Positioning method, positioning testing method, terminal, and network device

Abstract

The present disclosure relates to a positioning method, a positioning testing method, a terminal, and a network device. The method executed by a terminal comprises: determining first data, the first data being used for positioning the terminal on the basis of a positioning artificial intelligence (AI) model, and the first data comprising at least one of a measurement result of a positioning reference signal (PRS) and trajectory information of the terminal within a first duration. In this way, the terminal can determine the first data, and positioning of the terminal is performed on the basis of the positioning AI model, thereby improving the performance of a communication system.

Description

Positioning method, positioning test method, terminal, network equipment Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a positioning method, a positioning test method, a terminal, and a network device. Background Art

[0002] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in various fields. AI is also rapidly interpenetrating into other disciplines, integrating knowledge from different disciplines while also providing new directions and methods for their development.

[0003] Summary of the Invention

[0004] The positioning method, positioning test method, terminal, and network device provided in the embodiments of the present disclosure are used to solve the problem of how to locate the terminal based on a positioning AI model.

[0005] The embodiments of the present disclosure provide a positioning method, a positioning test method, a terminal, and a network device.

[0006] According to a first aspect of an embodiment of the present disclosure, a positioning method is proposed, which is executed by a terminal, including: determining first data, wherein the first data is used to locate the terminal based on a positioning artificial intelligence AI model; the first data includes at least one of a measurement result of a positioning reference signal PRS and trajectory information of the terminal within a first duration period.

[0007] In the above embodiment, the terminal can determine the first data to locate the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0008] According to a second aspect of an embodiment of the present disclosure, a positioning method is proposed, which is executed by a network device, including: determining first data, wherein the first data is used to locate the terminal based on a positioning AI model; the first data includes at least one of the measurement results of the positioning reference signal PRS and the trajectory information of the terminal within a first duration period.

[0009] In the above embodiment, the network device can determine the first data to locate the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0010] According to a third aspect of an embodiment of the present disclosure, a positioning test method is proposed, which is executed by a terminal, including: determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method described in the first aspect above.

[0011] In the above embodiment, the terminal can determine a training data set to train the first AI model based on the training data set to obtain a positioning AI model, and then realize positioning of the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0012] According to a fourth aspect of an embodiment of the present disclosure, a positioning test method is proposed, which is executed by a network device, including: determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method described in the first aspect above; or receiving first indication information sent by a terminal, wherein the first indication information is used to indicate the positioning AI model in the positioning method described in the first aspect above.

[0013] In the above embodiment, the network device can determine a training data set to train the first AI model based on the training data set to obtain a positioning AI model, or the network device can receive a positioning AI model indicated by the terminal, and then realize positioning of the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0014] According to a fifth aspect of an embodiment of the present disclosure, a terminal is provided, including: a processing module for determining first data, wherein the first data is used to locate the terminal based on a positioning artificial intelligence (AI) model; the first data includes at least one of a measurement result of a PRS and trajectory information of the terminal within a first duration period.

[0015] According to the sixth aspect of an embodiment of the present disclosure, a network device is provided, including: a processing module for determining first data, wherein the first data is used to locate a terminal based on a positioning AI model; the first data includes at least one of a measurement result of a PRS and trajectory information of the terminal within a first duration period.

[0016] According to a seventh aspect of an embodiment of the present disclosure, a terminal is provided, comprising: a processing module for determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method described in the first aspect above.

[0017] According to an eighth aspect of an embodiment of the present disclosure, a network device is provided, including: a processing module for determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method as described in the second aspect above; or a transceiver module for receiving first indication information sent by a terminal, wherein the first indication information is used to indicate the positioning AI model in the positioning method as described in the second aspect above.

[0018] According to a ninth aspect of an embodiment of the present disclosure, a terminal is provided, comprising: one or more processors; a memory coupled to the processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the terminal executes the methods described in the first and third aspects.

[0019] According to the tenth aspect of the embodiment of the present disclosure, a network device is provided, comprising: one or more processors; a memory coupled to the processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the network device is used to execute the methods described in the second aspect and the fourth aspect.

[0020] According to the eleventh aspect of an embodiment of the present disclosure, a communication system is provided, including a terminal and a network device, wherein the terminal is configured to implement the methods described in the first and third aspects, and the network device is configured to implement the methods described in the second and fourth aspects.

[0021] According to the twelfth aspect of the embodiment of the present disclosure, a storage medium is provided, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first aspect, the third aspect or the second aspect, the fourth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure;

[0023] FIG2A is a flow chart of a positioning method provided by an embodiment of the present disclosure;

[0024] FIG2B is a flow chart of another positioning method provided by an embodiment of the present disclosure;

[0025] FIG2C is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0026] FIG3A is a flow chart of a positioning test method provided by an embodiment of the present disclosure;

[0027] FIG3B is a flow chart of another positioning test method provided by an embodiment of the present disclosure;

[0028] FIG4A is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0029] FIG4B is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0030] FIG4C is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0031] FIG4D is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0032] FIG5A is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0033] FIG5B is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0034] FIG6A is a flowchart of another positioning test method provided by an embodiment of the present disclosure;

[0035] FIG6B is a flowchart of another positioning test method provided by an embodiment of the present disclosure;

[0036] FIG7A is a flowchart of another positioning test method provided by an embodiment of the present disclosure;

[0037] FIG7B is a flowchart of another positioning test method provided by an embodiment of the present disclosure;

[0038] FIG8A is a flowchart of another positioning method provided by an embodiment of the present disclosure;

[0039] FIG8B is a block diagram of a positioning test method provided by an embodiment of the present disclosure;

[0040] FIG8C is a timing diagram of a positioning test method provided by an embodiment of the present disclosure;

[0041] FIG9A is a structural diagram of a terminal provided by an embodiment of the present disclosure;

[0042] FIG9B is a structural diagram of a network device provided by an embodiment of the present disclosure;

[0043] FIG10A is a structural diagram of a communication device provided by an embodiment of the present disclosure;

[0044] FIG10B is a schematic structural diagram of a chip provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] The embodiments of the present disclosure provide a positioning method, a positioning test method, a terminal, and a network device.

[0046] In a first aspect, an embodiment of the present disclosure proposes a positioning method, which is executed by a terminal, including: determining first data, wherein the first data is used to locate the terminal based on a positioning artificial intelligence AI model; the first data includes at least one of the measurement results of the positioning reference signal PRS and the trajectory information of the terminal within a first duration period.

[0047] In the above embodiment, the terminal can determine the first data to locate the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the terminal determines the measurement result of the PRS, including: receiving the PRS sent by the network device; and measuring the PRS to determine the measurement result of the PRS.

[0049] In the above embodiment, the terminal may receive the PRS sent by the network device, perform measurement, and obtain the measurement result of the PRS to determine the measurement result of the PRS in the first data.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the measurement result of the PRS includes at least one of TDoA, RSTD, and RSRP of the PRS.

[0051] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal receives a request message sent by the network device, wherein the request message is used to request the terminal to report the ability to support positioning of the terminal based on the positioning AI model; according to the request message, sending capability information to the network device, wherein the capability information is used to indicate that the terminal supports positioning of the terminal based on the positioning AI model.

[0052] In the above embodiment, the terminal can report to the network device whether the terminal supports the ability to locate the terminal based on the positioning AI model based on the request of the network device, so that the network device can execute the corresponding terminal positioning method based on the terminal's capability.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the terminal determines the trajectory information of the terminal within the first duration, including: according to the request message, the terminal supports positioning the terminal based on the positioning AI model, determines the position of the terminal saved in the subsequent first duration period, and determines the trajectory information of the terminal within the first duration.

[0054] In the above embodiment, the terminal may determine the trajectory information of the terminal within the first duration in the first data.

[0055] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal determines the predicted trajectory information based on the first data and the positioning AI model; and determines the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0056] In the above embodiment, the terminal can determine the location information of the terminal based on the first data and the positioning AI model to achieve positioning of the terminal.

[0057] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal determines the predicted trajectory information based on the first data and the positioning AI model; and sends the predicted trajectory information and the PRS measurement results to the network device.

[0058] In the above embodiment, the terminal can determine the predicted trajectory information based on the first data and the positioning AI model, and send the predicted trajectory information and the measurement results of the PRS to the network device, so as to determine the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS at the network device to achieve positioning of the terminal.

[0059] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the terminal sends first data to the network device, wherein the first data is used by the network device to determine the location information of the terminal based on the first data and the positioning AI model.

[0060] In the above embodiment, the terminal can send the first data to the network device so that the network device can determine the location information of the terminal based on the first data and the positioning AI model to achieve positioning of the terminal.

[0061] In the second aspect, an embodiment of the present disclosure proposes a positioning method, which is executed by a network device, including: determining first data, wherein the first data is used to locate the terminal based on a positioning AI model; the first data includes at least one of the measurement results of the positioning reference signal PRS and the trajectory information of the terminal within a first duration period.

[0062] In the above embodiment, the network device can determine the first data to locate the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0063] In combination with some embodiments of the second aspect, in some embodiments, the measurement result of the PRS includes at least one of TDoA, RSTD, and RSRP of the PRS.

[0064] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: the network device sends a request message to the terminal, wherein the request message is used to request the terminal to report the ability to support positioning of the terminal based on the positioning AI model; receiving capability information sent by the terminal, wherein the capability information is used to indicate that the terminal supports positioning of the terminal based on the positioning AI model.

[0065] In the above embodiment, the network device may request the terminal to report whether it supports the capability of locating the terminal based on the positioning AI model, so that the network device executes the corresponding terminal positioning method based on the capability of the terminal.

[0066] In combination with some embodiments of the second aspect, in some embodiments, the network device determines the measurement result of the PRS, including: receiving the measurement result of the PRS sent by the terminal; and determining the measurement result of the PRS.

[0067] In the above embodiment, the network device may receive the measurement result of the PRS sent by the terminal to determine the measurement result of the PRS in the first data.

[0068] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: the network device receives predicted trajectory information sent by the terminal, wherein the predicted trajectory information is determined by the terminal based on the first data and the positioning AI model; and determines the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0069] In the above embodiment, the network device can receive the predicted trajectory information sent by the terminal based on the first data and the positioning AI model, and then determine the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS to achieve positioning of the terminal.

[0070] In combination with some embodiments of the second aspect, in some embodiments, the network device determines the first data, including: receiving the first data sent by the terminal; and determining the first data.

[0071] In the above embodiment, the network device may receive the first data sent by the terminal to determine the first data.

[0072] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: the network device determines the predicted trajectory information based on the first data and the positioning AI model; and determines the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0073] In the above embodiment, the network device can determine the location information of the terminal based on the first data and the positioning AI model to achieve positioning of the terminal.

[0074] In a third aspect, an embodiment of the present disclosure proposes a positioning test method, which is executed by a terminal, including: determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method described in the first aspect above.

[0075] In the above embodiment, the terminal can determine a training data set to train the first AI model based on the training data set to obtain a positioning AI model, and then realize positioning of the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0076] In conjunction with some embodiments of the third aspect, in some embodiments, the training dataset includes:

[0077] The second data and the real location information of the terminal; wherein the second data includes at least one of the trajectory information of the terminal within the second duration period and the measurement results of multiple PRSs with a specified time interval.

[0078] In combination with some embodiments of the third aspect, in some embodiments, the above method also includes: the terminal trains the first AI model according to the training data set to obtain a positioning AI model.

[0079] In the above embodiment, the terminal can train the first AI model based on the training data set to obtain a positioning AI model, and then realize positioning of the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0080] In combination with some embodiments of the third aspect, in some embodiments, the above method also includes: first indication information sent by the terminal to the network device, wherein the first indication information is used to indicate the positioning AI model.

[0081] In the above embodiment, the terminal can send the trained positioning AI model to the network device, so as to locate the terminal based on the positioning AI model on the network device side to achieve positioning of the terminal.

[0082] In combination with some embodiments of the third aspect, in some embodiments, the above method also includes: the terminal sends a training data set to the network device, wherein the training data set is used by the network device to train the first AI model to obtain a positioning AI model.

[0083] In the above embodiment, the terminal can send a training data set to the network device, so that the first AI model is trained on the network device side based on the training data set to obtain a positioning AI model.

[0084] In combination with some embodiments of the third aspect, in some embodiments, the above method also includes: the terminal receives second indication information sent by the network device, wherein the second indication information is used to indicate the positioning AI model.

[0085] In the above embodiment, after the positioning AI model is trained at the network device, the terminal can receive the second indication information sent by the network device, determine the positioning AI model, and locate the terminal based on the positioning AI model.

[0086] In a fourth aspect, an embodiment of the present disclosure proposes a positioning test method, which is executed by a network device, including: determining a training data set, wherein the training data set is used to train a first AI model to obtain a positioning AI model in the positioning method described in the second aspect above; or receiving a first indication information sent by a terminal, wherein the first indication information is used to indicate the positioning AI model in the positioning method described in the second aspect above.

[0087] In the above embodiment, the network device can determine a training data set to train the first AI model based on the training data set to obtain a positioning AI model, or the network device can receive a positioning AI model indicated by the terminal, and then realize positioning of the terminal based on the positioning AI model, which can improve the performance of the communication system.

[0088] In conjunction with some embodiments of the fourth aspect, in some embodiments, the training dataset includes:

[0089] The second data and the real location information of the terminal; wherein the second data includes at least one of the trajectory information of the terminal within the second duration period and the measurement results of multiple PRSs with a specified time interval.

[0090] In combination with some embodiments of the fourth aspect, in some embodiments, the network device determines the training data set, including: receiving the training data set sent by the terminal; and determining the training data set.

[0091] In combination with some embodiments of the fourth aspect, in some embodiments, the above method also includes: the network device trains the first AI model according to the training data set to obtain a positioning AI model.

[0092] In combination with some embodiments of the fourth aspect, in some embodiments, the above method also includes: the network device sends second indication information to the terminal, wherein the second indication information is used to indicate the positioning AI model.

[0093] In a fifth aspect, an embodiment of the present disclosure proposes a terminal, which includes at least one of a transceiver module and a processing module; wherein the terminal is used to execute the optional implementation methods of the first aspect and the third aspect.

[0094] In a sixth aspect, an embodiment of the present disclosure proposes a network device, which includes at least one of a transceiver module and a processing module; wherein the network device is used to execute the optional implementation methods of the second aspect and the fourth aspect.

[0095] In the seventh aspect, an embodiment of the present disclosure proposes a terminal, which includes: one or more processors; wherein, a memory coupled to the processor, the memory storing instructions, when the instructions are executed by the processor, enables the terminal to execute the optional implementation methods of the first aspect and the third aspect.

[0096] In an eighth aspect, an embodiment of the present disclosure proposes a network device, which includes: one or more processors; a memory coupled to the processor, on which instructions are stored, and when the instructions are executed by the processor, the network device is used to execute the optional implementation methods of the second and fourth aspects.

[0097] In the ninth aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the optional implementation of the first and third aspects, and the network device is configured to execute the method described in the optional implementation of the second and fourth aspects.

[0098] In the tenth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first aspect, the third aspect, the second aspect, and the fourth aspect.

[0099] In an eleventh aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation of the first aspect, the third aspect, the second aspect, and the fourth aspect.

[0100] In a twelfth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first and second aspects.

[0101] In a thirteenth aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first, third, second, and fourth aspects above.

[0102] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0103] The embodiments of the present disclosure provide a positioning method, a positioning test method, a terminal, and a network device. In some embodiments, the terms positioning method, information processing method, communication method, etc. can be used interchangeably.

[0104] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0105] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0106] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0107] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0108] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0109] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0110] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0111] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0112] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0113] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0114] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0115] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0116] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0117] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0118] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0119] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0120] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0121] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0122] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0123] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0124] In some embodiments, when the network device is LMF, the information sent and received between the terminal and the network device can be sent and received through AMF.

[0125] In some embodiments, when the network device is LMF, the information sent and received between the terminal and the network device can be sent and received through AMF and access network equipment.

[0126] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0127] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure.

[0128] As shown in FIG1 , a communication system 100 includes a terminal 10 and a network device 20 .

[0129] In some embodiments, the terminal 10 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0130] In some embodiments, the network device 20 may include at least one of an access network device and a core network device.

[0131] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0132] In some embodiments, the core network device may be a single device including a first network function, a second network function, etc., or may be a plurality of devices or a group of devices each including all or part of the first network function, the second network function, etc. The network function may be virtual or physical. The core network may include, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0133] In some embodiments, the first network function is, for example, an access and mobility management function (AMF).

[0134] In some embodiments, the first network function is used for access control and mobility management of the terminal accessing the operator network, for example, including functions such as mobile status management, allocation of user temporary identity, authentication and authorization of users, etc., and its name is not limited to this.

[0135] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0136] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0137] The embodiments of the present disclosure may be applied to long term evolution (LTE), LTE-advanced (LTE-A), LTE-beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), global system for mobile communications (GSM (registered trademark)), CDMA2000, ultra mobile broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, ultra-wideband (UWB), and the like. band, UWB), Bluetooth (registered trademark), public land mobile network (PLMN) network, device-to-device (D2D) system, machine-to-machine (M2M) system, Internet of Things (IoT) system, vehicle-to-everything (V2X), systems using other communication methods, and next-generation systems based on them. In addition, multiple systems can also be combined (for example, a combination of LTE or LTE-A and 5G) for application.

[0138] In related technologies, how to locate the terminal based on the positioning AI model is a technical problem that needs to be solved urgently.

[0139] Based on this, embodiments of the present disclosure provide a positioning method, a positioning test method, a terminal, and a network device. The method, performed by the terminal, includes determining first data, where the first data is used to locate the terminal based on a positioning artificial intelligence (AI) model; the first data includes at least one of a measurement result of a positioning reference signal (PRS) and trajectory information of the terminal within a first duration. Thus, the terminal can determine the first data to locate the terminal based on the positioning AI model, thereby improving the performance of the communication system.

[0140] FIG2A is an interactive schematic diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2A , the embodiment of the present disclosure relates to a positioning method, which includes:

[0141] S201A, the network device sends a request message to the terminal.

[0142] In some embodiments, the terminal receives a request message sent by the network device.

[0143] In some embodiments, the request message is used to request the terminal to report the ability to support positioning of the terminal based on a positioning AI model.

[0144] In some embodiments, the request message is used to request the terminal to report whether it supports positioning the terminal based on a positioning AI model.

[0145] In some embodiments, the name of the request message is not limited, and may be, for example, "first message", "request information", etc.

[0146] In some embodiments, a network device sends Long Term Evolution Positioning Protocol (LPP) capability request information to a terminal. The LPP request capabilities information includes a request message. However, the network device may also send other information including a request message to the terminal.

[0147] In some embodiments, the network device sends the request message to the terminal on its own, or sends the request message to the terminal when specific conditions are met.

[0148] Exemplarily, when determining that the terminal needs to be located, the network device sends a request message to the terminal.

[0149] Exemplarily, when the network device determines that it is necessary to use the positioning AI model to locate the terminal, it sends a request message to the terminal.

[0150] Exemplarily, when the network device determines that the terminal needs to use the positioning AI model to locate the terminal, the network device sends a request message to the terminal.

[0151] In some embodiments, the network device is a location management function (LMF).

[0152] In some embodiments, the network device is a LMF, and the network device sends a request message to the terminal, including: sending the request message to the terminal through an access and mobility management function (AMF).

[0153] In some embodiments, the network device is LMF, and the network device sends a request message to the terminal, including: sending a request message to the terminal through AMF and access network device.

[0154] S202A: The terminal sends capability information to the network device.

[0155] In some embodiments, the network device receives capability information sent by the terminal. In some embodiments, the capability information is used to indicate that the terminal supports positioning the terminal based on a positioning AI model. In some embodiments, the capability information is used to indicate that the terminal does not support positioning the terminal based on a positioning AI model. In some embodiments, the name of the capability information is not limited, and it can be, for example, "capability indication", "capability", etc. In some embodiments, the terminal sends the capability information to the network device on its own, or can also send the capability information to the network device when specific conditions are met.

[0156] Exemplarily, when the terminal determines that a positioning AI model is needed to locate the terminal, the terminal sends capability information to the network device. Exemplarily, when the terminal determines that a positioning AI model is needed to locate the terminal, the terminal sends capability information to the network device. Exemplarily, when the terminal receives a request message sent by the network device, and when the request message is used to request the terminal to report its ability to support positioning of the terminal based on the positioning AI model, the terminal sends capability information to the network device.

[0157] In some embodiments, the terminal sends LPP provided capability information to the network device, wherein the LPP provided capability information includes capability information. However, the network device may also send other information including capability information to the terminal.

[0158] Optionally, the network device receives LPP provided capability information sent by the terminal, where the LPP provided capability information includes a request message.

[0159] S203A, the terminal determines first data, where the first data is used to locate the terminal based on a positioning AI model.

[0160] In some embodiments, the first data includes at least one of the following: a measurement result of a positioning reference signal PRS; and trajectory information of the terminal within a first duration period.

[0161] In an embodiment of the present disclosure, a terminal determines first data, where the first data includes a measurement result of a positioning reference signal (PRS).

[0162] In some embodiments, the terminal determines the measurement result of the PRS, including: receiving the PRS sent by the network device; and measuring the PRS to determine the measurement result of the PRS.

[0163] In the embodiment of the present disclosure, the terminal may receive a PRS sent by a network device, and then measure the PRS to determine a measurement result of the PRS.

[0164] In some embodiments, the measurement result of the PRS includes at least one of the following: time difference of arrival TDoA; reference signal time difference of arrival RSTD; and reference signal received power RSRP of the PRS.

[0165] In the embodiment of the present disclosure, the measurement result of the PRS includes a time difference of arrival (TDOA).

[0166] In the embodiment of the present disclosure, the measurement result of the PRS includes a reference signal time difference (RSTD).

[0167] In the embodiment of the present disclosure, the measurement result of the PRS includes the reference signal receiving power (RSRP) of the PRS.

[0168] In an embodiment of the present disclosure, the terminal may determine first data, where the first data includes trajectory information of the terminal within a first duration period.

[0169] The trajectory information of the terminal during the first duration period may include the location of the terminal at at least one moment in the first duration period. Optionally, when the trajectory information of the terminal during the first duration period includes the location of the terminal at multiple moments in the first duration period, the multiple moments may be separated by the same duration.

[0170] Exemplarily, when the first duration period is 1 minute (min), the trajectory information of the terminal in the first duration period includes the position of the terminal at the 1st minute from the start time of the first duration period.

[0171] Exemplarily, when the first duration period is 10 minutes, the trajectory information of the terminal within the first duration period includes the position of the terminal at the 1st minute from the start time of the first duration period, the position of the terminal at the 2nd minute, the position of the terminal at the 3rd minute, the position of the terminal at the 4th minute, the position of the terminal at the 5th minute, the position of the terminal at the 6th minute, ..., the position of the terminal at the 10th minute.

[0172] It should be noted that the above examples are only for illustration and are not intended to limit the embodiments of the present disclosure.

[0173] It is understandable that the terminal can save the location of the terminal within the first duration period by itself, and then determine the trajectory information of the terminal within the first duration period.

[0174] In some embodiments, the terminal determines the trajectory information of the terminal within the first duration, including: according to the request message, the terminal supports positioning the terminal based on the positioning AI model, determines the position of the terminal saved in the subsequent first duration period, and determines the trajectory information of the terminal within the first duration.

[0175] In an embodiment of the present disclosure, after receiving a request message sent by a network device, the terminal can start saving the terminal's location if it determines that the terminal supports positioning the terminal based on a positioning AI model, save the terminal's location within a subsequent first duration period, and determine the terminal's trajectory information within the first duration period.

[0176] In some embodiments, if the terminal supports positioning the terminal based on a positioning AI model, the moment when the terminal receives the request message sent by the network device can be used as the starting moment of the first duration period.

[0177] In some embodiments, the duration of the first duration period is 1 minute, or 10 minutes, etc., and the embodiments of the present disclosure do not impose specific limitations on this and can be set as needed.

[0178] In some embodiments, the name of the first data is not limited, and it can be, for example, “positioning reference data”, “positioning measurement data”, etc.

[0179] S204A: The terminal determines the location information of the terminal based on the first data and the positioning AI model.

[0180] In an embodiment of the present disclosure, when the terminal determines the first data and the positioning AI model, the terminal can determine the location information of the terminal based on the first data and the positioning AI model.

[0181] In some embodiments, the positioning AI model is deployed on the terminal side, and the terminal can determine the positioning AI model. In some embodiments, the positioning AI model can be configured to the terminal by a network device. In some embodiments, the terminal or network device can train the first AI model based on the training data set to obtain the positioning AI model. In some embodiments, the name of the positioning AI model is not limited, and it can be, for example, "AI model", "first model", "positioning model", "positioning function", "machine learning (ML) model", "machine learning", etc.

[0182] In some embodiments, the method for the terminal to determine the first data can refer to the relevant description of S203A, which will not be repeated here.

[0183] In some embodiments, the terminal determines predicted trajectory information based on the first data and the positioning AI model, and determines the location information of the terminal based on the predicted trajectory information and the measurement result of the PRS.

[0184] In an embodiment of the present disclosure, the terminal determines the location information of the terminal based on the first data and the positioning AI model. It can first determine the predicted trajectory information based on the first data and the positioning AI model, and then determine the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0185] In some embodiments, the first data is input into a positioning AI model, and the output data obtained is the predicted trajectory information.

[0186] In some embodiments, first data including trajectory information of the terminal within a first duration period is input into a positioning AI model to obtain predicted trajectory information.

[0187] In some embodiments, first data including trajectory information of the terminal within a first duration and a measurement result of the PRS is input into a positioning AI model to obtain predicted trajectory information.

[0188] In some embodiments, the location information of the terminal is the location coordinates of the terminal, such as the relative location coordinates of the terminal, or the absolute location coordinates of the terminal.

[0189] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0190] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0191] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0192] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0193] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0194] The communication method involved in the embodiments of the present disclosure may include at least one of S201A to S204A. For example, S201A may be implemented as an independent embodiment, S202A may be implemented as an independent embodiment, S203A may be implemented as an independent embodiment, S204A may be implemented as an independent embodiment, S201A+S202A may be implemented as an independent embodiment, S202A+S203A may be implemented as an independent embodiment, S203A+S204A may be implemented as an independent embodiment, and S202A+S203A+S204A may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0195] In some embodiments, S202A and S203A may be executed synchronously or in an exchanged order.

[0196] In some embodiments, S201A and S202A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0197] In some embodiments, S201A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0198] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0199] FIG2B is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2B , the embodiment of the present disclosure relates to a positioning method, which includes:

[0200] S201B, the network device sends a request message to the terminal, where the request message is used to request the terminal to report its ability to support positioning of the terminal based on a positioning AI model.

[0201] S202B: The terminal sends capability information to the network device according to the request message, wherein the capability information is used to indicate that the terminal supports positioning of the terminal based on the positioning AI model.

[0202] S203B, the terminal determines first data, where the first data is used to locate the terminal based on the positioning AI model.

[0203] In some embodiments, the first data includes at least one of a measurement result of the PRS and trajectory information of the terminal during the first duration period.

[0204] For the related descriptions of S201B to S203B, reference may be made to the related descriptions of S201A to S203A in FIG2A , which will not be repeated here.

[0205] Among them, the optional implementation methods of S201B to S203B can refer to the optional implementation methods of S201A to S203A in 2A and other related parts in the embodiment involved in Figure 2A, and will not be repeated here.

[0206] S204B: The terminal determines predicted trajectory information based on the first data and the positioning AI model.

[0207] In an embodiment of the present disclosure, when determining the first data and the positioning AI model, the terminal can determine the predicted trajectory information based on the first data and the positioning AI model.

[0208] In some embodiments, the positioning AI model is deployed on the terminal side, and the terminal can determine the positioning AI model.

[0209] In some embodiments, the positioning AI model can be configured for the terminal by the network device.

[0210] In some embodiments, the terminal or network device may train the first AI model based on the training data set to obtain a positioning AI model.

[0211] In some embodiments, the name of the positioning AI model is not limited, and may be, for example, "AI model", "first model", "positioning model", "positioning function", "machine learning (ML) model", "machine learning", etc.

[0212] In some embodiments, the method for the terminal to determine the first data can refer to the relevant description of S203A in Figure 2A, which will not be repeated here.

[0213] S205B: The terminal sends the predicted trajectory information and the PRS measurement result to the network device.

[0214] In some embodiments, the network device receives the predicted trajectory information and the PRS measurement result sent by the terminal.

[0215] In some embodiments, the terminal determines first data, where the first data includes a measurement result of a PRS.

[0216] Optionally, the terminal determines the first data, and the first data includes a PRS measurement result. For related descriptions, please refer to the related descriptions in the above embodiments, which will not be repeated here.

[0217] In some embodiments, the terminal determines the first data, and then determines the predicted trajectory information based on the first data and the positioning AI model.

[0218] Optionally, the terminal determines the first data, and for the related description of determining the predicted trajectory information based on the first data and the positioning AI model, please refer to the related description in the above embodiment, which will not be repeated here.

[0219] In some embodiments, the terminal sends an LPP message to the network device, where the LPP message includes the predicted trajectory information and the PRS measurement result. However, the network device may also send other information including the predicted trajectory information and the PRS measurement result to the terminal.

[0220] Optionally, the network device receives an LPP message sent by the terminal, where the LPP message includes predicted trajectory information and a PRS measurement result.

[0221] S206B: The network device determines the location information of the terminal based on the predicted trajectory information and the PRS measurement result.

[0222] In some embodiments, the network device receives the predicted trajectory information and the PRS measurement result sent by the terminal, and determines the predicted trajectory information and the PRS measurement result.

[0223] In the embodiment of the present disclosure, the network device may determine the location information of the terminal based on the predicted trajectory information and the measurement result of the PRS.

[0224] In some embodiments, the location information of the terminal is the location coordinates of the terminal, such as the relative location coordinates of the terminal, or the absolute location coordinates of the terminal.

[0225] The communication method involved in the embodiment of the present disclosure may include at least one of S201B to S206B. For example, S201B can be implemented as an independent embodiment, S202B can be implemented as an independent embodiment, S203B can be implemented as an independent embodiment, S204B can be implemented as an independent embodiment, S205B can be implemented as an independent embodiment, S206B can be implemented as an independent embodiment, S201B+S202B can be implemented as an independent embodiment, S202B+S203B can be implemented as an independent embodiment, S203B+S204B can be implemented as an independent embodiment, S202B+S203B+S204B can be implemented as an independent embodiment, S205B+S206B can be implemented as an independent embodiment, S204B+S205B+S206B can be implemented as an independent embodiment, and S203B+S204B+S205B+S206B can be implemented as an independent embodiment, but are not limited to this.

[0226] In some embodiments, S202B and S203B may be executed synchronously or in an exchanged order.

[0227] In some embodiments, S201B, S202B, S204B, S205B, and S206B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0228] In some embodiments, S201B and S202B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0229] In some embodiments, S201B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0230] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2B .

[0231] FIG2C is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2C , the embodiment of the present disclosure relates to a positioning method, which includes:

[0232] S201C, the network device sends a request message to the terminal, where the request message is used to request the terminal to report its ability to support positioning of the terminal based on a positioning AI model.

[0233] S202C: The terminal sends capability information to the network device according to the request message, where the capability information is used to indicate that the terminal supports positioning of the terminal based on the positioning AI model.

[0234] S203C, the terminal determines first data, where the first data is used to locate the terminal based on the positioning AI model.

[0235] For the related descriptions of S201C to S203C, reference may be made to the related descriptions of S201A to S203A in FIG2A , which will not be repeated here.

[0236] Among them, the optional implementation methods of S201C to S203C can refer to the optional implementation methods of S201A to S203A in 2A and other related parts in the embodiment involved in Figure 2A, and will not be repeated here.

[0237] S204C: The terminal sends first data to the network device.

[0238] In some embodiments, the network device receives first data sent by the terminal.

[0239] In some embodiments, the first data includes at least one of a measurement result of the PRS and trajectory information of the terminal during the first duration period.

[0240] In some embodiments, upon determining the first data, the terminal sends the first data to the network device.

[0241] In some embodiments, the terminal sends the first data to the network device on its own, or sends the first data to the network device when specific conditions are met.

[0242] Exemplarily, when determining that the network device is required to locate the terminal, the terminal sends the first data to the network device.

[0243] Exemplarily, when the terminal determines that the network device needs to locate the terminal based on the positioning AI model, the terminal sends the first data to the network device.

[0244] In some embodiments, the terminal sends an LPP message to the network device, wherein the LPP message includes the first data. However, the present invention is not limited thereto, and the network device may also send other information including the first data to the terminal.

[0245] Optionally, the network device receives an LPP message sent by the terminal, where the LPP message includes first data.

[0246] S205C, the network device determines the first data.

[0247] In some embodiments, the network device receives first data sent by the terminal and determines the first data.

[0248] It should be noted that, for the relevant description of the first data, reference can be made to the relevant description in the above embodiment, which will not be repeated here.

[0249] In some embodiments, the network device determines the measurement result of the PRS, including: receiving the measurement result of the PRS sent by the terminal; and determining the measurement result of the PRS.

[0250] S206C: The network device determines the location information of the terminal based on the first data and the positioning AI model.

[0251] In some embodiments, after determining the first data and the positioning AI model, the network device can determine the location information of the terminal based on the first data and the positioning AI model.

[0252] It should be noted that, for the relevant description of how the network device determines the first data, reference can be made to the relevant description in the above embodiment, which will not be repeated here.

[0253] In some embodiments, the positioning AI model is deployed on the network device side, and the network device can determine the positioning AI model.

[0254] In some embodiments, the positioning AI model may be indicated to the network device by the terminal.

[0255] In some embodiments, the terminal or network device may train the first AI model based on the training data set to obtain a positioning AI model.

[0256] In some embodiments, the name of the positioning AI model is not limited, and may be, for example, "AI model", "first model", "positioning model", "positioning function", "machine learning (ML) model", "machine learning", etc.

[0257] In some embodiments, the network device determines the predicted trajectory information based on the first data and the positioning AI model; and determines the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0258] In an embodiment of the present disclosure, the network device determines the location information of the terminal based on the first data and the positioning AI model. It can first determine the predicted trajectory information based on the first data and the positioning AI model, and then determine the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0259] In some embodiments, the location information of the terminal is the location coordinates of the terminal, such as the relative location coordinates of the terminal, or the absolute location coordinates of the terminal.

[0260] The communication method involved in the embodiment of the present disclosure may include at least one of S201C to S206C. For example, S201C can be implemented as an independent embodiment, S202C can be implemented as an independent embodiment, S203C can be implemented as an independent embodiment, S204C can be implemented as an independent embodiment, S205C can be implemented as an independent embodiment, S206C can be implemented as an independent embodiment, S201C+S202C can be implemented as an independent embodiment, S202C+S203C can be implemented as an independent embodiment, S203C+S204C can be implemented as an independent embodiment, S202C+S203C+S204C can be implemented as an independent embodiment, S205C+S206C can be implemented as an independent embodiment, S204C+S205C+S206C can be implemented as an independent embodiment, and S203C+S204C+S205C+S206C can be implemented as an independent embodiment, but are not limited to these.

[0261] In some embodiments, S202C and S203C may be executed synchronously or in a swapped order.

[0262] In some embodiments, S201C, S202C, S204C, S205C, and S206C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0263] In some embodiments, S201C, S202C, S203C, S204C, and S206C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0264] In some embodiments, S201C and S202C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0265] In some embodiments, S201C is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0266] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2C .

[0267] FIG3A is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a positioning test method, which includes:

[0268] S301A, the terminal determines a training data set.

[0269] In some embodiments, the training data set is used to train the first AI model to obtain a positioning AI model in the positioning method described in some of the above embodiments.

[0270] In some embodiments, the training data set includes: the second data and the real location information of the terminal.

[0271] The second data includes at least one of the trajectory information of the terminal in the second duration period and the measurement results of multiple PRSs with a specified time interval.

[0272] In the embodiment of the present disclosure, the training data set includes the second data and the real location information of the terminal.

[0273] In an embodiment of the present disclosure, the second data includes trajectory information of the terminal within the first duration period.

[0274] In the embodiment of the present disclosure, the second data includes measurement results of a plurality of PRSs spaced at intervals of a specified time length.

[0275] Exemplarily, the terminal determines a training data set, first saves the trajectory information of the terminal within a second duration period, then measures the PRS sent by the network device at specified intervals, obtains the measurement results of the PRS, and determines the actual location information of the terminal at the moment of measuring the PRS, thereby obtaining the training data set.

[0276] In some embodiments, when determining that a positioning AI model needs to be obtained, the terminal determines a training data set to train the first AI model based on the training data set to obtain a positioning AI model.

[0277] In some embodiments, when the terminal determines that a positioning AI model is needed, it determines a training data set and sends the training data set to a network device, and trains the first AI model based on the training data set on the network device side to obtain a positioning AI model.

[0278] S302A: The terminal trains the first AI model according to the training data set to obtain a positioning AI model.

[0279] In an embodiment of the present disclosure, after the terminal determines the training data set, it can train the first AI model according to the training data set to obtain a positioning AI model.

[0280] Among them, the first AI model can adopt an open source AI model.

[0281] Exemplarily, the first AI model may be a machine learning model.

[0282] In an embodiment of the present disclosure, the terminal may obtain a first AI model, and when a training data set is determined, train the first AI model based on the training data set to obtain a positioning AI model.

[0283] S303A, the terminal sends first indication information to the network device, where the first indication information is used to indicate the positioning AI model.

[0284] In some embodiments, the network device receives first indication information sent by the terminal.

[0285] In some embodiments, the first indication information is used to indicate a positioning AI model.

[0286] In some embodiments, the first indication information carries a positioning AI model.

[0287] In some embodiments, the name of the first indication information is not limited, and it can be, for example, "model indication information", "indication information", etc.

[0288] The communication method involved in the embodiments of the present disclosure may include at least one of S301A to S303A. For example, S301A may be implemented as an independent embodiment, S302A may be implemented as an independent embodiment, S303A may be implemented as an independent embodiment, and S301A+S302A may be implemented as independent embodiments, but the present invention is not limited thereto.

[0289] In some embodiments, S302A and S303A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0290] In some embodiments, S303A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0291] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3A .

[0292] FIG3B is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a positioning test method, which includes:

[0293] S301B, the terminal determines a training data set.

[0294] In some embodiments, the training data set is used to train the first AI model to obtain a positioning AI model in the positioning method described in some of the above embodiments.

[0295] For the related description of S301B, please refer to the related description of S301A in FIG3A , which will not be repeated here.

[0296] Among them, the optional implementation method of S301B can refer to the optional implementation method of S301A in 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0297] S302B: The terminal sends a training data set to the network device.

[0298] In some embodiments, the network device receives a training data set sent by the terminal.

[0299] In some embodiments, the training data set is used by the network device to train the first AI model to obtain a positioning AI model.

[0300] S303B, the network device determines a training data set.

[0301] In some embodiments, the network device receives a training data set sent by the terminal and determines the training data set.

[0302] In some embodiments, the network device collects relevant information of the terminal and determines the training data set.

[0303] In some embodiments, the training data set includes: the second data and the real location information of the terminal.

[0304] The second data includes at least one of the following:

[0305] Track information of the terminal during the second duration period;

[0306] The measurement results of multiple PRSs separated by a specified time interval.

[0307] Exemplarily, the network device collects the trajectory information of the terminal during the second duration period, and measurement results of multiple PRSs measured by the terminal at intervals of a specified duration.

[0308] S304B: The network device trains the first AI model according to the training data set to obtain a positioning AI model.

[0309] In an embodiment of the present disclosure, after determining a training data set and a first AI model, the network device can train the first AI model according to the training data set to obtain a positioning AI model.

[0310] Among them, the first AI model can adopt an open source AI model.

[0311] Exemplarily, the first AI model may be a machine learning model.

[0312] In an embodiment of the present disclosure, the network device may obtain a first AI model, and when a training data set is determined, train the first AI model based on the training data set to obtain a positioning AI model.

[0313] S305B, the network device sends second indication information to the terminal, where the second indication information is used to indicate the positioning AI model.

[0314] In some embodiments, the terminal receives second indication information sent by the network device.

[0315] In some embodiments, the second indication information is used to indicate a positioning AI model.

[0316] In some embodiments, the second indication information carries a positioning AI model.

[0317] In some embodiments, the name of the second indication information is not limited, and it can be, for example, "model indication information", "indication information", etc.

[0318] The communication method involved in the embodiments of the present disclosure may include at least one of S301B to S305B. For example, S301B can be implemented as an independent embodiment, S302B can be implemented as an independent embodiment, S303B can be implemented as an independent embodiment, S304B can be implemented as an independent embodiment, S305B can be implemented as an independent embodiment, S301B+S302B can be implemented as an independent embodiment, S302B+S303B can be implemented as an independent embodiment, S303B+S304B can be implemented as an independent embodiment, S304B+S305B can be implemented as an independent embodiment, S303B+S304B+S305B can be implemented as an independent embodiment, and S302B+S303B+S304B+S305B can be implemented as an independent embodiment, but the present invention is not limited thereto.

[0319] In some embodiments, S301B, S302B, S304B, and S305B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0320] In some embodiments, S301B, S304B, and S305B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0321] In some embodiments, S301B and S305B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0322] In some embodiments, S301B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0323] In some embodiments, S305B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0324] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3B .

[0325] FIG4A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a positioning method, which is executed by a terminal and includes:

[0326] S401A, determine first data.

[0327] Among them, the optional implementation of S401A can refer to the optional implementation of S203A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0328] In some embodiments, the first data is used to locate the terminal based on a positioning AI model.

[0329] The first data includes at least one of the following:

[0330] Positioning reference signal (PRS) measurement results;

[0331] The trajectory information of the terminal during the first duration period.

[0332] In some embodiments, the terminal determines the measurement result of the PRS, including: receiving the PRS sent by the network device; and measuring the PRS to determine the measurement result of the PRS.

[0333] In some embodiments, the measurement result of the PRS includes at least one of TDoA, RSTD, and RSRP of the PRS.

[0334] In some embodiments, the terminal determines the trajectory information of the terminal within the first duration, including: according to the request message sent by the network device, when the terminal supports positioning the terminal based on the positioning AI model, determining the position of the terminal saved in the subsequent first duration period, and determining the trajectory information of the terminal within the first duration.

[0335] S402A: Determine the location information of the terminal based on the first data and the positioning AI model.

[0336] Among them, the optional implementation of S402A can refer to the optional implementation of S204A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0337] In some embodiments, the terminal determines predicted trajectory information based on the first data and the positioning AI model, and determines the location information of the terminal based on the predicted trajectory information and the measurement result of the PRS.

[0338] The communication method involved in the embodiments of the present disclosure may include at least one of S401A to S402A. For example, S401A may be implemented as an independent embodiment, and S402A may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0339] In some embodiments, S402A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0340] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4A .

[0341] FIG4B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a positioning method, which is executed by a terminal and includes:

[0342] S401B, obtain request message.

[0343] Among them, the optional implementation of S401B can refer to the optional implementation of S201A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0344] In some embodiments, the terminal receives a request message sent by a network device, but is not limited thereto and may also receive a request message sent by other entities.

[0345] In some embodiments, the terminal obtains a request message specified by the protocol.

[0346] In some embodiments, the terminal obtains the request message from upper layer(s).

[0347] In some embodiments, the terminal performs processing to obtain the request message.

[0348] In some embodiments, S401B is omitted, and the terminal autonomously implements the function indicated by the request message, or the above function is default or by default.

[0349] In some embodiments, the request message is used to request the terminal to report the ability to support positioning of the terminal based on a positioning AI model.

[0350] S402B, sending capability information.

[0351] Among them, the optional implementation of S402B can refer to the optional implementation of S202A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0352] In some embodiments, the terminal sends capability information to the network device, but is not limited thereto and may also send capability information to other entities.

[0353] Optionally, the capability information is used by the network device to determine whether the terminal supports positioning of the terminal based on a positioning AI model.

[0354] In some embodiments, the terminal receives a request message sent by the network device, and sends capability information to the network device according to the request message.

[0355] In some embodiments, the capability information is used to indicate that the terminal supports positioning the terminal based on a positioning AI model.

[0356] S403B, determine the first data.

[0357] Among them, the optional implementation of S403B can refer to the optional implementation of S203A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0358] In some embodiments, the first data is used to locate the terminal based on a positioning AI model.

[0359] The first data includes at least one of the following:

[0360] Positioning reference signal (PRS) measurement results;

[0361] The trajectory information of the terminal during the first duration period.

[0362] In some embodiments, the terminal determines the measurement result of the PRS, including: receiving the PRS sent by the network device; and measuring the PRS to determine the measurement result of the PRS.

[0363] In some embodiments, the measurement result of the PRS includes at least one of TDoA, RSTD, and RSRP of the PRS.

[0364] In some embodiments, the terminal determines the trajectory information of the terminal within the first duration, including: according to the request message sent by the network device, when the terminal supports positioning the terminal based on the positioning AI model, determining the position of the terminal saved in the subsequent first duration period, and determining the trajectory information of the terminal within the first duration.

[0365] S404B: Determine the location information of the terminal based on the first data and the positioning AI model.

[0366] Among them, the optional implementation of S404B can refer to the optional implementation of S204A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0367] In some embodiments, the terminal determines predicted trajectory information based on the first data and the positioning AI model, and determines the location information of the terminal based on the predicted trajectory information and the measurement result of the PRS.

[0368] The communication method involved in the embodiments of the present disclosure may include at least one of S401B to S404B. For example, S401B may be implemented as an independent embodiment, S402B may be implemented as an independent embodiment, S403B may be implemented as an independent embodiment, S404B may be implemented as an independent embodiment, S401B+S402B may be implemented as an independent embodiment, S402B+S403B may be implemented as an independent embodiment, S403B+S404B may be implemented as an independent embodiment, and S402B+S403B+S404B may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0369] In some embodiments, S402B and S403B may be executed synchronously or in an exchanged order.

[0370] In some embodiments, S401B and S402B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0371] In some embodiments, S401B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0372] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4B .

[0373] FIG4C is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4B , an embodiment of the present disclosure relates to a positioning method, which is executed by a terminal and includes:

[0374] S401C, determine the first data.

[0375] Among them, the optional implementation of S401C can refer to the optional implementation of S203A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0376] In some embodiments, before determining the first data, the terminal may also receive a request message sent by the network device. For example, please refer to the relevant description of S401B.

[0377] In some embodiments, upon receiving the request message sent by the network device, the terminal may send capability information to the network device. For example, please refer to the relevant description of S402B.

[0378] S402C: Determine predicted trajectory information based on the first data and the positioning AI model.

[0379] Among them, the optional implementation of S402C can refer to the optional implementation of S204B in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0380] S403C: Send the predicted trajectory information and the PRS measurement result.

[0381] Among them, the optional implementation of S403C can refer to the optional implementation of S205B in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0382] In some embodiments, the terminal sends the predicted trajectory information and the PRS measurement result to the network device, but is not limited thereto. The predicted trajectory information and the PRS measurement result may also be sent to other entities.

[0383] Optionally, the predicted trajectory information and PRS measurement results are used by the network device to determine the terminal's location information based on the predicted trajectory information and the PRS measurement results. For optional implementations, see the optional implementation of S206B in FIG. 2B and other related parts of the embodiment involved in FIG. 2B , which will not be repeated here.

[0384] The communication method involved in the embodiments of the present disclosure may include at least one of S401C to S403C. For example, S401C may be implemented as an independent embodiment, S402C may be implemented as an independent embodiment, S403C may be implemented as an independent embodiment, S401C + S402C may be implemented as an independent embodiment, and S402C + S403C may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0385] In some embodiments, S402C and S403C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0386] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4C .

[0387] FIG4D is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4D , the embodiment of the present disclosure relates to a positioning method, which is executed by a terminal and includes:

[0388] S401D, determine the first data.

[0389] Among them, the optional implementation of S401D can refer to the optional implementation of S203A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0390] In some embodiments, before determining the first data, the terminal may also receive a request message sent by the network device. For example, please refer to the relevant description of S401B.

[0391] In some embodiments, upon receiving the request message sent by the network device, the terminal may send capability information to the network device. For example, please refer to the relevant description of S402B.

[0392] S402D, send first data.

[0393] Among them, the optional implementation of S402D can refer to the optional implementation of S204C in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0394] In some embodiments, the terminal sends the first data to the network device, but is not limited thereto, and the first data may also be sent to other entities.

[0395] Optionally, the first data is used by the network device to determine the location information of the terminal based on the first data and the positioning AI model. Its optional implementation method can be referred to the optional implementation method of S206C in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0396] The communication method involved in the embodiments of the present disclosure may include at least one of S401D to S402D. For example, S401D may be implemented as an independent embodiment, and S402D may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0397] In some embodiments, S402D is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0398] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 4D .

[0399] FIG5A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG5A , the embodiment of the present disclosure relates to a positioning method, which is executed by a network device and includes:

[0400] S501A, determine first data.

[0401] Among them, the optional implementation of S501A can refer to the optional implementation of S205C in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0402] In some embodiments, the first data is used to locate the terminal based on a positioning AI model.

[0403] In some embodiments, the first data includes at least one of a measurement result of the PRS and trajectory information of the terminal during the first duration period.

[0404] In some embodiments, the network device receives first data sent by the terminal and determines the first data.

[0405] In some embodiments, the network device determines the measurement result of the PRS, including: receiving the measurement result of the PRS sent by the terminal; and determining the measurement result of the PRS.

[0406] S502A: Determine the location information of the terminal based on the first data and the positioning AI model.

[0407] Among them, the optional implementation of S502A can refer to the optional implementation of S206C in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0408] In some embodiments, the network device determines the predicted trajectory information based on the first data and the positioning AI model; and determines the location information of the terminal based on the predicted trajectory information and the measurement results of the PRS.

[0409] The communication method involved in the embodiments of the present disclosure may include at least one of S501A to S502A. For example, S501A may be implemented as an independent embodiment, and S502A may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0410] In some embodiments, S502A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0411] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 5A .

[0412] FIG5B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG5B , the embodiment of the present disclosure relates to a positioning method, which is executed by a network device and includes:

[0413] S501B, send a request message.

[0414] Among them, the optional implementation of S501B can refer to the optional implementation of S201A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0415] In some embodiments, the network device sends a request message to the terminal, but is not limited thereto and may also send a request message to other entities.

[0416] Optionally, the request message is used by the terminal to send capability information to the network device. For optional implementations, see the optional implementations of S202A in FIG2A and other related parts of the embodiment involved in FIG2A , which will not be described in detail here.

[0417] In some embodiments, the request message is used to request the terminal to report the ability to support positioning of the terminal based on the positioning AI model;

[0418] S502B, obtain capability information.

[0419] Among them, the optional implementation of S502B can refer to the optional implementation of S202A in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0420] In some embodiments, the network device receives capability information sent by the terminal, but is not limited thereto and may also receive capability information sent by other entities.

[0421] In some embodiments, the network device obtains capability information specified by the protocol.

[0422] In some embodiments, the network device obtains capability information from upper layer(s).

[0423] In some embodiments, the network device performs processing to obtain the capability information.

[0424] In some embodiments, S502B is omitted, and the network device autonomously implements the functions indicated by the capability information, or the above functions are default or by default.

[0425] In some embodiments, the capability information is used to indicate that the terminal supports positioning the terminal based on a positioning AI model.

[0426] S503B, determine the first data.

[0427] Among them, the optional implementation of S503B can refer to the optional implementation of S205C in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0428] The first data includes a measurement result of the PRS.

[0429] In some embodiments, the network device receives the PRS measurement result sent by the terminal and determines the PRS measurement result.

[0430] S504B, obtain predicted trajectory information.

[0431] Among them, the optional implementation of S504B can refer to the optional implementation of S205B in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0432] In some embodiments, the network device receives the predicted trajectory information sent by the terminal, but is not limited thereto and may also receive the predicted trajectory information sent by other entities.

[0433] S505B: Determine the location information of the terminal based on the predicted trajectory information and the PRS measurement result.

[0434] Among them, the optional implementation of S505B can refer to the optional implementation of S206B in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0435] The communication method involved in the embodiments of the present disclosure may include at least one of S501B to S505B. For example, S501B may be implemented as an independent embodiment, S502B may be implemented as an independent embodiment, S503B may be implemented as an independent embodiment, S504B may be implemented as an independent embodiment, S505B may be implemented as an independent embodiment, S501B+S502B may be implemented as an independent embodiment, and S503B+S504B+S505B may be implemented as independent embodiments, but the present invention is not limited thereto.

[0436] In some embodiments, S502B and S503B may be executed simultaneously or in a swapped order.

[0437] In some embodiments, S503B, S504B, and S505B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0438] In some embodiments, S501B and S502B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0439] In some embodiments, S501B, S502B, S504B, and S505B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0440] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 5B .

[0441] FIG6A is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG6A , the embodiment of the present disclosure relates to a positioning test method, which is executed by a terminal and includes:

[0442] S601A, determine a training data set.

[0443] Among them, the optional implementation of S601A can refer to the optional implementation of S301A in Figure 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0444] In some embodiments, the training data set includes: the second data and the real location information of the terminal.

[0445] The second data includes at least one of the following:

[0446] Track information of the terminal during the second duration period;

[0447] The measurement results of multiple PRSs separated by a specified time interval.

[0448] In some embodiments, the terminal receives a PRS sent by a network device, measures the PRS, and determines a measurement result of the PRS.

[0449] S602A: Train the first AI model according to the training data set to obtain a positioning AI model.

[0450] Among them, the optional implementation of S602A can refer to the optional implementation of S302A in Figure 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0451] S603A, sending first indication information.

[0452] Among them, the optional implementation of S603A can refer to the optional implementation of S303A in Figure 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0453] In some embodiments, the terminal sends the first indication information to the network device, but is not limited thereto, and may also send the first indication information to other entities.

[0454] Optionally, the first indication information is used by the network device to determine a positioning AI model and locate the terminal based on the positioning AI model.

[0455] In some embodiments, the first indication information is used to indicate a positioning AI model.

[0456] The communication method involved in the embodiments of the present disclosure may include at least one of S601A to S603A. For example, S601A may be implemented as an independent embodiment, S602A may be implemented as an independent embodiment, S603A may be implemented as an independent embodiment, and S601A+S602A may be implemented as independent embodiments, but the present invention is not limited thereto.

[0457] In some embodiments, S602A and S603A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0458] In some embodiments, S603A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0459] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 6A .

[0460] FIG6B is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG6B , the embodiment of the present disclosure relates to a positioning test method, which is executed by a terminal and includes:

[0461] S601B, determine the training data set.

[0462] Among them, the optional implementation of S601B can refer to the optional implementation of S301A in Figure 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0463] S602B, sending the training data set.

[0464] Among them, the optional implementation of S602B can refer to the optional implementation of S302B in Figure 3B and other related parts in the embodiment involved in Figure 3B, which will not be repeated here.

[0465] In some embodiments, the terminal sends the training data set to the network device, but is not limited thereto and may also send the training data set to other entities.

[0466] Optionally, the training data set is used by the network device to determine the training data set, and to train the first AI model based on the training data set to obtain a positioning AI model. For optional implementations, see S303B and S304B of FIG. 3B , as well as other related parts of the embodiment involved in FIG. 3B , which will not be described in detail here.

[0467] S603B, obtain second indication information.

[0468] Among them, the optional implementation of S603B can refer to the optional implementation of S305B in Figure 3B and other related parts in the embodiment involved in Figure 3B, which will not be repeated here.

[0469] In some embodiments, the terminal receives the second indication information sent by the network device, but is not limited thereto, and may also receive the second indication information sent by other entities.

[0470] In some embodiments, the terminal obtains second indication information specified by the protocol.

[0471] In some embodiments, the terminal obtains the second indication information from an upper layer(s).

[0472] In some embodiments, the terminal performs processing to obtain the second indication information.

[0473] In some embodiments, S603B is omitted, and the terminal autonomously implements the function indicated by the second indication information, or the above function is default or acquiescent.

[0474] In some embodiments, the second indication information is used to indicate a positioning AI model.

[0475] The communication method involved in the embodiments of the present disclosure may include at least one of S601B to S603B. For example, S601B may be implemented as an independent embodiment, S602B may be implemented as an independent embodiment, S603B may be implemented as an independent embodiment, and S601B+S602B may be implemented as independent embodiments, but are not limited thereto.

[0476] In some embodiments, S602B and S603B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0477] In some embodiments, S603B is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0478] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 6B .

[0479] FIG7A is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG7A , the embodiment of the present disclosure relates to a positioning test method, which is executed by a network device and includes:

[0480] S701A, determine the training data set.

[0481] Among them, the optional implementation of S701A can refer to the optional implementation of S303B in Figure 3B and other related parts in the embodiment involved in Figure 3B, which will not be repeated here.

[0482] In some embodiments, the training data set includes: the second data and the real location information of the terminal.

[0483] The second data includes at least one of the following:

[0484] Track information of the terminal during the second duration period;

[0485] The measurement results of multiple PRSs separated by a specified time interval.

[0486] In some embodiments, the network device determines the training data set, including: receiving the training data set sent by the terminal; and determining the training data set.

[0487] S702A: Train the first AI model according to the training data set to obtain a positioning AI model.

[0488] Among them, the optional implementation of S702A can refer to the optional implementation of S304B in Figure 3B and other related parts in the embodiment involved in Figure 3B, which will not be repeated here.

[0489] S703A, send second indication information.

[0490] Among them, the optional implementation of S703A can refer to the optional implementation of S305B in Figure 3B and other related parts in the embodiment involved in Figure 3B, which will not be repeated here.

[0491] In some embodiments, the network device sends the second indication information to the terminal, but is not limited thereto, and the second indication information may also be sent to other entities.

[0492] Optionally, the second indication information is used by the terminal to determine a positioning AI model and to locate the terminal based on the positioning AI model.

[0493] The communication method involved in the embodiments of the present disclosure may include at least one of S701A to S703A. For example, S701A may be implemented as an independent embodiment, S702A may be implemented as an independent embodiment, S703A may be implemented as an independent embodiment, and S701A+S702A may be implemented as independent embodiments, but the present invention is not limited thereto.

[0494] In some embodiments, S702A and S703A are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0495] In some embodiments, S703A is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0496] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 7A .

[0497] FIG7B is a flow chart of a positioning test method according to an embodiment of the present disclosure. As shown in FIG7B , the embodiment of the present disclosure relates to a positioning test method, which is executed by a network device and includes:

[0498] S701B, obtain first indication information.

[0499] Among them, the optional implementation of S701B can refer to the optional implementation of S301A in Figure 3A and other related parts in the embodiment involved in Figure 3A, which will not be repeated here.

[0500] In some embodiments, the network device receives the first indication information sent by the terminal, but is not limited thereto, and may also receive the first indication information sent by other entities.

[0501] In some embodiments, the network device obtains first indication information specified by a protocol.

[0502] In some embodiments, the network device obtains the first indication information from an upper layer(s).

[0503] In some embodiments, the network device performs processing to obtain the first indication information.

[0504] In some embodiments, S701A is omitted, and the network device autonomously implements the function indicated by the first indication information, or the above function is default or by default.

[0505] To facilitate understanding of the embodiments of the present disclosure, an exemplary embodiment is provided.

[0506] As shown in FIG8A , in an exemplary embodiment, the network device is taken as an LMF as an example for description, and the terminal (UE) assists in positioning based on PRS measurement and trajectory prediction.

[0507] 1. The MF requests the terminal to report its ability to support joint positioning with a positioning AI model (such as an ML model).

[0508] 2. The terminal can save the terminal's trajectory information for a specific duration.

[0509] 3. LMF configures the necessary PRS for the terminal.

[0510] 4. The terminal measures the PRS to obtain necessary physical layer measurement results (such as TDoA, RSTD, and RSRP of PRS).

[0511] 5. The terminal can obtain predicted trajectory information based on the saved trajectory information through the positioning AI model (such as the ML model).

[0512] 6. The terminal can report the physical layer measurement results and predicted trajectory information to the LMF, and the network device determines the location estimate. For example, the network device determines the terminal's location information based on the predicted trajectory information sent by the terminal and the PRS measurement results.

[0513] It should be noted that after determining the physical layer measurement results and the predicted trajectory information, the terminal may also determine a position estimate based on the physical layer measurement results and the predicted trajectory information. For example, the terminal determines the terminal's position information based on the predicted trajectory information and the PRS measurement results.

[0514] To facilitate understanding of the embodiments of the present disclosure, an exemplary embodiment is provided.

[0515] As shown in FIG8A , in an exemplary embodiment, a testing method for positioning an AI model is shown.

[0516] When testing positioning in conjunction with a positioning AI model, the metrics to be tested should include the terminal's true location estimate, rather than purely physical layer measurements. For example, the terminal's ground location information should be used during testing.

[0517] Therefore, in the test environment, the test equipment (TE) requires an additional AL / ML simulator (that is, the first AI model in the above embodiment), and / or the AI / ML training model in the device under test (DUT) (that is, the positioning AI model in the above embodiment) also requires an additional AL / ML simulator (that is, the first AI model in the above embodiment).

[0518] In some embodiments, TE is a terminal and DUT is a network device.

[0519] In some embodiments, TE is a network device and DUT is a terminal.

[0520] Test process: In addition to the terminal test measurement on the PRS, additional durations (such as T0 and T1) should be considered to allow the TE to generate sufficient terminal trajectory information and model training process datasets.

[0521] As shown in Figure 8C, in some embodiments, at T0, the terminal collects its trajectory information. At T1, the terminal determines the PRS measurement results. The terminal uses the trajectory information during T0, the PRS measurement results obtained during T1, and the terminal's actual location information during T1 as a training dataset to train a first AI model to obtain a positioning AI model. At T3, the terminal determines the PRS measurement results and obtains the terminal's historical trajectory information, and determines the terminal's location information based on the positioning AI model.

[0522] In other embodiments, at time T0, the network device collects the terminal's trajectory information during the T0 time period. At time T1, the network device determines the terminal's PRS measurement results during the T1 time period. The collected trajectory information, the PRS measurement results during the T1 time period, and the terminal's actual location information during the T1 time period are used as a training dataset to train a first AI model to obtain a positioning AI model. At time T3, the terminal determines the PRS measurement results and obtains the terminal's historical trajectory information, and determines the terminal's location information based on the positioning AI model.

[0523] Based on the above exemplary embodiments, in some embodiments, the positioning method provided by the embodiments of the present disclosure can predict the location information of the terminal based on the measurement results of the terminal PRS and the trajectory information of the terminal based on a machine learning model.

[0524] In some embodiments, the measurement result of the PRS of the terminal may include TDoA (eg RSTD) or RSRP of the PRS.

[0525] In some embodiments, the terminal may predict the location information of the terminal based on the trajectory information of the terminal before the real-time measurement and the measurement result of the PRS.

[0526] In some embodiments, the terminal may predict the location information of the terminal based on information including but not limited to the trajectory information, measured power, and measured time difference information of the terminal.

[0527] In some embodiments, the terminal may be deployed in a high-speed train.

[0528] In some embodiments, the terminal may report the measurement result of the PRS and its predicted trajectory information to the network device, so that the network device can determine the location information of the terminal.

[0529] In some embodiments, the terminal may calculate its position and predict its position based on the measurement results of the PRS.

[0530] In some embodiments, the test environment can include both the AL / ML simulator required in the TE and the AI / ML training model in the DUT.

[0531] In some embodiments, it is necessary to allow the UE to collect the necessary training data set when testing.

[0532] In some embodiments, it may be necessary to allow the terminal to train the dataset at test time.

[0533] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0534] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0535] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0536] FIG9A is a schematic diagram of the structure of a terminal according to an embodiment of the present disclosure. As shown in FIG9A , the terminal 10 may include at least one of a transceiver module 11 and a processing module 12 .

[0537] In some embodiments, the above-mentioned processing module 12 is used to determine the first data, wherein the first data is used to locate the terminal based on the positioning AI model; the first data includes at least one of the measurement results of the PRS and the trajectory information of the terminal within the first duration period.

[0538] The transceiver module 11 is configured to execute at least one of the communication steps (e.g., S201A-S204A, S201B-S206B, S201C-S206C, but not limited thereto) such as sending and / or receiving performed by the terminal 10 in any of the above methods, and will not be described in detail here. Optionally, the processing module 12 is configured to execute at least one of the other steps (e.g., S201A-S204A, S201B-S206B, S201C-S206C, but not limited thereto) performed by the terminal 10 in any of the above methods, and will not be described in detail here.

[0539] In other embodiments, the processing module 12 is used to determine a training data set, wherein the training data set is used to train the first AI model to obtain a positioning AI model in the positioning method described in some of the above embodiments.

[0540] The transceiver module 11 is configured to execute at least one of the communication steps (e.g., S301A to S303A, S301B to S305B, but not limited thereto) such as sending and / or receiving performed by the terminal 10 in any of the above methods, and will not be described in detail here. Optionally, the processing module 12 is configured to execute at least one of the other steps (e.g., S301A to S303A, S301B to S305B, but not limited thereto) performed by the terminal 10 in any of the above methods, and will not be described in detail here.

[0541] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0542] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0543] FIG9B is a schematic diagram of the structure of a network device according to an embodiment of the present disclosure. As shown in FIG9B , the network device 20 may include at least one of a transceiver module 21 and a processing module 22 .

[0544] In some embodiments, the processing module 22 is used to determine first data, wherein the first data is used to locate the terminal based on the positioning AI model; the first data includes at least one of the measurement results of the PRS and the trajectory information of the terminal within the first duration period.

[0545] The transceiver module 21 is configured to execute at least one of the communication steps (e.g., S201A-S204A, S201B-S206B, S201C-S206C, but not limited thereto) such as sending and / or receiving performed by the network device 20 in any of the above methods, which are not described in detail here. Optionally, the processing module 22 is configured to execute at least one of the other steps (e.g., S201A-S204A, S201B-S206B, S201C-S206C, but not limited thereto) performed by the network device 20 in any of the above methods, which are not described in detail here.

[0546] In other embodiments, the processing module 22 is used to determine a training data set, wherein the training data set is used to train the first AI model to obtain a positioning AI model in the positioning method described in some of the above embodiments.

[0547] The transceiver module 21 is configured to execute at least one of the communication steps (e.g., S301A to S303A, S301B to S305B, but not limited thereto) such as sending and / or receiving performed by the network device 20 in any of the above methods, and will not be described in detail here. Optionally, the processing module 22 is configured to execute at least one of the other steps (e.g., S301A to S303A, S301B to S305B, but not limited thereto) performed by the network device 20 in any of the above methods, and will not be described in detail here.

[0548] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0549] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0550] Figure 10A is a schematic diagram of the structure of a communication device 8100 proposed in an embodiment of the present disclosure. Communication device 8100 can be a network device, a terminal (e.g., user equipment), a chip, a chip system, or a processor that supports a network device in implementing any of the above methods, or a chip, a chip system, or a processor that supports a terminal in implementing any of the above methods. Communication device 8100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0551] As shown in Figure 10A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, baseband chip, terminal device, terminal device chip, DU or CU, etc.), execute programs, and process program data. The communication device 8100 is used to perform any of the above methods.

[0552] In some embodiments, the communication device 8100 further includes one or more memories 8102 for storing instructions. Optionally, all or part of the memories 8102 may be located outside the communication device 8100.

[0553] In some embodiments, the communication device 8100 further includes one or more transceivers 8103. When the communication device 8100 includes one or more transceivers 8103, the transceiver 8103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, S201A to S204A, S201B to S206B, S201C to S206C, S301A to S303A, S301B to S305B, but not limited thereto), and the processor 8101 performs at least one of the other steps (for example, S201A to S204A, S201B to S206B, S201C to S206C, S301A to S303A, S301B to S305B, but not limited thereto).

[0554] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0555] In some embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuit 8104 is connected to the memory 8102. The interface circuit 8104 may be configured to receive signals from the memory 8102 or other devices, and may be configured to send signals to the memory 8102 or other devices. For example, the interface circuit 8104 may read instructions stored in the memory 8102 and send the instructions to the processor 8101.

[0556] The communication device 8100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 8100 described in the present disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 10A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0557] FIG10B is a schematic diagram of the structure of the chip 8200 proposed in an embodiment of the present disclosure. If the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 8200 shown in FIG10B , but the present disclosure is not limited thereto.

[0558] The chip 8200 includes one or more processors 8201 , and the chip 8200 is configured to execute any of the above methods.

[0559] In some embodiments, the chip 8200 further includes one or more interface circuits 8202. Optionally, the interface circuit 8202 is connected to the memory 8203. The interface circuit 8202 can be used to receive signals from the memory 8203 or other devices, and can be used to send signals to the memory 8203 or other devices. For example, the interface circuit 8202 can read instructions stored in the memory 8203 and send the instructions to the processor 8201.

[0560] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, S201A~S204A, S201B~S206B, S201C~S206C, S301A~S303A, S301B~S305B, but not limited to these), and the processor 8201 performs at least one of the other steps (for example, S201A~S204A, S201B~S206B, S201C~S206C, S301A~S303A, S301B~S305B, but not limited to these).

[0561] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0562] In some embodiments, the chip 8200 further includes one or more memories 8203 for storing instructions. Alternatively, all or part of the memories 8203 may be outside the chip 8200.

[0563] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 8100, causes the communication device 8100 to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

[0564] The present disclosure also provides a program product, which, when executed by the communication device 8100, enables the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0565] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

[0566] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0567] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0568] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that: The method is executed by a terminal, and includes: Determine first data, wherein the first data is used to locate the terminal based on a positioning artificial intelligence (AI) model; The first data includes at least one of the following: The measurement result of the positioning reference signal PRS; The terminal's trajectory information during a first duration period.

2. The method according to claim 1, characterized in that The measurement result of the PRS includes at least one of the following: Time difference of arrival TDoA; Reference signal arrival time difference RSTD; The reference signal received power RSRP of the PRS.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Receiving a request message sent by a network device, wherein the request message is used to request the terminal to report a capability of supporting positioning of the terminal based on a positioning AI model; According to the request message, capability information is sent to the network device, wherein the capability information is used to indicate that the terminal supports positioning the terminal based on a positioning AI model.

4. The method according to claim 3, characterized in that The determining the trajectory information of the terminal within the first duration includes: According to the request message, the position of the terminal saved in a subsequent first duration period is determined, and the trajectory information of the terminal in the first duration period is determined, and the terminal supports positioning the terminal based on a positioning AI model.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Determine predicted trajectory information according to the first data and the positioning AI model; The location information of the terminal is determined according to the predicted trajectory information and the measurement result of the PRS.

6. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Determine predicted trajectory information according to the first data and the positioning AI model; The predicted trajectory information and the measurement result of the PRS are sent to a network device.

7. The method according to any one of claims 1 to 4, characterized in that The method further comprises: The first data is sent to a network device.

8. A positioning method, characterized in that: The method is performed by a network device and includes: Determine first data, wherein the first data is used to locate the terminal based on the positioning AI model; The first data includes at least one of the following: Measurement results of PRS; The terminal's trajectory information during a first duration period.

9. The method according to claim 8, characterized in that The measurement result of the PRS includes at least one of the following: TDoA; RSTD; The RSRP of the PRS.

10. The method according to claim 8 or 9, characterized in that The method further comprises: Sending a request message to the terminal, wherein the request message is used to request the terminal to report a capability of supporting positioning of the terminal based on a positioning AI model; Receive capability information sent by the terminal, wherein the capability information is used to indicate that the terminal supports positioning the terminal based on a positioning AI model.

11. The method according to any one of claims 8 to 10, characterized in that The determining of the measurement result of the PRS comprises: receiving a measurement result of the PRS sent by the terminal; A measurement result of the PRS is determined.

12. The method according to claim 11, characterized in that The method further comprises: Receiving predicted trajectory information sent by the terminal, wherein the predicted trajectory information is determined by the terminal based on the first data and the positioning AI model; The location information of the terminal is determined according to the predicted trajectory information and the measurement result of the PRS.

13. The method according to any one of claims 8 to 10, characterized in that The determining of the first data comprises: receiving the first data sent by the terminal; The first data is determined.

14. The method according to claim 13, characterized in that The method further comprises: Determine predicted trajectory information according to the first data and the positioning AI model; The location information of the terminal is determined according to the predicted trajectory information and the measurement result of the PRS.

15. A positioning test method, characterized in that: The method is executed by a terminal, and includes: Determine a training data set, wherein the training data set is used to train the first AI model to obtain the positioning AI model in the positioning method according to any one of claims 1 to 8.

16. The method according to claim 15, characterized in that The training data set includes: The second data and the real location information of the terminal; The second data includes at least one of the following: Track information of the terminal during a second duration period; The measurement results of multiple PRSs separated by a specified time interval.

17. The method according to claim 15 or 16, characterized in that The method further comprises: The first AI model is trained according to the training data set to obtain the positioning AI model.

18. The method according to claim 17, characterized in that The method further comprises: First indication information sent to a network device, wherein the first indication information is used to indicate the positioning AI model.

19. The method according to claim 15 or 16, characterized in that The method further comprises: The training data set is sent to a network device, wherein the training data set is used by the network device to train the first AI model to obtain the positioning AI model.

20. The method of claim 19, wherein: The method further comprises: Receive second indication information sent by the network device, wherein the second indication information is used to indicate the positioning AI model.

21. A positioning test method, characterized in that: The method is performed by a network device and includes: Determine a training data set, wherein the training data set is used to train the first AI model to obtain the positioning AI model in the positioning method according to any one of claims 9 to 15; or The first indication information sent by the receiving terminal is used to indicate the positioning AI model in the positioning method according to any one of claims 9 to 15.

22. The method according to claim 21, characterized in that The training data set includes: The second data and the real location information of the terminal; The second data includes at least one of the following: Track information of the terminal during a second duration period; The measurement results of multiple PRSs separated by a specified time interval.

23. The method according to claim 21 or 22, characterized in that The determining of the training data set comprises: Receiving the training data set sent by a terminal; The training data set is determined.

24. The method according to any one of claims 21 to 23, characterized in that The method further comprises: The first AI model is trained according to the training data set to obtain the positioning AI model.

25. The method of claim 24, wherein: The method further comprises: Send second indication information to the terminal, wherein the second indication information is used to indicate the positioning AI model.

26. A terminal, characterized in that: include: A processing module, configured to determine first data, wherein the first data is used to locate the terminal based on a positioning artificial intelligence (AI) model; The first data includes at least one of the following: Measurement results of PRS; The terminal's trajectory information during a first duration period.

27. A network device, characterized in that: include: A processing module, configured to determine first data, wherein the first data is used to locate the terminal based on a positioning AI model; The first data includes at least one of the following: Measurement results of PRS; The terminal's trajectory information during a first duration period.

28. A terminal, characterized in that: include: A processing module is used to determine a training data set, wherein the training data set is used to train the first AI model to obtain the positioning AI model in the positioning method as described in any one of claims 1 to 8.

29. A network device, characterized in that: include: A processing module, used to determine a training data set, wherein the training data set is used to train the first AI model to obtain the positioning AI model in the positioning method according to any one of claims 9 to 15; or A transceiver module, used to receive first indication information sent by a terminal, wherein the first indication information is used to indicate a positioning AI model in a positioning method as described in any one of claims 9 to 15.

30. A terminal, characterized in that: include: one or more processors; A memory coupled to the processor, wherein instructions are stored in the memory, and when the instructions are executed by the processor, the terminal executes the method according to any one of claims 1 to 7 and 15 to 20.

31. A network device, characterized in that: include: one or more processors; A memory coupled to the processor, wherein instructions are stored in the memory, and when the instructions are executed by the processor, the network device executes the method according to any one of claims 8 to 14 and 21 to 25.

32. A communication system, characterized in that: It comprises a terminal and a network device, wherein the terminal is configured to implement the method according to any one of claims 1 to 7 and 15 to 20, and the network device is configured to implement the method according to any one of claims 8 to 14 and 21 to 25.

33. A storage medium storing instructions, characterized in that: When the instructions are executed on a communication device, the communication device is caused to execute the method according to any one of claims 1 to 7, 8 to 14, 15 to 20, and 21 to 25.

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