Positioning information reporting method and device, terminal equipment, base station, network equipment and storage medium

Abstract

The invention relates to a positioning information reporting method and device, terminal equipment, a base station, network equipment and a computer readable storage medium. The method comprises the following steps: determining first positioning related time information of a target terminal according to a first artificial intelligence / machine learning AI / ML function or model; and sending positioning information to a network device, the positioning information including the first positioning related time information, and the positioning information not including the first LOS / NLOS index. By adopting the method, the LOS / NLOS index reporting method can be provided, so that the positioning related time information can better assist positioning, and the UE positioning precision is improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, terminal equipment, base station, network equipment, and storage medium for reporting location information. Background Technology

[0002] With the development of AI (Artificial Intelligence) and ML (Machine Learning), it has become a trend to use AI / ML functions or models to improve the performance of communication systems, including: using AI / ML functions or modules for assisted positioning.

[0003] When using AI / ML functions or models for assisted localization, the output of the AI / ML functions or models is localization-related measurements, such as localization-related time information and / or LOS / NLOS indicators (Light of Sight / Non-Light of Sight indicator).

[0004] However, there is currently a lack of a reporting method for location-related time information output by LOS / NLOS metrics, AI / ML functions, or models, so that the location-related measurements output by AI / ML functions or models can better assist in localization. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, terminal device, base station, network device, and computer-readable storage medium for reporting location-related time information output by LOS / NLOS indicators, AI / ML functions, or models, so that the location-related measurements output by AI / ML functions or models can better assist in positioning.

[0006] Firstly, this application provides a method for reporting location information, including:

[0007] Determine the first location-related time information of the target terminal based on the functions or models of the first artificial intelligence / machine learning (AI / ML).

[0008] Send location information to network devices. The location information includes first location-related time information, but does not include first LOS / NLOS metrics.

[0009] In one embodiment, the method further includes sending timing quality indication information to a network device, the timing quality indication information being used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

[0010] In one embodiment, the method further includes:

[0011] Send a second LOS / NLOS metric to the network device, wherein the second LOS / NLOS metric is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS metric is a predefined value, the predefined value being 1 or a fixed value.

[0012] In one embodiment, the method further includes:

[0013] The second positioning-related time information of the target terminal is measured; if the first LOS / NLOS index and the preset threshold value meet the preset relationship, the first positioning-related time information is sent to the network device; if the first LOS / NLOS index and the preset threshold value do not meet the preset relationship, the second positioning-related time information is sent to the network device.

[0014] In one embodiment, the method further includes:

[0015] While sending the first location-related time information or the second location-related time information to the network device, the system also sends the first LOS / NLOS indicator to the network device.

[0016] In one embodiment, the method further includes:

[0017] The second positioning-related time information of the target terminal is measured; the second positioning-related time information and the first LOS / NLOS index are sent to the network device; the first positioning-related time information and the second LOS / NLOS index are sent to the network device.

[0018] In one embodiment, the method includes:

[0019] The second location-related time information of the target terminal is measured; the third LOS / NLOS index of the target terminal is determined through the function or model of the second AI / ML; and the second location-related time information and the third LOS / NLOS index are sent to the network device.

[0020] In one embodiment, the method is applied to a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the method is applied to a base station or a Transmitter-Receiver Node (TRP), and the first positioning-related time information includes the uplink relative arrival time.

[0021] Secondly, this application provides a method for reporting location information, the method including:

[0022] The receiver sends location information, which includes first location-related time information and does not include first LOS / NLOS indicators. The first location-related time information is the location-related time information of the target terminal determined according to the function or model of first artificial intelligence / machine learning (AI / ML).

[0023] In one embodiment, the method further includes:

[0024] The system receives timing quality indication information sent by the transmitting end. The timing quality indication information is used to indicate the inference quality of the first positioning-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0025] In one embodiment, the method further includes:

[0026] The receiver sends a second LOS / NLOS index, wherein the second LOS / NLOS index is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

[0027] In one embodiment, the method further includes:

[0028] The system receives first positioning-related time information sent by the transmitting end, wherein the first LOS / NLOS index satisfies a preset relationship with a preset threshold value; or, the system receives second positioning-related time information sent by the transmitting end, wherein the first LOS / NLOS index does not satisfy a preset relationship with a preset threshold value, and the second positioning-related time information is the positioning-related time information of the target terminal obtained by the transmitting end through measurement.

[0029] In one embodiment, the method further includes:

[0030] While receiving the first or second location-related time information sent by the sending end, it also receives the first LOS / NLOS indicator sent by the sending end.

[0031] In one embodiment, the method further includes:

[0032] The system receives the second location-related time information and the first LOS / NLOS index sent by the transmitting end. The second location-related time information is the location-related time information of the target terminal obtained by the transmitting end through measurement. The system also receives the first location-related time information and the second LOS / NLOS index sent by the transmitting end.

[0033] In one embodiment, the method further includes:

[0034] The receiver sends a second location-related time information and a third LOS / NLOS index. The second location-related time information is the location-related time information of the target terminal obtained by the receiver through measurement, and the third LOS / NLOS index is the LOS / NLOS index of the target terminal determined by the receiver through the function or model of the second AI / ML.

[0035] In one embodiment, the transmitting end is a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the transmitting end is a base station or a transmitting and receiving node, and the first positioning-related time information includes the uplink relative arrival time.

[0036] Thirdly, this application also provides a location information reporting device, comprising:

[0037] The first determining module is used to determine the first positioning-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML).

[0038] The first sending module is used to send location information to the network device. The location information includes first location-related time information, but does not include the first LOS / NLOS metric.

[0039] In one embodiment, the device further includes:

[0040] The second sending module is used to send timing quality indication information to the network device. The timing quality indication information is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

[0041] In one embodiment, the device further includes:

[0042] The third sending module is used to send a second LOS / NLOS index to the network device. The second LOS / NLOS index is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

[0043] In one embodiment, the device further includes:

[0044] The first measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0045] The fourth sending module is used to send first positioning-related time information to the network device if the first LOS / NLOS indicator meets the preset relationship with the preset threshold value; and to send second positioning-related time information to the network device if the first LOS / NLOS indicator does not meet the preset relationship with the preset threshold value.

[0046] In one embodiment, the fourth sending module is further configured to:

[0047] While sending the first location-related time information or the second location-related time information to the network device, the system also sends the first LOS / NLOS indicator to the network device.

[0048] In one embodiment, the device further includes:

[0049] The second measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0050] The fifth sending module is used to send the second positioning-related time information and the first LOS / NLOS index to the network device; and to send the first positioning-related time information and the second LOS / NLOS index to the network device.

[0051] In one embodiment, the device further includes:

[0052] The third measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0053] The second determination module is used to determine the third LOS / NLOS metric of the target terminal through the functions or models of the second AI / ML;

[0054] The sixth sending module is used to send the second positioning-related time information and the third LOS / NLOS indicator to the network device.

[0055] In one embodiment, the device is applied to a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the device is applied to a base station or a Transmitter-Receiver Node (TRP), and the first positioning-related time information includes the uplink relative arrival time.

[0056] Fourthly, this application also provides a device for reporting location information, including:

[0057] The first receiving module is used to receive the first location-related time information sent by the sending end. The first location-related time information is the location-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning (AI / ML).

[0058] In one embodiment, the device further includes:

[0059] The second receiving module is used to receive timing quality indication information sent by the sending end. The timing quality indication information is used to indicate the inference quality of the first positioning-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0060] In one embodiment, the device further includes:

[0061] The third receiving module is used to receive the second LOS / NLOS index sent by the sending end. The second LOS / NLOS index is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

[0062] In one embodiment, the device further includes:

[0063] The fourth receiving module is used to receive first positioning-related time information sent by the sending end, wherein the first LOS / NLOS index satisfies a preset relationship with a preset threshold value; or, to receive second positioning-related time information sent by the sending end, wherein the first LOS / NLOS index does not satisfy a preset relationship with the preset threshold value, and the second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement.

[0064] In one embodiment, the fourth receiving module is further configured to:

[0065] While receiving the first or second location-related time information sent by the sending end, it also receives the first LOS / NLOS indicator sent by the sending end.

[0066] In one embodiment, the device further includes:

[0067] The fifth receiving module is used to receive the second positioning-related time information and the first LOS / NLOS index sent by the sending end. The second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement. The module also receives the first positioning-related time information and the second LOS / NLOS index sent by the sending end.

[0068] In one embodiment, the device further includes:

[0069] The sixth receiving module is used to receive the second positioning-related time information and the third LOS / NLOS index sent by the sending end. The second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement, and the third LOS / NLOS index is the LOS / NLOS index of the target terminal determined by the sending end through the function or model of the second AI / ML.

[0070] In one embodiment, the transmitting end is a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the transmitting end is a base station or a transmitting and receiving node, and the first positioning-related time information includes the uplink relative arrival time.

[0071] Fifthly, a terminal device is provided, including: a memory, a transceiver, and a processor.

[0072] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and execute any of the location information reporting methods provided in the first aspect.

[0073] Sixthly, a base station is provided, comprising: a memory, a transceiver, and a processor.

[0074] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and execute any of the location information reporting methods provided in the first aspect.

[0075] In a seventh aspect, a network device is provided, comprising: a memory, a transceiver, and a processor.

[0076] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer programs in the memory and execute any of the location information reporting methods provided in the second aspect.

[0077] Eighthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-mentioned methods for reporting location information.

[0078] Ninthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for reporting any location information.

[0079] In a tenth aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of a method for reporting any location information.

[0080] The aforementioned location information reporting method, apparatus, terminal device, base station, network device, and computer-readable storage medium, when determining the first location-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML), the terminal, base station, or TRP only sends the first location-related time information to the network device and does not send the first LOS / NLOS index to the network device. This avoids interference caused by the lack of correlation between the first LOS / NLOS index and the first location-related time information when the network device calculates the terminal location based on the first location-related time information, which can greatly improve the positioning accuracy of the terminal, avoid unnecessary reporting overhead, and save network resources. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1a This is a schematic diagram of the communication path in one embodiment;

[0083] Figure 1b This is a schematic diagram of the communication path in another embodiment;

[0084] Figure 2a This is a schematic diagram illustrating the use of AI / ML methods to infer LOS / NLOS metrics in one embodiment.

[0085] Figure 2b This is a schematic diagram illustrating the use of AI / ML methods to infer location-related time measurements in another embodiment.

[0086] Figure 3 This is a flowchart illustrating a method for reporting location information in one embodiment;

[0087] Figure 4 This is a schematic diagram of signaling interaction when the transmitting end does not report LOS / NLOS indicators in one embodiment.

[0088] Figure 5 This is a schematic diagram of the signaling interaction when the transmitting end reports timing quality indication information in one embodiment;

[0089] Figure 6 This is a schematic diagram of the signaling interaction when the transmitting end reports LOS / NLOS indicators in one embodiment;

[0090] Figure 7 This is a schematic diagram of signaling interaction when the sending end selects to report location-related time information in one embodiment;

[0091] Figure 8 This is a schematic diagram of signaling interaction when the transmitting end reports two location-related time information in one embodiment;

[0092] Figure 9 This is a schematic diagram of signaling interaction when the transmitting end reports the LOS / NLOS index determined by the AI ​​method in one embodiment;

[0093] Figure 10 This is a schematic diagram of a location information reporting system in an example.

[0094] Figure 11 This is a structural block diagram of a location information reporting device in one embodiment;

[0095] Figure 12 This is a structural block diagram of a location information reporting device in another embodiment;

[0096] Figure 13 This is an internal structure diagram of a terminal device in one embodiment;

[0097] Figure 14 This is a diagram of the internal structure of a base station in one embodiment;

[0098] Figure 15 This is a diagram of the internal structure of a network device in one embodiment. Detailed Implementation

[0099] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0100] The LOS / NLOS metric indicates the likelihood of a channel condition being either line-of-sight (LOS) or line-of-sight (NLOS). The closer the current channel condition is to LOS, the closer the soft value of the LOS / NLOS metric is to 1, or the more likely the hard value is to report 1. For example, refer to... Figure 1a As shown, if there are no obstacles obstructing the line of sight between the base station or TRP (Transmission Reception Point) and the UE (User Equipment / Terminal), then if the UE reports a soft value for the LOS / NLOS metric, it is likely to be closer to 1; if it reports a hard value, it is likely to report 1. (Refer to...) Figure 1b As shown, if there is an obstacle blocking the line of sight between the base station or TRP and the UE, the UE may report a soft value for the LOS / NLOS index, which is likely to be closer to 0. If it reports a hard value, it is likely to report 0.

[0101] In the context of non-AI / ML assisted positioning technologies, taking the measurement of positioning-related quantities on the UE side as an example, the positioning-related quantities measured by the UE may include LOS / NLOS indicators and DL RSTD (Downline Reference Signal Timing Difference); taking the measurement of positioning-related quantities on the base station or TRP side as an example, the positioning-related quantities measured by the base station or TRP may include LOS / NLOS indicators and ULRTOA (Upline Relative Time of Arrival).

[0102] There is a certain correlation between the DL RSTD obtained by the UE from receiving / measuring a certain PRS (Positioning Reference Signal) and the LOS / NLOS index, and between the UL RTOA obtained by the base station or TRP from receiving / measuring a certain SRS-pos (Sounding Reference Signal – positioning) and the LOS / NLOS index.

[0103] Taking the measurement of positioning-related quantities on the UE side as an example: the larger the LOS / NLOS index value, the closer the DLRSTD measured by the UE may be to or directly equal to the propagation time of electromagnetic waves on the direct path (LOS path) between the base station or TRP and the UE; the smaller the LOS / NLOS index value, the greater the DLRSTD measured by the UE may be than the propagation time of electromagnetic waves on the direct path between the base station or TRP and the UE, because the PRS received / measured by the UE is propagated through reflection, diffraction and other propagation methods, and is not propagated in a straight line.

[0104] Similarly, taking the measurement of positioning-related quantities on the base station or TRP side as an example, the larger the LOS / NLOS index value, the closer the UL RTOA measured by the base station or TRP may be to or directly equal to the propagation time of electromagnetic waves on the direct path (LOS path) between the base station or TRP and the UE; the smaller the LOS / NLOS index value, the larger the UL RTOA measured by the base station or TRP may be than the propagation time of electromagnetic waves on the direct path between the base station or TRP and the UE. This is because the SRS-pos received / measured by the base station or TRP is propagated through reflection, diffraction, and other means, and is not propagated in a straight line.

[0105] Continuing with the example of UE measurement reporting, in AI / ML-based assisted positioning, the following different situations exist: (Refer to...) Figure 2a As shown, the AI / ML function or model output of the UE is the LOS / NLOS index, that is, using AI / ML methods to infer the LOS / NLOS probability of the measured channel; while referring to Figure 2b As shown, the UE's AI / ML function or model outputs positioning-related time measurements, such as DL RSTD, or the time information required to calculate DL RSTD. Alternatively, the UE's AI / ML function or model can simultaneously output LOS / NLOS metrics and positioning-related time measurements.

[0106] Similarly, continuing with the example of measurement reporting via base stations or TRPs, in AI / ML-based assisted positioning, the following different situations exist: (Refer to...) Figure 2a As shown, the AI / ML function or model output of the base station or TRP is the LOS / NLOS index, that is, using AI / ML methods to infer the LOS / NLOS probability of the measured channel; while referring to Figure 2b As shown, the AI / ML function or model of the base station or TRP outputs location-related time measurements, such as UL RTOA, or the time information required to calculate UL RTOA. Alternatively, the AI / ML function or model of the base station or TRP can simultaneously output LOS / NLOS metrics and location-related time measurements.

[0107] However, when the training objective of AI / ML functions or models is to obtain more accurate LOS / NLOS metrics, the LOS / NLOS metrics measured based on AI / ML have the same meaning as the LOS / NLOS metrics estimated by non-AI / ML methods. That is, at this time, the LOS / NLOS metrics based on AI / ML and the DL RSTD or UL RTOA estimated by non-AI / ML methods still have the original relationship.

[0108] When the training objective of an AI / ML function or model is to overcome the signal arrival time delay error caused by the NLOS path, the output of the AI / ML function or model can be considered as the "time measurement corresponding to the virtual LOS path," where the virtual LOS path is... Figure 2b The path is shown by the dashed line. At this point, the LOS / NLOS index estimated by non-AI / ML methods indicates the actual NLOS path, meaning that the LOS / NLOS index does not have the original correlation with the DL RSTD or UL RTOA estimated by AI / ML methods.

[0109] If the relationship between LOS / NLOS metrics and location-related time measurements no longer holds, and the traditional LOS / NLOS metric reporting method is still used when reporting location-related time information obtained from AI / ML functions or models, it will mislead network devices in calculating the UE's location, leading to a reduction in UE positioning accuracy.

[0110] This application provides a method and apparatus for reporting location information, and a method for reporting location-related time measurements and LOS / NLOS indices. This method avoids reporting LOS / NLOS indices that are unrelated to location-related time measurements, thus preventing interference with UE positioning accuracy and improving UE positioning accuracy. The method and apparatus are based on the same concept, and since the principles underlying the problems solved are similar, their implementations can be referred to interchangeably; repeated details will not be elaborated further.

[0111] The technical solutions provided in this application can be applied to a variety of systems. For example, applicable systems may include Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE Time Division Duplex (TDD) systems, Long Term Evolution Advanced (LTE-A) systems, Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) systems, 5G New Radio (NR) systems and their evolved communication systems, and 6G (sixth generation mobile communication technology) systems. These systems may include terminal equipment and network equipment. The systems may also include a core network component, such as an Evolved Packet Core (EPC) or a 5G core network (5GC).

[0112] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can be USB storage devices, other personal computer memory devices, and dongles. They can also communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples of such devices include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), personal computers, tablets, and Machine-type Communication (MTC) terminal devices. Wireless terminal devices can also be referred to as systems, subscriber units, subscriber stations, mobile stations, mobile terminals, remote stations, access points, remote terminals, access terminals, user terminals, user agents, user devices, and wireless access devices and routers / modems that meet the limitations of this definition; however, this application does not limit the scope of the embodiments described.

[0113] The network device involved in the embodiments of this application can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, the base station may also be called an access point, or a device in the access network that communicates with wireless terminal devices through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network device involved in the embodiments of this application may be an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, or a Home evolved Node B (HeNB), relay node, femto, pico, network testing equipment, etc., and is not limited in the embodiments of this application. In some network architectures, network devices may include centralized unit (CU) nodes and distributed unit (DU) nodes, which may also be geographically separated.

[0114] In this application embodiment, the terminal device sending relevant information or similar descriptions to the network device only indicates that the terminal device sends relevant information via wireless signals, and the destination recipient is the network device, which can obtain the relevant information by receiving the wireless signals.

[0115] In one exemplary embodiment, the transmitting end (UE, base station, or TRP, hereinafter not described) only reports the first positioning-related time information of the target terminal determined according to the AI / ML function or model, and does not report LOS / NLOS indicators. For example, Figure 3 As shown, a method for reporting location information is provided, which can be applied to... Figure 1a Taking a UE, base station, or TRP as an example, the explanation includes the following steps 301 to 302. Wherein:

[0116] Step 301: Determine the first location-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML);

[0117] Step 302: Send location information to the network device. The location information includes first location-related time information, but does not include the first LOS / NLOS metric.

[0118] In this embodiment, to distinguish between the location-related time information determined by AI / ML functions or models and the location-related time information measured by non-AI / ML methods, the location-related time information determined by AI / ML functions or models is referred to as the first location time-related information, and the location-related time information measured by non-AI / ML methods is referred to as the second location time-related information. In this embodiment, the AI / ML function or model used to determine the first location time-related information is referred to as the first AI / ML function or model, and the AI / ML function or model used to determine the LOS / NLOS index is referred to as the second AI / ML function or model. The first AI / ML function or model and the second AI / ML function or model can be the same or different, and this embodiment does not specifically limit this.

[0119] In one example, let's consider an application to a UE. The UE receives a PRS signal and measures channel information based on the PRS signal. The channel information may include information such as channel impulse response, channel frequency domain estimation, signal strength, time delay, and signal quality. The UE provides the channel information as input to the first AI / ML function or model at the UE. The output of the first AI / ML function or model includes the UE's (target terminal's) first positioning-related time information. The first positioning-related time information may include downlink reference signal time difference (DL RSTD), or related data used to calculate DL RSTD. Alternatively, the output of the first AI / ML function or model may be information used to calculate the first positioning-related time information, such as Time of Arrival (TOA) information.

[0120] In another example, let's consider an application to a base station or TRP. The base station or TRP receives / measures the SRS-pos sent by the UE to obtain channel information. This channel information may include channel impulse response, channel frequency domain estimation, signal strength, time delay, signal quality, etc. The base station or TRP provides this channel information as input to the first AI / ML function or model. The output of the first AI / ML function or model includes the UE's first positioning-related time information. This first positioning-related time information may include uplink relative time of arrival (UL RTOA), or relevant data used to calculate UL RTOA. Alternatively, the output of the first AI / ML function or model may be information used to calculate the first positioning-related time information, such as time of arrival (TOA) information.

[0121] After determining the first location-related time information of the target terminal through the first AI / ML function or model, the first location-related time information can be sent to the network device providing location-related services, without sending the first LOS / NLOS index measured by non-AI / ML methods to the network device. For example, refer to... Figure 4 As shown:

[0122] Step 4.1: The sending end can determine the first location-related time information through AI / ML functions or models;

[0123] Step 4.2: The sending end can report the first location-related time information to the network device, but does not report the first LOS / NLOS index.

[0124] Correspondingly, the network device can receive location information sent by the transmitter. This location information includes first location-related time information but does not include a first LOS / NLOS metric. The first location-related time information is the location-related time information of the target terminal determined based on the function or model of the first artificial intelligence / machine learning (AI / ML). In this way, the network device can perform location calculations for the target terminal based on the first location-related time information. Since the first LOS / NLOS metric estimated by a non-AI / ML method is not received at this time, one possibility is that the network device can default the first LOS / NLOS metric to 1. That is, it defaults to a LOS path or virtual LOS path between the UE and the TRP / base station. This avoids interference caused by the lack of correlation between the first LOS / NLOS metric and the first location-related time information, and can greatly improve the positioning accuracy of the terminal.

[0125] By employing the location information reporting method provided in this application embodiment, when the first location-related time information of the target terminal is determined based on the function or model of the first artificial intelligence / machine learning (AI / ML), the terminal, base station, or TRP only sends the first location-related time information to the network device and does not send the first LOS / NLOS index to the network device. This avoids interference caused by the lack of correlation between the first LOS / NLOS index and the first location-related time information when the network device calculates the terminal location based on the first location-related time information. This can greatly improve the positioning accuracy of the terminal and avoid unnecessary reporting overhead, thus saving network resources.

[0126] In an exemplary embodiment, the method for reporting the location information described above may further include:

[0127] A timing quality indication message is sent to the network device. The timing quality indication message is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

[0128] In this embodiment, the transmitting end (UE, base station, or TRP) only sends the first location-related time information to the network device and does not send the first LOS / NLOS index to the network device. However, it can report information such as the inference quality of the first location-related time information, the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path to the network device through timing quality indication information (nr-TimingQuality).

[0129] The similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path can be determined by the first LOS / NLOS index. For example, if the value of the first LOS / NLOS index is larger, it means that the location is closer to the LOS path, and the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path is higher. Conversely, if the value of the first LOS / NLOS index is smaller, it means that the location is closer to the NLOS path, and the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path is lower.

[0130] Alternatively, the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path can be further determined by combining the current communication quality and channel quality. For example, if the current network quality, communication quality, and channel quality are good, the accuracy of the first LOS / NLOS index is good. In this case, if the value of the first LOS / NLOS index is larger, it indicates that the location is closer to the LOS path, and the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path is higher. Conversely, if the value of the first LOS / NLOS index is smaller, it indicates that the location is closer to the NLOS path, and the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path is lower. Otherwise, if the current network quality, communication quality, and channel quality are poor, the accuracy of the first LOS / NLOS index is poor, and regardless of the value of the first LOS / NLOS index, the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path is low.

[0131] In one exemplary embodiment, reference is made to Figure 5 As shown:

[0132] Step 5.1: The sending end can determine the first location-related time information through AI / ML functions or models;

[0133] Step 5.2: The sending end constructs timing quality indication information based on inference quality, confidence level, or similarity (the specific process can be referred to the relevant description in the foregoing embodiments, and will not be repeated here in this embodiment). It should be noted that this embodiment does not specify the execution order of steps 5.1 and 5.2. Step 5.2 can be executed first and then step 5.1, or steps 5.1 and 5.2 can be performed simultaneously.

[0134] Step 5.3: The sending end reports the first positioning-related time information and timing quality indication information to the network device, but does not report the first LOS / NLOS index.

[0135] Correspondingly, the network device can receive timing quality indication information sent by the transmitter. The timing quality indication information is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

[0136] Using the location information reporting method provided in this embodiment, the sending end sends first location-related time information and timing quality indication information to the network device to indicate the inference quality of the first location-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path. The network device can combine the inference quality of the first location-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path with the first location-related time information to calculate the UE's location. This can reduce the calculation complexity, for example, by reducing the calculation weight of low-quality or low-confidence first location-related time information, or ignoring them during calculation, thereby greatly improving the UE's positioning accuracy and positioning speed.

[0137] In an exemplary embodiment, the transmitting end may report LOS / NLOS metrics simultaneously with the first location-related time information of the target terminal determined by AI / ML functions or models. The method may further include:

[0138] Send a second LOS / NLOS metric to the network device; the network device can receive the second LOS / NLOS metric sent by the sender, wherein the second LOS / NLOS metric is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path.

[0139] In this embodiment, when the sending end sends the first location-related time information to the network device, it can also report to the network device the inference quality of the first location-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the LOS path (or it can also be a virtual LOS path) through the second LOS / NLOS metric. The method for determining the similarity is as described in the foregoing embodiments, and will not be repeated here. For example, refer to... Figure 6 As shown:

[0140] Step 6.1: The sending end determines the first location-related time information through AI / ML functions or models;

[0141] Step 6.2a: The sending end can construct a second LOS / NLOS index based on inference quality, confidence, or similarity (the specific process can be referred to the relevant description in the foregoing embodiments, and will not be repeated here in the embodiments of this application).

[0142] Step 6.3: The sending end can report the first location-related time information and the second LOS / NLOS indicator to the network device, but does not report the first LOS / NLOS indicator.

[0143] Using the location information reporting method provided in this embodiment, the sending end sends first location-related time information and a second LOS / NLOS index to the network device to indicate the inference quality of the first location-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the LOS path. The network device can combine the inference quality of the first location-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first location-related time information and the location-related time information corresponding to the LOS path with the first location-related time information to calculate the UE's location. This can reduce the calculation complexity, for example, by reducing the calculation weight of low-quality or low-confidence first location-related time information, or ignoring them during calculation, thereby greatly improving the UE's positioning accuracy and positioning speed.

[0144] In another example, the above method may further include: the sending end sending a second LOS / NLOS indicator to the network device; the network device receiving the second LOS / NLOS indicator, wherein the value of the second LOS / NLOS indicator is a predefined value, which is 1 or a fixed value.

[0145] In this embodiment, when the value carried in the second LOS / NLOS indicator is 1, the network device can determine that the current path is a LOS path through the second LOS / NLOS indicator; or when the value carried in the second LOS / NLOS indicator is a fixed value, the network device can determine that the current path is a virtual LOS path through the second LOS / NLOS indicator. Specifically, the soft value in the first LOS / NLOS indicator indicates the probability of the channel condition being Line of Sight (LOS) / Non-Line of Sight (NLOS), while the soft value in the second LOS / NLOS indicator is a fixed value. The fixed value can include a fixed soft value, a fixed hard value, or other values. The fixed soft value is a preset fixed value between 0 and 1, used to indicate that the channel condition is a virtual LOS path; the fixed hard value is a preset 0 or a preset 1, also used to indicate that the channel condition is a virtual LOS path; and other values ​​are preset other values ​​or symbols, also used to indicate that the channel condition is a virtual LOS path. For example, refer to... Figure 6 As shown:

[0146] Step 6.1: The sending end determines the first location-related time information through AI / ML functions or models;

[0147] Step 6.2b: The sending end can construct a second LOS / NLOS metric based on a predefined value (the specific process can be referred to the relevant description in the foregoing embodiments, and will not be repeated here in the embodiments of this application).

[0148] Step 6.3: The sending end can report the first location-related time information and the second LOS / NLOS indicator to the network device, but does not report the first LOS / NLOS indicator.

[0149] The location information reporting method provided in this embodiment sends first location-related time information and a second LOS / NLOS index to the network device to indicate that the current path is a LOS path or a virtual LOS path. The network device can combine the second LOS / NLOS index and the first location-related time information to calculate the UE's location, which can reduce the calculation complexity, greatly improve the UE's positioning accuracy and positioning speed, and can make the most of the existing reporting parameters, thus achieving low complexity.

[0150] In one exemplary embodiment, the sender may report a LOS / NLOS metric and location-related time information to the network device. The method further includes:

[0151] The transmitting end measures and obtains the second positioning-related time information of the target terminal;

[0152] If the first LOS / NLOS metric satisfies a preset relationship with a preset threshold, the sending end sends the first location-related time information to the network device; the network device receives the first location-related time information sent by the sending end; or...

[0153] If the first LOS / NLOS metric does not meet the preset relationship with the preset threshold, the sending end sends the second location-related time information to the network device, and the network device receives the second location-related time information sent by the sending end.

[0154] In this embodiment, if the first LOS / NLOS index is less than a preset threshold value, it can be determined that the first LOS / NLOS index and the preset threshold value satisfy a preset relationship; conversely, if the first LOS / NLOS index is greater than or equal to the preset threshold value, then the first LOS / NLOS index and the preset threshold value do not satisfy the preset relationship. Alternatively, if the first LOS / NLOS index is less than or equal to the preset threshold value, it can be determined that the first LOS / NLOS index and the preset threshold value satisfy a preset relationship; conversely, if the first LOS / NLOS index is greater than the preset threshold value, then the first LOS / NLOS index and the preset threshold value do not satisfy the preset relationship. The preset threshold value is a pre-set value between 0 and 1. For example, the preset threshold value can be set to a value close to 0.

[0155] The sending end measures or estimates the second location-related time information of the target terminal using non-AI / ML functions or models. If it is determined that the first LOS / NLOS index meets the preset relationship with the preset threshold value, it indicates that the current path is likely to be an NLOS path. At this time, the accuracy of the second location-related time information measured or estimated by non-AI / ML functions or models is low. Then the sending end can send the first location-related time information to the network device, and the network device can receive the second location-related time information sent by the sending end.

[0156] Conversely, if it is determined that the first LOS / NLOS index does not meet the preset relationship with the preset threshold, it indicates that the current path is likely to be a LOS path. In this case, the accuracy of the second location-related time information measured or estimated by non-AI / ML functions or model methods is high. Then, the sending end can send the second location-related time information to the network device, and the network device can receive the second location-related time information sent by the sending end.

[0157] In an exemplary embodiment, while sending the first location-related time information or the second location-related time information to the network device, the sending end may also send the first LOS / NLOS indicator to the network device. That is, while receiving the first location-related time information or the second location-related time information sent by the sending end, the network device may also receive the first LOS / NLOS indicator sent by the network device. For example, refer to... Figure 7 As shown:

[0158] Step 7.1: The sending end can determine the first location-related time information through AI / ML functions or models;

[0159] Step 7.2: The sending end measures the second positioning-related time information using a non-AI method; if the first LOS / NLOS index and the preset threshold value satisfy a preset relationship, then execute 7.3a; or, if the first LOS / NLOS index and the preset threshold value do not satisfy a preset relationship, then execute 7.3b; It should be noted that in this embodiment, the execution order of steps 7.1 and 7.2 is not specifically limited. Step 7.2 can be executed first and then step 7.1, or steps 7.1 and 7.2 can be performed simultaneously.

[0160] Step 7.3a: The sending end reports the first location-related time information and the first LOS / NLOS index to the network device;

[0161] Step 7.3b: The sending end reports the second positioning-related time information and the first LOS / NLOS index to the network device.

[0162] By using the location information reporting method provided in this embodiment, the sending end can select more accurate location-related time information to report to the network device based on the current first LOS / NLOS index, which can not only save reporting overhead, but also greatly improve the positioning accuracy of the UE.

[0163] In one exemplary embodiment, the sender may report two LOS / NLOS metrics and two location-related time information to the network device. The method further includes:

[0164] The transmitting end measures the second location-related time information of the target terminal; the transmitting end sends the second location-related time information and the first LOS / NLOS index to the network device, and sends the first location-related time information and the second LOS / NLOS index to the network device.

[0165] The network device can receive the second location-related time information and the first LOS / NLOS index sent by the sending end, and can also receive the first location-related time information and the second LOS / NLOS index sent by the sending end.

[0166] In this embodiment of the disclosure, the sending end measures or estimates the second location-related time information of the target terminal through a non-AI / ML function or model method, and can send the second location-related time information and the corresponding first LOS / NLOS index to the network device, and can also send the first location-related time information and the corresponding second LOS / NLOS index determined according to the AI / ML function or model to the network device.

[0167] The second LOS / NLOS metric is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path (or virtual LOS path); or, the value of the second LOS / NLOS metric is a predefined value, which is 1 or a fixed value. The explanation of the second LOS / NLOS metric can be found in the relevant descriptions of the foregoing embodiments, and will not be repeated here in the embodiments of this application. For example, refer to... Figure 8 As shown:

[0168] Step 8.1: The sending end can determine the first location-related time information through AI / ML functions or models;

[0169] Step 8.2: The sending end measures the second positioning-related time information using a non-AI method;

[0170] Step 8.3: The sending end constructs a second LOS / NLOS metric based on inference quality, confidence, similarity, or a predefined value;

[0171] Step 8.4: The sending end reports the second positioning-related time information and the first LOS / NLOS index to the network device, and also reports the first positioning-related time information and the second LOS / NLOS index.

[0172] By using the location information reporting method provided in this embodiment, the network device can obtain all location-related time information determined by AI / ML functions or models and location-related time information estimated by non-AI methods, as well as their respective quality evaluation or confidence information. Thus, the network device can have the greatest flexibility to calculate the UE's location, which can improve the UE's positioning accuracy and positioning efficiency.

[0173] In an exemplary embodiment, when the location-related time information is measured by the sending end using a non-AI method, and the LOS / NLOS metric is determined by the sending end using AI / ML functions or models, the sending end can report a third LOS / NLOS metric to the network device, and also report the location-related time information measured by the non-AI method to the network device. (Refer to...) Figure 9 As shown, the method also includes:

[0174] Step 9.1: The transmitting end measures and obtains the second positioning-related time information of the target terminal;

[0175] Step 9.2: The sending end determines the third LOS / NLOS metric of the target terminal through the function or model of the second AI / ML;

[0176] Step 9.3: The sending end sends the second location-related time information and the third LOS / NLOS indicator to the network device, but does not report the first LOS / NLOS indicator.

[0177] Network devices can receive the second location-related time information and the third LOS / NLOS indicator sent by the transmitter.

[0178] To enable those skilled in the art to better understand the location information reporting method provided in the embodiments of this application, the embodiments of this application are illustrated below through specific examples.

[0179] This application's embodiments are mainly applied to 5G NR systems, including network equipment and terminal equipment. The network equipment may include base stations, gNB (next generation Node B, fifth-generation mobile communication system base station), TRP, LMF (Location Management Function), and NWDAF (Network Data Analysis Function). The terminal equipment may include user equipment or UE, etc. Alternatively, it can also be applied to other systems, such as 6G systems, as long as the system requires the UE and / or TRP to report location measurement quantities based on AI / ML inference to the core network elements, such as LMF.

[0180] Reference Figure 10 As shown, in the NR system, multiple UEs, including UE1 and UE2, request wireless network connection services; the gNB provides these services. Data interaction and transmission occur between the gNB and UE1 and UE2 via wireless communication. For example, the gNB provides communication services to UE1 and UE2, including AI / ML-related communication services, such as transmitting various indication information to UE1 and UE2. Furthermore, the Location Management Function (LMF) resides on the core network side and provides location-related services. UEs or TRPs may report location-related measurement information to the LMF, including location-related measurement information for AI / ML inference.

[0181] In one embodiment, this embodiment applies to situations where location-related time information (such as DL RSTD or UL RTOA) is inferred from the AI / ML model of the UE or TRP, and only location-related time information based on AI / ML is reported to the LMF.

[0182] When the channel obtained by the UE through receiving / measuring the PRS is used as the input to the UE-side AI / ML model, and the location-related time information is obtained through the function or model inference of the UE-side AI / ML, the DL RSTD reported by the UE to the LMF is the result of the location-related time information based on the functional model inference of AI / ML. Then, the corresponding LOS / NLOS indicator processing method is one of the following:

[0183] (1) Do not report LOS / NLOS indicators.

[0184] Since the positioning-related time information obtained from AI / ML functions or model inference can be considered as the "time measurement corresponding to the virtual LOS path," and is positioning-related time information after overcoming the signal arrival time delay error caused by NLOS, the positioning-related time information obtained from AI / ML functions or model inference is closer to the "positioning-related time information when there is a virtual LOS path between the TRP and the UE." In this case, the LOS / NLOS metric can no longer reflect the degree to which the currently reported positioning-related time information is subject to delay errors introduced by the NLOS path. Therefore, the LOS / NLOS metric can be discontinued to reduce reporting overhead and avoid positioning interference caused by the lack of correlation between the LOS / NLOS metric and positioning-related time information, thereby improving positioning accuracy.

[0185] In one example, the LOS / NLOS metric definition can be assumed to remain unchanged. Although the UE does not report the LOS / NLOS metric, the LMF can understand and assume that its value is 1. That is, whether it is reported as a soft value or a hard value, it is always understood as a virtual LOS path.

[0186] In another example, the nr-TimingQuality reported along with the location-related timing information indicates the inference quality of the location-related timing information, or the inference confidence of the AI / ML function or model, or the similarity between the location-related timing information and the location-related timing information corresponding to the virtual LOS path. In this way, nr-TimingQuality can reflect the reliability / confidence / quality of AI inference, allowing the UE to more accurately provide information related to the timing measurements associated with AI-inferred positioning to the LMF, assisting the LMF in performing more accurate UE location calculations.

[0187] (2) The LOS / NLOS indicator is reported, and its value is fixed at 1 or other predefined fixed value.

[0188] As mentioned earlier, the location-related time information from AI / ML functions or model inference can be considered as "the time measurement corresponding to a virtual LOS path." Therefore, the location-related time information from AI / ML functions or model inference can be understood as "the location-related time information when there is a virtual LOS path between the TRP and the UE." If it is necessary to report a LOS / NLOS metric corresponding to AI-based location-related time information, it is advisable to report a fixed value of 1 or other predefined fixed values, representing that the location-related time information corresponds to a virtual LOS path.

[0189] Specifically, for example, regardless of whether soft or hard value reporting is used, "1" can be reported to represent "certainly LOS"; alternatively, a predefined special state / value can be reported, representing the location-related time information obtained through AI / ML functions or model inference, assuming the existence of a LOS path. This not only maximizes the use of existing reporting parameters, reducing complexity, but also avoids positioning interference caused by LMS / NLOS indicators and location-related time information not being correlated, thus improving positioning accuracy.

[0190] (3) The LOS / NLOS index is reported, and it indicates the "confidence of AI inference", or the "quality of AI inference", or the "similarity between the location-related time information and the location-related time information corresponding to the virtual LOS path".

[0191] Referring to the approach of reusing nr-TimingQuality but redefining / reinterpreting / reusing it to a certain extent in the aforementioned embodiments, LOS / NLOS is used to indicate "the reliability / confidence / quality of AI inference (location-related time information)" instead of the original meaning. This allows the UE to provide more accurate information related to the time measurement quantities associated with AI inference positioning to the LMF, assisting the LMF in performing more accurate UE location calculation.

[0192] In another embodiment, this embodiment applies to situations where location-related time information (such as DL RSTD or UL RTOA) is obtained by the AI / ML function or model inference of the UE or TRP, and the results of both AI / ML inference and non-AI / ML measurement of location time-related information may be reported.

[0193] When a UE receives / measures the channel obtained from PRS, and this information may be used as input to the UE-side AI / ML function or model, or used for inference of location-related time information by the UE-side AI / ML function or model, or may be estimated using non-AI methods, the location-related time information reported by the UE to the LMF may be the result of location-related time information based on AI / ML function or model inference, or the reported location-related time information may be the result of estimation of location-related information using non-AI methods, or both of the above. In this case, the corresponding LOS / NLOS metric processing method is one of the following:

[0194] (1) Only one location-related time information is reported, and:

[0195] When the value of the LOS / NLOS metric is less than or equal to (or less than) a predefined or configured threshold, only location-related time information obtained based on AI / ML function or model inference is reported;

[0196] When the LOS / NLOS metric value is greater than (or greater than or equal to) a predefined or configured threshold, report the location-related time information estimated by non-AI methods.

[0197] Furthermore, a LOS / NLOS metric can also be reported: The LOS / NLOS metric can be reported along with the location-related time information obtained from AI / ML functional or model inference; or, the LOS / NLOS metric can be reported along with the location-related time information estimated by non-AI methods.

[0198] This is because the existing LOS / NLOS metric measures the probability of a channel being a LOS path in non-AI scenarios. A smaller value indicates a higher likelihood of the actual channel being an NLOS path, leading to less accurate positioning-related timing information estimated using non-AI methods. Therefore, AI / ML-based functional or model inference results should be used to obtain the positioning-related timing information. Conversely, a larger value indicates a higher likelihood of the actual channel being a LOS path, resulting in more accurate positioning-related timing information estimated using non-AI methods. This approach saves reporting overhead and allows for the selection of the most suitable positioning-related timing information for reporting.

[0199] (2) Two LOS / NLOS metrics are reported, corresponding to the location-related time information determined by AI / ML functions or models and non-AI / ML methods, respectively, and:

[0200] The definitions of LOS / NLOS for non-AI / ML remain unchanged;

[0201] The definition of LOS / NLOS metrics for AI / ML functions or models includes: LOS / NLOS metrics being reported, with a fixed value of 1 or other predefined fixed values; or LOS / NLOS metrics indicating "confidence of AI inference," "quality of AI inference," or "the degree of similarity between location-related time information and location-related time information corresponding to the virtual LOS path."

[0202] In this way, LMF can obtain all location-related time information obtained from AI / ML-based functions or models and non-AI methods, as well as their respective quality assessments or confidence information, thus giving LMF the greatest flexibility to calculate the UE's location.

[0203] In another embodiment, when the channel obtained by the UE receiving / measuring the PRS is used as the input of the AI / ML function or model on the UE side, and the AI / ML function or model on the UE side performs inference and outputs the LOS / NLOS index, the LOS / NLOS index is reported as follows: the LOS / NLOS index obtained by the AI / ML function or model inference, as well as the positioning-related time information obtained by non-AI methods are reported.

[0204] It should be noted that the UE receives / measures the PRS sent by the TRP, the AI / ML function or model deployed on the UE side, and the location-related measurement information reported by the UE to the LMF. The above method also applies to the base station or TRP, that is, the base station or TRP receives / measures the SRS-pos sent by the UE, the AI / ML function or model deployed on the base station or TRP side, and the location-related measurement information reported by the base station or TRP to the LMF. The details will not be repeated in the embodiments of this application, but can be referred to the relevant descriptions in the foregoing embodiments.

[0205] The location information reporting method provided in this application provides a certain correlation between the LOS / NLOS metrics measured by non-AI methods and the location-related time information obtained by non-AI methods. However, the correlation between the LOS / NLOS metrics and the location-related time measurements obtained based on AI / ML functional or model inference no longer holds. If the traditional LOS / NLOS metric reporting method is still used, it will mislead the LMF and negatively impact the LMF's UE location calculation. The location information reporting method provided in this application can correctly indicate the relevant quality or confidence evaluation information of the location-related time information obtained based on AI / ML functional or model inference, which is beneficial for the LMF to calculate the UE location and can avoid unnecessary reporting overhead, saving network resources.

[0206] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0207] Based on the same inventive concept, this application also provides a location information reporting device for implementing the location information reporting method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more location information reporting device embodiments provided below can be found in the limitations of the location information reporting method described above, and will not be repeated here.

[0208] In one exemplary embodiment, such as Figure 11 As shown, a location information reporting device 1100 is provided, including: a first determining module 1110 and a first sending module 1120, wherein:

[0209] The first determining module 1110 is used to determine the first positioning-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning AI / ML.

[0210] The first sending module 1120 is used to send location information to the network device. The location information includes first location-related time information, but does not include the first LOS / NLOS index.

[0211] The aforementioned location information reporting device, when determining the first location-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML), the terminal, base station, or TRP only sends the first location-related time information to the network device and does not send the first LOS / NLOS index to the network device. This avoids interference caused by the lack of correlation between the first LOS / NLOS index and the first location-related time information when the network device calculates the terminal location based on the first location-related time information. This can greatly improve the positioning accuracy of the terminal, avoid unnecessary reporting overhead, and save network resources.

[0212] In one embodiment, the device further includes:

[0213] The second sending module is used to send timing quality indication information to the network device. The timing quality indication information is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

[0214] In one embodiment, the device further includes:

[0215] The third sending module is used to send a second LOS / NLOS index to the network device. The second LOS / NLOS index is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

[0216] In one embodiment, the device further includes:

[0217] The first measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0218] The fourth sending module is used to send first positioning-related time information to the network device if the first LOS / NLOS indicator meets the preset relationship with the preset threshold value; and to send second positioning-related time information to the network device if the first LOS / NLOS indicator does not meet the preset relationship with the preset threshold value.

[0219] In one embodiment, the fourth sending module is further configured to:

[0220] While sending the first location-related time information or the second location-related time information to the network device, the system also sends the first LOS / NLOS indicator to the network device.

[0221] In one embodiment, the device further includes:

[0222] The second measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0223] The fifth sending module is used to send the second positioning-related time information and the first LOS / NLOS index to the network device; and to send the first positioning-related time information and the second LOS / NLOS index to the network device.

[0224] In one embodiment, the device further includes:

[0225] The third measurement module is used to measure and obtain the second positioning-related time information of the target terminal;

[0226] The second determination module is used to determine the third LOS / NLOS metric of the target terminal through the functions or models of the second AI / ML;

[0227] The sixth sending module is used to send the second positioning-related time information and the third LOS / NLOS indicator to the network device.

[0228] In one embodiment, the device is applied to a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the device is applied to a base station or a Transmitter-Receiver Node (TRP), and the first positioning-related time information includes the uplink relative arrival time.

[0229] In one exemplary embodiment, such as Figure 12 As shown, a location information reporting device 1200 is provided, including: a first receiving module 1210, wherein:

[0230] The first receiving module 1210 is used to receive the first location-related time information sent by the sending end. The first location-related time information is the location-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning (AI / ML).

[0231] The aforementioned location information reporting device, when determining the first location-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML), the terminal, base station, or TRP only sends the first location-related time information to the network device and does not send the first LOS / NLOS index to the network device. This avoids interference caused by the lack of correlation between the first LOS / NLOS index and the first location-related time information when the network device calculates the terminal location based on the first location-related time information. This can greatly improve the positioning accuracy of the terminal, avoid unnecessary reporting overhead, and save network resources.

[0232] In one embodiment, the device further includes:

[0233] The second receiving module is used to receive timing quality indication information sent by the sending end. The timing quality indication information is used to indicate the inference quality of the first positioning-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0234] In one embodiment, the device further includes:

[0235] The third receiving module is used to receive the second LOS / NLOS index sent by the sending end. The second LOS / NLOS index is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or, the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

[0236] In one embodiment, the device further includes:

[0237] The fourth receiving module is used to receive first positioning-related time information sent by the sending end, wherein the first LOS / NLOS index satisfies a preset relationship with a preset threshold value; or, to receive second positioning-related time information sent by the sending end, wherein the first LOS / NLOS index does not satisfy a preset relationship with the preset threshold value, and the second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement.

[0238] In one embodiment, the fourth receiving module is further configured to:

[0239] While receiving the first or second location-related time information sent by the sending end, it also receives the first LOS / NLOS indicator sent by the sending end.

[0240] In one embodiment, the device further includes:

[0241] The fifth receiving module is used to receive the second positioning-related time information and the first LOS / NLOS index sent by the sending end. The second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement. The module also receives the first positioning-related time information and the second LOS / NLOS index sent by the sending end.

[0242] In one embodiment, the device further includes:

[0243] The sixth receiving module is used to receive the second positioning-related time information and the third LOS / NLOS index sent by the sending end. The second positioning-related time information is the positioning-related time information of the target terminal obtained by the sending end through measurement, and the third LOS / NLOS index is the LOS / NLOS index of the target terminal determined by the sending end through the function or model of the second AI / ML.

[0244] In one embodiment, the transmitting end is a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the transmitting end is a base station or a transmitting and receiving node, and the first positioning-related time information includes the uplink relative arrival time.

[0245] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0246] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application.

[0247] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0248] In one exemplary embodiment, a terminal device is provided, the structure of which can be as follows: Figure 13 As shown. The terminal device includes a memory 1320, a transceiver 1310, and a processor 1300.

[0249] A transceiver is used to receive and send data under the control of a processor.

[0250] processor,

[0251] Among them, Figure 13In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits represented by one or more processors (represented by processors) and memories (represented by memory). The bus architecture can also link various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium, including wireless channels, wired channels, optical fibers, etc. For different user equipment, the user interface can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0252] The processor is responsible for managing the bus architecture and general processing, while the memory can store the data used by the processor 1300 during operation.

[0253] Optionally, the processor can be a CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or CPLD (Complex Programmable Logic Device), and the processor can also adopt a multi-core architecture.

[0254] The processor executes any of the methods described in the embodiments of this application according to the obtained executable instructions by calling a program stored in memory. The processor and memory may also be physically separated.

[0255] It should be noted that the terminal device provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0256] In one exemplary embodiment, a base station is provided, the structure of which can be as follows: Figure 14 As shown. The base station includes a memory 1420, a transceiver 1410, and a processor 1400.

[0257] A transceiver is used to receive and send data under the control of a processor.

[0258] Among them, Figure 14In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (represented by a processor) and memory (represented by memory). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor is responsible for managing the bus architecture and general processing, while the memory can store data used by the processor during operation.

[0259] The processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0260] The processor executes any of the methods described above in the embodiments of this application according to the obtained executable instructions by calling a program stored in memory. The processor and memory may also be physically separated.

[0261] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0262] In one exemplary embodiment, a network device is provided, the structure of which can be as follows: Figure 15 As shown. The base station includes a memory 1520, a transceiver 1510, and a processor 1500.

[0263] A transceiver is used to receive and send data under the control of a processor.

[0264] Among them, Figure 15In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (represented by a processor) and memory (represented by memory). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor is responsible for managing the bus architecture and general processing, while the memory can store data used by the processor during operation.

[0265] The processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0266] The processor executes any of the methods described above in the embodiments of this application according to the obtained executable instructions by calling a program stored in memory. The processor and memory may also be physically separated.

[0267] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0268] In one exemplary embodiment, a location information reporting device is provided. The location information reporting device may be a terminal device, a base station, or a network device, and includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0269] In one exemplary embodiment, a processor-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0270] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0271] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0272] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0273] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0274] These processor-executable instructions may also be stored in a processor-readable memory that can instruct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0275] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0276] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0277] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0278] These processor-executable instructions may also be stored in a processor-readable memory that can instruct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0279] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for reporting location information, characterized in that, The method includes: Determine the first location-related time information of the target terminal based on the functions or models of the first artificial intelligence / machine learning (AI / ML). Location information is sent to network devices, the location information including the first location-related time information, and the location information does not include the first LOS / NLOS metric.

2. The method according to claim 1, characterized in that, The method further includes: The timing quality indication information is sent to the network device. The timing quality indication information is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

3. The method according to claim 1, characterized in that, The method further includes: Send a second LOS / NLOS metric to the network device, wherein the second LOS / NLOS metric is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or... The value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

4. The method according to claim 1, characterized in that, The method further includes: The second positioning-related time information of the target terminal was obtained by measurement; If the first LOS / NLOS indicator satisfies a preset relationship with a preset threshold value, the first positioning-related time information is sent to the network device; If the first LOS / NLOS metric does not satisfy the preset relationship with the preset threshold value, the second location-related time information is sent to the network device.

5. The method according to claim 4, characterized in that, The method further includes: While sending the first location-related time information or the second location-related time information to the network device, the first LOS / NLOS indicator is also sent to the network device.

6. The method according to claim 3, characterized in that, The method further includes: The second positioning-related time information of the target terminal was obtained by measurement; Send the second location-related time information and the first LOS / NLOS index to the network device; Send the first location-related time information and the second LOS / NLOS index to the network device.

7. The method according to claim 1, characterized in that, The method includes: The second positioning-related time information of the target terminal was obtained by measurement; The third LOS / NLOS metric of the target terminal is determined by the function or model of the second AI / ML; Send the second location-related time information and the third LOS / NLOS index to the network device.

8. The method according to any one of claims 1 to 7, characterized in that, The method is applied to a terminal, where the first positioning-related time information includes the downlink reference signal time difference; or, the method is applied to a base station or a Transmitter-Receiver Node (TRP), where the first positioning-related time information includes the uplink relative arrival time.

9. A method for reporting location information, characterized in that, The method includes: The system receives location information sent by the sending end. The location information includes first location-related time information, but does not include a first LOS / NLOS index. The first location-related time information is the location-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning (AI / ML).

10. The method according to claim 9, characterized in that, The method further includes: The system receives timing quality indication information sent by the sending end. The timing quality indication information is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the virtual LOS path.

11. The method according to claim 9, characterized in that, The method further includes: The system receives a second LOS / NLOS metric sent by the transmitting end, wherein the second LOS / NLOS metric is used to indicate the inference quality of the first location-related time information, or to indicate the inference confidence of the first AI / ML function or model, or to indicate the similarity between the first location-related time information and the location-related time information corresponding to the LOS path; or... The value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value.

12. The method according to claim 9, characterized in that, The method further includes: The system receives the first positioning-related time information sent by the transmitting end, wherein the first LOS / NLOS index satisfies a preset relationship with a preset threshold value; or... The system receives second location-related time information sent by the transmitting end, wherein the first LOS / NLOS index does not satisfy the preset relationship with the preset threshold value, and the second location-related time information is the location-related time information of the target terminal obtained by the transmitting end through measurement.

13. The method according to claim 12, characterized in that, The method further includes: While receiving the first location-related time information or the second location-related time information sent by the sending end, the device also receives the first LOS / NLOS index sent by the sending end.

14. The method according to claim 11, characterized in that, The method further includes: The system receives the second location-related time information and the first LOS / NLOS index sent by the transmitting end, wherein the second location-related time information is the location-related time information of the target terminal obtained by the transmitting end through measurement; The system receives the first location-related time information and the second LOS / NLOS index sent by the sending end.

15. The method according to claim 9, characterized in that, The method further includes: The system receives second location-related time information and a third LOS / NLOS index sent by the transmitting end. The second location-related time information is the location-related time information of the target terminal obtained by the transmitting end through measurement, and the third LOS / NLOS index is the LOS / NLOS index of the target terminal determined by the transmitting end through the function or model of the second AI / ML.

16. The method according to any one of claims 9 to 15, characterized in that, The transmitting end is a terminal, and the first positioning-related time information includes the downlink reference signal time difference; or, the transmitting end is a base station or a transmitting and receiving node, and the first positioning-related time information includes the uplink relative arrival time.

17. A device for reporting location information, characterized in that, The device includes: The first determining module is used to determine the first positioning-related time information of the target terminal based on the function or model of the first artificial intelligence / machine learning (AI / ML). The first sending module is used to send location information to the network device. The location information includes the first location-related time information, but does not include the first LOS / NLOS index.

18. A device for reporting location information, characterized in that, The device includes: The first receiving module is used to receive the first location-related time information sent by the sending end. The first location-related time information is the location-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning (AI / ML).

19. A terminal device, characterized in that, The terminal device includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: The first location-related time information of the target terminal is obtained by predicting using the target model; Location information is sent to the target network device. The location information includes the first location-related time information, but does not include the first LOS / NLOS metric.

20. A base station, characterized in that, The base station includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: Determine the first location-related time information of the target terminal based on the functions or models of the first artificial intelligence / machine learning (AI / ML). Location information is sent to network devices, the location information including the first location-related time information, and the location information does not include the first LOS / NLOS metric.

21. A network device, characterized in that, The network device includes: a memory, a transceiver, and a processor. A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: The receiver receives first location-related time information sent by the sender, wherein the first location-related time information is the location-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning (AI / ML).

22. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 16.

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