Communication methods and communication devices

Abstract

This application provides an information transmission method and apparatus for use in scenarios in which AI is combined with a wireless network, particularly in scenarios in which an AI model used for positioning is combined with a wireless network. The method includes the steps of: receiving instruction information from a location management function network element, wherein the instruction information indicates an artificial intelligence AI positioning function or AI positioning model, the AI ​​positioning function or AI positioning model belongs to a plurality of AI positioning functions or AI positioning models, the plurality of AI positioning functions or AI positioning models correspond to a plurality of different sets of measurements, the set of measurements includes one or more measurements; and transmitting a channel positioning result to the location management function network element based on the instruction information, wherein the measurement corresponding to the channel positioning result corresponds to the AI ​​positioning function or artificial intelligence positioning model indicated by the instruction information. The solution of this application is based on a correspondence between input / output measurements for an AI positioning case and different AI positioning functions or AI positioning models, and can reduce signaling overhead.

Description

[Technical Field]

[0001] The present application claims the priority of Chinese Patent Application No. 202311014244.0, titled "Communication Method and Communication Apparatus", filed with the China National Intellectual Property Administration on August 10, 2023, which is incorporated herein by reference in its entirety.

[0002] Technical Field The present application relates to the field of communication technology, and more specifically, to a communication method and a communication apparatus. [Background Art]

[0003] In positioning technology based on artificial intelligence (artificial intelligence, AI), an AI positioning model can be deployed on different devices or nodes. For example, the AI positioning model is typically deployed on the positioning device side, for example, on a location management function network element (location management function, LMF), or deployed on the base station (gNodeB, gNB) side. The AI positioning model takes the channel measurement result reported by the channel measurement network element as input, and outputs the position of the terminal device. Therefore, the channel measurement network element typically needs to report a channel measurement report to the location management function network element.

[0004] Protocols support that, in uplink positioning, the LMF transmits an NR positioning protocol A (NR positioning protocol A, NRPPa) measurement request to a base station (gNodeB, gNB), the gNB measures the sounding reference signal (sounding reference signal, SRS) transmitted by a UE, obtains a reference signal received path power (reference signal received path power, RSRPP) result, and transmits the RSRPP result to the LMF. Studies have shown that the signaling overhead is high, therefore, how to reduce signaling overhead is an urgent problem to be solved. [Overview of the project] [Problems that the invention aims to solve]

[0005] This application provides a communication method and communication device that can be used in AI positioning scenarios to reduce signaling overhead. [Means for solving the problem]

[0006] According to the first aspect, a communication method is provided. The method may be performed by a first device, or by a chip or circuit of the first device. This is not limited to the present application. For the sake of ease of explanation, the following examples will be used for illustrative purposes, in which the method is performed by a first device.

[0007] The method includes the steps of: receiving instruction information from a location management function network element, wherein the instruction information indicates an AI positioning function, the AI ​​positioning function belongs to a plurality of AI positioning functions, the plurality of AI positioning functions correspond to a plurality of different sets of measurement quantities, and the set of measurement quantities includes one or more measurement quantities; and transmitting channel measurement results to the location management function network element based on the instruction information, wherein the measurement quantity corresponding to the channel measurement result corresponds to an AI positioning function indicated by the instruction information.

[0008] Optionally, the first device determines the AI ​​positioning function based on the instruction information, and then determines the measurement quantity corresponding to the AI ​​positioning function.

[0009] For example, a location management network element could be a positioning node or positioning device, such as an LMF, configured to manage the location of terminal devices.

[0010] It should be understood that multiple AI positioning functions correspond one-to-one with multiple different sets of measurement quantities, each set of measurement quantities containing one or more measurement quantities, which may be line-of-sight identification results, arrival time estimation results, arrival angle estimation results, line-of-sight identification results corresponding to each path in the first path set, delays corresponding to each path in the second path set, amplitudes corresponding to each path in the third path set, or phases corresponding to each path in the fourth path set. At least two of the first, second, third, and fourth path sets may be the same or different. For a specific explanation, please refer to the related explanations below.

[0011] According to the solution provided herein, a first device can determine a channel measurement result to be transmitted to a location management function network element based on an AI positioning function indicated by instruction information. For example, the AI ​​positioning model may be an AI positioning model used for positioning. The input to the AI ​​positioning model is assumed to be a channel measurement result. Since there is a correspondence between the measurement quantity corresponding to the channel measurement result and the AI ​​positioning function indicated by the instruction information, the first device can determine a channel measurement result to be reported to the location management function network element based on the instruction information and the correspondence. That is, based on the received instruction information, the first device can implicitly know the channel measurement result expected by the location management function network element, or in other words, it can know the corresponding channel measurement reporting requirement. Correspondingly, by parsing the received channel measurement result, the location management function network element can implicitly know the AI ​​positioning function associated with the channel measurement result and then perform AI positioning.

[0012] Referring to the first aspect, in some implementations of the first aspect, instruction information is carried in measurement request messages from location management function network elements.

[0013] Optionally, measurement request messages and instruction information transmitted by a location management function network element may be transmitted together or independently. In other words, instruction information does not have to be carried in the measurement request message. In this case, the measurement request message and instruction information may be transmitted simultaneously or separately. This is not limited to the present invention.

[0014] Based on the aforementioned solution, once the instruction information is determined, the location management function network element includes the instruction information in a measurement request message and sends the measurement request message to the first device, which then determines the AI ​​positioning function based on the instruction information and reports the channel measurement result corresponding to the measurement quantity corresponding to the AI ​​positioning function to the location management function network element, based on the correspondence between the measurement quantity corresponding to the channel measurement result and the AI ​​positioning function indicated by the instruction information. This implementation helps reduce signaling overhead.

[0015] Referring to the first aspect, in some implementations of the first aspect, the AI ​​positioning function corresponds to one or more of the following measurements: Line of sight (LOS) identification results; Estimated time of arrival (TOA); Angle of arrival (AOA) estimation results; LOS identification result corresponding to each route in the first route set; The delay corresponding to each path in the second set of paths; The amplitude corresponding to each path in the third set of paths; or The phase corresponding to each path in the fourth set of paths.

[0016] At least two of the first, second, third, and fourth route sets are either the same or different.

[0017] For example, the LOS identification result includes line-of-sight LOS and non-line of sight (NLOS), and / or LOS probability. The LOS probability is used to determine the degree of NLOS in the channel environment. Different degrees of NLOS (i.e., different channel conditions) affect the channel measurement result to different degrees in the inference accuracy of the AI ​​positioning model. Therefore, the first device can know the degree of NLOS of the channel based on the channel measurement result, and then know the current channel conditions.

[0018] The line-of-sight identification result, arrival time estimation result, and arrival angle estimation result may be considered as channel measurement quantities, and it should be understood that a single channel may contain one or more paths (e.g., a set of paths). For example, the line-of-sight identification result may be the average LOS probability of line-of-sight identification results corresponding to all paths in the channel, or the LOS or NLOS identification result with the highest frequency. The arrival time estimation result may be the average of the arrival time estimation results corresponding to all paths in the channel. The arrival angle estimation result may be the average of the arrival angle estimation results corresponding to all paths in the channel.

[0019] Based on the aforementioned solution, the first device can determine a measurement corresponding to the channel measurement result corresponding to the AI ​​positioning function based on the received instruction information, then acquire the channel measurement result based on the channel measurement, and report the channel measurement result corresponding to the AI ​​positioning function indicated by the instruction information. This implementation helps reduce signaling overhead.

[0020] Referring to the first aspect, in some implementations of the first aspect, the first device is an access network device, and the channel measurement result is obtained based on a first channel measurement, wherein the first channel measurement comprises measuring a sounding reference signal from a terminal device; or, the first device is a terminal device, and the channel measurement result is obtained based on a second channel measurement, wherein the second channel measurement comprises measuring a positioning reference signal or a channel state information reference signal from an access network device; or, the first device is a first terminal device, and the channel measurement result is obtained based on a third channel measurement, wherein the third channel measurement comprises measuring a sidelink positioning reference signal from a second terminal device.

[0021] Based on the foregoing solutions, channel measurement methods in uplink AI positioning scenarios, downlink AI positioning scenarios, and sidelink AI positioning scenarios are provided. A correspondence between a measurement quantity corresponding to a channel measurement result and an AI positioning function indicated by indication information is defined, thereby reducing signaling overhead.

[0022] Referring to the first aspect, in some implementations of the first aspect, the AI positioning function further corresponds to one of the following reporting methods: measurement results of each path in the same measurement quantity are reported independently, or measurement results of each path in the same measurement quantity are reported differentially.

[0023] Based on the foregoing solution, the first device determines an AI positioning function based on the received indication information, determines a corresponding reporting method for a channel measurement result based on the AI positioning function, then obtains the channel measurement result based on channel measurement, and can report the channel measurement result associated with the AI positioning function to a location management function network element by using the corresponding reporting method. Compared with current solutions in which a plurality of signaling is used to separately indicate the reporting method and the AI positioning function, or a plurality of fields in one signaling is used to indicate the reporting method and the AI positioning function, the technical solution of the present application is based on a correspondence between an AI positioning function or an AI positioning model and a measurement quantity, and is further based on a correspondence between an AI positioning function or an AI positioning model and a reporting method, so that both the measurement quantity and the reporting method corresponding to the AI positioning function or the AI positioning model can be indicated by transmitting the indication information, thereby reducing signaling overhead.

[0024] With reference to the first aspect, in some implementations of the first aspect, the channel measurement result is included in a measurement report, and differentially reporting the measurement results of each path for the same measurement quantity includes differentially reporting the measurement results of each path for the same measurement quantity in the same measurement report.

[0025] That is, measurement results of a same measurement quantity in different measurement reports are reported independently.

[0026] Based on the aforementioned solution, it is conceivable to report the measurement results for each path differentially for reporting the same measurement quantity within the same measurement report, thereby reducing signaling overhead. For example, this could include a first path and a second path. The differential delay measurement results for multiple paths would include the delay measurement result corresponding to the first path and a first differential value, where the first differential value indicates the difference between the delay measurement result corresponding to the second path and the delay measurement result corresponding to the first path. Compared to directly reporting the delay measurement results corresponding to the first and second paths, the differential reporting method can reduce signaling overhead.

[0027] Referring to the first aspect, in some implementations of the first aspect, the AI ​​positioning function includes one or more AI positioning models, and the measurement corresponding to the channel measurement result corresponds to one or more AI positioning models.

[0028] Based on the above solution, the AI ​​positioning function may further include at least one AI positioning model. In this case, each AI positioning model may correspond to one quantifier and / or reporting method corresponding to a channel measurement result. Thus, after receiving instruction information, the first device may determine the AI ​​positioning function, implicitly determine one or more AI positioning models corresponding to the AI ​​positioning function, and the quantifier and / or reporting method for channel measurement results corresponding to each AI positioning model, and then, based on the instruction information, transmit the corresponding measurement results to the location management function network element. Compared to current solutions in which the reporting method and AI positioning function are indicated separately, this solution can reduce signaling overhead.

[0029] A second aspect provides a communication method. The method may be performed by a location management function network element, or by a chip or circuit of the location management function network element. This is not limited to the present application. For the sake of clarity, the following example uses a method performed by a location management function network element.

[0030] The method includes the steps of: transmitting instruction information to a first device, wherein the instruction information indicates an AI positioning function, the AI ​​positioning function belongs to a plurality of AI positioning functions, the plurality of AI positioning functions correspond to a plurality of different sets of measurement quantities, and the set of measurement quantities includes one or more measurement quantities; and receiving a channel measurement result from the first device, wherein the measurement quantity corresponding to the channel measurement result corresponds to an AI positioning function indicated by the instruction information.

[0031] Optionally, the method includes the following: A location management function network element determines instruction information before transmitting it to the first device, and performs measurements on the UE after receiving the channel AI results.

[0032] According to the solution provided in this application, a location management function network element can implicitly indicate a measured quantity of a channel measurement result corresponding to an AI positioning function by transmitting instruction information to a first device. For example, the AI ​​model may be an AI model used for positioning. The input to the AI ​​positioning model is assumed to be a channel measurement result. Since there is a correspondence between the measured quantity corresponding to the channel measurement result and the AI ​​positioning function indicated by the instruction information, the first device can determine the channel measurement result to be reported to the location management function network element based on the instruction information and the correspondence. That is, based on the received instruction information, the first device can implicitly know the channel measurement result expected by the location management function network element, or in other words, it can know the corresponding channel measurement reporting requirement. Correspondingly, by parsing the received channel measurement result, the location management function network element can implicitly know the AI ​​positioning function associated with the corresponding channel measurement result and then perform AI positioning.

[0033] Referring to the second aspect, in some implementations of the second aspect, instruction information is carried in measurement request messages transmitted by location management function network elements.

[0034] Referring to the second aspect, in some implementations of the second aspect, the AI ​​positioning function corresponds to one of the following measurements: line of sight (LOS) identification result, time of arrival (TOA) estimation result, angle of arrival (AOA) estimation result, LOS identification result corresponding to each route in the first route set, delay corresponding to each route in the second route set, amplitude corresponding to each route in the third route set, or phase corresponding to each route in the fourth route set. At least two of the first, second, third, and fourth route sets are either the same or different.

[0035] Referring to the second aspect, in some implementations of the second aspect, the first device is an access network device, and the channel measurement result is obtained based on a first channel measurement, the first channel measurement includes measuring a prospecting reference signal from a terminal device; or the first device is a terminal device, and the channel measurement result is obtained based on a second channel measurement, the second channel measurement includes measuring a positioning reference signal or a channel status information reference signal from an access network device; or the first device is a first terminal device, and the channel measurement result is obtained based on a third channel measurement, the third channel measurement includes measuring a sidelink positioning reference signal from a second terminal device.

[0036] Referring to the second aspect, in some implementations of the second aspect, the AI ​​positioning function further corresponds to one of the following reporting methods: either the measurement results for each route on the same measurement quantity are reported independently, or the measurement results for each route on the same measurement quantity are reported differentially.

[0037] Referring to the second aspect, in some implementations of the second aspect, channel measurement results are included in the measurement report, and the differential reporting of measurement results for each path at the same measurement quantity includes the differential reporting of measurement results for each path at the same measurement quantity in the same measurement report.

[0038] In other words, the measurement results for the same quantity in different measurement reports are reported independently.

[0039] Referring to the second aspect, in some implementations of the second aspect, the AI ​​positioning function includes one or more AI positioning models, and the measurement corresponding to the channel measurement result corresponds to one or more AI positioning models.

[0040] For the second aspect and the beneficial effects of some implementations of the second aspect, please refer to the corresponding explanation in the first aspect. Further details will not be provided here.

[0041] A third aspect provides a communication method. The method may be performed by a first device, or by a chip or circuit of the first device. This is not limited to the present application. For the sake of clarity, the following examples will use the method performed by the first device.

[0042] The method includes the steps of: receiving instruction information from a location management function network element, the instruction information indicating an AI positioning model, the AI ​​positioning model belonging to a plurality of AI positioning models, the plurality of AI positioning models corresponding to a plurality of different sets of measurements, the set of measurements including one or more measurements; and transmitting a channel positioning result to the location management function network element based on the instruction information, the measurement corresponding to the channel positioning result corresponding to an AI positioning model indicated by the instruction information.

[0043] Optionally, the first device determines an AI positioning model based on the instruction information, and then determines a measurement quantity corresponding to the AI ​​positioning model.

[0044] Multiple AI positioning models correspond one-to-one with multiple different sets of measurements, each set of measurements containing one or more measurements, which may be, namely, line-of-sight identification results, arrival time estimation results, arrival angle estimation results, line-of-sight identification results corresponding to each route in the first route set, delays corresponding to each route in the second route set, amplitudes corresponding to each route in the third route set, or phases corresponding to each route in the fourth route set. At least two of the first, second, third, and fourth route sets may be the same or different. For a specific explanation, please refer to the relevant explanations mentioned above.

[0045] According to the solution provided herein, a first device can determine a channel measurement result to be transmitted to a location management function network element based on an AI positioning model indicated by instruction information. For example, the AI ​​model may be an AI model used for positioning. The input to the AI ​​positioning model is assumed to be a channel measurement result. Since there is a correspondence between the measurement quantity corresponding to the channel measurement result and the AI ​​positioning model indicated by the instruction information, the first device can determine a channel measurement result to be reported to the location management function network element based on the instruction information and the correspondence. That is, based on the received instruction information, the first device can implicitly know the channel measurement result expected by the location management function network element, or in other words, it can know the corresponding channel measurement reporting requirement. Correspondingly, by parsing the received channel measurement result, the location management function network element can implicitly know the AI ​​positioning model associated with the corresponding channel measurement result and then perform AI positioning.

[0046] Referring to the third aspect, in some implementations of the third aspect, instruction information is carried in measurement request messages from location management function network elements.

[0047] Referring to the third aspect, in some implementations of the third aspect, the AI ​​positioning model corresponds to one or more of the following measurements: Line of Sight (LOS) identification results; Time of Arrival (TOA) Estimate Result; Estimated arrival angle of arrival (AOA); LOS identification result corresponding to each route in the first route set; The delay corresponding to each path in the second set of paths; The amplitude corresponding to each path in the third set of paths; or The phase corresponding to each path in the fourth set of paths.

[0048] At least two of the first, second, third, and fourth route sets are either the same or different. For a relevant explanation of the measured quantities, please refer to the relevant explanation above.

[0049] Referring to the third aspect, in some implementations of the third aspect, the first device is an access network device, and the channel measurement result is obtained based on the first channel measurement, which includes measuring a prospecting reference signal from a terminal device; or the first device is a terminal device, and the channel measurement result is obtained based on the second channel measurement, which includes measuring a positioning reference signal or a channel status information reference signal from an access network device; or the first device is a first terminal device, and the channel measurement result is obtained based on the third channel measurement, which includes measuring a sidelink positioning reference signal from a second terminal device.

[0050] Referring to the third aspect, in some implementations of the third aspect, the AI ​​positioning model further supports one of the following reporting methods: either the measurement results for each path on the same measurement quantity are reported independently, or the measurement results for each path on the same measurement quantity are reported differentially.

[0051] In relation to the third aspect, in some implementations of the third aspect, channel measurement results are included in the measurement report, and the differential reporting of measurement results for each path at the same measurement quantity includes the differential reporting of measurement results for each path at the same measurement quantity in the same measurement report.

[0052] In other words, the measurement results for each pathway at the same measurement quantity in different measurement reports are reported independently.

[0053] For the third aspect and the beneficial effects of some implementations of the third aspect, please refer to the relevant explanations in the first aspect. Further details will not be provided here.

[0054] A fourth aspect provides a communication method. The method may be performed by a location management function network element, or by a chip or circuit of the location management function network element. This is not limited to the present application. For the sake of clarity, the following example uses a method performed by a location management function network element.

[0055] The method includes the steps of: transmitting instruction information to a first device, wherein the instruction information indicates an AI positioning model, the AI ​​positioning model belongs to a plurality of AI positioning models, the plurality of AI positioning models correspond to a plurality of different sets of measurement quantities, the set of measurement quantities includes one or more measurement quantities; and receiving a channel measurement result from the first device, wherein the measurement quantity corresponding to the channel measurement result corresponds to an AI positioning model indicated by the instruction information.

[0056] Optionally, the method includes the following: A location management function network element determines instruction information before transmitting it to the first device, and performs AI positioning on the UE after receiving the channel measurement result.

[0057] According to the solution provided in this application, a location management function network element may implicitly indicate a measured quantity of a channel measurement result corresponding to an AI positioning model by transmitting instruction information to a first device. For example, the AI ​​model may be an AI model used for positioning. The input to the AI ​​positioning model is assumed to be a channel measurement result. Since there is a correspondence between the measured quantity corresponding to the channel measurement result and the AI ​​positioning model indicated by the instruction information, the first device can determine the channel measurement result to be reported to the location management function network element based on the instruction information and the correspondence. That is, based on the received instruction information, the first device can implicitly know the channel measurement result expected by the location management function network element, or in other words, it can know the corresponding channel measurement reporting requirements. Correspondingly, by parsing the received channel measurement result, the location management function network element can implicitly know the AI ​​positioning model associated with the corresponding channel measurement result and then perform AI positioning.

[0058] In relation to the fourth aspect, in some implementations of the fourth aspect, instruction information is carried within measurement request messages transmitted by location management function network elements.

[0059] Referring to the fourth aspect, in some implementations of the fourth aspect, the AI ​​positioning model corresponds to one of the following measurements: line of sight (LOS) identification result, time of arrival (TOA) estimation result, angle of arrival (AOA) estimation result, LOS identification result corresponding to each route in the first route set, delay corresponding to each route in the second route set, amplitude corresponding to each route in the third route set, or phase corresponding to each route in the fourth route set. At least two of the first, second, third, and fourth route sets are either the same or different.

[0060] Referring to the fourth aspect, in some implementations of the fourth aspect, the first device is an access network device, and the channel measurement result is obtained based on a first channel measurement, the first channel measurement includes measuring a prospecting reference signal from a terminal device; or the first device is a terminal device, and the channel measurement result is obtained based on a second channel measurement, the second channel measurement includes measuring a positioning reference signal or a channel status information reference signal from an access network device; or the first device is a first terminal device, and the channel measurement result is obtained based on a third channel measurement, the third channel measurement includes measuring a sidelink positioning reference signal from a second terminal device.

[0061] Referring to the fourth aspect, some implementations of the fourth aspect further support one of the following reporting methods: either the measurement results for each path on the same measurement are reported independently, or the measurement results for each path on the same measurement are reported differentially.

[0062] In relation to the fourth aspect, in some implementations of the fourth aspect, channel measurement results are included in the measurement report, and the differential reporting of measurement results for each path at the same measurement quantity includes the differential reporting of measurement results for each path at the same measurement quantity in the same measurement report.

[0063] In other words, the measurement results for each pathway at the same measurement quantity in different measurement reports are reported independently.

[0064] For the fourth aspect and the beneficial effects of some implementations of the fourth aspect, please refer to the corresponding explanation in the third aspect. Further details will not be provided here.

[0065] According to the fifth aspect, a communication device is provided. The device includes a transceiver unit configured to receive instruction information from a location management function network element, the instruction information indicating an AI positioning function. The transceiver unit is further configured to transmit channel measurement results to the location management function network element based on the instruction information, the measurement corresponding to the channel measurement result corresponds to the AI ​​positioning function indicated by the instruction information.

[0066] The processing unit is optionally configured to determine the AI ​​positioning function based on the instruction information and to determine the measurement quantity corresponding to the AI ​​positioning function.

[0067] The transceiver unit may perform receiving and transmitting processes on the first side, and the processing unit of the communication device may perform processes other than receiving and transmitting on the first side.

[0068] According to the sixth aspect, a communication device is provided. The device includes a transceiver unit configured to transmit instruction information to a first device, the instruction information indicating an AI positioning function. The transceiver unit is further configured to receive channel measurement results from the first device, the measurement corresponding to the channel measurement results corresponding to the AI ​​positioning function indicated by the instruction information.

[0069] Optionally, the processing unit is configured to determine instruction information and perform AI positioning on the UE after receiving channel measurement results.

[0070] The transceiver unit may perform receiving and transmitting processes on the second side, and the processing unit of the communication device may perform processes other than receiving and transmitting on the second side.

[0071] According to the seventh aspect, a communication device is provided. The device includes a transceiver unit configured to receive instruction information from a location management function network element, the instruction information indicating an AI positioning model. The transceiver unit is further configured to transmit channel measurement results to the location management function network element based on the instruction information, the measurement corresponding to the channel measurement results corresponding to the AI ​​positioning model indicated by the instruction information.

[0072] Optionally, the processing unit is configured to determine an AI positioning model based on the instruction information and to determine the measurement quantity corresponding to the AI ​​positioning model. The transceiver unit may perform receiving and transmitting processing in the third aspect, and the processing unit of the communication device may perform processing other than receiving and transmitting in the third aspect.

[0073] According to the eighth aspect, a communication device is provided. The device includes a transceiver unit configured to transmit instruction information to a first device, the instruction information indicating an AI positioning model. The transceiver unit is further configured to receive channel measurement results from the first device, the measurement corresponding to the channel measurement results corresponding to the AI ​​positioning model indicated by the instruction information.

[0074] Optionally, the processing unit is configured to determine instruction information and perform AI positioning on the UE after receiving channel measurement results.

[0075] The transceiver unit may perform receiving and transmitting processes on the fourth side, and the processing unit of the communication device may perform processes other than receiving and transmitting on the fourth side.

[0076] According to the ninth aspect, a communication device including a processor is provided. The processor is configured to execute a computer program, enabling the device to perform a method in any one of the possible implementations of the first to fourth aspects and the first to fourth aspects.

[0077] Optionally, there may be one or more processors.

[0078] Optionally, the communication device further includes memory, which is configured to store computer programs, and one or more memories exist.

[0079] Optionally, the memory may be integrated with the processor, or the memory and processor may be located separately, or the memory may be located within the processor.

[0080] Optionally, the communication device further includes transceiver circuits, such as transceivers or input / output circuits.

[0081] According to the tenth aspect, a communication system is provided which includes a first device and a location management function network element. The first device is configured to perform a method in any one of the first aspect or the third aspect and a possible implementation of the first aspect or the third aspect. The location management function network element is configured to perform a method in any one of the second aspect or the fourth aspect and a possible implementation of the second aspect or the fourth aspect.

[0082] According to the eleventh aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program or code. When the computer program or code is executed on the computer, the computer becomes capable of performing any of the methods in the first through fourth aspects and any one of the possible implementations of the first through fourth aspects.

[0083] According to the twelfth aspect, a chip is provided which includes at least one processor. The processor is configured to execute a computer program that enables a device on which the chip is installed to perform a method in any one of the possible implementations of the first through fourth aspects and the first through fourth aspects.

[0084] The chip may include an output circuit or interface configured to transmit information or data, and an input circuit or interface configured to receive information or data.

[0085] According to the thirteenth aspect, a computer program product is provided. The computer program product includes computer program code. When the computer program code is executed on a computer, the computer becomes capable of performing any of the methods in the first through fourth aspects and any one of the possible implementations of the first through fourth aspects. [Brief explanation of the drawing]

[0086] [Figure 1] This is a diagram of a wireless communication system 100 suitable for one embodiment of the present invention.

[0087] [Figure 2] This is a diagram of a wireless communication system 200 suitable for one embodiment of the present invention.

[0088] [Figure 3] This is a diagram of a wireless communication system 300 suitable for one embodiment of the present invention.

[0089] [Figure 4] This is a diagram of a network element according to one embodiment of the present invention.

[0090] [Figure 5] This is a diagram of an AI / ML network element or module.

[0091] [Figure 6] This is a diagram of an AI positioning model framework.

[0092] [Figure 7] This is a diagram of TDOA positioning.

[0093] [Figure 8] This is a diagram of LOS and NLOS.

[0094] [Figure 9] This is a schematic flowchart of a communication method 900 according to one embodiment of the present invention.

[0095] [Figure 10] This is a schematic flowchart of a communication method 1000 according to one embodiment of the present invention.

[0096] [Figure 11] This is a schematic flowchart of a communication method 1100 according to one embodiment of the present invention.

[0097] [Figure 12] This is a schematic flowchart of a communication method 1200 according to one embodiment of the present invention.

[0098] [Figure 13] This is a diagram of a communication device 1300 according to one embodiment of the present invention.

[0099] [Figure 14] This is a diagram of another communication device 1400 according to one embodiment of the present invention.

[0100] [Figure 15] This is a diagram of a chip system 1500 according to one embodiment of the present invention. [Modes for carrying out the invention]

[0101] The technical solution in this application will be described below with reference to the attached drawings.

[0102] The technical solutions provided in this application may be applied to various communication systems, such as future communication systems including 5th generation (5G) or new radio (NR) systems, long-term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, 6th generation mobile communication systems, or convergence systems of multiple systems. The technical solutions provided in this application may also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), Internet of Things (IoT) communication systems, or other communication systems.

[0103] A device in a communication system can transmit signals to or receive signals from another device. These signals may include information, signaling, data, etc. A device may be replaced by an entity, network entity, communication device, communication module, node, communication node, etc. In this application, a device is used as an example for illustrative purposes. For example, a communication system may include at least one terminal device and at least one network device. The network device may transmit downlink signals to the terminal device, and / or the terminal device may transmit uplink signals to the network device. It can be understood that the terminal device / network device in this application may be replaced by a first device that performs the corresponding communication method in this application using a location management function network element.

[0104] The terminal devices in embodiments of the present application include a variety of devices having wireless communication capabilities and may be configured to connect to people, objects, machines, etc. Terminal devices can be widely used in a variety of scenarios, such as cellular communication, D2D, V2X, peer-to-peer (P2P), M2M, MTC, IoT, virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, unmanned aerial vehicles, robots, remote sensing, passive sensing, positioning, navigation and tracking, and automated delivery. The terminal device may be a terminal in any of the aforementioned scenarios, for example, an MTC terminal or an IoT terminal.Terminal devices include user equipment (UE), terminals, fixed devices, mobile station devices or mobile devices, subscriber units, handheld devices, in-vehicle devices, wearable devices, cellular phones, smartphones, session initiation protocol (SIP) phones, wireless data cards, personal digital assistants (PDAs), computers, tablet computers, notebook computers, wireless modems, handsets, laptop computers, computers with wireless transceiver functionality, smartbooks, vehicles, satellites, and global positioning systems (GPS). The terminal device may be a system (GPS) device, a target tracking device, an aircraft (e.g., an unmanned aerial vehicle, helicopter, multi-helicopter, four-engine helicopter, or airplane), a ship, a remote control device, a smart home device, an industrial device, equipment placed in the aforementioned device (e.g., a communication module, modem, or chip within the aforementioned device), or another processing device connected to a wireless modem. For the sake of clarity, the following examples will use the terminal device as a terminal or UE for illustrative purposes.

[0105] It should be understood that in some scenarios, the UE can be further configured to function as a base station. For example, the UE can act as a scheduling entity providing sidelink signals between UEs in V2X, D2D, P2P, or other scenarios.

[0106] In embodiments of the present application, the device configured to implement the functions of a terminal device may be a terminal device, or a device capable of supporting a terminal device in implementing its functions, such as a chip system or a chip. The device may be installed in the terminal device. In embodiments of the present application, the chip system may include a chip, or include a chip and other discrete components.

[0107] In embodiments of the present invention, a network device may be a device configured to communicate with a terminal device. A network device may also be called an access network device or a radio access network device. For example, a network device may be a base station. In embodiments of the present invention, a network device may be a radio access network (RAN) node (or device) that connects terminal devices to a wireless network. In a broad sense, a base station may cover, or be replaced by, the following names: Node B, evolved Node B (eNB), next generation Node B (gNB), relay station, access point, transmission reception point (TRP), transmission point (TP), primary station, secondary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmitting node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. A base station can be a macro base station, a micro base station, a relay node, a donor node, etc., or a combination thereof. Alternatively, a base station may be a communication module, modem, or chip located in the aforementioned device or apparatus. Alternatively, a base station may be a mobile switching center, a device performing base station functions in D2D, V2X, or M2M communications, a network-side device in a 6G network, a device performing base station functions in a future communication system, etc.Base stations may support a network using the same or different access technologies. The specific technologies and device configurations used for network devices are not limited to those of the present invention.

[0108] Base stations may be fixed or mobile. For example, a helicopter or unmanned aerial vehicle may be configured as a mobile base station, and one or more cells may move based on the location of the mobile base station. In another example, a helicopter or unmanned aerial vehicle may be configured as a device for communicating with another base station.

[0109] In some developments, the network device referred to in the embodiments of the present application may include a CU or DU, or a device including both a CU and a DU, or a device including a CU control plane node (central unit-control plane, CU-CP), a CU user plane node (central unit-user plane, CU-UP), and a DU node. For example, the network device may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0110] In some deployments, multiple RAN nodes cooperate to assist terminals in implementing radio access, with each different RAN node implementing a portion of the base station's functionality. For example, a RAN node may be a CU, DU, CU-CP, CU-UP, or RU. CUs and DUs may be located separately or be included in the same network element, such as a BBU. An RU may be included in a radio frequency device or radio frequency unit, such as an RRU, AAU, or RRH.

[0111] A RAN node can support one or more categories of fronthaul interfaces, with different fronthaul interfaces corresponding to DUs and RUs, each with different functionalities. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is an interface of a different category than the CPRI implementation, some of the downlink and / or uplink baseband functions are moved from the DU to the RU for implementation. For example, for the downlink, one or more of precoding, digital beamforming (BF), or inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition are moved from the DU to the RU for implementation. For uplinks, one or more of the following are moved from the DU to the RU for implementation: digital beamforming (BF) or fast Fourier transform (FFT) / cyclic prefix removal. In one possible implementation, the interface may be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, different partitioning schemes between the DU and RU correspond to different categories (Cats) of eCPRI, such as eCPRI Cats A, B, C, D, E, and F.

[0112] eCPRI Cat A is used as an example. For downlink transmissions, splitting is performed in layer mapping. The DU is configured to implement layer mapping and one or more functions prior to layer mapping (i.e., encoding, rate matching, scrambling, modulation, or layer mapping), while other functions after layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to the RU for implementation. For uplink transmissions, splitting is performed in RE demapping. The DU is configured to implement demapping and one or more pre-demapping functions (i.e., one or more functions from decoding, rate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, or RE demapping), while other post-demapping functions (e.g., one or more from digital BF or fast Fourier transform (FFT) / CP rejection) are moved to the RU for implementation. For functional descriptions of DUs and RUs corresponding to various categories of eCPRIs, it is understandable to refer to the eCPRI standards. Further details are not described herein.

[0113] In one possible design, the processing unit for implementing baseband functionality within the BBU is called the baseband high (BBH) unit, and the processing unit for implementing baseband functionality within the RRU / AAU / RRH is called the baseband low (BBL) unit.

[0114] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meanings. For example, in the ORAN system, CU may be called O-CU (open CU), DU may be called O-DU, CU-CP may be called O-CU-CP, CU-UP may be called O-CU-UP, and RU may be called O-RU. Any of CU (or CU-CP or CU-UP), DU, and RU in this application may be implemented by using a software module, a hardware module, or a combination of a software module and a hardware module.

[0115] In embodiments of the present application, the device configured to implement the functionality of a network device may be a network device, or a device capable of supporting a network device in implementing functionality, such as a chip system or a chip. The device may be installed in a network device. In embodiments of the present application, the chip system may include a chip, or include a chip and other discrete components.

[0116] Network devices and terminal devices may be deployed on land, on water, or in the air, including indoor or outdoor devices, handheld devices, or in-vehicle devices, and may be deployed on airplanes, balloons, and satellites. The scenarios in which network devices and terminal devices are located are not limited to the embodiments of this application. In addition, terminal devices and network devices may be hardware devices, or software functions running on dedicated hardware or general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities comprising dedicated or general-purpose hardware devices and software functions. The specific forms of terminal devices and network devices are not limited in this application.

[0117] In embodiments of the present application, the location management function network element may be a positioning node or positioning device, such as an LMF, configured to perform positioning management for the location of a terminal device. For example, a device configured to perform positioning or management for the location of a terminal device may be a location management function network element, or a chip system, chip, or circuit of a location management function network element. The chip system, chip, or circuit may be installed in the location management function network element. The chip system may include a chip, or it may include a chip and another discrete device.

[0118] Optionally, a location management function network element may be a core network device. A core network device is a device located within a core network (CN) that provides service support for terminal devices. A core network device may include one or more core network elements. A 5G core network is used as an example. A 5G core network includes an access and mobility management function (AMF) network element responsible for mobility management, access management, and other services; a session management function (SMF) network element responsible for session management; a user plane function (UPF) network element responsible for user plane data packet routing and forwarding, as well as quality of service (QoS) control; a policy control function (PCF) network element, and so on. Core network elements may operate independently or may be combined to implement several control functions. For example, the AMF, SMF, and PCF may be combined as a single core network device. All or part of the core network elements may be independent in form or integrated into the same device. This is not limited herein.

[0119] In embodiments of the present application, the first device may be a terminal device or a network device, or a component of a terminal device or network device (e.g., a chip or circuit). Optionally, the network device may be a network device comprising one or more AI modules. For example, the network device may be one or more devices of a core network device, an access network node (RAN node), or an OAM. For example, the AI ​​module may be a RAN intelligent controller (RIC), for example, a quasi-real-time RIC or a non-real-time RIC. For example, a quasi-real-time RIC may be located in a RAN node (e.g., a CU or DU), and a non-real-time RIC may be located in an OAM, a cloud server, a core network device, or another network device. The location management function network element may be a network element for training an AI positioning model and / or for storing an AI positioning model library, or it may also be a network element for selecting / inferring an AI positioning model. For example, an AI positioning model used for positioning may be configured in the location management function network element.

[0120] For example, the first device and the location management network element may be logically deployed separately. In various implementations, the first device and the location management network element may be physically deployed in the same network element or in different network elements. This is not limited. For example, the first device may be a terminal device, and the location management network element may be a server (also called a host) or cloud device in an over-the-top (OTT) system, and the terminal device may communicate with the server or cloud device in the OTT system over the internet. In another example, the first device may be a module within the terminal device (e.g., a module in the physical layer), and the location management network element may be another module within the device (e.g., an LMF) (e.g., a module in the application layer, such as an application module connected to an OTT server). In embodiments of the present application, it may be understood that the module may be implemented by hardware, software, or a combination of hardware and software. This is not limited.

[0121] First, a brief description of a communication system suitable for the embodiment of this invention will be given below.

[0122] Figure 1 is a diagram of a wireless communication system 100 suitable for one embodiment of the present invention. As shown in Figure 1, the wireless communication system includes a radio access network 100. The radio access network 100 may be a next-generation (e.g., 6G or higher) radio access network or a conventional (e.g., 5G, 4G, 3G, 2G) radio access network. One or more terminal devices (120a to 120j, collectively referred to as 120) may be connected to each other or to one or more network devices (110a and 110b, collectively referred to as 110) in the radio access network 100. Figure 1 is merely a diagram. The wireless communication system may further include other devices, for example, core network devices, wireless relay devices, wireless backhaul devices, etc., which are not shown in Figure 1.

[0123] In practical applications, a wireless communication system may include multiple network devices and multiple terminal devices, but is not limited to this. One network device may provide services to one or more terminal devices. One terminal device may access one or more network devices. The number of terminal devices and network devices included in a wireless communication system is not limited to the embodiments of this application.

[0124] Figure 2 is a diagram of a wireless communication system 200 suitable for an embodiment of the present invention. As shown in Figure 2, the wireless communication system 200 may include at least one network device, for example, the network device 210 shown in Figure 2. The wireless communication system 200 may further include at least one terminal device, for example, terminal devices 220 and 230 shown in Figure 2. The wireless communication system 200 may further include a positioning device, for example, the positioning device 240 shown in Figure 2. For example, the positioning device 240 is a positioning management function LMF network element, abbreviated as LMF.

[0125] Positioning devices and network devices can communicate with each other using interface messages. For example, network device 210 is a gNB and positioning device 240 is an LMF. The gNB and LMF can exchange information using NRPPa messages. In another example, network device 210 is an eNB and positioning device 240 is an LMF. The eNB and LMF can exchange information using LTE positioning protocol (LPP) messages.

[0126] The terminal device and the positioning device may communicate with each other directly or through another device, which may be, for example, a network device and / or a core network element. For example, as shown in Figure 2, the terminal device 220 and the positioning device 240 may communicate with each other through a network device 210.

[0127] Optionally, the positioning device and the network device may be different modules of the same device, or they may be different, separate devices.

[0128] Figure 3 is a diagram of a wireless positioning system suitable for one embodiment of the present invention. As shown in Figure 3, the wireless positioning system mainly includes an access network device, a terminal device, and a positioning device. The positioning device is mainly responsible for receiving positioning service requests, collecting positioning-related measurement results, calculating positioning results, and providing corresponding wireless positioning services. Optionally, the positioning device may receive positioning service requests from an access network device or a higher-layer application. In one example, the positioning device may be a location management function network element, such as an LMF. See the above description for the access network device and the terminal device.

[0129] Figure 4 is a diagram of a device according to one embodiment of the present application. As shown in Figure 4, it includes a UE, a location management function network element, and an access network device. Optionally, an access and mobility management function (AMF) network element is further included. In one example, the access network device may be an ng-eNB or a gNB. An ng-eNB represents a 4G base station that can access a 5G core network, and a gNB represents a 5G base station; both are NR-RAN network elements. Radio access network devices or base stations referred to in embodiments of the present application may be ng-eNBs or gNBs; this is not limited to them. The UE and the radio access network device communicate with each other via corresponding interfaces. For example, the UE and the gNB communicate via an NR-Uu interface, and the UE and the ng-eNB communicate via an LTE-Uu interface. In embodiments of the present application, the NR-Uu interface and the LTE-Uu interface are used for positioning-related signaling and / or data transmission. In addition, the NG-C interface is used for communication between the gNB and the AMF, and between the ng-eNB and the AMF, for example, for positioning-related signaling transmission. The NL1 interface is used for communication between the AMF and the LMF, for example, for positioning-related signaling transmission. Optionally, the UE interacts with the LMF based on the LTE positioning protocol (LPP), and the NG-RAN interacts with the LMF based on the NRPPa protocol, which is used for transparent transmission across the AMF. It should be understood that the NG-RAN is used merely as an example. If the technical solution of this application is applied to a future wireless communication system, such as a 6G system, the NG-RAN would correspondingly be the access network device in the 6G system. Similarly, the names of network elements, the names of interfaces between network elements, message names, etc., are merely examples.In future wireless communication systems, network elements, interfaces, and interface messages having the same or similar functionality may be used to implement the technical solutions of this invention.

[0130] Furthermore, to support machine learning capabilities in wireless communication systems, AI nodes may be introduced into the wireless communication system.

[0131] Optionally, the communication system further includes at least one AI node.

[0132] Optionally, an AI node may be deployed in one or more of the following: a network device, a terminal device, a core network, or a positioning device. Alternatively, an AI node may be deployed separately, for example, in a location other than one of the above-mentioned devices. An AI node may communicate with another device in the communication system, which may be one or more of the following: a network device, a terminal device, a core network element, or a positioning device.

[0133] Optionally, an AI node can be configured to perform AI-related operations. For example, AI-related operations may include one or more of the following: model failure testing, model performance testing, model training testing, or data collection.

[0134] For example, a network device may transfer data to an AI node that is related to an AI positioning model and reported by a terminal device, and the AI ​​node performs an AI-related operation. In another example, a network device or terminal device may transfer data related to an AI positioning model to an AI node, and the AI ​​node performs an AI-related operation. In yet another example, the AI ​​node may send one or more outputs of an AI-related operation, such as a trained neural network model, model evaluation results, or model test results, to a network device and / or terminal device. For example, the AI ​​node may send the output of an AI-related operation directly to a network device and a terminal device. In yet another example, the AI ​​node may send the output of an AI-related operation to a terminal device via a network device. In yet another example, the AI ​​node may send the output of an AI-related operation to a network device via a terminal device.

[0135] It should be understood that the number of AI nodes is not limited in this application. For example, when there are multiple AI nodes, these multiple AI nodes may be obtained through a function-based division. For example, different AI nodes may be responsible for different functions.

[0136] It can be further understood that AI nodes may be independent devices, or integrated into the same device to implement different functions, or may be network elements within a hardware device, or may be software functions running on dedicated hardware, or may be virtualization functions instantiated on a platform (e.g., a cloud platform). The specific forms of AI nodes are not limited herein.

[0137] For example, an AI node may be an AI network element or an AI module.

[0138] Figure 5 is a diagram of an AI / ML network element or module. When an AI network element is introduced, it indicates that the AI ​​network element corresponds to an independent network element. When an AI module is introduced, the AI ​​module may be located inside the network element. As described above, the network element in embodiments of the present application includes a UE, a radio access network device, and an LMF, and optionally further includes an AMF. One or more of the UE, radio access network device, AMF (if a network element is included), and LMF may have an AI module located inside. Alternatively, the corresponding AI network element is introduced for one or more of the UE, radio access network device, AMF, and LMF. Or, these two methods may be used in combination. This is not limited to the present application.

[0139] An AI module is configured to implement a corresponding AI function. AI modules deployed in different network elements may be the same or different. The model of an AI module is constructed based on different parameters, and AI modules may implement different functions. The model of an AI module may be constructed based on one or more of the following parameters: structural parameters (e.g., at least one of the number of layers in the neural network, the width of the neural network, the connectivity between layers, the weights of neurons, the activation function of neurons, or the bias of the activation function), input parameters (e.g., the type of the input parameters and / or the dimensions of the input parameters), or output parameters (e.g., the type of the output parameters and / or the dimensions of the output parameters). The bias in the activation function may also be called the bias of the neural network.

[0140] An AI module may have one or more models. A model may obtain one output through inference, the output containing one or more parameters. The learning processes, training processes, or inference processes of different models may be deployed on different nodes or devices, or on the same node or device.

[0141] It should be understood that when a corresponding AI network element is deployed for one or more of the following: UE, radio access network device, AMF, and LMF, and the AI ​​operation is performed by the corresponding AI network element, the UE, radio access network device, AMF, or LMF must transmit information related to the AI ​​operation to the corresponding AI network element. For example, suppose a corresponding AI network element is deployed for an LMF, and the AI ​​network element performs the inference operation of an AI model. In this case, after receiving a channel measurement report from the first device (e.g., the access network device or UE), the LMF transmits the channel measurement results carried in the channel measurement report to the corresponding AI network element. In another example, in uplink positioning, the corresponding AI network element is deployed for an access network device. Assume that the input to the AI ​​model is channel features extracted from channel measurement results. Channel measurement results are obtained by the access network device by measuring the SRS of the UE. Therefore, after obtaining the channel measurement results, the access network device transmits the channel measurement results to the corresponding AI network element. The AI ​​network element extracts channel features from channel measurement results using an AI model, and then returns the extracted channel features to the access network device, which then transmits the channel features to the LMF.

[0142] It should be further understood that Figures 1-5 are simplified illustrative diagrams for ease of understanding. The wireless communication system may further include other network devices, other terminal devices, or other AI nodes not shown in Figures 1-5.

[0143] To facilitate understanding of the embodiments of this application, the terminology used in these embodiments will be briefly explained below.

[0144] (1) Artificial intelligence AI:

[0145] Artificial intelligence enables machines to learn and accumulate experience, thereby allowing them to solve problems that humans can solve through experience, such as natural language understanding, image recognition, and playing chess. Artificial intelligence may also be understood as intelligence represented by a man-made machine. In general, artificial intelligence is the technology that presents human intelligence through the use of computer programs. The goal of artificial intelligence includes understanding intelligence by constructing symbolic reasoning and computer programs for reasoning.

[0146] (2) Machine learning (ML):

[0147] ML is an implementation of artificial intelligence. Machine learning is a method that enables machines to learn to perform functions that cannot be directly achieved through programming. In practice, machine learning is a method of training a model by using data and then making predictions by using the model. There are many machine learning methods, such as neural networks (NN), decision trees, and support vector machines. Machine learning theory mainly designs and analyzes several algorithms that enable computers to learn automatically. Machine learning algorithms are algorithms that automatically analyze data to obtain rules and predict unknown data by using those rules.

[0148] (3) AI model:

[0149] An AI model is an algorithm or computer program capable of implementing AI functionality. An AI model represents a mapping relationship between the model's inputs and outputs. In other words, an AI model is a functional model that maps inputs in one dimension to outputs in another dimension. The parameters of a functional model can be obtained through machine learning and training. For example, f(x) = mx 2 +n is a quadratic function model and can be considered an AI model, where m and n are parameters of the AI ​​model, and m and n can be obtained through machine learning and training. For example, the AI ​​models referred to in the following embodiments of the present application are not limited to neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q-learning models, or other machine learning (ML) models.

[0150] The design of an AI model primarily includes a data acquisition phase (e.g., collecting training data and / or inference data), a model training phase, and a model inference phase, and may further include an inference result application phase. In the data acquisition phase, a data source is used to provide training datasets and inference data. In the model training phase, the AI ​​model is acquired through analysis or training using the training data provided by the data source. Acquiring the AI ​​model through learning using a model training host is equivalent to acquiring a mapping relationship between the inputs and outputs of the AI ​​model through learning using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, and the inference results are acquired. This phase may be understood as inputting the inference data into the AI ​​model and acquiring the output by using the AI ​​model. The output is the inference result. The inference result may indicate configuration parameters used (executed) by the actor object, and / or actions performed by the actor object. The inference result is released in the inference result application phase. For example, inference results may be centrally planned by an actor entity. For instance, the actor entity may send the inference results to one or more actor objects (e.g., a core network device, an access network device, or a terminal device) for execution. In another example, the actor entity can further feed back the performance of the AI ​​model to a data source to facilitate subsequent update training of the AI ​​model.

[0151] It can be understood that AI models may be implemented using hardware circuits, software, or a combination of software and hardware. This is not limited to these. Non-exclusive examples of software include program code, programs, subprograms, instructions, instruction sets, code, code segments, software modules, application programs, and software application programs.

[0152] (4) Neural network (NN):

[0153] Neural networks are a concrete implementation of AI or machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, and as a result, they have the ability to learn any mapping.

[0154] A neural network may include neurons. Neurons are x s This may also be an computational unit that takes intercept 1 as input. A neural network is a network formed by connecting many single neurons, that is, the output of one neuron can be the input of another neuron. The input of each neuron is connected to the local receptive field of the previous layer, and features of the local receptive field can be extracted. The local receptive field may be a region containing several neurons.

[0155] For example, one type of AI model is a neural network. The AI ​​model in this disclosure may be a deep neural network (DNN). Based on the network construction mode, DNNs may include feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and so on.

[0156] (5) Fingerprint:

[0157] Since multipath propagation of signals is environment-dependent, the multipath structure of a channel at each location is unique. Radio waves transmitted by a terminal are reflected and refracted, resulting in a multipath signal that is in a specific mode and closely related to the surrounding environment. Such multipath features are sometimes called the "fingerprint" information of that location. A method of positioning based on this "fingerprint" information is sometimes called fingerprint positioning. For example, positioning on a mobile terminal is performed based on the "fingerprint" information of the mobile terminal.

[0158] (6) Training dataset and inference data:

[0159] In the field of machine learning, the correct answer (ground truth) is typically considered to be accurate data or true data.

[0160] A training dataset is used to train an AI model. The training dataset may contain the inputs to the AI ​​model, or it may contain both the inputs and target outputs of the AI ​​model. The training dataset contains one or more training data points. The training data may contain training samples that are input to the AI ​​model, or it may also contain the target outputs of the AI ​​model. The target outputs may also be called tags, sample tags, or tag samples. Tags are considered correct answers.

[0161] In the field of communications, training datasets may include simulated data collected by a simulation platform, experimental data collected in experimental scenarios, or actual measurement data collected in actual communication networks. The geographical environment and channel conditions under which the data is generated differ. For example, indoor or outdoor environments, movement speeds, frequency bands, or antenna configurations may differ. Therefore, the collected data can be classified at the time of acquisition. For example, data with the same channel propagation environment and the same antenna configuration may be classified into one type.

[0162] Model training is essentially the process of learning certain features of training data from training data. During training of an AI model (e.g., a neural network model), the output of the AI ​​model is expected to be as close as possible to the value that is actually expected to be predicted. Therefore, the network's current predictions may be compared to the target value that is actually expected, and then the weight vectors of each layer of the AI ​​model are updated based on the difference between the two (of course, an initialization process is usually performed before the first update, i.e., parameters are pre-configured for each layer of the AI ​​model). For example, if the network's prediction is larger, the weight vectors are adjusted to obtain smaller predictions. The weight vectors are continuously adjusted until the AI ​​model can predict the target value that is actually expected, or a value that is very close to the target value that is actually expected. Therefore, "how to compare the prediction to the target value and obtain the difference" needs to be predefined. This results in a loss function or objective function. The loss function and objective function are important formulas that measure the difference between the prediction and the target value. The loss function is used as an example. A larger output value (loss) of the loss function indicates a greater difference. In this case, training an AI model is a process of minimizing the loss, thereby ensuring that the value of the loss function is below a threshold or that the value of the loss function meets the target requirements. For example, the AI ​​model is a neural network, and tuning the model parameters of the neural network includes tuning at least one of the following: the number of layers in the neural network, the width of the neural network, the weights of the neurons, or the parameters in the activation function of the neurons.

[0163] Inference data can be used as input to a trained AI model for inference by the AI ​​model. During model inference, the inference data can be input to the AI ​​model to obtain the corresponding output, i.e., the inference result.

[0164] Figure 6 shows the AI ​​application framework.

[0165] In the data acquisition phase, a data source is used to provide training datasets and inference data. In the model training phase, an AI model is acquired through analysis or training using the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's inputs and outputs. Acquiring an AI model through learning using a model training host is equivalent to acquiring the mapping relationship between the model's inputs and outputs through learning using training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, and the inference results are acquired. This phase may also be understood as inputting the inference data into the AI ​​model and acquiring the output by using the AI ​​model. The output is the inference result. The inference result may represent the configuration parameters used (executed) by the actor object, and / or the actions performed by the actor object. The inference result is released in the inference result application phase. For example, inference results may be centrally planned by an actor entity, which may send the inference results to one or more actor objects (e.g., a network device or a terminal device) for execution. In another example, the actor entity may further feed back the model's performance to a data source to facilitate subsequent update training of the model.

[0166] It can be understood that a communication system may include network elements having artificial intelligence capabilities. The above phases relating to AI model design may be performed by one or more network elements having artificial intelligence capabilities. In one possible design, the AI ​​functionality (e.g., an AI module or AI entity) may be configured within an existing network element in the communication system to perform AI-related operations, such as AI model training and / or inference. For example, such existing network element may be a network device or a terminal device. Alternatively, in another possible design, a separate network element may be introduced into the communication system to perform AI-related operations, such as AI model training. The separate network element may be called an AI network element, an AI node, etc. The names are not limited to the embodiments of this application. For example, the AI ​​network element may be directly connected to a network device in the communication system, or indirectly connected to a network device via a third-party network element. Third-party network elements may be, but are not limited to, core network elements such as authentication management function (AMF) network elements or user plane function (UPF) network elements, operation administration and maintenance (OAM), cloud servers, or other network elements. For example, independent network elements may be deployed on one or more of the following: network device side, terminal device side, or core network side. Optionally, independent network elements may be deployed on cloud servers.

[0167] The training processes for different models may be deployed on different devices or nodes, or on the same device or node. The inference processes for different models may be deployed on different devices or nodes, or on the same device or node. For example, the model parameters of an AI model may include one or more of the following: structural parameters of the model (e.g., the number of layers and / or weights of the model), input parameters of the model (e.g., the number of input dimensions or input ports), or output parameters of the model (e.g., the number of output dimensions or output ports). It can be understood that the input dimension may be the size of the pieces of input data. For example, if the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may be the amount of input data. Similarly, the output dimension may be the size of the pieces of output data. For example, if the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may be the amount of output data.

[0168] (7) Time difference of arrival (TDOA): This is a method of positioning that utilizes the time difference.

[0169] Figure 7 is a diagram of TDOA positioning. As shown in Figure 7, it is assumed that the distance between network device #1 and the terminal device is d1, and the transmission time spent on signal transmission between network device #1 and the terminal device is t1; the distance between network device #2 and the terminal device is d2, and the transmission time spent on signal transmission between network device #2 and the terminal device is t2; and the distance between network device #3 and the terminal device is d3, and the transmission time spent on signal transmission between network device #3 and the terminal device is t3.

[0170] In TDOA, for example, multiple network devices can transmit reference signals, such as positioning reference signals (PRS), to a terminal device, and the terminal device determines its position by measuring the TDOA between the reference signals. The TDOA between reference signals is sometimes called the reference signal time difference (RSTD). Figure 7 is used as an example. Suppose that the reference signal transmitted to the terminal device by network device #1 is P1, the reference signal transmitted to the terminal device by network device #2 is P2, and the reference signal transmitted to the terminal device by network device #3 is P3. The terminal device measures the TDOA between P2 and P1, i.e., t2-t1. Based on t2-t1, the terminal device can estimate the distance difference d2-d1 between network device #2 and network device #1 and obtain a curve. Each point on the curve satisfies the distance difference d2-d1 between network device #2 and network device #1. Similarly, the terminal device measures the TDOA between P3 and P1, i.e., t3-t1. Based on t3-t1, the terminal device can estimate the distance difference d3-d1 between network device #3 and network device #1 and obtain another curve. Each point on the curve satisfies the distance difference d3-d1 between network device #3 and network device #1. The position of the terminal device can be determined by using the intersection of the two aforementioned curves, which can be represented by using a mathematical model as shown in Equation 1 below.

number

[0171] (a i ,b i) represents the position coordinates of network device #i. (a,b) represents the position coordinates of the terminal device to be acquired. For example, a represents the position coordinates of the terminal device to be acquired on the X axis, and b represents the position coordinates of the terminal device to be acquired on the Y axis. c represents the speed of light.

[0172] Due to synchronization errors between different network devices, the corresponding measurements are somewhat uncertain, which corresponds to the range shown by the dashed line in Figure 7.

[0173] The above describes a method of positioning based on a reference signal transmitted by a network device to a terminal device, with reference to Figure 7. Such a method is sometimes called the downlink TDOA (DL-TDOA) method or the observation arrival time difference (OTDOA) method. Similarly, positioning may alternatively be performed based on a reference signal transmitted by a terminal device to a network device, such as an SRS. Such a positioning method is sometimes called the uplink TDOA (UL-TDOA) method.

[0174] It may be understood that positioning may be performed through angle measurement in addition to time difference measurement. The angle may be the angle of arrival (AOA) or the angle of departure (AOD). The angle of arrival represents the angle between the direction in which the receiving end receives the signal and the reference direction. The angle of departure represents the angle between the direction in which the transmitting end transmits the signal and the reference direction. The reference direction may be a direction determined based on the position and / or shape of the antenna.

[0175] In actual communication scenarios, noise and interference can cause specific measurement errors in time or angle measurements, and consequently, specific errors in positioning results.

[0176] Figure 8 shows the LOS and NLOS. As shown in Figure 8, the LOS between the network device and the terminal device (dashed line in Figure 8) is blocked by an obstacle, and the reference signal transmitted between the network device and the terminal device actually passes through the reflected NLOS (solid line in Figure 8). From the figure, it can be seen that the length of the NLOS (i.e., d2 + d3) is greater than the length of the LOS (i.e., d1). If the NLOS is considered as the LOS during position estimation, a large measurement error can occur. Therefore, the classification of LOS and NLOS is also important for positioning accuracy.

[0177] In AI-based positioning technology, the AI ​​positioning model is typically deployed in the LMF. The AI ​​positioning model uses channel measurement results reported by channel measurement network elements as input and outputs the location of the terminal device. Therefore, channel measurement network elements typically need to report channel measurement reports to the location management function network elements. For example, the protocol supports the LMF sending an NRPPa measurement request to the gNB to obtain measurement results in uplink positioning. Correspondingly, the gNB measures the SRS signal transmitted by the UE to obtain the RSRPP result and sends the RSRPP result to the LMF. RSRPP is defined as the average linear power value of the i-th path of the channel response obtained by measuring the SRS on the corresponding subband. Furthermore, the protocol supports the reporting method of the RSRPP result including reporting the absolute value of the RSRPP and reporting the differential value of the RSRPP. In current solutions, during terminal device positioning function management of location management network elements, the AI ​​positioning function or AI positioning model, the monitoring policy for the AI ​​positioning function or AI positioning model, the update policy for the AI ​​positioning function or AI positioning model, the quantifiers corresponding to the AI ​​positioning function or AI positioning model, and the reporting method for the quantifiers obtained by measuring them are typically shown separately by using multiple signalings or separately by using multiple fields within a single signaling. This can result in high signaling overhead.

[0178] In view of this, embodiments of the present invention provide a communication method and apparatus. Based on the correspondence between the input / output measurements of an AI positioning case and an AI positioning function or AI positioning model, the AI ​​positioning function or AI positioning model may be indicated by transmitting instruction information, thereby enabling a first terminal device to determine the measurement corresponding to the AI ​​positioning function or AI positioning model. That is, both the AI ​​positioning function or AI positioning model and the measurement corresponding to the AI ​​positioning function or AI positioning model can be determined by acquiring instruction information, thereby reducing signaling overhead.

[0179] The communication methods provided in the embodiments of this application will be described in detail below with reference to the attached drawings. The embodiments provided in this application may be applied to the communication system shown in Figure 1 or Figure 2, but are not limited thereto.

[0180] Figure 9 is a schematic flowchart of a communication method 900 according to one embodiment of the present invention. As shown in Figure 9, the method 900 may include the following steps. It should be understood that the method may be performed by a first device and location management network element, or by a chip or circuit of the first device and location management network element. This is not limited to the present invention. For ease of explanation, an example in which the first device and location management network element performs the method will be used below for illustrative purposes.

[0181] The S910 location management function network element transmits instruction information to the first device (i.e., the network element that performs channel measurement; abbreviated as the channel measurement network element, or referred to as the reference signal measurement node, etc.). Correspondingly, the first device receives instruction information from the location management function network element.

[0182] The instruction information indicates an AI positioning function or AI positioning model, the AI ​​positioning function or AI positioning model belongs to a plurality of AI positioning functions or AI positioning models, the plurality of AI positioning functions or AI positioning models corresponds to a plurality of different sets of measurement quantities, and the set of measurement quantities includes one or more measurement quantities.

[0183] In this embodiment of the present invention, the AI ​​model represents an AI model used for positioning (abbreviated as the AI ​​positioning model).

[0184] Optionally, instruction information is carried in a measurement request message from a location management function network element. For example, a location management function network element sends a measurement request message to a first device, and the measurement request message carries instruction information. The measurement request message is used to instruct the first device to perform a channel measurement. In other words, the measurement request message is used to trigger the first device to perform a channel measurement.

[0185] Optionally, measurement request messages and instruction information transmitted by a location management function network element may be transmitted together or independently. In other words, instruction information does not have to be carried in the measurement request message. In this case, the measurement request message and instruction information may be transmitted simultaneously or separately. This is not limited to the present invention.

[0186] S920 The first device transmits the channel measurement results to the location management function network element based on the instruction information. Correspondingly, the location management function network element receives the channel measurement results from the first device.

[0187] The measured quantity corresponding to the channel measurement result corresponds to the AI ​​positioning function or AI positioning model indicated by the instruction information.

[0188] Optionally, channel measurement results are included in the measurement report. For example, the first device sends the measurement report to a location management function network element, and the measurement report carries the channel measurement results.

[0189] In this application, a channel measurement report is used to report channel measurement results (or channel features extracted from channel measurement results; here, channel measurement results are used as examples for explanatory purposes in embodiments), and the channel measurement results are used by a location management function network element to perform AI-related operations, such as model inference based on an AI positioning model. Thus, the AI ​​positioning model uses the channel measurement results (or channel features extracted from channel measurement results) as input and outputs the location of a terminal device. Therefore, the purpose of the channel measurement report is to report the input to the AI ​​positioning model. Based on this premise, the channel measurement results in the channel measurement report in this application are determined based on the measured AI positioning function or AI positioning model indicated by instructional information from the location management function network element. The channel measurement network element (e.g., UE or gNB) feeds back to the location management function network element the measured quantities corresponding to the channel measurement results corresponding to the AI ​​positioning function or AI positioning model. Based on the correspondence between input / output measurements for an AI positioning case and different AI positioning functions or AI positioning models, the AI ​​positioning function or AI positioning model can be determined by using instruction information. Then, the measurement corresponding to the AI ​​positioning function or AI positioning model can be determined to perform positioning for the terminal device's location. Compared to indicating the AI ​​positioning function or AI positioning model and the measurement corresponding to said AI positioning function or AI positioning model through multiple signaling steps, this can reduce signaling overhead.

[0190] For example, in an uplink positioning scenario, when the first device is a network device, the channel measurement result is obtained based on a first channel measurement, which includes the network device measuring a prospect reference signal (e.g., SRS) from a terminal device.

[0191] For example, in a downlink positioning scenario, when the first device is a terminal device, the channel measurement result is obtained based on a second channel measurement, which includes: the terminal device measuring a positioning reference signal (e.g., PRS or preamble) or a channel state information reference signal (CSI-RS) from an access network device.

[0192] For example, in a sidelink positioning scenario, when the first device is the first terminal device, the channel measurement result is obtained based on a third channel measurement, which includes the following: The first terminal device measures the sidelink positioning reference signal (SL-PRS) from the second terminal device.

[0193] In this embodiment of the present application, the first device may be a terminal device or a network device. Optionally, when the first device is a network device, an AI positioning model configured within the network device is used for assisted positioning, and an AI positioning model configured within a location management function network element is used for direct positioning.

[0194] In this embodiment of the present application, the input to the AI ​​positioning model is a channel measurement result, and the output of the AI ​​positioning model is used directly or indirectly to determine the location of a terminal device. For example, the output of the AI ​​positioning model may be the location of a mobile terminal, or it may be supported positioning information used to determine the location of a terminal device. This is not limited herein.

[0195] In one example, channel measurement results may be used as input to an AI positioning model. For the AI ​​positioning model, the input to the AI ​​positioning model may be channel measurement results obtained based on reference signal measurements, and the output to the AI ​​positioning model may be the location of the terminal device.

[0196] For example, an AI positioning model is used to perform direct positioning of a terminal device. In this case, the input to the AI ​​positioning model is the channel measurement result obtained based on a reference signal measurement, and the output to the AI ​​positioning model is the terminal device's position, for example, the terminal device's position coordinates on the X and Y axes.

[0197] In another example, an AI positioning model is used to perform aided positioning (also called indirect positioning) for the location of a terminal device. In this case, the input to the AI ​​positioning model is the channel measurement result obtained based on reference signal measurements, and the output of the AI ​​positioning model is aided positioning information, which is used to determine the location of the terminal device. The aided positioning information includes, but is not limited to, at least one of the following: LOS probability, AOA, AOD, time of arrival (TOA), or TDOA between reference signals.

[0198] In another example, supported positioning information based on channel measurement results may be used as input to an AI positioning model. For an AI positioning model, the input to the AI ​​positioning model may be supported positioning information based on channel measurement results, and the output to the AI ​​positioning model may be the location of a terminal device. Supported positioning information based on channel measurement results may be acquired in a conventional manner or may be the output of an AI positioning model. This is not limited to the above. Supported positioning information may include, but is not limited to, at least one of the following: LOS probability, AOA, AOD, time of arrival (TOA), or TDOA between reference signals.

[0199] For example, an AI positioning model is used to perform direct positioning of a terminal device. In this case, the input to the AI ​​positioning model is supported positioning information based on channel measurement results, and the output to the AI ​​positioning model is the terminal device's location.

[0200] In another example, an AI positioning model is used to perform aided positioning (or indirect positioning) for the location of a terminal device. In this case, the input to the AI ​​positioning model is aided positioning information based on channel measurement results, and the output of the AI ​​positioning model is other aided positioning information, which is used to determine the location of the terminal device.

[0201] It should be noted that examples in which channel measurement results are obtained through channel measurement based on a reference signal are primarily used for the purposes of describing this embodiment of the present application. This is not limited to these examples. For example, channel measurement results may alternatively include pedestrian dead-reckoning (PDR) measurement results from a terminal. Alternatively, channel measurement results may include environmental monitoring and identification results from a camera, for example, environmental monitoring and identification results from a surveillance camera in an indoor factory.

[0202] In this embodiment of the present application, the AI ​​positioning function or AI positioning model corresponds to one or more of the following measurement quantities.

[0203] (1) Line of Sight Line Identification Results

[0204] For example, the LOS identification result includes LOS or NLOS, and / or the LOS probability.

[0205] LOS means that a signal is transmitted at a distance where the transmitting antenna and the receiving antenna can "see each other." This can be understood as there being no obstacles affecting signal transmission between the two antennas, and the signal can be transmitted completely. Non-line-of-sight means that a signal is transmitted at a distance where the transmitting antenna and the receiving antenna cannot "see each other." This can be understood as there being obstacles affecting signal transmission between the two antennas, and the signal cannot be transmitted completely. The LOS probability can be used to determine the degree of NLOS in a channel environment. Different degrees of NLOS (i.e., different channel conditions) affect the inference accuracy of the AI ​​positioning model to different degrees. Therefore, a first device can know the degree of NLOS of a channel based on the channel measurement result, and then know the current channel condition. For example, LOS(1) or NLOS(0) may indicate that the LOS between the network device and the terminal device is not blocked by obstacles, and the reference signal transmitted between the network device and the terminal device is unaffected.

[0206] The channel propagation condition for LOS paths is a scenario in which direct paths exist, but NLOS paths may also be included. In other words, there is at least one LOS path (i.e., a pure LOS path), but the number of NLOS paths is not limited. The channel propagation condition for NLOS paths is a scenario in which no direct paths exist, i.e., only NLOS paths exist. For example, the line-of-sight identification result can be considered a metric of channel measurement, and a channel may contain one or more paths (e.g., a set of paths), and the line-of-sight identification result may be the average LOS probability of the line-of-sight identification results corresponding to all paths in the channel, or the LOS or NLOS identification result with the highest frequency of occurrence.

[0207] (2) Arrival time TOA estimate results

[0208] For example, TOA is a method for calculating physical distance by using the transmission delay of radio signals between two nodes. That is, the location is determined by measuring the time interval between sending and receiving a signal, and typically the timing needs to be synchronized at the receiving node. For example, the estimated arrival time result may be considered a measure of channel measurement, and a channel may contain one or more paths (e.g., a set of paths), and the estimated arrival time result may be the average of the estimated arrival time results corresponding to all paths in the channel.

[0209] (3) Estimated arrival angle AOA results

[0210] For example, a low-energy (LE) device can make its orientation available to a peer device by transmitting data packets with direction-finding capabilities using a single antenna. The peer device includes a radio frequency switch and an antenna array, which switches antennas when it receives a portion of the data packets and obtains IQ samples. The IQ samples may be used to calculate the phase difference between radio signals received by different elements of the antenna array, which can then be used to estimate the angle of arrival (AOA). For example, the estimated AOA result may be considered a measure of channel measurement, where a channel may contain one or more paths (e.g., a set of paths), and the estimated AOA result may be the average of the estimated AOA results corresponding to all paths within the channel.

[0211] (4) LOS identification result corresponding to each route in the first route set

[0212] For example, the first set of routes includes one or more routes, and the LOS identification result corresponding to each route includes LOS or NLOS and / or LOS probability, and the LOS identification results corresponding to those routes may be the same or different. The number of routes is not limited in this application. For example, the first set of routes includes route #1, and the LOS identification result for route #1 may be LOS(1) or NLOS(0), indicating that route #1 is a direct route, i.e., there are no obstacles between the network device and the terminal device and the signal can be transmitted completely. Optionally, the first set of routes further includes route #2, and the LOS identification result for route #2 may be LOS(0) or NLOS(1), indicating that route #2 is not a direct route, i.e., there are obstacles between the network device and the terminal device and the signal cannot be transmitted completely. In other words, the LOS identification results corresponding to route #1 and route #2 are different.

[0213] (5) The delay corresponding to each path in the second set of paths.

[0214] For example, the second set of routes may include one or more routes, and the delays corresponding to those routes may be the same or different. The number of routes is not limited in this application. For example, the second set of routes may include route #3, and the delay for route #3 is delay #1. Optionally, the second set of routes may further include route #4, and the delay for route #4 is delay #2. Delay #1 = 10 ms, delay #2 = 8 ms, i.e., the delays corresponding to route #3 and route #4 are different. Alternatively, delay #1 = delay #2 = 5 ms, i.e., the delays corresponding to route #3 and route #4 are the same.

[0215] (6) The amplitude corresponding to each path in the third path set.

[0216] For example, a third set of paths may include one or more paths, and the amplitudes corresponding to those paths may be the same or different. The number of paths is not limited in this application. For example, a third set of paths may include path #5, and the amplitude of path #5 is amplitude 1. Optionally, a third set of paths may further include path #6, and the amplitude of path #6 is amplitude 2. For example, amplitude 1 = 1, amplitude 2 = 2, i.e., the amplitudes corresponding to path #5 and path #6 are different. Alternatively, amplitude 1 = amplitude 2 = 0.5, i.e., the amplitudes corresponding to path #5 and path #6 are the same.

[0217] (7) The phase corresponding to each path in the fourth path set.

[0218] For example, the fourth path set includes one or more paths, and the phases corresponding to those paths may be the same or different. The number of paths is not limited in this application. For example, the fourth path set includes path #7, and the phase of path #7 is phase 1. Optionally, the fourth path set may further include path #8, and the phase of path #8 is phase 2. For example, phase 1 = 30°, phase 2 = 45°, i.e., the phases corresponding to path #7 and path #8 are different. Or, phase 1 = phase 2 = 10°, i.e., the phases corresponding to path #7 and path #8 are the same.

[0219] It should be noted that at least two of the first, second, third, and fourth route sets are either the same or different. For example, assuming that the first and second route sets are the same, for instance, that both the first and second route sets include routes #1, #2, #3, and #4, the channel results obtained by the first device through channel measurements based on a reference signal will include LOS identification results and delays corresponding to the four routes.

[0220] For example, route #1 corresponds to an LOS identification result LOS(1) and a delay of 2ms, route #2 corresponds to an LOS identification result LOS(1) and a delay of 3ms, route #3 corresponds to an LOS identification result NLOS(1) and a delay of 6ms, and route #4 corresponds to an LOS identification result NLOS(1) and a delay of 4ms. From the above, it can be seen that routes #1 and #2 have the same LOS identification result but different delays, while routes #3 and #4 have the same LOS identification result but different delays.

[0221] In another example, assuming that the second, third, and fourth path sets are the same, for example, that the second, third, and fourth path sets all include path #3, path #4, path #5, path #6, path #7, and path #8, the channel results obtained by the first device through channel measurements based on a reference signal would include delay, amplitude, and phase corresponding to the six paths. For example, path #3 corresponds to a delay of 2 ms, amplitude of 1, and phase of 30°; path #4 corresponds to a delay of 2 ms, amplitude of 2, and phase of 20°; path #5 corresponds to a delay of 5 ms, amplitude of 1, and phase of 45°; path #6 corresponds to a delay of 2 ms, amplitude of 0, and phase of 30°; path #7 corresponds to a delay of 4 ms, amplitude of 2, and phase of 45°; and path #8 corresponds to a delay of 6 ms, amplitude of 2, and phase of 30°. From the above, it can be seen that paths #3, #4, and #6 have the same delay, paths #3 and #5 have the same amplitude, paths #4, #7, and #8 have the same amplitude, paths #3, #6, and #8 have the same phase, and paths #5 and #7 have different amplitudes.

[0222] In this embodiment of the present application, the AI ​​positioning function or AI positioning model further corresponds to one of the following reporting methods.

[0223] (1) Measurement results for each pathway for the same quantity are reported independently.

[0224] For example, reporting the measurement results for each path for the same measurement quantity independently includes reporting the measurement results for each path for the same measurement quantity independently in the same measurement report. For example, assuming there are paths #1, #2, #3, and #4, and the measurement quantity corresponding to the AI's positioning function or AI's positioning model indicated by the instruction information is the delay corresponding to each path, then in the same measurement report, the delays corresponding to paths #1, #2, #3, and #4 are reported independently. For example, the delay corresponding to path #1 is 2ms, the delay corresponding to path #2 is 4ms, the delay corresponding to path #3 is 6ms, and the delay corresponding to path #4 is 5ms. That is, the measurement report may include paths #1, #2, #3, and #4 and their corresponding delay results, e.g., 2ms, 4ms, 6ms, and 5ms. The number of paths is not limited in this application.

[0225] Optionally, the measurement results for each path of the same measured quantity in different measurement reports may be reported independently.

[0226] (2) The measurement results for each pathway for the same measurement quantity are reported in a differential manner.

[0227] For example, reporting the measurement results for each path for the same measurement quantity differentially includes reporting the measurement results for each path for the same measurement quantity differentially in the same measurement report. For example, suppose there are paths #1, #2, #3, and #4, and the measurement quantity corresponding to the AI's positioning function or AI's positioning model indicated by the instruction information is the delay corresponding to each path, then in the same measurement report, the delays corresponding to paths #1, #2, #3, and #4 may be reported differentially based on a reference path, and the delay corresponding to the reference path is reported by using an absolute value. For example, the delay corresponding to path #1 is 2ms, the delay corresponding to path #2 is 4ms, the delay corresponding to path #3 is 6ms, and the delay corresponding to path #4 is 5ms. In this case, the measurement report may include delay #1 of 2 ms, which is the delay of route #1; delay #2 of 2 ms, which is the delay between route #2 and route #1; delay #3 of 2 ms, which is the delay between route #3 and route #2; and delay #4 of -1 ms, which is the delay between route #4 and route #3. Then, after receiving the measurement report, the location management function network element may determine, based on delays #1 and Δ1, that delay #2, which is the delay of route #2, is 4 ms; based on delays #2 and Δ2, that delay #3, which is the delay of route #3, is 6 ms; and based on delays #3 and Δ3, that delay #4, which is the delay of route #4, is 5 ms. The number of routes is not limited herein.

[0228] Please understand that reporting measurement results differentially can reduce signaling overhead.

[0229] Optionally, the measurement results for the same quantity in different measurement reports may be reported differentially. In this way, signaling overhead can be further reduced.

[0230] For example, in an uplink positioning scenario, assuming that the measurement corresponding to the AI ​​positioning function or AI positioning model indicated by the position management function network element is the estimated time of arrival (TOA), the terminal device separately measures two PRSs from the access network device and reports the channel measurement results in different measurement reports. For example, the terminal device performs a channel measurement on PRS#1 from the access network device and obtains channel measurement result #1, i.e., TOA#1. The terminal device performs a channel measurement on PRS#2 from the access network device and obtains channel measurement result #2, i.e., TOA#2. In this case, measurement report #1 carries measurement result #1, i.e., TOA#1, and measurement report #2 carries ΔTOA. TOA#1 + ΔTOA = TOA#2. Then, after receiving measurement reports #1 and #2, the position management function network element may further determine, based on TOA#1 and ΔTOA, that the TOA corresponding to measurement result #2 is TOA#2. In other words, based on the two acquired measurement reports, the location management function network element can explicitly know channel measurement result #1, i.e., TOA#1, and implicitly know channel measurement result #2, i.e., TOA#2, and so on.

[0231] In conclusion, the first device can determine the corresponding measurement and / or the corresponding reporting method based on the AI ​​positioning function or AI positioning model indicated by the received instruction information, and then transmit the corresponding channel measurement result to the location management function network element using the determined reporting method.

[0232] For example, there is a mapping relationship between an AI positioning function or AI positioning model and a quantifiable measure and / or reporting method corresponding to a channel measurement result. That is, the first device can determine the quantifiable measure and / or reporting method corresponding to the channel measurement result based on the received instruction information and the mapping relationship, and then transmit the channel measurement result to a location management function network element. In other words, different instruction information indicates different AI positioning functions or AI positioning models, and the quantifiable measure and / or reporting method corresponding to the corresponding channel measurement result may also be different.

[0233] Mapping relationships may be predefined, and the predefinition may include definitions that are performed in advance, such as definitions in a protocol. Alternatively, mapping relationships may be configured or pre-configured. Pre-configuration may be implemented by pre-storing corresponding code or corresponding tables in the device, or in another way in which the relevant information can be indicated. Specific implementations of pre-configuration are not limited herein.

[0234] Optionally, mapping relationships may exist in the form of tables, functions, text, or strings, for example, stored or transmitted.

[0235] In embodiments of the present application, an AI positioning function or AI positioning model belongs to a plurality of AI positioning functions or a plurality of AI positioning models, and the plurality of AI positioning functions or a plurality of AI positioning models correspond one-to-one with a plurality of different sets of quantifiable parameters, each set of quantifiable parameters includes one or more quantifiable parameters, the quantifiable parameters include one of the following: line-of-sight identification result, arrival time estimation result, arrival angle estimation result, line-of-sight identification result corresponding to each route in a first set of routes, delay corresponding to each route in a second set of routes, amplitude corresponding to each route in a third set of routes, and phase corresponding to each route in a fourth set of routes. For a specific description of the quantifiable parameters, please refer to the relevant descriptions above.

[0236] In the following, a table format is used as an example to show the mapping relationship between the AI ​​positioning model or AI positioning function indicated by the instruction information and the measured quantity / reporting method corresponding to the channel measurement result. The mapping relationship between the measured quantity corresponding to the channel measurement result and the AI ​​positioning function may satisfy Table 1 below, and the mapping relationship between the reporting method of the channel measurement result and the AI ​​positioning function may satisfy Table 2 below. It should be noted that Tables 1 and 2 provided herein are applicable to uplink, downlink, or sidelink positioning scenarios and are provided only as examples for ease of understanding and do not constitute any limitation to this solution. Optionally, the number of correspondences between AI positioning functions and measured quantities in Table 1 (e.g., rows in the table) is not limited herein. For example, AI positioning functions #1 through #7 and AI positioning functions #8 through #33 in Table 1 may be formed independently into a new table. In other words, Table 1 may be divided into, for example, several other tables. This is not limited herein, nor is the method of division limited. [Table 1] TIFF2026529619000004.tif142170

[0237] In one example, let's assume that a value of "1" in the LOS identification result indicates LOS, and a value of "0" in the LOS identification result indicates NLOS. If the value of the LOS identification result obtained by the first device through measurement, corresponding to AI positioning function #1, is 1, it indicates that the measurement result corresponding to AI positioning function #1 indicates LOS. If the value of the LOS identification result obtained through measurement, corresponding to AI positioning function #1, is 0, it indicates that the measurement result corresponding to AI positioning function #1 indicates NLOS. It should be understood that the LOS identification result is a parameter that can reflect the channel environment or channel conditions. In other words, changes in the channel environment can be reflected in the measurement result of the LOS identification result. For example, the LOS identification result is a parameter that can reflect the degree of NLOS of the channel.

[0238] In one example, suppose that TOA1 is the estimated time of arrival (TOA) value obtained by the first device through measurement, corresponding to AI positioning function #2. TOA1 may be a specific value or a range of values. This is not limited herein.

[0239] In one example, suppose that AOA1 is the value of the estimated arrival angle AOA result obtained by the first device through measurement, corresponding to AI positioning function #3. AOA1 may be a specific value or a range of values. This is not limited herein.

[0240] In one example, a line-of-sight LOS (LOS) identification result value of "1" indicates LOS, a line-of-sight LOS identification result value of "0" indicates NLOS, and the first route set includes route #1 and route #2. If the LOS identification result value of route #1 in the first route set is 1, and the LOS identification result value of route #2 in the first route set is 0, and these correspond to AI positioning function #4 and are acquired by the first device through measurement, this indicates that the measurement result of route #1 in the first route set indicates LOS, and the measurement result of route #2 in the first route set indicates NLOS. The number of routes in the first set is not limited in this application.

[0241] In one example, the second set of routes includes routes #1, #2, and #3. Route #1 in the second set of routes has a delay of 4ms, route #2 has a delay of 3ms, and route #3 has a delay of 4ms, and these correspond to AI positioning function #5 and are acquired by the first device through measurement. The number of routes in the second set is not limited in this application.

[0242] In one example, the third set of routes includes routes #1 and #2. The amplitude of route #1 in the third set of routes is amplitude 1, and the amplitude of route #2 in the third set of routes is amplitude 2, corresponding to AI positioning function #6 and acquired by the first device through measurement. The amplitudes corresponding to each route may be specific values ​​or ranges; this is not limited herein. The number of routes in the third set is not limited herein.

[0243] In one example, the fourth route set includes route #1 and route #2. The phase of route #1 in the fourth route set is phase 1, and the phase of route #2 in the fourth route set is phase 2, corresponding to AI positioning function #7 and acquired by the first device through measurement. The phase corresponding to each route may be a specific value or a range of values; this is not limited herein. The number of routes in the fourth set is not limited herein.

[0244] The aforementioned mapping relationships between AI positioning functions #1 through #7 and measured quantities are merely examples for ease of understanding. For specific implementations of other mapping relationships between AI positioning functions #8 through #33 and measured quantities, please refer to the relevant explanations mentioned above. For brevity, further details will not be explained here.

[0245] Optionally, for Table 1, an AI positioning function may correspond to one or more AI positioning models, i.e., the measured quantity corresponding to the channel measurement result corresponds to one or more AI positioning models. AI positioning function #3 is used as an example. If AI positioning function #3 includes AI positioning model #1 and AI positioning model #2, the estimated arrival angle AOA result corresponding to AI positioning model #1 may be AOA11, and the estimated arrival angle AOA result corresponding to AI positioning model #2 may be AOA12, and these are obtained by measurement by the first device. If the instruction information received by the first device indicates AI positioning function #3, the measurement results reported by the first device may be the estimated arrival angle AOA result AOA11 corresponding to AI positioning model #1 included in AI positioning function #3, and the estimated arrival angle AOA result AOA12 corresponding to AI positioning model #2 included in AI positioning function #3. Note that this implementation is also applicable to other AI positioning functions in Table 1. For brevity, further details are not described herein.

[0246] In this embodiment of the present application, the AI ​​positioning function further corresponds to one of the following reporting methods: the measurement results for each path on the same measurement quantity are reported independently (abbreviated as independent reporting); or the measurement results for each path on the same measurement quantity are reported differentially (abbreviated as differential reporting). That is, each of the AI ​​positioning functions in Table 1 may correspond to independent reporting or differential reporting. In this case, an AI positioning function in Table 1 that can correspond to independent reporting and differential reporting may be replaced by two AI positioning functions, one corresponding to independent reporting and the other to differential reporting. Hereafter, Table 2 will be used as an example to illustrate the mapping relationship between each AI positioning function and reporting method shown in Table 1. It should be understood that Table 2 is provided merely as an example for ease of understanding and does not constitute any limitation to the technical solution of the present application. For example, the reporting method corresponding to AI positioning function #1 may be differential reporting, or the reporting method corresponding to AI positioning function #5 may be independent reporting. This is not limited to the present application. [Table 2] TIFF2026529619000006.tif53170

[0247] As shown in Table 2, the reporting method for AI positioning function #5, AI positioning function #12, AI positioning function #15, AI positioning function #16, AI positioning function #18 to AI positioning function #23, AI positioning function #26, AI positioning function #27, AI positioning function #29, AI positioning function #30, AI positioning function #32, and AI positioning function #33 is a difference-based reporting method, while the reporting method for AI positioning function #1 to AI positioning function #4, AI positioning function #6 to AI positioning function #11, positioning function #13, positioning function #14, positioning function #17, positioning function #24, positioning function #25, positioning function #28, and AI positioning function #31 is an independent reporting method.

[0248] Optionally, the reporting methods in Table 2 may be explicitly or implicitly indicated. For example, Table 2 may only show the mapping relationship between AI positioning functions and differential reporting methods. In this case, if the reporting method corresponding to the AI ​​positioning function indicated by the instruction information is empty after the first device receives instruction information from the location management function network element, it indicates that the measurement result of the measurement quantity corresponding to the AI ​​positioning function is reported to the location management function network element by the independent reporting method.

[0249] It should be understood that the mapping relationships between the AI ​​positioning function and the corresponding measured quantity shown in Table 1, and the mapping relationships between the AI ​​positioning function and the measurement result reporting method shown in Table 2, may be implemented independently or in combination. This is not limited to the present invention. For example, one or more rows from Table 1 and the corresponding reporting methods from Table 2 may be shown in a single table. That is, after receiving instruction information from a location management function network element, the first device can know both the measured quantity and the measurement result reporting method corresponding to the AI ​​positioning function.

[0250] For example, if the instruction information transmitted by the location management function network element indicates AI positioning function #19, the first device transmits to the location management function network element, in a differential reporting manner, the measurement results of the delay corresponding to each path in the first set of paths and the measurement results of the amplitude corresponding to each path in the first set of paths. For example, the first set of paths includes paths #1, #2, and #3. In this case, the first device may perform channel measurements based on a reference signal to obtain the delays and amplitudes corresponding to paths #1, #2, and #3, and the first device may decide that the measurement results of the delays and amplitudes corresponding to paths #1, #2, and #3 corresponding to AI positioning function #19 can be reported differentially to the location management function network element. For example, the measurement results reported by the first device include: delay #1 being 4ms, which is the delay of path #1; delay difference Δ1 between path #2 and path #1 being -1ms; delay difference Δ2 between path #3 and path #2 being 1ms; amplitude of path #1 being amplitude 1; amplitude difference between path #2 and path #1 being Δ1'; and amplitude difference between path #3 and path #2 being Δ2' being path #3. After receiving these measurement results, the location management network element may determine that the delay of route #2 is 3 ms based on delays #1 and Δ1, which are the delays of route #1; that the delay of route #3 is 4 ms based on delays #2 and Δ2, which are the delays of route #2; that the amplitude of route #2 is amplitude 2 = amplitude 1 + Δ1' based on amplitudes 1 and Δ1', which are the amplitudes of route #1; and that the amplitude of route #3 is amplitude 3 = amplitude 2 + Δ2' based on amplitudes 2 and Δ2', which are the amplitudes of route #2. Optionally, the first device may instead report the delays and amplitudes corresponding to route #1 and route #3 to the location management network element differentially (provided that these are predefined between the location management network element and the first device) by using the delays and amplitudes corresponding to route #2 as a reference. This is not limited to the present invention. Implementations of other combinations are similar. For brevity, further details are again not described herein.

[0251] Tables 1 and 2 are provided merely as examples to facilitate understanding and should not be understood as constituting any limitation to the technical solutions of this application.

[0252] It should be noted that Tables 1 and 2 described above represent the mapping relationships between the AI ​​positioning function and the measured quantity, and between the AI ​​positioning function and the reporting method corresponding to the measured result, respectively. Optionally, the mapping relationships between the AI ​​positioning model and the measured value, and between the AI ​​positioning model and the reporting method corresponding to the measured result, in this embodiment of the present application may be shown in table form. For specific implementations, please refer to Tables 1 and 2. For example, the AI ​​positioning function in Tables 1 and 2 may be replaced with an AI positioning model to describe the mapping relationships between AI positioning models #1 to #33 and the measured quantity, and the mapping relationships between AI positioning models #1 to #33 and the reporting method corresponding to the measured result.

[0253] Optionally, the mapping relationship between the AI ​​positioning model and the reporting method may be explicitly or implicitly indicated. For example, only the mapping relationship between the AI ​​positioning model and the differential reporting method may be indicated. In this case, if, after the first device receives instruction information from the location management network element, the reporting method corresponding to the AI ​​positioning model indicated by the instruction information is empty, it indicates that the measurement result of the quantifier corresponding to the AI ​​positioning model will be reported to the location management network element by the independent reporting method.

[0254] It should be understood that the mapping relationship between the AI ​​positioning model and the measured quantity corresponding to the measurement result, and the mapping relationship between the AI ​​positioning model and the method for reporting the measurement result, may be implemented independently or in combination. This is not limited to the present invention. For example, the mapping relationship between the AI ​​positioning model, the measured quantity corresponding to the measurement result, and the method for reporting the measurement result may be shown in a single table. That is, after receiving instruction information from a location management function network element, the first device can know both the measured quantity and the method for reporting the measurement result corresponding to the AI ​​positioning model.

[0255] For example, if the instruction information transmitted by the location management function network element indicates AI positioning function #21, the first device transmits to the location management function network element, in a differential reporting manner, the measurement result of the delay corresponding to each path in the first set of paths, and the measurement result of the phase corresponding to each path in the first set of paths. For example, the first set of paths includes paths #1 and #2. In this case, the first device may perform channel measurements based on a reference signal to obtain the delay and phase corresponding to paths #1 and #2, and the first device may decide that the measurement results of the delay and phase corresponding to paths #1 and #2 corresponding to AI positioning function #21 may be reported differentially to the location management function network element. For example, the measurement results reported by the first device include delay #1, which is the delay of path #1 and -1m, the delay difference Δ1 between path #1 and path #2 and path #1, the phase of path #1 and Δ1'. Subsequently, after receiving the measurement results, the location management function network element may determine that delay #2, which is the delay of path #1, is 3 ms based on delay #1 and Δ1, which are the delays of path #1, and that the phase of path #2 is phase 2 = phase 1 + Δ1' based on phase 1 and Δ1', which are the phases of path #1. Optionally, the first device may alternatively report the delay and phase corresponding to path #1 to the location management function network element differentially by using the delay and phase corresponding to path #2 as a reference (provided that this is predefined between the location management function network element and the first device). This is not limited to the present application. Implementations of other combinations are similar. For brevity, further details are again not described herein.

[0256] Optionally, the first device may further determine, based on the instruction information, a monitoring policy for the AI ​​positioning function or AI positioning model, an update policy for the AI ​​positioning function or AI positioning model, and so on.

[0257] For example, a monitoring policy for an AI positioning function or AI positioning model includes at least one of the following:

[0258] (1) Monitoring based on the data distribution of measurement results of the measured quantities input to the corresponding AI positioning function / model. For example, monitoring is performed based on the signal-to-interference plus noise ratio (SINR) of the channel measurement results. If the SINR satisfies the first condition, the current monitoring criteria are met and no action is required on the AI ​​positioning function / model; otherwise, an action is required on the AI ​​positioning function / model. The first condition includes the SINR being greater than or equal to a first threshold and less than or equal to a second threshold, or the SINR being greater than or equal to a first threshold and less than or equal to a second threshold, or the SINR being greater than a first threshold and less than or equal to a second threshold.

[0259] (2) Monitoring based on the data distribution of results output by the AI ​​positioning function / model. For example, monitoring is performed based on the position results output by the AI ​​direct positioning function / model. For the same AI direct positioning function / model, if two adjacent output position results satisfy the second condition, an action must be taken on the AI ​​positioning function / model; otherwise, no action must be taken on the AI ​​positioning function / model. Two adjacent output position results satisfying the second condition includes the difference between the two adjacent output position results being greater than the third threshold, or the difference between the two adjacent output position results being greater than or equal to the third threshold.

[0260] (3) Monitoring based on the correct value of the result output by the AI ​​positioning function / model. For example, monitoring is performed based on the location result output by the AI ​​direct positioning function / model. For the same AI direct positioning function / model, if the output location result satisfies the third condition for a first input whose output is known, i.e., an existing output tag or a ground truth, then an action must be taken on the AI ​​positioning function / model; otherwise, no action must be taken on the AI ​​positioning function / model. The output location result satisfies the third condition, which includes the difference between the output location result and the correct value of the output location result, i.e., the ground truth, being greater than the third threshold, or the difference between the output location result and the ground truth being greater than or equal to the third threshold.

[0261] Actions performed on the AI ​​positioning function / model include one or more of the following: switching, updating, or deactivating.

[0262] For example, an update policy for an AI positioning function or AI positioning model includes at least one of the following:

[0263] (1) Update all parameters of the AI ​​positioning function or AI positioning model.

[0264] (2) Update some of the parameters of the AI ​​positioning function or the AI ​​positioning model.

[0265] (3) Switch to the specified AI positioning function or AI positioning model.

[0266] Based on monitoring and update policies for the AI ​​positioning function or AI positioning model, the first device can monitor and update the AI ​​positioning function or AI positioning model in a timely manner, thereby enabling it to obtain a more accurate location of the terminal device and thereby effectively manage the terminal device.

[0267] In conclusion, a correspondence is defined between the measured quantity corresponding to the channel measurement result and the AI ​​positioning function or AI positioning model indicated by the instruction information, and a correspondence is defined between the AI ​​positioning function or AI positioning model and the method for reporting the measurement result. As a result, after receiving the instruction information, the first device can determine the channel measurement result expected by the location management function network element and report the measurement result to the location management function network element using the corresponding reporting method. Therefore, signaling overhead can be reduced.

[0268] The following describes the application of embodiments of the present application to uplink positioning scenarios, downlink positioning scenarios, and sidelink positioning scenarios, with reference to Figures 10 to 12, using examples. It should be understood that the method embodiments shown in Figures 9 to 12 may be combined with each other, and the steps in the method embodiments shown in Figures 9 to 12 may refer to each other. For example, in embodiments of the present application, the method embodiments shown in Figures 10 to 12 may be considered as possible implementations that implement the functionality of the method embodiment shown in Figure 9. Figure 10 mainly describes the uplink positioning scenario, Figure 11 mainly describes the downlink positioning scenario, and Figure 12 mainly describes the sidelink positioning scenario.

[0269] Figure 10 is a schematic flowchart of a communication method 1000 according to one embodiment of the present invention. As shown in Figure 10, for example, LMF is a location management function network element, the first device is a gNB, and the second device is an UE. Model selection / inference in this implementation is performed on the LMF side. The corresponding channel measurement result obtained by the gNB by measuring the SRS transmitted by the UE is used as input to an AI positioning model that outputs the UE's location. It should be understood that the relevant descriptions in the embodiment shown in Figure 9 are also applicable to this implementation. The same or similar technical means may exist between Figure 9 and Figure 10. The contents of Figure 10 that were described in the embodiment of Figure 9 will not be described in detail again.

[0270] S1010. The LMF sends an AI positioning measurement request #1 (that is, an example of a measurement request message) to the gNB. Correspondingly, the gNB receives the AI positioning measurement request #1 from the LMF. The AI positioning measurement request #1 carries indication information, and the indication information indicates an AI positioning function or an AI positioning model.

[0271] For example, the LMF sends the AI positioning measurement request #1 to the gNB by using NRPPa signaling, to request the gNB to report a SRS measurement result.

[0272] Optionally, the indication information may not be carried in the AI positioning measurement request #1, that is, the AI positioning measurement request #1 and the indication information may be carried in different messages, and the two may be transmitted simultaneously or separately.

[0273] S1020. The gNB configures a UE to transmit an SRS based on the AI positioning measurement request #1.

[0274] S1030. The UE transmits the SRS to the gNB. Correspondingly, the gNB receives the SRS from the UE.

[0275] S1040. The gNB measures the SRS to obtain a measurement result #1 (that is, an example of a channel measurement result).

[0276] S1050. The gNB transmits the measurement result #1 to the LMF based on the indication information.

[0277] For a specific implementation of how the gNB transmits measurement result #1 to the LMF based on the instruction information, please refer to the relevant explanation in Method 900. The AI ​​positioning function or AI positioning model indicated by the instruction information corresponds to the measured quantity corresponding to the channel measurement result. In addition, the AI ​​positioning function or AI positioning model indicated by the instruction information further corresponds to the reporting method. For specific details and explanations of the measured quantity and reporting method, please refer to the relevant explanation in Method 900. For example, if the instruction information indicates AI positioning function #5 shown in Table 1, then Table 2 shows that the reporting method corresponding to AI positioning function #5 is differential reporting. The gNB then configures the UE to transmit SRS, measures the SRS from the UE to obtain the corresponding measurement result #1, and reports measurement result #1 to the LMF. If the second set of routes includes routes #1, #2, and #3, then measurement result #1 includes delay #1, which is the delay corresponding to route #1, which is 4 ms; delay #2 and the delay difference Δ1 between route #2 and route #1, which is -1 ms; and delay #3 and the delay difference Δ2 between route #3 and route #2, which is 1 ms. Then, after receiving measurement result #1, the location management function network element may determine, based on delays #1 and Δ1, that delay #2, which is the delay corresponding to route #2, is 3 ms, and based on delays #2 and Δ2, that delay #3, which is the delay corresponding to route #3, is 4 ms. Optionally, the first device may instead report the delays corresponding to routes #1 and #3 differentially by using the delay corresponding to route #2 as a reference (provided that it is predefined between the location management function network element and the first device).

[0278] Optionally, the method may further include step S1060 (not shown).

[0279] S1060. The LMF selects an AI positioning model based on measurement result #1 and performs AI positioning. For example, measurement result #1 is used as input to an AI positioning model, and the AI ​​positioning model outputs the UE's position. The AI ​​positioning model may be deployed on the LMF side. Alternatively, the UE / base station may extract channel response features by using an AI positioning model, and the LMF uses the extracted features as input to an AI positioning model to obtain the UE's position.

[0280] For example, the UE transmits the uplink SRS to the base station, and the gNB obtains the channel response. For example, an AI positioning model is deployed on the LMF side, the gNB transmits the channel response to the LMF, and the LMF uses the channel responses of multiple gNBs as input to the AI ​​positioning model to output the UE's location information. If the gNB has 16 antennas and 4096 subcarriers, each gNB needs to transmit 16*4096 pieces of complex information to the LMF. In another example, an AI positioning model is deployed on both the gNB and LMF sides. Each gNB uses the channel response as input. The AI ​​positioning model on the gNB side extracts channel response features and transmits them to the LMF. The LMF uses the received channel response features as input, and the AI ​​positioning model on the LMF side obtains the UE's location. For example, the channel response features may be the corresponding noise signal when the signal power is weak, or the corresponding signal multipath response when the signal power is strong, and may include one or more of the following: the number of paths, the amplitude, phase, or angle information of each path. The dimension of the channel response feature is determined by the output dimension of the AI ​​positioning model on the gNB side. For example, the gNB may extract a channel response feature of dimension

[0128] from a channel response of dimension [16,4096] and transmit this channel response feature to the LMF.

[0281] It should be noted that in this embodiment of the present application, information / or data transmission between devices is not limited to direct transmission, indirect transmission (including transparent transmission), etc. Therefore, device A sending information to device B may include device A directly sending information to device B by using an interface between device A and device B, and may also include device A sending a message to device C, and device C sending a message to device B. In addition, the number of relays used for transfer from device A to device B is not limited. For example, UE sending measurement result #1 to LMF may include multiple implementations, for example, UE directly sending measurement result #1 to LMF by using an LPP message, or UE sending measurement result #1 to LMF via a gNB, or UE sending measurement result #1 to LMF via a gNB and AMF, or UE sending measurement result #1 to LMF via an AMF. This is not limited. Interactions between other devices are similar and will be understood by those skilled in the art. Further details are not described again. For details regarding inter-device interface messages, please refer to the description of Figure 4.

[0282] In this embodiment of the present application, the LMF selects an adaptive AI positioning model based on measurement result #1. It can be understood that an AI positioning model may be selected for positioning inference if the AI ​​positioning model uses the delay, amplitude, and phase of multiple paths included in measurement result #1 as input. For example, if the payload of the channel measurement report contains information on 32 paths, and in the LMF's model library, AI positioning model A uses information on 256 paths as input, and AI positioning model B uses information on 32 paths as input, the LMF selects AI positioning model B for positioning inference.

[0283] In this embodiment of the present invention, the reference signal measurement node gNB knows the measurement result #1 required by the LMF through a parse based on the AI ​​positioning function indicated by the LMF, and reports the measurement result #1, thereby allowing the LMF to use the measurement result #1 as input to the AI ​​positioning model and obtain the position of the UE as output to the AI ​​positioning model. In this way, AI positioning is performed, and the signaling overhead that would arise from indicating the reporting method and the AI ​​positioning function separately is reduced.

[0284] Figure 11 is a schematic flowchart of a communication method 1100 according to one embodiment of the present invention. As shown in Figure 11, for example, LMF is a location management function network element, the first device is an UE, and the second device is a gNB. Model selection / inference in this implementation is performed on the LMF side. The corresponding channel measurement result obtained by the UE by measuring the PRS transmitted by the gNB is used as input to an AI positioning model, which outputs the UE's position. It should be understood that the relevant descriptions in the embodiment shown in Figure 9 are also applicable to this implementation. The same or similar technical means may exist between Figure 9 and Figure 11. The contents of Figure 11 that were described in the embodiment of Figure 9 will not be described in detail again.

[0285] S1110. The LMF sends AI positioning measurement request #2 (i.e., an example of a measurement request message) to the UE. Correspondingly, the UE receives AI positioning measurement request #2 from the LMF. AI positioning measurement request #2 carries instruction information, which indicates the AI ​​positioning function or AI positioning model.

[0286] For example, the LMF sends AI positioning measurement request #2 to the UE by using LPP signaling, requesting the UE to report the PRS measurement results.

[0287] Optionally, instruction information does not have to be carried in AI positioning request #2; that is, AI positioning request #2 and instruction information may be carried in different messages, and the two may be transmitted simultaneously or separately.

[0288] S1120. The LMF configures the gNB to send PRS.

[0289] S1130. The gNB sends a PRS to the UE based on the LMF configuration. In response, the UE receives a PRS from the gNB.

[0290] S1140. The UE measures the PRS from the gNB to obtain measurement result #2 (i.e., an example of a channel measurement result).

[0291] S1150. The UE sends measurement result #2 to the LMF based on the instruction information.

[0292] For a specific implementation of how the UE transmits measurement result #2 to the LMF based on the instruction information, see the relevant description in Method 900. The AI ​​positioning function or AI positioning model indicated by the instruction information corresponds to the measured quantity corresponding to the channel measurement result. In addition, the AI ​​positioning function or AI positioning model indicated by the instruction information further corresponds to the reporting method. For specific details and descriptions of the measured quantity and reporting method, see the relevant description in Method 900. For brevity, further details are not described herein.

[0293] Optionally, the method may further include step S1160 (not shown).

[0294] S1160. The LMF selects an AI positioning model based on measurement result #2 and performs AI positioning. For example, measurement result #2 is used as input to the AI ​​positioning model, and the AI ​​positioning model outputs the UE's position. The AI ​​positioning model may be deployed on the LMF side. Alternatively, the UE / base station may extract channel response features by using an AI positioning model, and the LMF uses the extracted features as input to the AI ​​positioning model to obtain the UE's position.

[0295] For example, the gNB transmits a downlink PRS to the UE, which obtains a channel response and transmits the channel response or features extracted from the channel response by an AI positioning model to the LMF. The features based on the channel response may be the channel response itself, or the features extracted from the channel response by an AI positioning model.

[0296] In this embodiment of the present invention, the reference signal measurement node gNB knows and reports the measurement result #1 required by the LMF through a parse based on the AI ​​positioning capabilities shown by the LMF, thereby allowing the LMF to use measurement result #1 as input to the AI ​​positioning model and obtain the position of the UE as output to the AI ​​positioning model. In this way, AI positioning is performed and signaling overhead can be reduced.

[0297] FIG. 12 is a schematic flowchart of a communication method 1200 according to an embodiment of the present application. As shown in FIG. 12, for example, the LMF is a location management function network element, the first device is UE#1, and the second device is UE#2. Model selection / inference in this implementation is performed on the LMF side. The corresponding channel measurement result obtained by UE#1 by measuring the SL-PRS transmitted by UE#2 is used as an input to an AI positioning model, and the AI positioning model outputs the position of UE#1. It should be understood that the relevant description in the embodiment shown in FIG. 9 is also applicable to this implementation. The same or similar technical means may exist in FIGS. 9 to 12. The content of FIG. 12 that has been described in the embodiments of FIGS. 9 to 11 will not be described in detail again.

[0298] S1210. The LMF transmits an AI positioning measurement request #3 (that is, an example of a measurement request message) to UE#1. Correspondingly, UE#1 receives the AI positioning measurement request #3 from the LMF. The AI positioning measurement request #3 carries indication information, and the indication information indicates an AI positioning function or an AI positioning model.

[0299] For example, the LMF transmits AI positioning measurement request #3 to UE#1 by using LPP signaling to request UE#1 to report the SL-PRS measurement result.

[0300] Optionally, the indication information may not be carried in the AI positioning measurement request #3, that is, the AI positioning measurement request #3 and the indication information may be carried in different messages, and the two may be transmitted simultaneously or separately.

[0301] S1220. The LMF configures UE#2 to transmit a PRS.

[0302] S1230. UE#2 transmits the SL-PRS to UE#1 based on the configuration of the LMF. Correspondingly, UE#1 receives the SL-PRS from UE#2.

[0303] S1240. UE#1 measures the SL-PRS from UE#2 and obtains measurement result #3 (i.e., an example of a channel measurement result).

[0304] S1250. UE#1 sends measurement result #3 to the LMF based on the instruction information.

[0305] For a specific implementation of how UE#1 transmits measurement result #3 to the LMF based on the instruction information, please refer to the relevant description in Method 900. The AI ​​positioning function or AI positioning model indicated by the instruction information corresponds to the measured quantity corresponding to the channel measurement result. In addition, the AI ​​positioning function or AI positioning model indicated by the instruction information further corresponds to the reporting method. For specific details and descriptions of the measured quantity and reporting method, please refer to the relevant description in Method 900. For brevity, further details are not described herein.

[0306] Optionally, the method may further include step S1260 (not shown).

[0307] S1260. The LMF selects an AI positioning model based on measurement result #3 and performs AI positioning. For example, measurement result #3 is used as input to an AI positioning model, and the AI ​​positioning model outputs the position of the UE. The AI ​​positioning model may be deployed on the LMF side. Alternatively, UE#1 / UE#2 may extract channel response features by using an AI positioning model, and the LMF uses the extracted features as input to an AI positioning model to obtain the position of the UE.

[0308] For example, UE#2 transmits a sidelink SL-PRS to UE#1, UE#1 obtains a channel response, and transmits the channel response or features extracted from the channel response by an AI positioning model to the LMF. The features based on the channel response may be the channel response itself, or the features extracted from the channel response by an AI positioning model.

[0309] In this embodiment of the present invention, the reference signal measurement node gNB knows and reports the measurement result #1 required by the LMF through a parse based on the AI ​​positioning function shown by the LMF, thereby allowing the LMF to use measurement result #1 as input to the AI ​​positioning model and obtain the position of the UE as output to the AI ​​positioning model. In this way, AI positioning is performed and signaling overhead can be reduced.

[0310] The method provided in the embodiments of the present application has been described in detail above with reference to Figures 1 to 12. Hereafter, the apparatus provided in the embodiments of the present application will be described in detail with reference to Figures 13 to 15. It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, please refer to the method embodiment described above. For the sake of brevity, further details will not be described again in this specification.

[0311] Figure 13 shows a communication device 1300 according to one embodiment of the present application. The device 1300 includes a transceiver unit 1310 and optionally further includes a processing unit 1320. The transceiver unit 1310 may be configured to implement corresponding communication functions. The transceiver unit 1310 may also be referred to as a communication interface or communication unit. The processing unit 1320 may be configured to perform processing.

[0312] Optionally, the device 1300 may further include a storage unit. The storage unit may be configured to store instructions and / or data. The processing unit 1320 may read instructions and / or data from the storage unit, enabling the device to carry out the method embodiments described above.

[0313] For example, the communication device 1300 may be the first device, or a communication device used in or in combination with the first device that can implement a method performed by the first device, such as a chip, chip system, or circuit. For further details, see the relevant description of the chip system shown in Figure 15.

[0314] For example, the communication device 1300 may be a location management function network element, or a communication device, such as a chip, chip system, or circuit, that is used in or in combination with a location management function network element and can implement a method performed by the location management function network element. For further details, see the relevant description of the chip system shown in Figure 15. For example, the location management function network element may be an LMF in the method embodiment described above.

[0315] In one design, the apparatus 1300 is configured to perform steps or procedures performed by the first device in the method embodiments shown in Figures 9 to 12, the transceiver unit 1310 is configured to perform the receive / transmit related operations on the first device side in the aforementioned method embodiments, and the processing unit 1320 is configured to perform the processing related operations on the first device side in the method embodiments shown in Figures 9 to 12.

[0316] In an alternative design, the device 1300 is configured to perform steps or procedures performed by the location management function network element in the method embodiments of Figures 9 to 12, the transceiver unit 1310 is configured to perform transmit / receive related operations on the location management function network element side in the aforementioned method embodiments, and the processing unit 1320 is configured to perform processing related operations on the location management function network element side in the method embodiments of Figures 9 to 12.

[0317] It should be understood that the specific process by which the unit performs the corresponding steps described above is described in detail in the method embodiments described above. For the sake of brevity, the details will not be described here.

[0318] It should be further understood that the apparatus 1300 described herein is embodied in the form of a functional unit. The term “unit” as used herein may mean an application-specific integrated circuit (ASIC), an electronic circuit, a processor configured to run one or more software or firmware programs (e.g., a shared processor, a dedicated processor, or a group processor), memory, merged logic circuits, and / or other suitable components that support the functions described. In an optional example, those skilled in the art will understand that the apparatus 1300 may specifically be a network element in the embodiments described above (e.g., a first device or a location management function network element) and may be configured to perform procedures and / or steps corresponding to the network element in the method embodiments described above. To avoid repetition, further details are not described here again.

[0319] The apparatus 1300 in the aforementioned solution has the function of implementing the corresponding steps performed by a network element (e.g., a first device or a location management function network element) in the aforementioned method. The function may be implemented by hardware or by hardware running corresponding software. The hardware or software includes one or more modules corresponding to the function. For example, a transceiver unit may be replaced by a transceiver (e.g., a transmitting unit in a transceiver unit may be replaced by a transmitter, and a receiving unit in a transceiver unit may be replaced by a receiver), and another unit, for example, a processing unit may be replaced by a processor, which may perform the receive / transmit operations and associated processing operations in the method embodiment, respectively.

[0320] In addition, the transceiver unit 1310 may alternatively be a transceiver circuit (for example, it may include a receiving circuit and a transmitting circuit), and the processing unit may be a processing circuit.

[0321] It should be noted that the apparatus in Figure 13 may be a network element in the embodiments described above, or it may be a chip or chip system, such as a system on a chip (SoC). The transceiver unit may be an input / output circuit or a communication interface. The processing unit is a processor, microprocessor, or integrated circuit on a chip. This is not limited to the foregoing.

[0322] Figure 14 is a diagram of another communication device 1400 according to one embodiment of the present application. As shown in Figure 14, the device 1400 includes a transceiver 1430. The transceiver 1430 is configured to receive and / or transmit signals. For example, a processor 1410 is configured to control the transceiver 1430 to receive and / or transmit signals.

[0323] Optionally, the device 1400 further includes a processor 1410 and a memory 1420. The processor 1410 is coupled to the memory 1420. The memory 1420 is configured to store computer programs or instructions and / or data. The processor 1410 is configured to execute computer programs or instructions stored in the memory 1420 or to read data stored in the memory 1420 to perform the method in the embodiment of the method described above. For example, the processor 1410 is configured to control a transceiver 1430 to receive and / or transmit signals.

[0324] Optionally, there may be one or more processors 1410.

[0325] Optionally, there is one or more memory 1420s.

[0326] Optionally, the memory 1420 and the processor 1410 may be integrated together or located separately.

[0327] For example, the processor 1410 may have the functions of the processing unit 1320 shown in Figure 13, the memory 1420 may have the functions of a storage unit, and the transceiver 1430 may have the functions of the transceiver unit 1310 shown in Figure 13.

[0328] For example, the communication device 1400 may be the first device, or a communication device used in or in combination with the first device that can implement a method performed by the first device, such as a chip, chip system, or circuit. For further details, see the relevant description of the chip system shown in Figure 15.

[0329] For example, the communication device 1400 may be a location management function network element, or a communication device, such as a chip, chip system, or circuit, that is used in or in combination with a location management function network element and can implement a method performed by the location management function network element. For further details, see the relevant description of the chip system shown in Figure 15. For example, the location management function network element may be an LMF in the embodiment of the method described above.

[0330] In one design, the device 1400 is configured to perform steps or procedures performed by the first device in the method embodiments shown in Figures 9 to 12, the transceiver 1430 is configured to perform receive / transmit related operations on the first device side in the aforementioned method embodiments, and the processor 1410 is configured to perform processing related operations on the first device side in the method embodiments shown in Figures 9 to 12.

[0331] In an alternative design, the device 1400 is configured to perform steps or procedures performed by the location management function network element in the method embodiments of Figures 9 to 12, the transceiver 1430 is configured to perform transmit / receive related operations on the location management function network element side in the aforementioned method embodiments, and the processor 1410 is configured to perform processing related operations on the location management function network element side in the method embodiments of Figures 9 to 12.

[0332] Optionally, the processor 1410 is configured to execute a computer program or instruction stored in memory 1420 to perform the relevant operations of the network element (e.g., the first device or location management function network element) in the embodiments of the method described above.

[0333] It should be understood that the processor referred to in this embodiment of the present application may be any of the following devices, or part of a circuit configured for processing functions in any of the following devices: a central processing unit (CPU) or another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and so on. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0334] Furthermore, it should be understood that the memory referred to in this embodiment of the present application may be volatile memory and / or non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). For example, RAM may be used as an external cache. As an example, rather than an exhaustive list, RAM includes multiple forms such as static random access memory (static RAM, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchlink dynamic random access memory (synchlink DRAM, SLDRAM), and direct rambus random access memory (direct rambus RAM, DR RAM).

[0335] Note that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or another programmable logic device, discrete gate or transistor logic device, or discrete hardware component, memory (storage modules) may be integrated into the processor.

[0336] It should be further noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0337] In embodiments of the present application, the methods shown in Figures 9 to 12 may be performed by a first device and location management network element, or by a chip, chip system, or circuit of the first device and location management network element. The chip, chip system, or circuit may be installed in the first device and location management network element. The chip system and location management network element in the first device will be described below with reference to Figure 15.

[0338] Figure 15 shows a chip system 1500 according to one embodiment of the present invention. The chip system 1500 (sometimes referred to as a processing system) includes a logic circuit 1510 and an input / output interface 1520.

[0339] The logic circuit 1510 may be a processing circuit within the chip system 1500. The logic circuit 1510 may be coupled to and connected to a memory unit and call instructions within the memory unit, enabling the chip system 1500 to implement the methods and functions of the embodiments of the present application. The input / output interface 1520 may be an input / output circuit within the chip system 1500, which outputs information processed by the chip system 1500 or inputs data or signaling information to be processed into the chip system 1500 for processing.

[0340] For example, when the chip system 1500 is installed in a first device, the logic circuit 1510 is coupled to an input / output interface 1520, which may input a reference signal to the logic circuit 1510 for processing, for example, to perform channel measurements on a reference signal to obtain measurement results.

[0341] In another example, if the chip system 1500 is installed in a location management function network element, the logic circuit 1510 is coupled to an input / output interface 1520, which can input measurement results from a first device to the logic circuit 1510 for processing.

[0342] In one solution, the chip system 1500 is configured to implement the operations performed by the device (for example, the first device, a positioning device, or a location management function network element) in the embodiments of the method described above.

[0343] For example, the logic circuit 1510 is configured to implement processing-related operations performed by a device (first device, positioning device, or location management function network element) in the method embodiment described above, and the input / output interface 1520 is configured to implement transmission and / or reception-related operations performed by a network element (first device, positioning device, or location management function network element) in the method embodiment described above.

[0344] One embodiment of the present invention provides a computer-readable storage medium that stores computer instructions for implementing a method performed by a device (for example, a first device, a positioning device, or a location management network element) in the aforementioned method embodiment.

[0345] For example, when a computer program is executed by a computer, the computer can implement methods performed by a device (e.g., a first device, a positioning device, e.g., a location management network element) in the method embodiments described above.

[0346] One embodiment of the present invention provides a computer program product including instructions. When the instructions are executed by a computer, the method performed by the device in the aforementioned method embodiment (e.g., a first device, a positioning device, e.g., a location management function network element) is implemented.

[0347] One embodiment of the present application provides a communication system. The communication system includes a first device and / or location management function network element in the embodiments described above. For example, the system includes a first device and / or location management function network element in the embodiments shown in Figures 9 to 11. In another example, the system includes a first device and / or location management function network element in the embodiments shown in Figures 9 to 11.

[0348] For a description of the relevant aspects and beneficial effects of any of the devices provided above, please refer to the corresponding method embodiments provided above. Further details are not described herein.

[0349] To facilitate understanding of the embodiments provided herein, the following points will be explained.

[0350] In this application, unless otherwise specified or unless there is a logical inconsistency, terminology and / or descriptions between different embodiments are consistent and may be mutually referenced, and technical features in different embodiments may be combined into new embodiments based on their internal logical relationships.

[0351] In this application, “at least one” means one or more, “multiple” means two or more, and “and / or” describes the relationship between the related objects, indicating that three relationships may exist. For example, A and / or B may indicate that only A exists, both A and B exist, and only B exists. A and B can be singular or plural. In the text description of this application, the letter “ / ” generally indicates an “or” relationship between the related objects, and “at least one of the following items (pieces)” or similar expressions means any combination of these items, including any combination of singular or plural items (pieces). For example, at least one item (piece) of a, b, and c could be a, b, c, a and b, a and c, b and c, or a, b and c.

[0352] In this application, “first,” “second,” and various numbers are used for distinction to facilitate explanation, rather than to limit the scope of the embodiments of this application, for example, to distinguish different messages rather than to describe a specific order or sequence. It should be understood that the subjects described in this manner are interchangeable in appropriate contexts, thereby allowing for the description of solutions other than the embodiments of this application.

[0353] In this application, the terms “includes” and “have,” and any other variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a list of steps or units, is not necessarily limited to those steps or units explicitly listed, and may include other steps or units that are not explicitly listed or are specific to such process, method, product, or device.

[0354] In this application, "to indicate" may include "to indicate directly" and "to indicate indirectly." When a single reference information is described as indicating A, that reference information may indicate A directly or indirectly, and does not necessarily mean that the reference information definitely carries A. Directly indicating information A means including information A. Implicitly indicating information A means indicating information A based on a correspondence between information A and information B by directly indicating information B. The correspondence between information A and information B may be predefined, pre-stored, pre-burned, or pre-configured.

[0355] It can be understood that some optional features in embodiments of the present application may be independent of other features in some scenarios, or may be combined with other features in some scenarios. This is not limited to this.

[0356] In some of the embodiments described above, it may be further understood that information transmission is referred to multiple times. For example, “Network element A transmits information A to network element B” may be understood as network element B being the destination end of information A or an intermediate network element in the transmission path to the destination end, and may include transmitting the information directly or indirectly to network element B; “Network element B receives information A from network element A” may be understood as network element A being the source end of information A or an intermediate network element in the transmission path to the source end, and may include receiving the information directly or indirectly from network element A. Necessary processing, such as formatting changes, may be performed on the information between the source and destination ends where the information is transmitted, but the destination end can understand valid information from the source end. Similar descriptions in this application may be similarly understood, and further details are not described herein.

[0357] In some of the embodiments described above, it may be understood that examples where the AI ​​model is used for positioning are primarily used for illustrative purposes. It may also be understood that the AI ​​model may be used for other purposes.

[0358] The solutions in the embodiments of this application may be appropriately combined for use, and it should be further understood that the definitions or descriptions of terms in the embodiments may be mutually referenced or explained in the embodiments. This is not limited to these definitions.

[0359] In embodiments of the methods described above, it may be further understood that methods and operations performed by the first device or positioning device may, alternatively, be performed by components of the first device or positioning device (e.g., chips or circuits). This is not limited to these.

[0360] A person skilled in the art will recognize, by referring to the units and algorithmic steps in the examples described in the embodiments disclosed herein, that the present application may be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or by software depends on the specific application and design constraints of the technical solution. A person skilled in the art may use different methods to implement the described functions for each specific application, but such implementations should not be considered to be beyond the scope of the present application.

[0361] For the sake of brevity, it will be readily apparent to those skilled in the art that the detailed operating processes of the aforementioned systems, apparatus, and units are described by referring to the corresponding processes in the method embodiments described above. Further details are not described herein.

[0362] In some embodiments provided herein, it should be understood that the disclosed systems, apparatus, and methods may be implemented in other ways. For example, the described apparatus embodiments are merely examples. For example, the division into units is merely a logical functional division. Other division methods may exist in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the mutual coupling, direct coupling, or communication connection shown or described may be implemented by using some interfaces. Indirect coupling or communication connection between apparatus or units may be implemented electronically, mechanically, or in other forms.

[0363] Units described as separate parts may or may not be physically separate, and parts shown as units may or may not be physical units, may be located in one place, or may be distributed across multiple network units. Some or all of the units may be selected based on the actual requirements for achieving the objectives of the solution of the embodiment.

[0364] In addition, the functional units in the embodiments of the present invention may be integrated into a single processing unit, or each unit may exist physically independently, or two or more units may be integrated into a single unit.

[0365] When a function is implemented in the form of a software function unit and sold or used as an independent product, the function may be stored on a computer-readable storage medium. Based on such understanding, the technical solution of the present application, in essence, or in part with respect to the prior art, or a part of the technical solution, may be implemented in the form of a software product. A computer software product includes several instructions stored on a storage medium for instructing a computer device (which may be a personal computer, server, network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. The storage medium includes a variety of media capable of storing program code, such as USB flash drives, removable hard disk drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0366] The foregoing description is merely an individual implementation of the present application and is not intended to limit the scope of protection of the present application. Any modifications or substitutions that are readily conceivable by a person skilled in the art within the scope of the art disclosed herein shall fall within the scope of protection of the present application. Accordingly, the scope of protection of the present application shall be subject to the scope of protection of the claims.

Claims

1. A communication method performed by a first device or a chip or circuit of the first device, the method being: Steps include: receiving instruction information from a location management function network element, wherein the instruction information indicates an artificial intelligence (AI) positioning function or AI positioning model, the AI ​​positioning function or AI positioning model belongs to a plurality of AI positioning functions or AI positioning models, the plurality of AI positioning functions or AI positioning models correspond to a plurality of different sets of measurement quantities, and the set of measurement quantities includes one or more measurement quantities; The step of transmitting channel measurement results to the location management function network element based on the instruction information, wherein the measured quantity corresponding to the channel measurement results corresponds to the AI ​​positioning function or the AI ​​positioning model indicated by the instruction information, method.

2. The method according to claim 1, wherein the instruction information is conveyed in a measurement request message from the location management function network element.

3. The AI ​​positioning function or AI positioning model measures the following quantities, namely: Line of Sight (LOS) identification results; Time of Arrival (TOA) Estimate Result; Estimated arrival angle of arrival (AOA); LOS identification result corresponding to each route in the first route set; The delay corresponding to each path in the second set of paths; The amplitude corresponding to each path in the third set of paths; or Phase corresponding to each path in the fourth path set Corresponds to one or more of the following: At least two of the first route set, the second route set, the third route set, and the fourth route set are either the same or different. The method according to claim 1 or 2.

4. The first device is an access network device, the channel measurement result is obtained based on a first channel measurement, the first channel measurement includes measuring a prospect reference signal from a terminal device; or, The first device is a terminal device, the channel measurement result is obtained based on a second channel measurement, the second channel measurement includes measuring a positioning reference signal or a channel status information reference signal from an access network device; or, The first device is a first terminal device, the channel measurement result is obtained based on a third channel measurement, the third channel measurement includes measuring a sidelink positioning reference signal from a second terminal device, The method according to any one of claims 1 to 3.

5. The AI ​​positioning function or AI positioning model is reported by the following method, namely: The measurement results for each pathway with the same measurement quantity are reported independently; or The measurement results for each pathway using the same measurement quantity are reported differentially. The method according to any one of claims 1 to 4, further corresponding to one of the above.

6. The channel measurement results are included in the measurement report, and the measurement results for each pathway at the same measurement quantity are reported differentially: This includes reporting the differential measurement results for each path for the same measured quantity in the same measurement report. The method according to claim 5.

7. The method according to any one of claims 1 to 6, wherein the AI ​​positioning function includes one or more AI positioning models, and the measured quantity corresponding to the channel measurement result corresponds to one or more AI positioning models.

8. A communication method performed by a location management function network element or a chip or circuit of said location management function network element, the method being: A step of transmitting instruction information to a first device, wherein the instruction information indicates an artificial intelligence (AI) positioning function or AI positioning model, the AI ​​positioning function or AI positioning model belongs to a plurality of AI positioning functions or AI positioning models, the plurality of AI positioning functions or AI positioning models correspond to a plurality of different sets of measurement quantities, and the set of measurement quantities includes one or more measurement quantities; The step of receiving channel measurement results from the first device, wherein the measured quantity corresponding to the channel measurement results corresponds to the AI ​​positioning function or AI positioning model indicated by the instruction information, and Methods that include...

9. The method according to claim 8, wherein the instruction information is conveyed in a measurement request message from the location management function network element.

10. The AI ​​positioning function or AI positioning model measures the following quantities, namely: Line of Sight (LOS) identification results; Time of Arrival (TOA) Estimate Result; Estimated arrival angle of arrival (AOA); LOS identification result corresponding to each route in the first route set; The delay corresponding to each path in the second set of paths; The amplitude corresponding to each path in the third set of paths; or Phase corresponding to each path in the fourth path set Corresponding to any one of the above, at least two of the first route set, the second route set, the third route set, and the fourth route set are the same or different. The method according to claim 8 or 9.

11. The first device is an access network device, the channel measurement result is obtained based on a first channel measurement, the first channel measurement includes measuring a prospect reference signal from a terminal device; or, The first device is a terminal device, and the channel measurement result is obtained based on a second channel measurement, the second channel measurement includes measuring a positioning reference signal or a channel status information reference signal from an access network device; or, The first device is a first terminal device, the channel measurement result is obtained based on a third channel measurement, the third channel measurement includes measuring a sidelink positioning reference signal from a second terminal device, The method according to any one of claims 8 to 10.

12. The AI ​​positioning function or AI positioning model is reported by the following method, namely: The measurement results for each pathway with the same measurement quantity are reported independently; or The measurement results for each pathway using the same measurement quantity are reported differentially. The method according to any one of claims 8 to 11, further corresponding to one of the above.

13. The channel measurement results are included in the measurement report, and the measurement results for each pathway at the same measurement quantity are reported differentially: This includes reporting the differential measurement results for each path for the same measured quantity in the same measurement report. The method according to claim 12.

14. The method according to any one of claims 8 to 13, wherein the AI ​​positioning function includes one or more AI positioning models, and the measured quantity corresponding to the channel measurement result corresponds to one or more AI positioning models.

15. A communication device having a module configured to perform the method described in any one of claims 1 to 7 or 8 to 14.

16. A communication system comprising a module configured to perform the method described in any one of claims 1 to 7, and a module configured to perform the method described in any one of claims 8 to 14.

17. A communication device having a processor, The processor is configured to execute computer instructions stored in memory so that the communication device can perform the method described in any one of claims 1 to 7, or so that the communication device can perform the method described in any one of claims 8 to 14. Communication device.

18. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer is enabled to perform the method according to any one of claims 1 to 7, or the computer is enabled to perform the method according to any one of claims 8 to 14.

19. A chip having a processor, which executes a computer program to enable a device on which the chip is installed to perform the method described in any one of claims 1 to 7, or enables a device on which the chip is installed to perform the method described in any one of claims 8 to 14.

20. A computer program product comprising a computer program executed by a computer to perform the method described in any one of claims 1 to 7, or a computer program executed by a computer to perform the method described in any one of claims 8 to 14.

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