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

Abstract

The present disclosure relates to a positioning information reporting method and apparatus, a terminal device, a base station, a network device, and a computer-readable storage medium. The method comprises: determining first positioning-related time information of a target terminal on the basis of a first artificial intelligence / machine learning (AI / ML) functionality or model; and sending positioning information to a network device, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise a first LOS / NLOS indicator.

Description

Reporting method and apparatus for positioning information, terminal device, base station, network device, and storage medium

[0001] The present disclosure claims priority to the Chinese patent application No. 2024110792871, filed on August 7, 2024, and entitled "Reporting method and apparatus for positioning information, terminal device, base station, network device, and storage medium", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present disclosure relates to the field of communication technology, and in particular, to a reporting method and apparatus for positioning information, a terminal device, a base station, a network device, and a storage medium. BACKGROUND

[0003] 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 models to assist positioning.

[0004] When using AI / ML functions or models to assist positioning, the output of the AI / ML functions or models is a positioning-related measurement, such as positioning-related time information and / or a LOS / NLOS indicator (Light of Sight / Non-Light of Sight indicator).

[0005] However, there is currently a lack of a reporting method for the LOS / NLOS indicator and the positioning-related time information output by the AI / ML functions or models, which makes the positioning-related measurement output by the AI / ML functions or models better assist positioning. SUMMARY

[0006] According to various embodiments of the present disclosure, a reporting method and apparatus for positioning information, a terminal device, a base station, a network device, and a computer-readable storage medium are provided.

[0007] In a first aspect, the present disclosure provides a reporting method for positioning information, comprising:

[0008] 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, wherein the first positioning-related time information is included in the positioning information, and the first LOS / NLOS indicator is not included in the positioning information.

[0009] In some embodiments of the present disclosure, the method further comprises: sending, to the network device, timing quality indication information, the timing quality indication information being used to indicate any of: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; and similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0010] In some embodiments of the present disclosure, the method further comprises:

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

[0012] In some embodiments of the present disclosure, the method further comprises:

[0013] measuring second positioning-related time information of the target terminal; sending, to the network device, the first positioning-related time information when the first LOS / NLOS indicator and a preset threshold value satisfy a preset relationship; and sending, to the network device, the second positioning-related time information when the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship.

[0014] In some embodiments of the present disclosure, the method further comprises:

[0015] sending, to the network device, the first positioning-related time information or the second positioning-related time information, and sending, to the network device, the first LOS / NLOS indicator.

[0016] In some embodiments of the present disclosure, the method further comprises:

[0017] measuring second positioning-related time information of the target terminal; sending, to the network device, the second positioning-related time information and the first LOS / NLOS indicator; and sending, to the network device, the first positioning-related time information and a second LOS / NLOS indicator.

[0018] In some embodiments of the present disclosure, the method comprises:

[0019] measuring second positioning-related time information of the target terminal; determining, by a function or model of a second AI / ML, a third LOS / NLOS indicator of the target terminal; and sending, to the network device, the second positioning-related time information and the third LOS / NLOS indicator.

[0020] In some embodiments of the present disclosure, the method is applied to one of: a terminal; a base station; a transmission-reception point (TRP); when the method is applied to the terminal, the first positioning-related time information comprises a downlink reference signal time difference; and when the method is applied to the base station or the transmission-reception point (TRP), the first positioning-related time information comprises an uplink relative time of arrival.

[0021] In a second aspect, the present disclosure provides a method for reporting positioning information, the method comprising:

[0022] receiving the positioning information sent by the sending end, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise the first LOS / NLOS indicator, and the first positioning-related time information is the positioning-related time information of the target terminal determined according to a first artificial intelligence / machine learning (AI / ML) function or model.

[0023] In some embodiments of the present disclosure, the method further comprises:

[0024] receiving timing quality indication information sent by the sending end, wherein the timing quality indication information is used to indicate one of: the inference quality of the first positioning-related time information; the inference confidence of the first AI / ML function or model; and the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0025] In some embodiments of the present disclosure, the method further comprises:

[0026] receiving a second LOS / NLOS indicator sent by the sending end, wherein the second LOS / NLOS indicator is used to indicate one of: the inference quality of the first positioning-related time information; the inference confidence of the first AI / ML function or model; and the similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path; or the value of the second LOS / NLOS indicator is a predefined value, and the predefined value is 1 or a fixed value.

[0027] In some embodiments of the present disclosure, the method further comprises:

[0028] receiving the first positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and a preset threshold value satisfy a preset relationship; or receiving the second positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, and the second positioning-related time information is the positioning-related time information of the target terminal measured by the sending end.

[0029] In some embodiments of the present disclosure, the method further comprises:

[0030] The first LOS / NLOS indicator is received from the sending end at the same time as the first positioning-related time information or the second positioning-related time information.

[0031] In some embodiments of the present disclosure, the method further includes:

[0032] The second positioning-related time information and the first LOS / NLOS indicator are received from the sending end, the second positioning-related time information being the positioning-related time information of the target terminal measured by the sending end; and the first positioning-related time information and the second LOS / NLOS indicator are received from the sending end.

[0033] In some embodiments of the present disclosure, the method further includes:

[0034] The second positioning-related time information and the third LOS / NLOS indicator are received from the sending end, the second positioning-related time information being the positioning-related time information of the target terminal measured by the sending end, and the third LOS / NLOS indicator being the LOS / NLOS indicator of the target terminal determined by the sending end through the second AI / ML function or model.

[0035] In some embodiments of the present disclosure, the sending end is any one of the following: a terminal; a base station; a sending-receiving node; when the sending end is a terminal, the first positioning-related time information includes a downlink reference signal time difference; or when the sending end is a base station or a sending-receiving node, the first positioning-related time information includes an uplink relative time of arrival.

[0036] In a third aspect, the present disclosure provides a reporting device for positioning information, comprising:

[0037] A first determining module is configured to determine the first positioning-related time information of the target terminal according to a first artificial intelligence / machine learning (AI / ML) function or model.

[0038] A first sending module is configured to send the positioning information to a network device, the positioning information containing the first positioning-related time information, and the positioning information not containing the first LOS / NLOS indicator.

[0039] In some embodiments of the present disclosure, the device further includes:

[0040] A second sending module is configured to send timing quality indication information to a network device, the timing quality indication information being used to indicate any one of the following: the inference quality of the first positioning-related time information; the inference confidence of the first AI / ML function or model; and the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0041] In some embodiments of the present disclosure, the device further includes:

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

[0043] In some embodiments of the present disclosure, the apparatus further includes:

[0044] The first measurement module is configured to measure the second positioning-related time information of the target terminal.

[0045] The fourth sending module is configured to send the first positioning-related time information to the network device when the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship, and send the second positioning-related time information to the network device when the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship.

[0046] In some embodiments of the present disclosure, the fourth sending module is further configured to:

[0047] The fourth sending module is further configured to send the first LOS / NLOS indicator to the network device at the same time of sending the first positioning-related time information or the second positioning-related time information to the network device.

[0048] In some embodiments of the present disclosure, the apparatus further includes:

[0049] The second measurement module is configured to measure the second positioning-related time information of the target terminal.

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

[0051] In some embodiments of the present disclosure, the apparatus further includes:

[0052] The third measurement module is configured to measure the second positioning-related time information of the target terminal.

[0053] The second determination module is configured to determine the third LOS / NLOS indicator of the target terminal by using the function or model of the second AI / ML.

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

[0055] In some embodiments of the present disclosure, the apparatus is applied to any one of: a terminal; a base station; a transmission-reception point, TRP;

[0056] When the apparatus is applied to the terminal, the first positioning-related time information comprises a downlink reference signal time difference; or, when the apparatus is applied to the base station or the transmission-reception point, TRP, the first positioning-related time information comprises an uplink relative time of arrival.

[0057] In a fourth aspect, the present disclosure further provides an apparatus for reporting positioning information, comprising:

[0058] A first receiving module is configured to receive first positioning-related time information sent by a sending end, wherein the first positioning-related time information is positioning-related time information of a target terminal determined according to a first artificial intelligence / machine learning, AI / ML, function or model.

[0059] In some embodiments of the present disclosure, the apparatus further comprises:

[0060] A second receiving module is configured to receive timing quality indication information sent by the sending end, wherein the timing quality indication information is used to indicate any one of: inference quality of the first positioning-related time information; inference confidence of the first AI / ML function or model; and similarity between the first positioning-related time information and positioning-related time information corresponding to a virtual LOS path.

[0061] In some embodiments of the present disclosure, the apparatus further comprises:

[0062] A third receiving module is configured to receive second LOS / NLOS indicators sent by the sending end, wherein the second LOS / NLOS indicators are used to indicate any one of: inference quality of the first positioning-related time information; inference confidence of the first AI / ML function or model; and similarity between the first positioning-related time information and positioning-related time information corresponding to a LOS path; or, a value of the second LOS / NLOS indicators is a predefined value, and the predefined value is 1 or a fixed value.

[0063] In some embodiments of the present disclosure, the apparatus further comprises:

[0064] A fourth receiving module is configured to receive the first positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicators and a preset threshold value satisfy a preset relationship; or, receive second positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicators and the preset threshold value do not satisfy the preset relationship, and the second positioning-related time information is positioning-related time information of the target terminal measured by the sending end.

[0065] In some embodiments of the present disclosure, the fourth receiving module is further configured to:

[0066] The first positioning-related time information or the second positioning-related time information sent by the sending end is received, and the first LOS / NLOS index sent by the sending end is also received.

[0067] In some embodiments of the present disclosure, the apparatus further comprises:

[0068] The fifth receiving module is configured 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 being the positioning-related time information of the target terminal measured by the sending end; and receive the first positioning-related time information and the second LOS / NLOS index sent by the sending end.

[0069] In some embodiments of the present disclosure, the apparatus further comprises:

[0070] The sixth receiving module is configured 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 being the positioning-related time information of the target terminal measured by the sending end, and the third LOS / NLOS index being the LOS / NLOS index of the target terminal determined by the sending end through the second AI / ML function or model.

[0071] In some embodiments of the present disclosure, the sending end is any one of the following: a terminal; a base station; a sending-receiving node;

[0072] When the sending end is a terminal, the first positioning-related time information comprises a downlink reference signal time difference; or when the sending end is a base station or a sending-receiving node, the first positioning-related time information comprises an uplink relative time of arrival.

[0073] In a fifth aspect, a terminal device is provided, comprising: a memory, a transceiver, and a processor.

[0074] The memory is configured to store a computer program; the transceiver is configured to transceive data under the control of the processor; and the processor is configured to read the computer program in the memory and execute any one of the reporting methods of the positioning information provided in the first aspect.

[0075] In a sixth aspect, a base station is provided, comprising: a memory, a transceiver, and a processor.

[0076] The memory is configured to store a computer program; the transceiver is configured to transceive data under the control of the processor; and the processor is configured to read the computer program in the memory and execute any one of the reporting methods of the positioning information provided in the first aspect.

[0077] In a seventh aspect, a network device is provided, comprising: a memory, a transceiver, and a processor.

[0078] The memory is configured to store a computer program; the transceiver is configured to transceive data under control of the processor; and the processor is configured to read the computer program in the memory and execute any one of the reporting methods of the positioning information provided by the second aspect.

[0079] In an eighth aspect, the disclosure also provides a computer device, comprising 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 one of the reporting methods of the positioning information.

[0080] In a ninth aspect, the disclosure also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any one of the reporting methods of the positioning information.

[0081] In a tenth aspect, the disclosure also provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the steps of any one of the reporting methods of the positioning information.

[0082] The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the disclosure or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or related art description. Obviously, the drawings in the following description are only some embodiments of the disclosure, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0084] FIG. 1a is a schematic diagram of a communication path in a related art;

[0085] FIG. 1b is a schematic diagram of a communication path in another related art;

[0086] FIG. 2a is a schematic diagram of using AI / ML inference LOS / NLOS indicators in a related art;

[0087] FIG. 2b is a schematic diagram of using AI / ML inference positioning-related time measurement quantities in another related art;

[0088] FIG. 3 is a flowchart of a reporting method of positioning information in an embodiment of the disclosure;

[0089] FIG. 4 is a signaling interaction schematic diagram when a sending end does not report LOS / NLOS indicators in an embodiment of the disclosure;

[0090] FIG. 5 is a signaling interaction diagram for reporting timing quality indication information by a sending terminal in an embodiment of the present disclosure;

[0091] FIG. 6 is a signaling interaction diagram for reporting LOS / NLOS indicators by a sending terminal in an embodiment of the present disclosure;

[0092] FIG. 7 is a signaling interaction diagram for selecting positioning-related time information to be reported by a sending terminal in an embodiment of the present disclosure;

[0093] FIG. 8 is a signaling interaction diagram for reporting two pieces of positioning-related time information by a sending terminal in an embodiment of the present disclosure;

[0094] FIG. 9 is a signaling interaction diagram for reporting AI-determined LOS / NLOS indicators by a sending terminal in an embodiment of the present disclosure;

[0095] FIG. 10 is a schematic diagram of a system for reporting positioning information in an example of the present disclosure;

[0096] FIG. 11 is a structural block diagram of a reporting device for positioning information in an embodiment of the present disclosure;

[0097] FIG. 12 is a structural block diagram of a reporting device for positioning information in another embodiment of the present disclosure;

[0098] FIG. 13 is a diagram of an internal structure of a terminal device in an embodiment of the present disclosure;

[0099] FIG. 14 is a diagram of an internal structure of a base station in an embodiment of the present disclosure;

[0100] FIG. 15 is a diagram of an internal structure of a network device in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0101] To make the objectives, technical solutions, and advantages of the present disclosure clearer, further detailed descriptions will be given to the present disclosure in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure, and are not used to limit the present disclosure.

[0102] In the description of the embodiments of the present application, the term “and / or” is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character “ / ” herein generally represents an “or” relationship between the associated objects.

[0103] In addition, if there are terms such as "first", "second", these terms are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" can be explicitly or implicitly included at least one of the features. In the description of the present application, if the term "multiple" appears, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise specifically limited.

[0104] The LOS / NLOS indicator indicates the possibility of channel conditions being line of sight (LOS) / non-line of sight (NLOS). The closer the current channel condition is to LOS, the closer the soft value of the LOS / NLOS indicator is to 1, or the hard value is more likely to report 1. Among them, the hard value (Hard Value): usually refers to the binary determination of LOS / NLOS (0 or 1, True or False), for example, based on threshold judgment (such as the received signal strength, delay spread, etc. Parameters exceeding a certain threshold are determined as NLOS); soft value (Soft Value): refers to the probability or confidence of LOS / NLOS (for example, 0.7 probability is LOS).

[0105] For example, referring to Figure 1a, there is no obstacle blocking the line of sight between the base station or TRP (Transmission Reception Point, transmission reception point) and the UE (User Equipment, user equipment / terminal). At this time, if the UE reports the soft value of the LOS / NLOS indicator, the reported soft value may be closer to 1, and if the hard value is reported, it is likely to report 1; referring to Figure 1b, there is an obstacle blocking the line of sight between the base station or TRP and the UE. At this time, if the UE reports the soft value of the LOS / NLOS indicator, the reported soft value may be closer to 0, and if the hard value is reported, it is likely to report 0.

[0106] In related technologies using non-AI / ML assisted positioning, taking measurement of positioning related measurement quantities at the UE side as an example, the positioning related measurement quantities measured by the UE can include LOS / NLOS indicators, DL RSTD (Downline Reference Signal Timing Difference, downlink reference signal time difference); taking measurement of positioning related measurement quantities at the base station or TRP side as an example, the positioning related measurement quantities measured by the base station or TRP can include LOS / NLOS indicators, UL RTOA (Upline Relative Time of Arrival, uplink relative arrival time).

[0107] There is a certain correlation between the DL RSTD obtained by the UE receiving / measuring a certain PRS (Positioning Reference Signal) and the LOS / NLOS indicator, or between the UL RTOA obtained by the base station or TRP receiving / measuring a certain SRS-pos (Sounding Reference Signal - positioning) and the LOS / NLOS indicator.

[0108] Taking the measurement of the positioning-related measurement quantity implemented by the UE as an example, the greater the value of the LOS / NLOS indicator, the more likely the DL RSTD measured by the UE is close to or directly equal to the time of electromagnetic wave propagation on the straight path (LOS path) between the base station or TRP and the UE; the smaller the value of the LOS / NLOS indicator, the greater the DL RSTD measured by the UE than the time of electromagnetic wave propagation on the straight path between the base station or TRP and the UE, because the PRS received / measured by the UE is through reflection, diffraction and other propagation modes, not straight-line propagation.

[0109] Similarly, taking the measurement of the positioning-related measurement quantity implemented by the base station or TRP as an example, the greater the value of the LOS / NLOS indicator, the more likely the UL RTOA measured by the base station or TRP is close to or directly equal to the time of electromagnetic wave propagation on the straight path (LOS path) between the base station or TRP and the UE; the smaller the value of the LOS / NLOS indicator, the greater the UL RTOA measured by the base station or TRP than the time of electromagnetic wave propagation on the straight path between the base station or TRP and the UE, because the SRS-pos received / measured by the base station or TRP is through reflection, diffraction and other propagation modes, not straight-line propagation.

[0110] Continuing to take the UE implementation measurement reporting as an example, in AI / ML-assisted positioning, there are different cases as follows: referring to FIG. 2a, the AI / ML function or model output of the UE is just the LOS / NLOS indicator, that is, the LOS / NLOS possibility of the measured channel is inferred by the AI / ML method; and referring to FIG. 2b, the AI / ML function or model output of the UE is a positioning-related time measurement quantity, such as DL RSTD, or time information required for calculating DL RSTD. Alternatively, the AI / ML function or model of the UE can output the LOS / NLOS indicator and the positioning-related time measurement quantity at the same time.

[0111] Similarly, taking the base station or TRP implementation of measurement reporting as an example, in AI / ML assisted positioning, there are different cases as follows: referring to FIG. 2a, the AI / ML function or model output of the base station or TRP is just the LOS / NLOS indicator, that is, the AI / ML method is used to infer the LOS / NLOS possibility of the measured channel; and referring to FIG. 2b, the AI / ML function or model output of the base station or TRP is a positioning related time measurement quantity, such as UL RTOA, or time information required for calculating UL RTOA. Alternatively, the AI / ML function or model of the base station or TRP can output both the LOS / NLOS indicator and the positioning related time measurement quantity.

[0112] However, when the training target of the AI / ML function or model is to obtain a more accurate LOS / NLOS indicator, the LOS / NLOS indicator obtained based on AI / ML measurement and the LOS / NLOS indicator estimated by a non-AI / ML method have the same meaning, that is, at this time, the LOS / NLOS indicator based on AI / ML still has the original correlation with the DL RSTD or UL RTOA estimated by the non-AI / ML method.

[0113] And when the training target of the 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 a "time measurement quantity corresponding to a virtual LOS path", and the virtual LOS path is the path shown by the dashed line in FIG. 2b. At this time, the LOS / NLOS indicator estimated by the non-AI / ML method indicates the NLOS path in reality, that is, the LOS / NLOS indicator does not have the original correlation with the DL RSTD or UL RTOA estimated based on the AI / ML method.

[0114] In the case where the correlation between the LOS / NLOS indicator and the positioning related time measurement quantity no longer exists, if the positioning related time information measured by the AI / ML function or model is reported, the traditional LOS / NLOS indicator reporting method is still used, which can mislead the network device to solve the UE position, resulting in the problem of reducing the UE positioning accuracy.

[0115] Embodiments of the present disclosure provide a positioning information reporting method and device, and provide a reporting method for a positioning related time measurement quantity and a LOS / NLOS indicator, which can avoid reporting the LOS / NLOS indicator irrelevant to the positioning related time measurement quantity, and can avoid interference to the UE positioning accuracy, thereby improving the UE positioning accuracy. The method and the device are based on the same application concept, and the implementation of the device and the method can be mutually referred to, and the repeated parts will not be described herein.

[0116] The technical solutions provided by the embodiments of the present disclosure can be applied to various systems. For example, the applicable systems can be a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a long term evolution advanced (LTE-A) system, a universal mobile system (UMTS), a worldwide interoperability for microwave access (WiMAX) system, a 5G new radio (NR) system and its evolution communication system, and a 6G (sixth generation mobile communication technology) system. The various systems can include terminal devices and network devices. The system can also include a core network part, such as an evolved packet system (EPC), a 5G core network (5GC), and the like.

[0117] The terminal device to which the embodiments of the present disclosure relate can refer to a device providing voice and / or data connectivity to a user, a handheld device with wireless connection function, or other processing devices connected to a wireless modem, etc. In different systems, the name of the terminal device can also be different, for example, in the 5G system, the terminal device can be called user equipment (UE). The wireless terminal device can be a USB storage device, other personal computer memory devices and a dongle, and can also communicate with one or more core networks (CN) through a radio access network (RAN). The wireless terminal device can be a mobile terminal device, such as a mobile phone (or called "cellular" phone) and a computer with a mobile terminal device, for example, it can be a portable, pocket, handheld, built-in computer or vehicle-mounted mobile device, which exchanges voice and / or data with a radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), personal computers, tablet computers, machine-type communication (MTC) terminal devices, etc. The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, an access point, a remote terminal, an access terminal, a user terminal, a user agent, a user device, and a wireless access device and a router / modem that meet the limitations of the present definition, etc. The embodiments of the present disclosure are not limited.

[0118] The network device involved in the embodiments of the present disclosure can be a base station, which can include multiple cells serving terminals. According to different application scenarios, the base station can also be referred to as an access point, or can be a device in an access network that communicates with wireless terminal devices through one or more sectors over an air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets as a router between the wireless terminal device and the rest of the access network, which can include an Internet Protocol (IP) communication network. The network device can also coordinate the management of the properties of the air interface. For example, the network device involved in the embodiments of the present disclosure can be an evolved network device (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a 5G network architecture, etc., and can also be a home evolved base station (HeNB), a relay node, a femto, a pico, a network test device, etc., which is not limited in the embodiments of the present disclosure. In some network structures, the network device can include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit can also be arranged geographically apart.

[0119] The terminal device in the embodiments of the present disclosure sends relevant information or the like to the network device, which only indicates that the terminal device sends relevant information in a wireless signal manner, and the receiving end of the relevant information is the network device, and the network device can obtain the relevant information by receiving the wireless signal.

[0120] In one exemplary embodiment, the sending end (UE or base station or TRP, which will not be described below) 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 the LOS / NLOS index. For example, as shown in FIG. 3, a positioning information reporting method is provided, which is applied to the UE or base station or TRP in FIG. 1a as an example for illustration, including the following steps 301 to 302.

[0121] Step 301: determining the first positioning-related time information of the target terminal according to the first artificial intelligence / machine learning AI / ML function or model;

[0122] Step 302: sending the positioning information to the network device, wherein the positioning information includes the first positioning-related time information, and the positioning information does not include the first LOS / NLOS index.

[0123] In the embodiments of the present disclosure, in order to distinguish the positioning related time information determined by the AI / ML function or model from the positioning related time information measured by the non-AI / ML method, the positioning related time information determined by the AI / ML function or model in the embodiments of the present disclosure is referred to as first positioning time related information, and the positioning related time information measured by the non-AI / ML method is referred to as second positioning time related information. In the embodiments of the present disclosure, the AI / ML function or model used to determine the first positioning time related information is referred to as a first AI / ML function or model, and the AI / ML function or model used to determine the LOS / NLOS indicator is referred to as a 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 the embodiments of the present disclosure do not limit this.

[0124] In one example, taking the method applied to a UE as an example, the UE in this example is a target terminal. The UE receives a PRS signal and measures channel information based on the PRS signal. The channel information can include channel impulse response, channel frequency domain estimation, signal strength, time delay, signal quality, and the like. The UE provides the channel information as input to the first AI / ML function or model at the UE end. The output of the first AI / ML function or model includes first positioning related time information of the UE. The first positioning related time information can include a downlink reference signal time difference (DL RSTD), or related data for calculating the DL RSTD. Alternatively, the output of the first AI / ML function or model is information used to calculate the first positioning related time information, such as time of arrival (TOA) information.

[0125] In another example, taking the method applied to a base station or TRP as an example. The base station or TRP receives / measures SRS-pos sent by the UE to obtain channel information. The channel information can include channel impulse response, channel frequency domain estimation, signal strength, time delay, signal quality, and the like. The base station or TRP provides the channel information as input to the first AI / ML function or model at the base station or TRP end. The output of the first AI / ML function or model includes first positioning related time information of the UE. The first positioning related time information can include an uplink relative time of arrival (UL RTOA), or related data for calculating the UL RTOA. Alternatively, the output of the first AI / ML function or model is information used to calculate the first positioning related time information, such as time of arrival (TOA) information.

[0126] After determining the first positioning-related time information of the target terminal through the function or model of the first AI / ML, the first positioning-related time information can be sent to the network device providing the positioning-related service, and the first LOS / NLOS indicator measured through the non-AI / ML method is not sent to the network device. For example, refer to FIG. 4:

[0127] Step 4.1, the sending end can determine the first positioning-related time information through the function or model of the AI / ML.

[0128] Step 4.2, the sending end can report the first positioning-related time information to the network device, and does not report the first LOS / NLOS indicator.

[0129] Correspondingly, the network device can receive the positioning information sent by the sending end, the positioning information containing the first positioning-related time information and not containing the first LOS / NLOS indicator, the first positioning-related time information being the positioning-related time information of the target terminal determined according to the function or model of the first artificial intelligence / machine learning AI / ML. In this way, the network device side can perform positioning calculation of the target terminal based on the first positioning-related time information, and since the first LOS / NLOS indicator estimated in the non-AI / ML manner is not received at this time, one possible case is that the network device can default the first LOS / NLOS indicator as 1, that is, it is defaulted at this time that the LOS path or virtual LOS path exists between the UE and the TRP / base station, which can avoid the interference caused by the fact that the first LOS / NLOS indicator does not have a correlation with the first positioning-related time information, and can greatly improve the positioning accuracy of the terminal.

[0130] By using the reporting method of the positioning information provided in the embodiments of the present disclosure, in the case of determining the first positioning-related time information of the target terminal according to the function or model of the first artificial intelligence / machine learning AI / ML, the terminal or the base station or the TRP only sends the first positioning-related time information to the network device, and does not send the first LOS / NLOS indicator to the network device, so that when the network device side performs terminal position calculation based on the first positioning-related time information, the interference caused by the fact that the first LOS / NLOS indicator does not have a correlation with the first positioning-related time information is avoided, the positioning accuracy of the terminal is greatly improved, and unnecessary reporting overhead is avoided, and network resources are saved.

[0131] In an exemplary embodiment, the reporting method of the positioning information can further include:

[0132] The timing quality indication information is sent to the network device, and the timing quality indication information is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; and similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0133] In the embodiments of the present disclosure, the sending end (UE or base station or TRP) only sends the first positioning-related time information to the network device, and does not send the first LOS / NLOS indicator to the network device, but can report the inference quality of the first positioning-related time information, or the inference confidence of the function or model of the first AI / ML, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path, and other information to the network device through the timing quality indication information (nr-TimingQuality).

[0134] The similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path can be determined by the first LOS / NLOS indicator, for example: the greater the value of the first LOS / NLOS indicator, the closer to the LOS path, and the higher the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path; on the contrary, the smaller the value of the first LOS / NLOS indicator, the closer to the NLOS path, and the smaller the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path.

[0135] Or, 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 in combination with the current communication quality, channel quality, and the like, for example: in the case of good current network quality, good communication quality, and good channel quality, the first LOS / NLOS indicator has good accuracy, so the greater the value of the first LOS / NLOS indicator, the closer to the LOS path, and the higher the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path; on the contrary, the smaller the value of the first LOS / NLOS indicator, the closer to the NLOS path, and the smaller the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path; otherwise, in the case of poor current network quality, poor communication quality, and poor channel quality, the first LOS / NLOS indicator has poor accuracy, so whether the value of the first LOS / NLOS indicator is large or small, the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path is small.

[0136] In an exemplary embodiment, refer to FIG. 5:

[0137] Step 5.1, the sending end can determine the first positioning-related time information through the function or model of AI / ML;

[0138] Step 5.2, the sending end constructs the timing quality indication information based on the inference quality or confidence or similarity. (The implementation process of this step can refer to the related description of the foregoing embodiments, and will not be described here in the embodiments of the present disclosure.) It should be noted that the order of execution of steps 5.1 and 5.2 is not limited in the embodiments of the present disclosure. Step 5.2 can be executed first, and then step 5.1 can be executed, or steps 5.1 and 5.2 can be performed synchronously.

[0139] Step 5.3, the sending end reports the first positioning-related time information and the timing quality indication information to the network device, and does not report the first LOS / NLOS index.

[0140] Correspondingly, the network device can receive the 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.

[0141] By using the positioning information reporting method provided in the embodiments of the present disclosure, the sending end sends the first positioning-related time information and the timing quality indication information used to indicate the inference quality of the first positioning-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path to the network device. The network device can combine the inference quality of the first positioning-related time information, or the inference confidence of the first AI / ML function or model, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the virtual LOS path and the first positioning-related time information to solve the position of the UE. The calculation complexity can be reduced, for example, the calculation weight of the first positioning-related time information with low quality or low confidence is reduced, or they are ignored during solving, thereby greatly improving the positioning accuracy and positioning speed of the UE.

[0142] In one exemplary embodiment, the sending end can report the LOS / NLOS index while reporting the first positioning-related time information of the target terminal determined through the function or model of AI / ML. The above method can further include:

[0143] The network device can receive the second LOS / NLOS indicator sent by the sending end, wherein the second LOS / NLOS indicator is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; and similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path.

[0144] In the embodiment of the present disclosure, when the sending end sends the first positioning-related time to the network device, the sending end can also report, to the network device, the inference quality of the first positioning-related time information, or the inference confidence of the function or model of the first AI / ML, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path (or a virtual LOS path), through the second LOS / NLOS indicator, wherein the determination manner of the similarity can refer to the related description of the foregoing embodiment, which will not be described herein again in the embodiment of the present disclosure. For example, referring to FIG. 6:

[0145] Step 6.1, the sending end determines the first positioning-related time information through the function or model of the AI / ML.

[0146] Step 6.2a, the sending end can construct the second LOS / NLOS indicator based on the inference quality or the confidence or the similarity (the implementation process of this step can refer to the related description of the foregoing embodiment, which will not be described herein again in the embodiment of the present disclosure);

[0147] Step 6.3, the sending end can report, to the network device, the first positioning-related time information and the second LOS / NLOS indicator, and does not report the first LOS / NLOS indicator.

[0148] By using the reporting method of the positioning information provided in the embodiment of the present disclosure, the sending end sends, to the network device, the first positioning-related time information and the second LOS / NLOS indicator used to indicate the inference quality of the first positioning-related time information, or the inference confidence of the function or model of the first AI / ML, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path, and the network device can combine the inference quality of the first positioning-related time information, or the inference confidence of the function or model of the first AI / ML, or the similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path, and the first positioning-related time information to solve the position of the UE, which can reduce the solving complexity, for example, reduce the calculation weight of the first positioning-related time information with low quality or low confidence, or ignore them when solving, thereby greatly improving the positioning accuracy and positioning speed of the UE.

[0149] In another example, the method can further include: sending, by the sending end, a second LOS / NLOS indicator to the network device; and receiving, by the network device, the second LOS / NLOS indicator, wherein the second LOS / NLOS indicator has a predefined value, and the predefined value is 1 or a fixed value.

[0150] In the embodiments of the present disclosure, 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. In the value in the first LOS / NLOS indicator is used to indicate the possibility of a direct line of sight (LOS) / non-direct line of sight (NLOS) channel condition, and the value in the second LOS / NLOS indicator is a fixed value, which can include a fixed soft value, a fixed hard value, or other values. The fixed soft value is a fixed numerical value between 0 and 1, which is used to indicate a virtual LOS path; the fixed hard value is a pre-set 0 or a pre-set 1, which is also used to indicate a virtual LOS path; and the other value is a pre-set other numerical value or a symbol value, which is also used to indicate a virtual LOS path. For example, refer to FIG. 6:

[0151] Step 6.1, determining, by the sending end, the first positioning-related time information through the function or model of AI / ML;

[0152] Step 6.2b, the sending end can construct the second LOS / NLOS indicator based on the predefined value. (The implementation process of this step can refer to the related description of the foregoing embodiments, which will not be described here in the embodiments of the present disclosure.)

[0153] Step 6.3, the sending end can report the first positioning-related time information and the second LOS / NLOS indicator to the network device, and does not report the first LOS / NLOS indicator.

[0154] By using the reporting method of positioning information provided in the embodiments of the present disclosure, the sending end sends the first positioning-related time information and the second LOS / NLOS indicator for indicating whether the current path is a LOS path or a virtual LOS path to the network device, and the network device can combine the second LOS / NLOS indicator and the first positioning-related time information to solve the position of the UE, which can reduce the complexity of solving, greatly improve the positioning accuracy and positioning speed of the UE, and can maximize the use of existing reporting parameters, thereby achieving low complexity.

[0155] In an exemplary embodiment, the sending end can report one LOS / NLOS indicator and one positioning-related time information to the network device. The method further includes:

[0156] The sending end measures second positioning-related time information of the target terminal;

[0157] When the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship, the sending end sends first positioning-related time information to the network device; the network device receives the first positioning-related time information sent by the sending end; or,

[0158] When the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, the sending end sends second positioning-related time information to the network device, and the network device receives the second positioning-related time information sent by the sending end.

[0159] In the embodiments of the present disclosure, when the first LOS / NLOS indicator is less than the preset threshold value, it can be determined that the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship, and vice versa, when the first LOS / NLOS indicator is greater than or equal to the preset threshold value, the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship; or, when the first LOS / NLOS indicator is less than or equal to the preset threshold value, it can be determined that the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship, and vice versa, when the first LOS / NLOS indicator is greater than the preset threshold value, the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship. The preset threshold value is a value between 0 and 1 set in advance, and for example, the preset threshold value can be set to a value close to 0.

[0160] The sending end measures or estimates the second positioning-related time information of the target terminal by a method of non-AI / ML function or model. When it is determined that the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship, it indicates that the current path is more likely to be an NLOS path. At this time, the accuracy of the second positioning-related time information measured or estimated by the method of non-AI / ML function or model is low, and then the sending end can send the first positioning-related time information to the network device, and the network device can receive the second positioning-related time information sent by the sending end.

[0161] Conversely, when it is determined that the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, it indicates that the current path is more likely to be an LOS path. At this time, the accuracy of the second positioning-related time information measured or estimated by the method of non-AI / ML function or model is high, and then the sending end can send the second positioning-related time information to the network device, and the network device can receive the second positioning-related time information sent by the sending end.

[0162] In an example embodiment, the sending end can send the first LOS / NLOS indicator to the network device while sending the first positioning-related time information or the second positioning-related time information to the network device, i.e., the network device can receive the first LOS / NLOS indicator sent by the network device while receiving the first positioning-related time information or the second positioning-related time information sent by the sending end. For example, refer to FIG. 7:

[0163] Step 7.1, the sending end can determine the first positioning-related time information through the function or model of AI / ML;

[0164] Step 7.2, the sending end measures the second positioning-related time information based on a non-AI method; if the first LOS / NLOS indicator meets the preset relationship with the preset threshold value, step 7.3a is performed; or if the first LOS / NLOS indicator does not meet the preset relationship with the preset threshold value, step 7.3b is performed; it should be noted that the order of steps 7.1 and 7.2 is not limited in the example embodiment of the present disclosure, and step 7.2 can be performed first and then step 7.1 is performed, or steps 7.1 and 7.2 are performed synchronously;

[0165] Step 7.3a, the sending end reports the first positioning-related time information and the first LOS / NLOS indicator to the network device;

[0166] Step 7.3b, the sending end reports the second positioning-related time information and the first LOS / NLOS indicator to the network device.

[0167] By using the reporting method of positioning information provided in the example embodiment of the present disclosure, the sending end can select the positioning-related time information with higher accuracy based on the current first LOS / NLOS indicator and report it to the network device, which can not only save the reporting cost, but also greatly improve the positioning accuracy of the UE.

[0168] In an example embodiment, the sending end can report two LOS / NLOS indicators and two positioning-related time information to the network device. The method further comprises:

[0169] The sending end measures the second positioning-related time information of the target terminal; the sending end sends the second positioning-related time information and the first LOS / NLOS indicator to the network device, and sends the first positioning-related time information and the second LOS / NLOS indicator to the network device.

[0170] The network device can receive the second positioning-related time information and the first LOS / NLOS indicator sent by the sending end, and receive the first positioning-related time information and the second LOS / NLOS indicator sent by the sending end.

[0171] In the embodiments of the present disclosure, the sending end measures or estimates the second positioning-related time information of the target terminal through a method of non-AI / ML function or model, can send the second positioning-related time information and the corresponding first LOS / NLOS index to the network device, and can send the first positioning-related time information determined according to the AI / ML function or model and the corresponding second LOS / NLOS index to the network device.

[0172] The second LOS / NLOS index is used to indicate the inference quality of the first positioning-related time information, or is used to indicate the inference confidence of the first AI / ML function or model, or is used to indicate the similarity between the first positioning-related time information and the positioning-related time information corresponding to the LOS path (or the virtual LOS path); or the value of the second LOS / NLOS index is a predefined value, which is 1 or a fixed value. The explanation of the second LOS / NLOS index can refer to the related description of the foregoing embodiments, which will not be repeated here. For example, referring to FIG. 8:

[0173] Step 8.1, the sending end can determine the first positioning-related time information through the AI / ML function or model;

[0174] Step 8.2, the sending end measures the second positioning-related time information based on a non-AI method;

[0175] Step 8.3, the sending end constructs the second LOS / NLOS index based on the inference quality or confidence or similarity or predefined value;

[0176] Step 8.4, the sending end reports the second positioning-related time information and the first LOS / NLOS index to the network device, and reports the first positioning-related time information and the second LOS / NLOS index to the network device.

[0177] By using the reporting method of positioning information provided in the embodiments of the present disclosure, the network device can obtain all the positioning-related time information determined based on the AI / ML function or model and the positioning-related time information estimated by the non-AI method, and the respective quality evaluation or confidence information, so that the network device can have the maximum flexibility to solve the position of the UE, and the positioning accuracy and efficiency of the UE can be improved.

[0178] In an exemplary embodiment, when the positioning-related time information is measured by the sending end through a non-AI method, and the LOS / NLOS index is determined by the sending end through an AI / ML function or model. The sending end can report a third LOS / NLOS index to the network device, and report the positioning-related time information measured by the non-AI method to the network device. Referring to FIG. 9, the method further comprises:

[0179] Step 9.1, the sending end measures the second positioning-related time information of the target terminal;

[0180] Step 9.2, the sending end determines the third LOS / NLOS index of the target terminal through the function or model of the second AI / ML;

[0181] Step 9.3, the sending end sends the second positioning-related time information and the third LOS / NLOS index to the network device, and does not report the first LOS / NLOS index.

[0182] The network device can receive the second positioning-related time information and the third LOS / NLOS index sent by the sending end.

[0183] In order for those skilled in the art to better understand the reporting method of positioning information provided by the embodiments of the present disclosure, the embodiments of the present disclosure are described below through some examples.

[0184] The embodiments of the present disclosure are mainly applied to the 5G NR system, which includes network devices and terminal devices, wherein the network devices can include base stations, gNBs (next generation Node B (fifth generation mobile communication system base station)), TRPs, LMFs (Location Management Function), NWDAFs (Network Data Analytics Function), and the terminal devices can include user equipment or UEs, etc.; or, it can also be applied to other systems, such as 6G system, as long as the UE and / or TRP in the system reports the positioning measurement based on AI / ML inference to the core network element, such as LMF.

[0185] Referring to FIG. 10, multiple UEs including UE1 and UE2 in the NR system apply for wireless network connection services; gNB provides wireless services for them. gNB and UE1, UE2 exchange and transmit data through wireless communication, for example, gNB provides communication services to UE1 and UE2, including AI / ML related communication services, such as transmitting various indication information to UE1 and UE2. In addition, the location management function LMF is on the core network side and provides positioning related services, and the UE or TRP can report the positioning related measurement information to the LMF, including the AI / ML inference positioning related measurement information.

[0186] In one embodiment, the present embodiment is applicable to the case where the positioning related time information (such as DL RSTD or UL RTOA) is inferred by the AI / ML model of the UE or TRP, and only the AI / ML based positioning related time information is reported to the LMF.

[0187] When the UE receives / measures the channel obtained by PRS as the input of the AI / ML model on the UE side, the function or model inference of the AI / ML on the UE side obtains the positioning-related time information, and the UE reports the DL RSTD to the LMF, which is the result of the positioning-related time information based on the function model inference of the AI / ML, then the corresponding LOS / NLOS indicator processing method is one of the following:

[0188] (1) No LOS / NLOS indicator is reported.

[0189] Since the positioning-related time information obtained by the function or model inference of the AI / ML can be considered as a "time measurement quantity corresponding to a virtual LOS path", it is the positioning-related time information after overcoming the signal arrival time delay error caused by NLOS, so the positioning-related time information obtained by the function or model inference of the AI / ML is closer to the "positioning-related time information under the condition that there is a virtual LOS path between the TRP and the UE". At this time, the LOS / NLOS indicator can no longer reflect the degree of delay error introduced by NLOS path to the currently reported positioning-related time information, so the LOS / NLOS indicator can no longer be reported to reduce the reporting overhead, and avoid the positioning interference caused by the fact that the LOS / NLOS indicator has no correlation with the positioning-related time information, and improve the positioning accuracy.

[0190] In one example, at this time, it can be considered that the definition of the LOS / NLOS indicator is unchanged, and although the UE does not report the LOS / NLOS indicator, the LMF can understand and assume that its value is 1, that is, whether it is a soft value or a hard value report, that is, it is always understood as a virtual LOS path.

[0191] In another example, at this time, the nr-TimingQuality reported together with the positioning-related time information indicates the inference quality of the positioning-related time information, or the inference confidence of the function or model of the AI / ML, or the similarity between the positioning-related time information and the positioning-related time information corresponding to the virtual LOS path. In this way, the reliability / con dence / quality of AI inference can be reflected through nr-TimingQuality, so that the UE can more accurately provide the information related to the AI inference positioning-related time measurement quantity to the LMF, and assist the LMF to more accurately calculate the UE position.

[0192] (2) The LOS / NLOS indicator is reported, and its value is fixed as 1 or other predefined fixed value.

[0193] As mentioned before, the positioning related time information of the AI / ML function or model inference can be considered as a "time measurement quantity corresponding to a virtual LOS path", thus the positioning related time information of the AI / ML function or model inference can be understood as "positioning related time information under the condition that there is a virtual LOS path between the TRP and the UE". If it is necessary to report a LOS / NLOS indicator corresponding to an AI-based positioning related time information, a fixed value of 1 or other pre-defined fixed value can be considered to be reported, representing that the positioning related time information corresponds to a virtual LOS path.

[0194] For example, whether the soft value is reported or the hard value is reported, "1" can be reported to represent "certainly LOS"; or a pre-defined state / value can be reported, representing that the positioning related time information is obtained through AI / ML function or model inference and assuming that there is a LOS path. In this way, not only the existing reporting parameters can be used to the greatest extent, but also the complexity is low, and the positioning interference to the LMF in the case that the LOS / NLOS indicator and the positioning related time information do not have a correlation relationship can be avoided, and the positioning accuracy can be improved.

[0195] (3) LOS / NLOS indicator is reported, and indicates "confidence of AI inference", or "quality of AI inference", or "similarity between positioning related time information and positioning related time information corresponding to virtual LOS path".

[0196] Referring to the method of redefining / reinterpreting / reusing nr-TimingQuality in the foregoing embodiments, the reliability / confidence / quality of AI inference (positioning related time information) is indicated by LOS / NLOS, instead of the original meaning, so that the UE can more accurately provide information related to the time measurement quantity of AI inference positioning to the LMF, and assist the LMF to more accurately calculate the position of the UE.

[0197] In another embodiment, the present embodiment is applicable to the case that the positioning related time information (such as DL RSTD or UL RTOA) is obtained by AI / ML function or model inference of the UE or TRP, and the results of AI / ML inference and non-AI / ML measurement of the positioning related time information can be reported.

[0198] When the UE receives / measures the channel obtained by PRS, and both the AI / ML function or model on the UE side and the non-AI method can estimate the positioning-related time information, the UE reports to the LMF the positioning-related time information that can be the result of the positioning-related time information estimated by the AI / ML function or model, or the positioning-related time information estimated by the non-AI method, or both. The corresponding LOS / NLOS index processing method is one of the following:

[0199] (1) Only one positioning-related time information is reported, and:

[0200] When the value of the LOS / NLOS index is less than or equal to (or less than) the pre-defined or configured threshold value, only the positioning-related time information estimated by the AI / ML function or model is reported.

[0201] When the value of the LOS / NLOS index is greater than (or greater than or equal to) the pre-defined or configured threshold value, the positioning-related time information estimated by the non-AI method is reported.

[0202] Further, one LOS / NLOS index can also be reported: when the positioning-related time information estimated by the AI / ML function or model is reported, the measured LOS / NLOS index is also reported; or when the positioning-related time information estimated by the non-AI method is reported, the measured LOS / NLOS index is also reported.

[0203] This is because, in the related art, the LOS / NLOS index is used to measure the possibility of the channel being LOS in the non-AI case. The smaller the value, the more likely the real channel is NLOS, and the positioning-related time information estimated by the non-AI method is less accurate, so the result of the AI / ML function or model should be used to obtain the positioning-related time information. Conversely, the larger the value, the more likely the real channel is LOS, and the positioning-related time information estimated by the non-AI method is more accurate, so the positioning-related time information estimated by the non-AI method can be used. In this way, the reporting overhead is saved, and the most suitable positioning-related time information is selected for reporting.

[0204] (2) Two LOS / NLOS indexes are reported, corresponding to the positioning-related time information determined by the AI / ML function or model and the non-AI / ML method respectively, and:

[0205] The definition of the non-AI / ML LOS / NLOS index remains unchanged.

[0206] The definition of the LOS / NLOS indicator corresponding to the function or model of AI / ML includes: the LOS / NLOS indicator reporting, and the value of which is fixed as 1 or other predefined fixed value, or the LOS / NLOS indicator indicates "confidence of AI inference", or "quality of AI inference", or "similarity between the positioning-related time information and the positioning-related time information corresponding to the virtual LOS path".

[0207] In this way, the LMF can obtain the positioning-related time information obtained by all AI / ML-based functions or models and non-AI methods, and the respective corresponding quality evaluation or confidence information, so that the LMF has the greatest flexibility to solve the position of the UE.

[0208] In another embodiment, when the UE receives / measures the channel obtained by the PRS 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 output as the LOS / NLOS indicator, the LOS / NLOS indicator reporting manner is: reporting the LOS / NLOS indicator obtained by the AI / ML function or model inference, and the positioning-related time information obtained by the non-AI method.

[0209] It should be noted that the UE receives / measures the PRS sent by the TRP, deploys the AI / ML function or model on the UE side, and reports the positioning-related measurement information to the LMF. The above method is also applicable to the base station or TRP, that is, the base station or TRP receives / measures the SRS-pos sent by the UE, deploys the AI / ML function or model on the base station or TRP side, and reports the positioning-related measurement information to the LMF. This will not be described here in the embodiment of the disclosure, and the related description of the foregoing embodiments can be referred to.

[0210] By using the positioning information reporting method provided in the embodiment of the disclosure, there is a certain correlation relationship between the LOS / NLOS indicator measured by the non-AI method and the positioning-related time information measured by the non-AI method, but the correlation relationship between the LOS / NLOS indicator and the positioning-related time measurement quantity obtained by the inference of the AI / ML-based function or model no longer holds. If the traditional LOS / NLOS indicator reporting method is still used, it will mislead the LMF, and have a negative impact on the UE position solving of the LMF. The positioning information reporting method provided in the embodiment of the disclosure can correctly indicate the related quality or confidence evaluation information of the positioning-related time information obtained by the inference of the AI / ML-based function or model, which is beneficial to the UE position solving of the LMF, and can avoid unnecessary reporting overhead and save network resources.

[0211] It should be understood that although each step in the flowchart involved in each embodiment as described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless explicitly stated herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0212] Based on the same inventive concept, the embodiments of the present disclosure also provide a positioning information reporting apparatus for implementing the positioning information reporting method described above. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, and therefore some limitations in one or more positioning information reporting apparatus embodiments provided below can refer to the limitations of the positioning information reporting method described above, which will not be repeated here.

[0213] In one exemplary embodiment, as shown in FIG. 11, a positioning information reporting apparatus 1100 is provided, comprising a first determination module 1110 and a first sending module 1120, wherein:

[0214] The first determination module 1110 is configured to determine first positioning-related time information of the target terminal according to a function or model of first artificial intelligence / machine learning AI / ML;

[0215] The first sending module 1120 is configured to send the positioning information to the network device, wherein the first positioning-related time information is contained in the positioning information, and the first LOS / NLOS indicator is not contained in the positioning information.

[0216] In the above positioning information reporting apparatus, in the case of determining the first positioning-related time information of the target terminal according to the function or model of the first artificial intelligence / machine learning AI / ML, the terminal or the base station or the TRP only sends the first positioning-related time information to the network device, and does not send the first LOS / NLOS indicator to the network device, so that when the network device side solves the terminal position based on the first positioning-related time information, the interference caused by the fact that the first LOS / NLOS indicator does not have a correlation with the first positioning-related time information is avoided, the positioning accuracy of the terminal can be greatly improved, unnecessary reporting overhead is avoided, and network resources are saved.

[0217] In the embodiments of the present disclosure, the apparatus further comprises:

[0218] The second sending module is configured to send the timing quality indication information to the network device, and the timing quality indication information is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; and similarity between the first positioning-related time information and positioning-related time information corresponding to a virtual LOS path.

[0219] In the embodiments of the present disclosure, the apparatus further includes:

[0220] The third sending module is configured to send the second LOS / NLOS indicator to the network device, where the second LOS / NLOS indicator is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; and similarity between the first positioning-related time information and positioning-related time information corresponding to a LOS path; or the value of the second LOS / NLOS indicator is a predefined value, and the predefined value is 1 or a fixed value.

[0221] In the embodiments of the present disclosure, the apparatus further includes:

[0222] The first measuring module is configured to measure the second positioning-related time information of the target terminal.

[0223] The fourth sending module is configured to send the first positioning-related time information to the network device when the first LOS / NLOS indicator and the preset threshold value satisfy a preset relationship, and send the second positioning-related time information to the network device when the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship.

[0224] In the embodiments of the present disclosure, the fourth sending module is further configured to:

[0225] The fourth sending module is configured to send the first positioning-related time information or the second positioning-related time information to the network device, and also send the first LOS / NLOS indicator to the network device.

[0226] In the embodiments of the present disclosure, the apparatus further includes:

[0227] The second measuring module is configured to measure the second positioning-related time information of the target terminal.

[0228] The fifth sending module is configured to send the second positioning-related time information and the first LOS / NLOS indicator to the network device, and send the first positioning-related time information and the second LOS / NLOS indicator to the network device.

[0229] In the embodiments of the present disclosure, the apparatus further includes:

[0230] The third measuring module is configured to measure the second positioning-related time information of the target terminal.

[0231] The second determining module is configured to determine a third LOS / NLOS indicator of the target terminal by using a function or a model of the second AI / ML.

[0232] The sixth sending module is configured to send the second positioning-related time information and the third LOS / NLOS indicator to the network device.

[0233] In the embodiments of the present disclosure, the apparatus is applied to any one of the following: a terminal; a base station; a transmission and reception point (TRP). When the apparatus is applied to the terminal, the first positioning-related time information comprises a downlink reference signal time difference. Alternatively, when the apparatus is applied to the base station or the TRP, the first positioning-related time information comprises an uplink relative time of arrival.

[0234] In one exemplary embodiment, as shown in FIG. 12, a reporting apparatus 1200 of positioning information is provided, comprising: a first receiving module 1210, wherein:

[0235] The first receiving module 1210 is configured to receive the first positioning-related time information sent by the sending end, wherein the first positioning-related time information is positioning-related time information of the target terminal determined according to a function or a model of the first artificial intelligence / machine learning (AI / ML).

[0236] In the case where the first positioning-related time information of the target terminal is determined according to the function or the model of the first artificial intelligence / machine learning (AI / ML), the terminal or the base station or the TRP only sends the first positioning-related time information to the network device, and does not send the first LOS / NLOS indicator to the network device, so that when the network device side performs terminal position calculation based on the first positioning-related time information, the interference caused by the fact that the first LOS / NLOS indicator and the first positioning-related time information do not have a correlation is avoided, the positioning accuracy of the terminal can be greatly improved, unnecessary reporting overhead is avoided, and network resources are saved.

[0237] In the embodiments of the present disclosure, the apparatus further comprises:

[0238] The second receiving module is configured to receive timing quality indication information sent by the sending end, wherein the timing quality indication information is used to indicate any one of the following: inference quality of the first positioning-related time information; inference confidence of the function or the model of the first AI / ML; and similarity between the first positioning-related time information and positioning-related time information corresponding to a virtual LOS path.

[0239] In the embodiments of the present disclosure, the apparatus further comprises:

[0240] The third receiving module is configured to receive the second LOS / NLOS indicator sent by the sending end, wherein the second LOS / NLOS indicator is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; similarity between the first positioning-related time information and positioning-related time information corresponding to the LOS path; or the value of the second LOS / NLOS indicator is a predefined value, and the predefined value is 1 or a fixed value.

[0241] In the embodiments of the present disclosure, the apparatus further comprises:

[0242] The fourth receiving module is configured to receive the first positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and the preset threshold value satisfy the preset relationship; or receive the second positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, and the second positioning-related time information is the positioning-related time information of the target terminal measured by the sending end.

[0243] In the embodiments of the present disclosure, the fourth receiving module is further configured to:

[0244] The first LOS / NLOS indicator is received by the receiving module at the same time as the first positioning-related time information or the second positioning-related time information sent by the sending end.

[0245] In the embodiments of the present disclosure, the apparatus further comprises:

[0246] The fifth receiving module is configured to receive the second positioning-related time information and the first LOS / NLOS indicator sent by the sending end, and the second positioning-related time information is the positioning-related time information of the target terminal measured by the sending end; and receive the first positioning-related time information and the second LOS / NLOS indicator sent by the sending end.

[0247] In the embodiments of the present disclosure, the apparatus further comprises:

[0248] The sixth receiving module is configured to receive the second positioning-related time information and the third LOS / NLOS indicator sent by the sending end, wherein the second positioning-related time information is the positioning-related time information of the target terminal measured by the sending end, and the third LOS / NLOS indicator is the LOS / NLOS indicator of the target terminal determined by the function or model of the second AI / ML.

[0249] In the embodiments of the present disclosure, the sending end is any of the following: a terminal; a base station; a sending and receiving node;

[0250] When the sending end is a terminal, the first positioning-related time information includes a downlink reference signal time difference; or when the sending end is a base station or a sending and receiving node, the first positioning-related time information includes an uplink relative time of arrival.

[0251] It should be noted that the division of units in the embodiments of the present disclosure is illustrative, and is only a logical function division. In actual implementation, another division manner can be used. In addition, each functional unit in each embodiment of the present disclosure can be integrated in one processing unit, or each unit can be physically present independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0252] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, the integrated unit can be stored in a processor-readable storage medium. Based on this understanding, the technical solutions of the present disclosure, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the methods in the various embodiments of the present disclosure.

[0253] It should be noted that the above-described apparatus provided by the embodiments of the present disclosure can realize all the method steps realized by the above-described method embodiments, and can achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments will not be described in detail.

[0254] In one exemplary embodiment, a terminal device is provided, and a structure of the terminal device can be as shown in FIG. 13. The terminal device includes a memory 1320, a transceiver 1310, and a processor 1300.

[0255] The transceiver is configured to receive and send data under the control of the processor.

[0256] In FIG. 13, the bus architecture can include any number of interconnected buses and bridges, specifically, various circuitry of the processor(s) represented by the processor and the memory represented by the memory linked together. The bus architecture can also link various other circuitry such as peripheral devices, voltage regulators, and power management circuitry, which are well known in the art, and thus, are not further described herein. The bus interface provides an interface. The transceiver can be a plurality of elements, i.e., including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, and the like. The user interface can also be an interface capable of coupling to various devices, including but not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like, for a user of the various user devices.

[0257] The processor is responsible for managing the bus architecture and general processing, and the memory can store data used by the processor 1300 in executing operations.

[0258] Optionally, the processor can be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a CPLD (Complex Programmable Logic Device), and the processor can also adopt a multi-core architecture.

[0259] The processor executes any of the methods provided by the embodiments of the present disclosure by invoking the program stored in the memory. The processor and the memory can also be physically arranged separately.

[0260] It should be noted that the terminal device provided by the embodiments of the present disclosure can implement all the method steps achieved by the above-mentioned method embodiments, and achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments are not described in detail herein.

[0261] In one exemplary embodiment, a base station is provided, and the structure of the base station can be as shown in FIG. 14. The base station includes a memory 1420, a transceiver 1410, and a processor 1400.

[0262] The transceiver is configured to receive and send data under the control of the processor.

[0263] In FIG. 14, the bus architecture can include any number of interconnected buses and bridges, specifically, various circuitry of one or more processors represented by the processor and memory represented by the memory linked together. The bus architecture can also link various other circuitry such as peripheral devices, voltage regulators, and power management circuitry, which are well known in the art, and thus, are not further described herein. The bus interface provides an interface. The transceiver can be a plurality of elements, i.e., including a transmitter and a receiver, providing a unit for communicating with various other apparatuses on transmission media, including wireless channels, wired channels, optical cables, and the like. The processor is responsible for managing the bus architecture and general processing, and the memory can store data used by the processor in performing operations.

[0264] 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), and the processor can also adopt a multi-core architecture.

[0265] The processor calls a program stored in the memory to execute any of the above methods provided by the embodiments of the present disclosure according to the executable instructions obtained. The processor and the memory can also be physically arranged separately.

[0266] It should be noted that the above device provided by the embodiments of the present disclosure can realize all the method steps realized by the above method embodiments, and can achieve the same technical effects. The same parts and beneficial effects of the method embodiments in this embodiment will not be described in detail.

[0267] In one exemplary embodiment, a network device is provided, and the structure of the network device can be as shown in FIG. 15. The base station includes a memory 1520, a transceiver 1510, and a processor 1500.

[0268] The transceiver is configured to receive and send data under the control of the processor.

[0269] In FIG. 15, the bus architecture can include any number of interconnected buses and bridges, which are well known in the art and thus, not further described herein. The bus interface provides an interface to the memory. The transceiver can be a plurality of elements, including a transmitter and a receiver, which are arranged to provide a communication link with various other devices over a transmission medium, including wireless channels, wired channels, optical cables, and the like. The processor is responsible for managing the bus architecture and general processing, and the memory can store data used by the processor in executing its operations.

[0270] 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), or the processor can be a multi-core processor.

[0271] 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), or the processor can be a multi-core processor.

[0272] It should be noted that the above-described apparatus provided by the embodiments of the present disclosure can implement all the method steps achieved by the above-described method embodiments and achieve the same technical effects. Therefore, the same parts and beneficial effects of the embodiments of the present disclosure are not described herein.

[0273] In one example embodiment, a positioning information reporting apparatus, which can be a terminal device, a base station or a network device, is provided, and includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps in the above-described method embodiments.

[0274] In one example embodiment, a processor-readable storage medium is provided, and the processor-readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps in the above-described method embodiments.

[0275] In one example embodiment, a computer program product is provided, and the computer program product includes a computer program. The computer program is executed by a processor to implement the steps in the above-described method embodiments.

[0276] The processor-readable storage media can be any available media or data storage device that can be accessed by a processor including, but not limited to, magnetic storage devices (e.g., floppy disks, hard disks, tape, etc.), optical storage devices (e.g., CD-ROMs, DVDs, BDs, HVDs, etc.), and semiconductor memory devices (e.g., ROM, EPROM, EEPROM, NAND FLASH, solid state drives (SSDs), etc.). The processor-readable storage media can be tangible and non-transitory.

[0277] Those skilled in the art will appreciate that embodiments of the disclosure can be supplied as a method, a system, or a computer program product. Thus, the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the disclosure can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, magnetic disks and optical storage media) embodying computer-readable program code.

[0278] The disclosure is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart 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, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate means for implementing the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram.

[0279] These processor executable instructions can also be stored in a processor readable memory that can direct the computer or other programmable data processing apparatus to function in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device that implements the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram.

[0280] It will be apparent that various modifications and variations can be made to the present disclosure without departing from the spirit and scope of the disclosure. Thus, it is intended that the present disclosure cover the modifications and variations of this disclosure provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method of reporting positioning information, wherein, The method comprises: determining first positioning-related time information of a target terminal according to a function or model of first artificial intelligence / machine learning (AI / ML); and sending positioning information to a network device, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise a first LOS / NLOS indicator.

2. The method of claim 1, wherein, The method further comprises: sending timing quality indication information to the network device, wherein the timing quality indication information is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; similarity between the first positioning-related time information and positioning-related time information corresponding to a virtual LOS path.

3. The method of claim 1, wherein, The method further comprises: sending a second LOS / NLOS indicator to the network device, wherein the second LOS / NLOS indicator is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; similarity between the first positioning-related time information and positioning-related time information corresponding to a LOS path; or a value of the second LOS / NLOS indicator is a predefined value, and the predefined value is 1 or a fixed value.

4. The method of claim 1, wherein, The method further comprises: measuring second positioning-related time information of the target terminal; when the first LOS / NLOS indicator and a preset threshold value satisfy a preset relationship, sending the first positioning-related time information to the network device; when the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, sending the second positioning-related time information to the network device.

5. The method of claim 4, wherein, The method further comprises: when the first positioning-related time information or the second positioning-related time information is sent to the network device, the first LOS / NLOS indicator is also sent to the network device.

6. The method of claim 3, wherein, The method further comprises: measuring second positioning-related time information of the target terminal; sending the second positioning-related time information and the first LOS / NLOS indicator to the network device; and sending the first positioning-related time information and the second LOS / NLOS indicator to the network device.

7. The method of claim 1, wherein, The method comprises: measuring second positioning-related time information of the target terminal; determining a third LOS / NLOS indicator of the target terminal through a function or model of second AI / ML; and sending the second positioning-related time information and the third LOS / NLOS indicator to the network device.

8. The method of any one of claims 1 to 7, wherein, The method is applied to any of the following: a terminal; a base station; a transmission and reception point (TRP); when the method is applied to a terminal, the first positioning-related time information comprises a downlink reference signal time difference; when the method is applied to a base station or a transmission and reception point (TRP), the first positioning-related time information comprises an uplink relative time of arrival.

9. A method of reporting positioning information, wherein, The method comprises: receive positioning information sent by a sending end, the positioning information containing first positioning-related time information and not containing a first LOS / NLOS indicator, the first positioning-related time information being positioning-related time information of a target terminal determined according to a function or model of first artificial intelligence / machine learning (AI / ML).

10. The method of claim 9, wherein, The method further includes: receive timing quality indication information sent by the sending end, the timing quality indication information being used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; similarity between the first positioning-related time information and positioning-related time information corresponding to a virtual LOS path.

11. The method of claim 9, wherein, The method further includes: receive a second LOS / NLOS indicator sent by the sending end, wherein the second LOS / NLOS indicator is used to indicate any of the following: inference quality of the first positioning-related time information; inference confidence of the function or model of the first AI / ML; similarity between the first positioning-related time information and positioning-related time information corresponding to a LOS path; or a value of the second LOS / NLOS indicator is a predefined value, and the predefined value is 1 or a fixed value.

12. The method of claim 9, wherein, The method further includes: receive the first positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and a preset threshold value satisfy a preset relationship; or receive second positioning-related time information sent by the sending end, wherein the first LOS / NLOS indicator and the preset threshold value do not satisfy the preset relationship, and the second positioning-related time information is positioning-related time information of the target terminal measured by the sending end.

13. The method of claim 12, wherein, The method further includes: receive the first LOS / NLOS indicator sent by the sending end at the same time of receiving the first positioning-related time information or the second positioning-related time information sent by the sending end.

14. The method of claim 11, wherein, The method further includes: receive second positioning-related time information and the first LOS / NLOS indicator sent by the sending end, the second positioning-related time information being positioning-related time information of the target terminal measured by the sending end; receive the first positioning-related time information and the second LOS / NLOS indicator sent by the sending end.

15. The method of claim 9, wherein, The method further includes: receive second positioning-related time information and a third LOS / NLOS indicator sent by the sending end, wherein the second positioning-related time information is positioning-related time information of the target terminal measured by the sending end, and the third LOS / NLOS indicator is a LOS / NLOS indicator of the target terminal determined by a function or model of second AI / ML.

16. The method of any one of claims 9 to 15, wherein, The sending end is any of the following: a terminal; a base station; a sending / receiving node; when the sending end is a terminal, the first positioning-related time information includes a downlink reference signal time difference; When the sending end is a base station or a transmission-reception node, the first positioning-related time information comprises an uplink relative time of arrival.

17. A reporting apparatus of positioning information, wherein, The apparatus comprises: a first determining module configured to determine first positioning-related time information of a target terminal according to a function or model of first artificial intelligence / machine learning (AI / ML); a first sending module configured to send positioning information to a network device, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise a first LOS / NLOS indicator.

18. A reporting apparatus of positioning information, wherein, The apparatus comprises: a first receiving module configured to receive first positioning-related time information sent by a sending end, wherein the first positioning-related time information is determined according to a function or model of first artificial intelligence / machine learning (AI / ML) and is related to positioning of a target terminal.

19. A terminal device, wherein, The terminal device comprises a memory, a transceiver, and a processor: the memory is configured to store a computer program; the transceiver is configured to transceive data under control of the processor; the processor is configured to read the computer program in the memory and perform the following operations:

20. A base station, wherein, determine first positioning-related time information of a target terminal according to a function or model of first artificial intelligence / machine learning (AI / ML); and send positioning information to a target network device, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise a first LOS / NLOS indicator. The base station comprises a memory, a transceiver, and a processor: the memory is configured to store a computer program; 21. A network device, wherein, the transceiver is configured to transceive data under control of the processor; the processor is configured to read the computer program in the memory and perform the following operations: determine first positioning-related time information of a target terminal according to a function or model of first artificial intelligence / machine learning (AI / ML); and 22. A computer readable storage medium having stored thereon a computer program, wherein, send positioning information to a network device, wherein the positioning information comprises the first positioning-related time information, and the positioning information does not comprise a first LOS / NLOS indicator. The network device comprises a memory, a transceiver, and a processor: the memory is configured to store a computer program; the transceiver is configured to transceive data under control of the processor; the processor is configured to read the computer program in the memory and perform the following operations: receive first positioning-related time information sent by a sending end, wherein the first positioning-related time information is determined according to a function or model of first artificial intelligence / machine learning (AI / ML) and is related to positioning of a target terminal. The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 16.

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