: a positioning accuracy, and at least one of the following: the positioning accuracy is estimated under pre-selected assumptions for the positioning configuration; or the positioning accuracy is provided from a look-up table, and parameters for determining the label quality indicator from the positioning accuracy.

15. The second device of claim 14, wherein the parameters for determining the label quality indicator comprise a maximum error value.

16. The second device of claim 13, wherein the label quality indicator is determined based on a target percentile of the positioning accuracy achieved by the first devices out of all the first devices, and a value of the percentile is provided from a look-up table as estimated under pre-selected assumptions for the positioning configuration.

17. The second device of claim 12, wherein the label quality indicator comprises a variable label quality indicator, and the positioning configuration comprises a positioning method and one or more parameters of the positioning method, and wherein the variable label quality indicator is determined at least based on the positioning configuration and an area where the first device is located.

18. The second device of claim 17, wherein the assistance data comprises a positioning precision map for the positioning configuration, and the label quality indicator is further determined based on the positioning precision map for the positioning configuration.

19. The second device of claim 18, wherein the positioning precision map matches a set of relative distance between a first device and a set of surrounding transmission and reception points (TRPs) to a statistic positioning accuracy, and the label quality indicator is further determined based on a matching between a relative position of the first device with respect to an identified TRP and a serving beam in the positioning precision map.

20. The second device of any of claims 13-19, wherein the one or more parameters of the positioning method comprise at least one of the following: a number of TRPs, IDs of TRPs, beam IDs; or a positioning reference signal (PRS) bandwidth.21 . The second device of any of claims 12-20, wherein the label quality indicator is a quality indicator of a label, and the label is a position of the first device, and wherein the label quality indicator is used for model training and / or monitoring.

22. The second device of any of claims 12-21 , wherein the first device comprises a user equipment (UE), the second device comprises a location management function (LMF), and the third device comprises a positioning reference unit (PRU).

23. A method comprising: receiving, from a second device, a positioning configuration for determining a label quality indicator, wherein the assistance data is determined based on the following: at least one ground truth position of at least one third device reported by the at least one third device to the second device, and at least one position of the at least one third device, wherein the at least one position is estimated by the second device based on the positioning configuration and measurements reported by the at least one third device; determine the label quality indicator based on the positioning configuration and the assistance data; and transmitting, to the second device, the determined label quality indicator.

24. A method comprising: determining assistance data for determining a label quality indicator based on the following: at least one ground truth position of at least one third device reported by the at least one third device, and at least one position of the at least one third device, wherein the at least one position is estimated by a second device based on a positioning configuration and measurements reported by the at least one third device; transmitting, to a first device, the positioning configuration and the assistance data; and receiving, from the first device, the determined label quality indicator.

25. A computer readable medium comprising program instructions for causing an apparatus to perform at least method of claim 23 or the method of claim 24.

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