Positioning measurement method, apparatus, device, communication system, and storage medium

Abstract

The present invention relates to the technical field of communications, and in particular to a positioning measurement method, an apparatus, a device, a communication system, and a storage medium. The positioning measurement method comprises: a first node determines to use artificial intelligence (AI)-assisted positioning measurement; and the first node sends a first message to a second node, wherein the first message comprises first information, and the first information is used for indicating a result of AI-assisted positioning measurement. On the basis of the present invention, when an AI model has been deployed on a centralized node, a positioning measurement result obtained at a multi-TRP can be inputted into the AI model, and a reasoning result output by the AI model is obtained, thereby achieving AI-assisted positioning measurement; and thus the position of the terminal can be calculated on the basis of an AI-assisted positioning result, thereby improving the accuracy of terminal positioning.

Description

Positioning measurement method, device, equipment, communication system and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a positioning measurement method, apparatus, device, communication system, and storage medium. Background Art

[0002] The widespread adoption of 5G technology is bringing tremendous changes to every aspect of people's lives. According to the International Telecommunication Union (ITU), 5G will permeate every area of ​​future society, building a comprehensive, user-centric information ecosystem. 5G user experience rates can reach 100Mbit / s to 1Gbit / s, enabling premium services like mobile virtual reality. 5G peak rates can reach 10Gbit / s to 20Gbit / s, with traffic density reaching 10Mbit / s / m², supporting future growth in mobile traffic exceeding a thousandfold. 5G connection density can reach 1 million connections / m², effectively supporting massive IoT devices. 5G transmission latency can reach milliseconds, meeting the stringent requirements of the Internet of Vehicles and industrial control. 5G can support mobile speeds of 500km / h, ensuring a superior user experience in high-speed rail environments. Thus, 5G, as a representative of new infrastructure, will reshape the future information society.

[0003] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in numerous fields. The continued development of fields such as intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home living, healthcare, retail, security, and other fields. This has brought convenience to people's lives while also promoting industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines, integrating knowledge from different disciplines while also providing new directions and methods for their development.

[0004] In 3GPP Release 18, a research project on artificial intelligence technology in wireless air interfaces was established within RAN1. This project aims to study how to introduce artificial intelligence technology into wireless air interfaces and explore how artificial intelligence technology can assist in improving wireless air interface transmission technologies.

[0005] For example, application cases of artificial intelligence may include but are not limited to: AI-based channel state information (CSI) enhancement, AI-based beam management, AI-based positioning, etc.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure propose positioning measurement methods, devices, equipment, communication systems, and storage media to solve the technical problem of how to implement AI-assisted positioning measurement when the AI ​​model is deployed on a centralized node.

[0008] According to a first aspect of an embodiment of the present disclosure, a positioning measurement method is provided, which is performed by a first node. The method includes:

[0009] Determine the use of artificial intelligence (AI)-assisted positioning measurements;

[0010] Send a first message to the second node, where the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0011] According to a second aspect of an embodiment of the present disclosure, a positioning measurement method is provided, which is performed by a second node. The method includes:

[0012] Receive a first message sent by a first node, where the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0013] According to a third aspect of the embodiments of the present disclosure, a first device is provided, comprising:

[0014] a processing module for determining positioning measurements using AI assistance;

[0015] A transceiver module is used to send a first message to the second node, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0016] According to a fourth aspect of the embodiments of the present disclosure, a second device is provided, comprising:

[0017] A transceiver module is configured to receive a first message sent by a first node, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0018] According to a fifth aspect of an embodiment of the present disclosure, a first device is proposed, comprising: one or more processors; wherein the first device is used to execute the positioning measurement method of the first aspect above.

[0019] According to a sixth aspect of an embodiment of the present disclosure, a second device is proposed, comprising: one or more processors; wherein the second device is used to execute the positioning measurement method of the second aspect above.

[0020] According to the seventh aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the processor is used to call instructions so that the communication device executes the positioning measurement method of the first aspect above, and / or the positioning measurement method of the second aspect above.

[0021] According to an eighth aspect of an embodiment of the present disclosure, a communication system is proposed, comprising a first device and a second device, wherein the first device is configured to implement the positioning measurement method of the first aspect, and the second device is configured to implement the positioning measurement method of the second aspect.

[0022] According to a ninth aspect of an embodiment of the present disclosure, a storage medium is proposed, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the positioning measurement method of the first aspect and / or the positioning measurement method of the second aspect.

[0023] According to an embodiment of the present disclosure, after determining to use AI-assisted positioning measurement, the first node can indicate the AI-assisted positioning measurement result to the second node by sending a first message to the second node. Accordingly, when the AI ​​model is deployed on a centralized node, it can support the input of positioning measurement results obtained on multi-TRP into the AI ​​model and obtain the inference result output by the AI ​​model, thereby realizing AI-assisted positioning measurement, and then the terminal's position can be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0026] FIG2 is an interactive schematic diagram illustrating a positioning measurement method according to an embodiment of the present disclosure.

[0027] FIG3A is a schematic diagram showing an AI-assisted positioning architecture according to an embodiment of the present disclosure.

[0028] FIG3B is a schematic diagram showing another AI-assisted positioning architecture according to an embodiment of the present disclosure.

[0029] FIG4 is a schematic flowchart showing a positioning measurement method according to an embodiment of the present disclosure.

[0030] FIG5 is a schematic flowchart showing another positioning measurement method according to an embodiment of the present disclosure.

[0031] FIG6 is an interactive schematic diagram showing a positioning measurement method according to an embodiment of the present disclosure.

[0032] FIG7 is an interactive schematic diagram showing another positioning measurement method according to an embodiment of the present disclosure.

[0033] FIG8 is a schematic block diagram showing a first device according to an embodiment of the present disclosure.

[0034] FIG9 is a schematic block diagram showing a second device according to an embodiment of the present disclosure.

[0035] FIG10 is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0036] FIG11 is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] Embodiments of the present disclosure provide a positioning measurement method, apparatus, device, communication system, and storage medium.

[0038] In a first aspect, an embodiment of the present disclosure proposes a positioning measurement method, which is performed by a first node. The method includes: determining to use AI-assisted positioning measurement; sending a first message to a second node, wherein the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0039] In the above embodiment, after determining to use AI-assisted positioning measurement, the first node can indicate the AI-assisted positioning measurement result to the second node by sending a first message to the second node. Accordingly, when the AI ​​model is deployed on a centralized node, it can support the input of positioning measurement results obtained on the multi-TRP into the AI ​​model and obtain the inference result output by the AI ​​model, thereby implementing AI-assisted positioning measurement. The terminal's position can then be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning.

[0040] In combination with some embodiments of the first aspect, in some embodiments, determining to use AI-assisted positioning measurement includes: receiving a second message sent by the second node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement; and determining to use AI-assisted positioning measurement based on the second information.

[0041] In combination with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following: an AI-assisted positioning measurement indication; and positioning measurement requirement information for requesting input information of an AI model.

[0042] In combination with some embodiments of the first aspect, in some embodiments, determining to use AI-assisted positioning measurement includes: determining to use AI-assisted positioning measurement based on implementation.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: determining positioning measurement requirement information, wherein the positioning measurement requirement information is used to request input information of the AI ​​model; and performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the manner of determining the positioning measurement requirement information includes at least one of the following: determining the positioning measurement requirement information according to second information in a second message sent by the second node; or determining the positioning measurement requirement information based on implementation.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the first node includes a first child node and a second child node;

[0046] The determining the positioning measurement requirement information includes: the first sub-node determining the positioning measurement requirement information, and sending the positioning measurement requirement information to the second sub-node;

[0047] The performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model includes: the second sub-node performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model, and sending the input information of the AI ​​model to the first sub-node.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the positioning measurement requirement information includes at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0049] In combination with some embodiments of the first aspect, in some embodiments, the input information of the AI ​​model includes at least one of the following: a TRP identifier; an AI model identifier; a CIR measurement result; a PDP measurement result; a DP measurement result; or a traditional positioning measurement result.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, an AI model is deployed on the first node, and the first information includes output information of the AI ​​model; the method further includes: performing AI model inference based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0051] In the above embodiment, the gNB-CU can request the gNB-DU to perform corresponding positioning measurements by sending positioning measurement request information to the gNB-DU, thereby obtaining input information for the AI ​​model. The gNB-CU can then perform AI model inference based on the AI ​​model input information sent by the gNB-DU, obtaining AI model output information. Consequently, when the AI ​​model is deployed on the gNB-CU, it can support inputting positioning measurement results obtained over multi-TRPs into the AI ​​model and obtaining inference results output by the AI ​​model, thereby implementing AI-assisted positioning measurements. The terminal's position can then be calculated based on the AI-assisted positioning results, thereby improving terminal positioning accuracy.

[0052] In combination with some embodiments of the first aspect, in some embodiments, an AI model is deployed on the second node, and the first information includes input information of the AI ​​model.

[0053] In the above embodiment, the gNB-CU can request the gNB-DU to perform corresponding positioning measurements to obtain input information for the AI ​​model by sending positioning measurement request information to the gNB-DU. The gNB-CU can then feed the AI ​​model input information back to the LMF, allowing the LMF to perform AI model inference based on the AI ​​model input information sent by the gNB-CU and obtain AI model output information. Accordingly, when the AI ​​model is deployed on the LMF, it can support inputting positioning measurement results obtained on multi-TRPs into the AI ​​model and obtaining inference results output by the AI ​​model, thereby implementing AI-assisted positioning measurement. The terminal's position can then be calculated based on the AI-assisted positioning results, thereby improving terminal positioning accuracy.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, the first node includes an access network node, and the second node includes a core network node.

[0055] In combination with some embodiments of the first aspect, in some embodiments, the first sub-node comprises a centralized unit in an access network node, and the second sub-node comprises a distributed unit in the access network node.

[0056] In a second aspect, an embodiment of the present disclosure proposes a positioning measurement method, which is performed by a second node. The method includes: receiving a first message sent by a first node, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0057] In the above embodiment, after determining to use AI-assisted positioning measurement, the first node can indicate the AI-assisted positioning measurement result to the second node by sending a first message to the second node. Accordingly, when the AI ​​model is deployed on a centralized node, it can support the input of positioning measurement results obtained on the multi-TRP into the AI ​​model and obtain the inference result output by the AI ​​model, thereby implementing AI-assisted positioning measurement. The terminal's position can then be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning.

[0058] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending a second message to the first node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following: an AI-assisted positioning measurement indication; and positioning measurement requirement information for requesting input information of an AI model.

[0060] In conjunction with some embodiments of the second aspect, in some embodiments, the positioning measurement requirement information includes at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the input information of the AI ​​model includes at least one of the following: a TRP identifier; an AI model identifier; a CIR measurement result; a PDP measurement result; a DP measurement result; or a traditional positioning measurement result.

[0062] In conjunction with some embodiments of the second aspect, in some embodiments, an AI model is deployed on the first node, the first information includes output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0063] In conjunction with some embodiments of the second aspect, in some embodiments, an AI model is deployed on the second node, and the first information includes input information of the AI ​​model; the method further includes: performing AI model inference based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0064] In conjunction with some embodiments of the second aspect, in some embodiments, the first node includes an access network node, and the second node includes a core network node.

[0065] In a third aspect, an embodiment of the present disclosure provides a first device, comprising:

[0066] a processing module for determining positioning measurements using AI assistance;

[0067] A transceiver module is used to send a first message to the second device, wherein the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0068] In a fourth aspect, an embodiment of the present disclosure provides a second device, comprising:

[0069] A transceiver module is used to receive a first message sent by a first device, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0070] In a fifth aspect, an embodiment of the present disclosure proposes a first device, comprising: one or more processors; wherein the first device is used to execute the positioning measurement method described in the first aspect and the optional embodiment of the first aspect.

[0071] In a sixth aspect, an embodiment of the present disclosure proposes a second device, comprising: one or more processors; wherein the second device is used to execute the positioning measurement method described in the second aspect and the optional embodiment of the second aspect.

[0072] In the seventh aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; one or more memories for storing instructions; wherein the processor is used to call the instructions so that the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0073] In the eighth aspect, an embodiment of the present disclosure proposes a communication system, which includes: a first device and a second device; wherein the first device is configured to execute the method described in the first and second aspects, and the optional embodiments of the first and second aspects, and the second device is configured to execute the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0074] In the ninth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0075] In a tenth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0076] In an eleventh aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the methods described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0077] It is understandable that the first device, the second device, the communication device, the communication system, the storage medium, the program product, and the computer program are all used to perform the method proposed in the embodiment of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding method and will not be repeated here.

[0078] The present disclosure provides positioning measurement methods, devices, equipment, communication systems, and storage media. In some embodiments, the terms positioning measurement method, information processing method, and communication method are interchangeable; the terms first device, second device, positioning measurement device, and communication device are interchangeable; and the terms positioning measurement system, communication system, and so on are interchangeable.

[0079] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional embodiments in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional embodiments of other embodiments.

[0080] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0081] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0082] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc.

[0083] For example, when using articles such as “a”, “an”, and “the” in English in translation, the noun following the article can be understood as a singular expression or a plural expression.

[0084] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0085] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0086] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0087] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0088] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restrictions on the position, order, priority, quantity or content of the description objects. For the statement of the description objects, please refer to the description in the context of the claims or embodiments, and no unnecessary restrictions should be constituted due to the use of prefixes.

[0089] For example, if the description object is "field," the ordinal number preceding "field" in "first field" and "second field" does not restrict the position or order of the "fields." "First" and "second" do not restrict whether the modified "fields" are in the same message, nor do they restrict the order of the "first field" and "second field." For another example, if the description object is "level," the ordinal number preceding "level" in "first level" and "second level" does not restrict the priority of the "levels." For another example, the number of description objects is not restricted by ordinal numbers and can be one or more. For example, in the case of "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the description object is "device," "first device" and "second device" can be the same or different devices, and their types can be the same or different. For another example, if the description object is "information," "first information" and "second information" can be the same or different information, and their content can be the same or different.

[0090] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0091] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0092] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0093] In some embodiments, devices and the like can be interpreted as physical or virtual, and their names are not limited to those in the embodiments.

[0094] The recorded names, "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and other terms can be used interchangeably.

[0095] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0096] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0097] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0098] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0099] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0100] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0101] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0102] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0103] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0104] As shown in FIG1 , a communication system 100 includes an access network device 101 and a core network device 102 .

[0105] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0106] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0107] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0108] In some embodiments, the communication system 100 further includes a terminal, which can communicate with the access network device 101 and can also communicate with the core network device 102 through the access network device 101 .

[0109] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0110] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0111] In some embodiments, each DU may have one or more Transmission and Receiving Points (TRPs), positioning measurements may be performed on each TRP, and the positioning measurement results obtained on each TRP may be used to calculate the position of the terminal.

[0112] In some embodiments, the core network device may include a location management function (LMF). The LMF may be used to locate the terminal.

[0113] In some embodiments, in an artificial intelligence (AI)-assisted positioning architecture, the AI ​​model can be deployed on the CU or LMF of the access network device.

[0114] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0115] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0116] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0117] FIG2 is an interactive schematic diagram illustrating a positioning measurement method according to an embodiment of the present disclosure.

[0118] As shown in Figure 2, the positioning measurement method includes:

[0119] In step S201 , the first node determines to use AI-assisted positioning measurement.

[0120] In some embodiments, the first node may include an access network device 101, and the second node may include a core network device 102. In one possible implementation, an AI model is deployed on the core network device 102.

[0121] In some embodiments, the first node may include a first subnode and a second subnode, wherein the first subnode may be a CU in the access network device 101, and the second subnode may be a DU in the access network device 101. In one possible implementation, an AI model is deployed on the CU in the access network device 101.

[0122] In some embodiments, the first node may determine to use AI-assisted positioning measurement based on an instruction from the second node, or may determine AI-assisted positioning measurement based on its own implementation.

[0123] In one possible implementation, the second node may decide to use AI-assisted positioning measurement. In this case, the first node may receive a second message sent by the second node, where the second message includes second information for requesting AI-assisted positioning measurement; and determine to use AI-assisted positioning measurement based on the second information. The second message may specifically include, but is not limited to, an NRPPa measurement request message, an NRPPa measurement request message, an NRPPa AI measurement request message, etc.

[0124] In some other possible implementations, the first node (e.g., gNB-CU) may decide to use AI-assisted positioning measurements. In this case, the first node may determine to use AI-assisted positioning measurements based on implementation.

[0125] In some embodiments, the second information may include at least one of the following: an AI-assisted positioning measurement indicator; positioning measurement requirement information for requesting input information of the AI ​​model.

[0126] In some embodiments, the positioning measurement requirement information may include at least one of the following: a requested TRP identifier; an AI model identifier; a channel impulse response (CIR) measurement request; a power delay profile (PDP) measurement request; a delay profile (DP) measurement request; or a traditional positioning measurement request.

[0127] Among them, the traditional positioning measurement request may specifically include but is not limited to: base station receive and transmit time difference (gNB-RxTxTimeDiff) measurement request, uplink sounding reference signal received power (UL-SRS-RSRP) measurement request, uplink angle of arrival (Uplink Angles of Arrival, UL-AoA) measurement request, uplink relative time of arrival (Uplink Relative Time of Arrival, UL-RTOA) measurement request, multiple uplink angles of arrival (Multiple UL-AoA) measurement request, uplink sounding reference signal reference signal received path power (UL SRS Reference Signal Received Path Power, UL SRS-RSRPP) measurement request, etc.

[0128] In some embodiments, the input information of the AI ​​model may include at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0129] Among them, the traditional positioning measurement results may specifically include but are not limited to: gNB-RxTxTimeDiff measurement results, UL-SRS-RSRP measurement results, UL-AoA measurement results, UL-RTOA measurement results, Multiple UL-AoA measurement results, UL SRS-RSRPP measurement results, etc.

[0130] In some embodiments, the method further includes: determining positioning measurement requirement information.

[0131] In some possible implementations, the first node may determine the positioning measurement requirement information based on an instruction from the second node. In this case, the method for determining the positioning measurement requirement information may include: determining the positioning measurement requirement information based on second information in a second message sent by the second node.

[0132] In some other possible implementations, the first node may determine the positioning measurement requirement information based on its own implementation. In this case, the manner of determining the positioning measurement requirement information includes: determining the positioning measurement requirement information based on implementation (by implementation).

[0133] In some embodiments, the first subnode determines positioning measurement requirement information and sends the positioning measurement requirement information to the second subnode; the second subnode performs positioning measurement according to the positioning measurement requirement information, obtains input information of the AI ​​model, and sends the input information of the AI ​​model to the first subnode.

[0134] In some possible implementations, the first subnode may send the positioning measurement requirement information to the second subnode via a fourth message, wherein the fourth message may be an F1AP AI measurement request message, which may specifically include but is not limited to an F1AP measurement request message.

[0135] In some possible implementations, the second subnode may send the AI ​​model input information to the first subnode via a fifth message. The fifth message may be an F1AP AI measurement feedback message, which may include but is not limited to an F1AP measurement response message, an F1AP measurement report message, or an F1AP AI measurement report message.

[0136] In one possible implementation, the second sub-node may first process the positioning measurement result, and then send the processed positioning result to the first sub-node as input information of the AI ​​model.

[0137] Step S202: The first node sends a first message to the second node.

[0138] In some embodiments, the second node receives the first message sent by the first node.

[0139] In some embodiments, the first message may include first information, which may be used to indicate the AI-assisted positioning measurement result.

[0140] In some embodiments, the first message may be an NRPPa-based measurement feedback message, wherein the NRPPa-based measurement feedback message may specifically include but is not limited to: an NRPPa measurement response message, an NRPPa measurement report message, an NRPPa AI positioning measurement feedback message, an NRPPa AI positioning measurement report message, etc.

[0141] In some embodiments, an AI model is deployed on the first node, and the first information includes output information of the AI ​​model. The method further includes: performing AI model inference based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0142] In some embodiments, an AI model is deployed on the second node, and the first information includes input information of the AI ​​model. The second node performs AI model inference based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0143] The communication method involved in the embodiments of the present disclosure may include at least one of steps S201 to 202. For example, step S201 may be implemented as an independent embodiment, step S202 may be implemented as an independent embodiment, and steps S201+S202 may be implemented as independent embodiments, but are not limited thereto.

[0144] In some embodiments, steps S201 and S202 may be performed in an interchangeable order or simultaneously.

[0145] In some embodiments, step S201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0146] In some embodiments, step S202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0147] In some embodiments, reference may be made to other optional embodiments described before or after the description corresponding to FIG. 2 .

[0148] In some embodiments, for the Next Generation Radio Access Network (NG-RAN), three artificial intelligence (AI)-based positioning architectures can be considered: AI-assisted positioning, where the AI ​​model is deployed on distributed nodes; AI-assisted positioning, where the AI ​​model is deployed on centralized nodes; and direct UE positioning.

[0149] It should be noted that the AI ​​model involved in this disclosure can also be described as a machine learning model (ML Model), model, etc., and this disclosure does not specifically limit this.

[0150] In one possible implementation, Figure 3A is a schematic diagram of an AI-assisted positioning architecture according to an embodiment of the present disclosure, wherein the AI ​​model is deployed on a centralized node (e.g., a gNB-CU).

[0151] As shown in Figure 3A, in a Central Unit-Distributed Unit (CU-DU) split architecture, an AI model can be deployed on the gNB-CU. Each distributed node (e.g., a gNB-DU) can have one or more Transmission and Receiving Points (TRPs). Measurement results from multiple TRPs (multi-TRPs) serve as input to the same or identical AI model deployed on the gNB-CU. The AI ​​model performs inference based on the input information and outputs inference results.

[0152] It's important to note that in a CU-DU separation architecture, the CU and DU are physically separated and deployed in different entities. In traditional wireless communication networks, the CU and DU are typically integrated into a single entity (such as a base station). By separating the CU and DU, the CU can centrally handle control plane tasks, optimizing network resources. At the same time, the DU can be closer to the terminal, reducing signal transmission latency. Therefore, the CU-DU separation architecture improves network performance, scalability, and reliability, laying the foundation for next-generation wireless communication networks such as 5G and 6G.

[0153] In another possible implementation, Figure 3B is a schematic diagram of another AI-assisted positioning architecture according to an embodiment of the present disclosure, wherein the AI ​​model is deployed on a centralized node (e.g., a location management function (LMF)).

[0154] As shown in Figure 3B, in a CU-DU split architecture, the AI ​​model can be deployed on the LMF. Each distributed node (e.g., gNB-DU) can have one or more Transmission Reception Points (TRPs). Measurement results from multiple transmission reception points (multi-TRPs) are applied as input to the same AI model deployed on the LMF. The AI ​​model can perform inference based on the input information and output the inference results.

[0155] It should be noted that the “AI model deployed on a centralized node” involved in this disclosure can also be described as “the AI ​​model is centrally deployed on the node”, “an AI model is deployed on the centralized node”, “one or more AI models are deployed on the centralized node”, etc. This disclosure does not impose any restrictions on this.

[0156] However, there are still some technical problems. For example, when the AI ​​model is deployed on a centralized node, how to implement AI-assisted positioning measurement, how to support the positioning measurement results on multiple different TRPs as input to the AI ​​model deployed on the centralized node, etc.

[0157] In a first aspect, embodiments of the present disclosure provide a positioning measurement method. Figure 4 is a schematic flow chart illustrating a positioning measurement method according to an embodiment of the present disclosure. The positioning measurement method illustrated in this embodiment may be executed by a first node.

[0158] In some embodiments, the first node may include an access network node, and the second node may include a core network node.

[0159] For example, as shown in Figure 3A, the first node may be a Next Generation Radio Access Network node (NG-RAN node), and the second node may be a Location Management Function (LMF); wherein an AI model is deployed on the NG-RAN node.

[0160] For another example, as shown in FIG3B , the first node may be an NG-RAN node, and the second node may be an LMF; wherein an AI model is deployed on the LMF.

[0161] In some embodiments, the first node may include a first sub-node and a second sub-node, wherein the first sub-node may include a centralized unit in the access network node, and the second sub-node may include a distributed unit in the access network node.

[0162] For example, as shown in Figure 3A, the NG-RAN node may include gNB-CU and distributed nodes such as gNB-DU1 and gNB-DU2; among which, an AI model is deployed on the gNB-CU.

[0163] It should be noted that the “first node”, “second node”, “first sub-node” and “second sub-node” can distinguish different nodes participating in the positioning measurement process, and do not represent any special limitation on the present disclosure.

[0164] As shown in FIG4 , the positioning measurement method may include the following steps:

[0165] In step S401 , it is determined to use AI-assisted positioning measurement.

[0166] In step S402, a first message is sent to the second node, where the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0167] For example, the NG-RAN node may determine to use AI-assisted positioning measurement; further, the NG-RAN node may send a measurement feedback message based on the New Radio Positioning Protocol A (NR Positioning Protocol A, NRPPa) to the LMF, wherein the measurement feedback message based on NRPPa may include first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0168] The NRPPa-based measurement feedback message may specifically include but is not limited to: an NRPPa measurement response message, an NRPPa measurement report message, an NRPPa AI positioning measurement feedback message, an NRPPa AI positioning measurement report message, and the like.

[0169] It should be noted that the embodiment shown in FIG. 4 can be implemented independently or in combination with at least one other embodiment in the present disclosure. The specific selection can be made as needed and the present disclosure does not limit it.

[0170] Optionally, the second node may receive a first message sent by the first node, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0171] According to an embodiment of the present disclosure, after determining to use AI-assisted positioning measurement, the first node can indicate the AI-assisted positioning measurement result to the second node by sending a first message to the second node. Accordingly, when the AI ​​model is deployed on a centralized node, it can support the input of positioning measurement results obtained on multi-TRP into the AI ​​model and obtain the inference result output by the AI ​​model, thereby realizing AI-assisted positioning measurement, and then the terminal's position can be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning.

[0172] In some embodiments, in step S401, the first node may determine to use AI-assisted positioning measurement according to an instruction of the second node, or may determine AI-assisted positioning measurement based on its own implementation.

[0173] In one possible implementation, the second node may decide to use AI-assisted positioning measurement. In this case, the determination to use AI-assisted positioning measurement may include: receiving a second message sent by the second node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement; and determining to use AI-assisted positioning measurement based on the second information.

[0174] Optionally, the second node sends a second message to the first node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

[0175] The second message may specifically include but is not limited to: NRPPa measurement request message, NRPPa measurement request message, NRPPa AI measurement request message, etc.

[0176] The second information may be, for example, AI-assisted positioning measurement request information (AI-assisted POS request).

[0177] The second information may include at least one of the following: an AI-assisted positioning measurement indicator; positioning measurement requirement information, used to request input information of the AI ​​model.

[0178] For example, the LMF may decide to use AI-assisted positioning measurement and may send a second message to the NG-RAN node, wherein the second message may include an AI-assisted positioning measurement indication and / or positioning measurement requirement information; further, the NG-RAN node may receive the second message sent by the LMF and may determine to use AI-assisted positioning measurement based on the AI-assisted positioning measurement indication and / or positioning measurement requirement information.

[0179] In the above embodiment, the LMF may only send the AI-assisted positioning measurement indication to the NG-RAN node, and the NG-RAN node may determine the input requirements of the AI ​​model on its own. Alternatively, the LMF may directly send the input requirements of the AI ​​model to the NG-RAN node.

[0180] In some other possible implementations, the first node (e.g., gNB-CU) may decide to use AI-assisted positioning measurement. In this case, the determination to use AI-assisted positioning measurement includes: determining to use AI-assisted positioning measurement based on implementation.

[0181] It should be noted that the "determining the use of AI-assisted positioning measurements based on implementation" referred to in this disclosure can also be described as "determining the use of AI-assisted positioning measurements based on at least one of the following information: configuration in the node; computing power of the node; energy consumption of the node; or load of the node." The configuration in the node can be pre-configured or configured by Operation Administration and Maintenance (OAM).

[0182] For example, the gNB-CU can determine whether to use AI-assisted positioning measurements based on the Operation Administration and Maintenance (OAM) configuration.

[0183] Optionally, the second node may send a third message to the first node, the third message including third information, the third information being used to request a traditional positioning measurement. The first node may receive the third message sent by the second node and determine, based on the third information, that positioning measurements need to be performed. Furthermore, the first node may determine, based on the OAM configuration, to use AI-assisted positioning measurements.

[0184] The third message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa measurement request message, etc. The third message does not include an AI-assisted positioning measurement indication and / or positioning measurement requirement information.

[0185] In some embodiments, the positioning measurement requirement information may include at least one of the following: a requested TRP identifier; an AI model identifier; a channel impulse response (CIR) measurement request; a power delay profile (PDP) measurement request; a delay profile (DP) measurement request; or a traditional positioning measurement request.

[0186] Among them, the requested TRP identifier is used to indicate the TRP requested among multiple TRPs. The AI ​​model identifier can be used to indicate the AI ​​model to which the requested positioning measurement result is applied. The DP measurement request may include configured threshold information, and the threshold information may be absolute threshold information or relative threshold information. Traditional positioning measurement requests may specifically include but are not limited to: base station transmit and receive time difference (gNB-RxTxTimeDiff) measurement request, uplink sounding reference signal received power (UL-SRS-RSRP) measurement request, uplink arrival angle (Uplink Angles of Arrival, UL-AoA) measurement request, uplink relative arrival time (Uplink Relative Time of Arrival, UL-RTOA) measurement request, multiple uplink arrival angles (Multiple UL-AoA) measurement request, uplink sounding reference signal reference signal received path power (UL SRS Reference Signal Received Path Power, UL SRS-RSRPP) measurement request, etc.

[0187] In some embodiments, the input information (AI input) of the AI ​​model may include at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0188] The TRP identifier is used to indicate that the input information of the AI ​​model comes from a specific TRP among multiple TRPs. The AI ​​model identifier is used to indicate that the input information of the AI ​​model is for a specific AI model. The CIR measurement result may include time information, amplitude information (including real and imaginary amplitude information), and / or phase information corresponding to the sampling point. The PDP measurement result may include sampling time information and / or amplitude information. The DP measurement result may include time information and / or threshold information, where the threshold information may be absolute threshold information or relative threshold information. Traditional positioning measurement results may specifically include, but are not limited to: gNB-RxTxTimeDiff measurement result, UL-SRS-RSRP measurement result, UL-AoA measurement result, UL-RTOA measurement result, multiple UL-AoA measurement result, UL SRS-RSRPP measurement result, etc.

[0189] In some embodiments, before step S402, the method may further include: determining positioning measurement requirement information, wherein the positioning measurement requirement information is used to request input information of the AI ​​model; performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model.

[0190] In some possible implementations, the first node may determine the positioning measurement requirement information based on an instruction from the second node. In this case, the method for determining the positioning measurement requirement information may include: determining the positioning measurement requirement information based on second information in a second message sent by the second node.

[0191] For example, the NG-RAN node can receive a second message sent by the LMF, and the second message includes positioning measurement requirement information. The NG-RAN node can perform positioning measurement according to the positioning measurement requirement information included in the second message to obtain input information of the AI ​​model.

[0192] In some other possible implementations, the first node may determine the positioning measurement requirement information based on its own implementation. In this case, the manner of determining the positioning measurement requirement information includes: determining the positioning measurement requirement information based on implementation.

[0193] It should be noted that the phrase "determining positioning measurement requirement information based on implementation" in this disclosure can also be described as "determining positioning measurement requirement information based on at least one of the following information: AI model requirements; node configuration." The node configuration can be pre-configured or configured by Operation Administration and Maintenance (OAM).

[0194] For example, the NG-RAN node can receive a second message sent by the LMF, where the second message only includes an AI-assisted positioning measurement indication but does not include positioning measurement requirement information. Alternatively, the NG-RAN node can receive a third message sent by the LMF and decide to use AI-assisted positioning measurement based on the OAM configuration. The NG-RAN node can then determine the positioning measurement requirement information based on the OAM configuration, and perform positioning measurement based on the positioning measurement requirement information to obtain input information of the AI ​​model.

[0195] In some other possible implementations, the first node may determine the positioning measurement requirement information by combining the two aforementioned methods, which are not further described here. For example, if an AI model is deployed on the gNB-CU, the gNB-CU may not only obtain the first positioning measurement requirement information indicated by the second information in the second message, but may also determine the second positioning measurement requirement information based on its own implementation. The gNB-CU may then adjust the second positioning measurement requirement information based on the first positioning measurement requirement information and request AI model input information from the gNB-DU based on the adjusted second positioning measurement requirement information.

[0196] In some embodiments, under the CU-DU separation architecture, the first node includes a first sub-node and a second sub-node. In this case, determining the positioning measurement requirement information includes: the first sub-node determines the positioning measurement requirement information and sends the positioning measurement requirement information to the second sub-node; performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model includes: the second sub-node performs positioning measurement according to the positioning measurement requirement information, obtains the input information of the AI ​​model, and sends the input information of the AI ​​model to the first sub-node.

[0197] For example, after the gNB-CU determines to use AI-assisted positioning measurement and determines positioning measurement requirement information, it may send a fourth message to the gNB-DU, where the fourth message includes the positioning measurement requirement information; further, the gNB-DU may perform positioning measurement based on the positioning measurement requirement information sent by the gNB-CU to obtain input information for the AI ​​model; further, the gNB-DU may send a fifth message to the gNB-CU, where the fifth message may include input information for the AI ​​model (AI inputs request).

[0198] The fourth message may be an F1AP AI measurement request message, which may specifically include but is not limited to an F1AP measurement request message, etc. The positioning measurement requirement information included in the fourth message may be, for example, AI input request information (AI inputs request).

[0199] The fifth message may be an F1AP AI measurement feedback message, which may specifically include, but is not limited to, an F1AP measurement response message, an F1AP measurement report message, and an F1AP AI measurement report message. In one possible implementation, the gNB-DU may first process the positioning measurement results and then send the processed positioning results as input information to the gNB-CU. For example, the input information to the AI ​​model sent by the gNB-DU to the gNB-CU may be eigenvalues ​​processed by singular value decomposition (SVD), but is not limited thereto.

[0200] In some embodiments, an AI model is deployed on the first node, and the first information includes output information of the AI ​​model; the method further includes: performing AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0201] For example, as shown in Figure 3A, an AI model is deployed on the gNB-CU; the gNB-CU can receive the input information of the AI ​​model sent by the gNB-DU, and can perform AI model inference based on the received AI model input information to obtain the output information (AI outputs) of the AI ​​model; further, the gNB-CU can also use the output information of the AI ​​model as the first information and send a first message including the first information to the LMF; the LMF can calculate the position of the terminal based on the output information of the AI ​​model included in the first message, that is, the LMF can locate the terminal based on the output information of the AI ​​model included in the first message.

[0202] In the above embodiment, the gNB-CU sends positioning measurement request information to the gNB-DU, requesting the gNB-DU to perform corresponding positioning measurements to obtain AI model input information. The gNB-CU then performs AI model inference based on the AI ​​model input information sent by the gNB-DU, obtaining AI model output information. Consequently, when the AI ​​model is deployed on the gNB-CU, it can support inputting positioning measurement results obtained over multi-TRPs into the AI ​​model and obtaining inference results from the AI ​​model output, thereby implementing AI-assisted positioning measurements. The terminal's position can then be calculated based on the AI-assisted positioning results, thereby improving terminal positioning accuracy.

[0203] In some embodiments, an AI model is deployed on the second node, and the first information includes input information of the AI ​​model.

[0204] Optionally, the second node may perform AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0205] For example, as shown in Figure 3B, an AI model is deployed on the LMF; the gNB-CU can receive the input information of the AI ​​model sent by the gNB-DU, and can use the input information of the AI ​​model as the first information to send a first message including the first information to the LMF; the LMF can perform AI model inference based on the input information of the AI ​​model included in the first message to obtain output information of the AI ​​model; the LMF can also calculate the position of the terminal based on the output information of the AI ​​model obtained by inference, that is, the LMF can locate the terminal based on the output information of the AI ​​model.

[0206] In the above embodiment, the gNB-CU can request the gNB-DU to perform corresponding positioning measurements to obtain AI model input information by sending positioning measurement request information to the gNB-DU. The gNB-CU can then feed the AI ​​model input information back to the LMF, allowing the LMF to perform AI model inference based on the AI ​​model input information sent by the gNB-CU and obtain AI model output information. Accordingly, when the AI ​​model is deployed on the LMF, it can support inputting positioning measurement results obtained on multi-TRPs into the AI ​​model and obtaining AI model inference results, thereby implementing AI-assisted positioning measurement. The terminal's position can then be calculated based on the AI-assisted positioning results, which helps improve terminal positioning accuracy.

[0207] In a second aspect, embodiments of the present disclosure provide a positioning measurement method. Figure 5 is a schematic flow chart illustrating another positioning measurement method according to an embodiment of the present disclosure. The positioning measurement method illustrated in this embodiment may be executed by a second node.

[0208] As shown in FIG5 , the positioning measurement method may include the following steps:

[0209] In step S501, a first message sent by a first node is received, where the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0210] For example, the first node may be an NG-RAN node, and the second node may be an LMF; the LMF may receive an NRPPa-based measurement feedback message sent by the NG-RAN node, wherein the NRPPa-based measurement feedback message may include first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0211] The NRPPa-based measurement feedback message may specifically include but is not limited to: an NRPPa measurement response message, an NRPPa measurement report message, an NRPPa AI positioning measurement feedback message, an NRPPa AI positioning measurement report message, and the like.

[0212] In some embodiments, the AI ​​model can be deployed on a centralized node, for example, the AI ​​model can be deployed on a gNB-CU or LMF.

[0213] It should be noted that the embodiment shown in FIG. 5 can be implemented independently or in combination with at least one other embodiment in the present disclosure. The specific selection can be made as needed and the present disclosure does not limit it.

[0214] Optionally, the first node may determine to use AI-assisted positioning measurements.

[0215] Optionally, the first node may send a first message to the second node, wherein the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0216] According to an embodiment of the present disclosure, after determining to use AI-assisted positioning measurement, the first node can indicate the AI-assisted positioning measurement result to the second node by sending a first message to the second node. Accordingly, when the AI ​​model is deployed on a centralized node, it can support the input of positioning measurement results obtained on multi-TRP into the AI ​​model and obtain the inference result output by the AI ​​model, thereby realizing AI-assisted positioning measurement, and then the terminal's position can be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning.

[0217] In some embodiments, the first node may determine to use AI-assisted positioning measurement based on an instruction from the second node, or may determine AI-assisted positioning measurement based on its own implementation.

[0218] In one possible implementation, the second node may decide to use AI-assisted positioning measurement. In this case, the method further includes: sending a second message to the first node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

[0219] Optionally, the first node may receive a second message sent by the second node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

[0220] The second message may specifically include but is not limited to: NRPPa measurement request message, NRPPa measurement request message, NRPPa AI measurement request message, etc.

[0221] The second information may be, for example, AI-assisted positioning measurement request information (AI-assisted POS request).

[0222] The second information may include at least one of the following: an AI-assisted positioning measurement indicator; positioning measurement requirement information, used to request input information of the AI ​​model.

[0223] For example, the LMF may decide to use AI-assisted positioning measurement and may send a second message to the NG-RAN node, wherein the second message may include an AI-assisted positioning measurement indication and / or positioning measurement requirement information; further, the NG-RAN node may receive the second message sent by the LMF and may determine to use AI-assisted positioning measurement based on the AI-assisted positioning measurement indication and / or positioning measurement requirement information.

[0224] In the above embodiment, the LMF may only send the AI-assisted positioning measurement indication to the NG-RAN node, and the NG-RAN node may determine the input requirements of the AI ​​model on its own. Alternatively, the LMF may directly send the input requirements of the AI ​​model to the NG-RAN node.

[0225] In some other possible implementations, the decision to use AI-assisted positioning measurements may be made by a first node (e.g., a gNB-CU), and the first node is expected to determine the use of AI-assisted positioning measurements based on the implementation. For example, the gNB-CU may determine the use of AI-assisted positioning measurements based on an OAM configuration. In this case, the method may further include: sending a third message to the first node, the third message including third information, the third information being used to request a traditional positioning measurement.

[0226] Optionally, the first node may receive a third message sent by the second node, and determine that positioning measurement needs to be performed based on the third information, and the first node may determine to use AI-assisted positioning measurement based on the configuration of OAM.

[0227] The third message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa measurement request message, etc. The third message does not include an AI-assisted positioning measurement indication and / or positioning measurement requirement information.

[0228] In some embodiments, the positioning measurement requirement information may include at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0229] The requested TRP identifier is used to indicate the requested TRP among multiple TRPs. The AI ​​model identifier can be used to indicate the AI ​​model to which the requested positioning measurement result is applied. The DP measurement request may include configured threshold information, which may be absolute threshold information or relative threshold information. Traditional positioning measurement requests may specifically include, but are not limited to: gNB-RxTxTimeDiff measurement request, UL-SRS-RSRP measurement request, UL-AoA measurement request, UL-RTOA measurement request, Multiple UL-AoA measurement request, UL SRS-RSRPP measurement request, etc.

[0230] In some embodiments, the input information of the AI ​​model (AI inputs) may include at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0231] The TRP identifier is used to indicate that the input information of the AI ​​model comes from a specific TRP among multiple TRPs. The AI ​​model identifier is used to indicate that the input information of the AI ​​model is for a specific AI model. The CIR measurement result may include time information, amplitude information (including real and imaginary amplitude information), and / or phase information corresponding to the sampling point. The PDP measurement result may include sampling time information and / or amplitude information. The DP measurement result may include time information and / or threshold information, where the threshold information may be absolute threshold information or relative threshold information. Traditional positioning measurement results may specifically include, but are not limited to: gNB-RxTxTimeDiff measurement result, UL-SRS-RSRP measurement result, UL-AoA measurement result, UL-RTOA measurement result, multiple UL-AoA measurement result, UL SRS-RSRPP measurement result, etc.

[0232] Optionally, the first node may determine positioning measurement requirement information. The first node may also perform positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model.

[0233] In some possible implementations, the first node may determine the positioning measurement requirement information according to an instruction of the second node. In this case, the first node may determine the positioning measurement requirement information according to second information in a second message sent by the second node.

[0234] For example, the NG-RAN node can receive a second message sent by the LMF, and the second message includes positioning measurement requirement information. The NG-RAN node can perform positioning measurement according to the positioning measurement requirement information included in the second message to obtain input information of the AI ​​model.

[0235] In some other possible implementations, the first node may determine the positioning measurement requirement information based on its own implementation. In this case, the first node may determine the positioning measurement requirement information based on the implementation.

[0236] For example, the NG-RAN node can receive a second message sent by the LMF, where the second message only includes an AI-assisted positioning measurement indication but does not include positioning measurement requirement information. Alternatively, the NG-RAN node can receive a third message sent by the LMF and decide to use AI-assisted positioning measurement based on the OAM configuration. The NG-RAN node can then determine the positioning measurement requirement information based on the OAM configuration, and perform positioning measurement based on the positioning measurement requirement information to obtain input information of the AI ​​model.

[0237] In some embodiments, under the CU-DU separation architecture, the first node includes a first sub-node and a second sub-node. In this case, the first sub-node determines positioning measurement requirement information and sends the positioning measurement requirement information to the second sub-node; the second sub-node performs positioning measurement according to the positioning measurement requirement information, obtains input information of the AI ​​model, and sends the input information of the AI ​​model to the first sub-node.

[0238] For example, after the gNB-CU determines to use AI-assisted positioning measurement and determines the positioning measurement requirement information, it can send the positioning measurement requirement information to the gNB-DU. Furthermore, the gNB-DU can perform positioning measurement based on the positioning measurement requirement information sent by the gNB-CU to obtain input information for the AI ​​model. Furthermore, the gNB-DU can send the input information for the AI ​​model to the gNB-CU.

[0239] In one possible implementation, the gNB-DU may first process the positioning measurement results and then send the processed positioning results to the gNB-CU as input to the AI ​​model. For example, the input to the AI ​​model sent by the gNB-DU to the gNB-CU may be eigenvalues ​​processed by SVD, but is not limited to this.

[0240] In some embodiments, an AI model is deployed on the first node, and the first information includes output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0241] Optionally, the first node may perform AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0242] For example, as shown in Figure 3A, an AI model is deployed on the gNB-CU; the gNB-CU can receive the input information of the AI ​​model sent by the gNB-DU, and can perform AI model inference based on the received AI model input information to obtain the output information (AI output) of the AI ​​model; further, the gNB-CU can also use the output information of the AI ​​model as the first information and send a first message including the first information to the LMF; the LMF can calculate the position of the terminal based on the output information of the AI ​​model included in the first message, that is, the LMF can locate the terminal based on the output information of the AI ​​model included in the first message.

[0243] In the above embodiment, the gNB-CU sends positioning measurement request information to the gNB-DU, requesting the gNB-DU to perform corresponding positioning measurements to obtain AI model input information. The gNB-CU then performs AI model inference based on the AI ​​model input information sent by the gNB-DU, obtaining AI model output information. Consequently, when the AI ​​model is deployed on the gNB-CU, it can support inputting positioning measurement results obtained over multi-TRPs into the AI ​​model and obtaining inference results from the AI ​​model output, thereby implementing AI-assisted positioning measurements. The terminal's position can then be calculated based on the AI-assisted positioning results, thereby improving terminal positioning accuracy.

[0244] In some embodiments, an AI model is deployed on the second node, and the first information includes input information of the AI ​​model. In this case, the method further includes: performing AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0245] For example, as shown in Figure 3B, an AI model is deployed on the LMF; the gNB-CU can receive the input information of the AI ​​model sent by the gNB-DU, and can use the input information of the AI ​​model as the first information to send a first message including the first information to the LMF; the LMF can perform AI model inference based on the input information of the AI ​​model included in the first message to obtain output information of the AI ​​model; the LMF can also calculate the position of the terminal based on the output information of the AI ​​model obtained by inference, that is, the LMF can locate the terminal based on the output information of the AI ​​model.

[0246] In the above embodiment, the gNB-CU can request the gNB-DU to perform corresponding positioning measurements to obtain AI model input information by sending positioning measurement request information to the gNB-DU. The gNB-CU can then feed the AI ​​model input information back to the LMF, allowing the LMF to perform AI model inference based on the AI ​​model input information sent by the gNB-CU and obtain AI model output information. Accordingly, when the AI ​​model is deployed on the LMF, it can support inputting positioning measurement results obtained on multi-TRPs into the AI ​​model and obtaining AI model inference results, thereby implementing AI-assisted positioning measurement. The terminal's position can then be calculated based on the AI-assisted positioning results, which helps improve terminal positioning accuracy.

[0247] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0248] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0249] In some embodiments, the terms "component carrier (CC)", "cell", "frequency carrier", "carrier frequency" and the like can be used interchangeably.

[0250] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0251] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0252] An optional embodiment of the method according to the first aspect is described below in conjunction with Figure 6. Figure 6 is an interactive schematic diagram showing a positioning measurement method according to an embodiment of the present disclosure.

[0253] As shown in Figure 6, the LMF can decide to use AI-assisted positioning measurement and request the AI-assisted positioning measurement from the NG-RAN node to obtain the AI-assisted positioning measurement results. The AI ​​model can be deployed on the gNB-CU or the LMF.

[0254] In some embodiments, the core network node may include an LMF, and the access network node may include a gNB-CU and a gNB-DU.

[0255] In step 601, the LMF decides to use AI-assisted positioning measurements.

[0256] In step 602, the LMF sends an NRPPa AI measurement request message to the gNB-CU.

[0257] In some embodiments, the NRPPa AI measurement request message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa measurement request message, etc.

[0258] In some embodiments, the NRPPa AI measurement request message may include second information, wherein the second information is used to request AI-assisted positioning measurement.

[0259] In some embodiments, the second information may include at least one of the following: an AI-assisted positioning measurement indicator; positioning measurement requirement information for requesting input information of an AI model.

[0260] In the above embodiment, the LMF may only send the AI-assisted positioning measurement indication to the NG-RAN node, and the NG-RAN node may determine the input requirements of the AI ​​model on its own. Alternatively, the LMF may directly send the input requirements of the AI ​​model to the NG-RAN node.

[0261] In some embodiments, the positioning measurement requirement information may include at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0262] The requested TRP identifier is used to indicate the requested TRP among multiple TRPs. The AI ​​model identifier can be used to indicate the AI ​​model to which the requested positioning measurement result is applied. The DP measurement request may include configured threshold information, which may be absolute threshold information or relative threshold information. Traditional positioning measurement requests may specifically include, but are not limited to: gNB-RxTxTimeDiff measurement request, UL-SRS-RSRP measurement request, UL-AoA measurement request, UL-RTOA measurement request, Multiple UL-AoA measurement request, UL SRS-RSRPP measurement request, etc.

[0263] In some embodiments, the input information of the AI ​​model (AI inputs) may include at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0264] The TRP identifier is used to indicate that the input information of the AI ​​model comes from a specific TRP among multiple TRPs. The AI ​​model identifier is used to indicate that the input information of the AI ​​model is for a specific AI model. The CIR measurement result may include time information, amplitude information (including real and imaginary amplitude information), and / or phase information corresponding to the sampling point. The PDP measurement result may include sampling time information and / or amplitude information. The DP measurement result may include time information and / or threshold information, where the threshold information may be absolute threshold information or relative threshold information. Traditional positioning measurement results may specifically include, but are not limited to: gNB-RxTxTimeDiff measurement result, UL-SRS-RSRP measurement result, UL-AoA measurement result, UL-RTOA measurement result, multiple UL-AoA measurement result, UL SRS-RSRPP measurement result, etc.

[0265]

[0266] Optionally, the gNB-CU may receive the NRPPa AI measurement request message sent by the LMF.

[0267] Optionally, the gNB-CU may determine to use AI-assisted positioning measurement based on the NRPPa AI measurement request message sent by the LMF.

[0268] In step 603, the gNB-CU sends a F1AP AI measurement request message to the gNB-DU.

[0269] In some embodiments, the F1AP AI measurement request message may specifically include but is not limited to: a F1AP measurement request message, etc.

[0270] In some embodiments, the F1AP AI measurement request message may include second information, where the second information is used to request AI-assisted positioning measurement.

[0271] In some embodiments, in addition to determining the positioning measurement requirement information based on the NRPPa AI measurement request message sent by the LMF, the gNB-CU may also determine the positioning measurement requirement information based on its own implementation (for example, based on the OAM configuration).

[0272] Optionally, the gNB-DU may receive the F1AP AI Measurement Request message sent by the gNB-CU.

[0273] In step 604, the gNB-DU performs positioning measurements.

[0274] In some embodiments, the gNB-DU may perform positioning measurements based on the positioning measurement requirement information to obtain input information for the AI ​​model.

[0275] In some embodiments, the gNB-DU may also first process the positioning measurement results (e.g., SVD processing) and then use the processed eigenvalues ​​as input information for the AI ​​model.

[0276] In step 605, the gNB-DU sends a F1AP AI measurement feedback message to the gNB-CU.

[0277] In some embodiments, the F1AP AI measurement feedback message may specifically include but is not limited to: a F1AP measurement response message, a F1AP measurement report message, a F1AP AI measurement report message, etc.

[0278] In some embodiments, the FIAP AI measurement feedback message may include input information of the AI ​​model.

[0279] Optionally, the gNB-CU may receive the F1AP AI measurement feedback message sent by the gNB-DU.

[0280] In a further embodiment, if the AI ​​model is deployed on the gNB-CU, steps 606 and 607 are performed; if the AI ​​model is deployed on the LMF, steps 608 and 609 are performed.

[0281] In step 606, the gNB-CU performs AI model inference based on the input information of the AI ​​model.

[0282] In step 607, the gNB-CU sends an NRPPa AI measurement feedback message to the LMF, where the NRPPa AI measurement feedback message includes output information of the AI ​​model.

[0283] In some embodiments, the NRPPa AI measurement feedback message may specifically include but is not limited to: NRPPa measurement response message, NRPPa measurement report message, NRPPa AI positioning measurement feedback message, NRPPa AI positioning measurement report message, etc.

[0284] In some embodiments, the NRPPa AI measurement feedback message may include first information indicating an AI-assisted positioning measurement result. When the AI ​​model is deployed on the gNB-CU, the first information includes output information (AI outputs) of the AI ​​model.

[0285] Optionally, the LMF may receive the NRPPa AI measurement feedback message sent by the gNB-CU to obtain the output information of the AI ​​model.

[0286] In a further embodiment, the LMF can locate the terminal based on the output information of the AI ​​model.

[0287] In step 608, the gNB-CU sends an NRPPa AI measurement feedback message to the LMF, where the NRPPa AI measurement feedback message includes input information of the AI ​​model.

[0288] In step 609 , the LMF performs AI model reasoning based on the input information of the AI ​​model.

[0289] In some embodiments, the NRPPa AI measurement feedback message may include first information, wherein the first information is used to indicate the AI-assisted positioning measurement result. When the AI ​​model is deployed on the LMF, the first information includes input information of the AI ​​model.

[0290] Optionally, the LMF may receive the NRPPa AI measurement feedback message sent by the gNB-CU to obtain input information for the AI ​​model.

[0291] In a further embodiment, the LMF can locate the terminal based on the output information of the AI ​​model.

[0292] An optional embodiment of the method according to the first aspect is described below in conjunction with Figure 7. Figure 7 is an interactive schematic diagram showing another positioning measurement method according to an embodiment of the present disclosure.

[0293] As shown in Figure 7, the gNB-CU can decide to use AI-assisted positioning measurements. The LMF can request positioning measurements (e.g., traditional positioning measurements) from the NG-RAN node, and the gNB-CU can decide to use AI-assisted positioning measurements to obtain AI-assisted positioning measurement results. The AI ​​model can be deployed on either the gNB-CU or the LMF.

[0294] In step 701, the LMF sends an NRPPa measurement request message to the gNB-CU.

[0295] In some embodiments, the NRPPa measurement request message specifically includes but is not limited to: an NRPPa measurement request message, etc. The NRPPa measurement request message does not include an AI-assisted positioning measurement indication and / or positioning measurement requirement information.

[0296] Optionally, the gNB-CU may receive the NRPPa measurement request message sent by the LMF.

[0297] In step 702, the gNB-CU decides to use AI-assisted positioning measurements.

[0298] In some embodiments, the gNB-CU determines to use AI-assisted positioning measurements based on implementation (e.g., OAM-based configuration).

[0299] In step 703, the gNB-CU sends a F1AP AI measurement request message to the gNB-DU.

[0300] In step 704, the gNB-DU performs positioning measurements.

[0301] In step 705, the gNB-DU sends a F1AP AI measurement feedback message to the gNB-CU.

[0302] In a further embodiment, if the AI ​​model is deployed on the gNB-CU, steps 706 and 707 are performed; if the AI ​​model is deployed on the LMF, steps 708 and 709 are performed.

[0303] In step 706, the gNB-CU performs AI model inference based on the input information of the AI ​​model.

[0304] In step 707, the gNB-CU sends an NRPPa AI measurement feedback message to the LMF, where the NRPPa AI measurement feedback message includes output information of the AI ​​model.

[0305] In step 708, the gNB-CU sends an NRPPa AI measurement feedback message to the LMF, where the NRPPa AI measurement feedback message includes input information of the AI ​​model.

[0306] In step 709 , the LMF performs AI model reasoning based on the input information of the AI ​​model.

[0307] The implementation of steps 703 to 709 is similar to that of steps 603 to 609. Please refer to other embodiments in the present disclosure and will not be repeated here.

[0308] Corresponding to the aforementioned embodiments of the positioning measurement method, the present disclosure also provides embodiments of a positioning measurement device.

[0309] FIG8 is a schematic block diagram of a first device according to an embodiment of the present disclosure. As shown in FIG8 , the first device 800 includes a processing module 801 and a transceiver module 802 .

[0310] In some embodiments, the processing module is configured to determine the use of AI-assisted positioning measurements;

[0311] The transceiver module is used to send a first message to the second device, wherein the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

[0312] In some embodiments, the processing module includes:

[0313] A first transceiver submodule, configured to receive a second message sent by the second device, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement;

[0314] The first processing submodule is used to determine the use of AI-assisted positioning measurement based on the second information.

[0315] In some embodiments, the second information includes at least one of the following: an AI-assisted positioning measurement indication; positioning measurement requirement information, used to request input information of the AI ​​model.

[0316] In some embodiments, the processing module includes:

[0317] The second processing submodule is configured to determine whether to use AI-assisted positioning measurements based on the implementation.

[0318] In some embodiments, the processing module is further configured to determine positioning measurement requirement information, wherein the positioning measurement requirement information is used to request input information of the AI ​​model;

[0319] The processing module is further used to perform positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model.

[0320] In some embodiments, the positioning measurement requirement information is determined in a manner including at least one of the following:

[0321] determining the positioning measurement requirement information according to second information in the second message sent by the second device;

[0322] The positioning measurement requirement information is determined based on implementation.

[0323] In some embodiments, the first device includes a first sub-device and a second sub-device;

[0324] The processing module includes:

[0325] a third processing submodule, configured for the first sub-device to determine positioning measurement requirement information and send the positioning measurement requirement information to the second sub-device;

[0326] The fourth processing submodule is used for the second sub-device to perform positioning measurement according to the positioning measurement requirement information, obtain input information of the AI ​​model, and send the input information of the AI ​​model to the first sub-device.

[0327] In some embodiments, the positioning measurement requirement information includes at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0328] In some embodiments, the input information of the AI ​​model includes at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0329] In some embodiments, an AI model is deployed on the first device, and the first information includes output information of the AI ​​model;

[0330] The processing module is further used to perform AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0331] In some embodiments, an AI model is deployed on the second device, and the first information includes input information of the AI ​​model.

[0332] In some embodiments, the first device comprises an access network node and the second device comprises a core network node.

[0333] In some embodiments, the first sub-device comprises a centralized unit in an access network node, and the second sub-device comprises a distributed unit in the access network node.

[0334] FIG9 is a schematic block diagram of a second device according to an embodiment of the present disclosure. As shown in FIG9 , the second device 900 includes a transceiver module 901 .

[0335] In some embodiments, the transceiver module is used to receive a first message sent by a first device, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

[0336] In some embodiments, the transceiver module is further used to send a second message to the first device, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

[0337] In some embodiments, the second information includes at least one of the following: an AI-assisted positioning measurement indication; positioning measurement requirement information, used to request input information of the AI ​​model.

[0338] In some embodiments, the positioning measurement requirement information includes at least one of the following: a requested TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; or a traditional positioning measurement request.

[0339] In some embodiments, the input information of the AI ​​model includes at least one of the following: TRP identifier; AI model identifier; CIR measurement result; PDP measurement result; DP measurement result; traditional positioning measurement result.

[0340] In some embodiments, an AI model is deployed on the first device, and the first information includes output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0341] In some embodiments, an AI model is deployed on the second device, and the first information includes input information of the AI ​​model; the second device 900 further includes:

[0342] A processing module is used to perform AI model reasoning based on the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

[0343] In some embodiments, the first device comprises an access network node and the second device comprises a core network node.

[0344] It should be noted that the modules included in the first device 800 and / or the second device 900 are not limited to the modules described in the above embodiments, and may also include other modules, such as a storage module, a computing module, an inference module, a display module, etc. Among them, the storage module can be used to store AI model related information and / or AI related resource information. The computing module can be used to calculate the location of the terminal based on the fourth information. The inference module can be used to perform AI inference based on the positioning measurement results obtained on multiple TRPs.

[0345] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0346] An embodiment of the present disclosure further proposes a first device, comprising: one or more processors; wherein the processor is used to call instructions to enable the first device to execute the positioning measurement method described in the first aspect and the optional embodiments of the first aspect.

[0347] An embodiment of the present disclosure further proposes a second device, comprising: one or more processors; wherein the processor is used to call instructions to enable the second device to execute the positioning measurement method described in the second aspect and the optional embodiment of the second aspect.

[0348] An embodiment of the present disclosure also proposes a communication device, comprising: one or more processors; wherein the processor is used to call instructions to enable the communication device to execute the positioning measurement method described in the first aspect, the optional embodiment of the first aspect, and / or the positioning measurement method described in the second aspect, the optional embodiment of the second aspect.

[0349] An embodiment of the present disclosure also proposes a communication system, including a first device and a second device, wherein the first device is configured to implement the positioning measurement method described in the first aspect and the optional embodiment of the first aspect, and the second device is configured to implement the positioning measurement method described in the second aspect and the optional embodiment of the second aspect.

[0350] An embodiment of the present disclosure also proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the positioning measurement method described in the first aspect and the optional embodiment of the first aspect, and / or the positioning measurement method described in the second aspect and the optional embodiment of the second aspect.

[0351] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0352] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0353] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit, and the logical relationship of the above hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration file and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0354] Figure 10 is a schematic diagram of the structure of a communication device 10100 proposed in an embodiment of the present disclosure. Communication device 10100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user device, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 10100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0355] As shown in Figure 10, the communication device 10100 includes one or more processors 10101. The processor 10101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. The processor 10101 is used to call instructions to enable the communication device 10100 to execute any of the above methods.

[0356] In some embodiments, the communication device 10100 further includes one or more memories 10102 for storing instructions. Optionally, all or part of the memory 10102 may be located outside the communication device 10100.

[0357] In some embodiments, the communication device 10100 further includes one or more transceivers 10103. When the communication device 10100 includes one or more transceivers 10103, the communication steps such as sending and receiving in the above method are performed by the transceiver 10103, and the other steps are performed by the processor 10101.

[0358] In some embodiments, a transceiver may include a receiver and a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0359] Optionally, the communication device 10100 further includes one or more interface circuits 10104, which are connected to the memory 10102. The interface circuits 10104 may be configured to receive signals from the memory 10102 or other devices, and may be configured to send signals to the memory 10102 or other devices. For example, the interface circuits 10104 may read instructions stored in the memory 10102 and send the instructions to the processor 10101.

[0360] The communication device 10100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 10100 described in the present disclosure is not limited thereto, and the structure of the communication device 10100 may not be limited by FIG. 10 . The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0361] FIG11 is a schematic diagram of the structure of a chip 11200 according to an embodiment of the present disclosure. If the communication device 10100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 11200 shown in FIG11 , but the present disclosure is not limited thereto.

[0362] The chip 11200 includes one or more processors 11201 , and the processor 11201 is used to call instructions so that the chip 11200 executes any of the above methods.

[0363] In some embodiments, the chip 11200 further includes one or more interface circuits 11202, which are connected to the memory 11203. The interface circuit 11202 can be used to receive signals from the memory 11203 or other devices, and can be used to send signals to the memory.

[0364] 11203 or other devices to send signals. For example, the interface circuit 11202 can read the instructions stored in the memory 11203 and send the instructions to the processor 11201. Optionally, the terms interface circuit, interface, transceiver pin, transceiver, etc. can be used interchangeably.

[0365] In some embodiments, the chip 11200 further includes one or more memories 11203 for storing instructions. Alternatively, all or part of the memories 11203 may be located outside the chip 11200.

[0366] The present disclosure also provides a storage medium having instructions stored thereon. When the instructions are executed on the communication device 10100, the communication device 10100 is caused to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0367] The present disclosure also provides a program product, which, when executed by the communication device 10100, enables the communication device 10100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0368] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A positioning measurement method, characterized in that: Executed by the first node, the method includes: Determine the use of artificial intelligence (AI)-assisted positioning measurements; Send a first message to the second node, wherein the first message includes first information, and the first information is used to indicate the AI-assisted positioning measurement result.

2. The method according to claim 1, characterized in that The determining to use AI-assisted positioning measurements includes: receiving a second message sent by the second node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement; Based on the second information, determine to use AI-assisted positioning measurement.

3. The method according to claim 2, characterized in that The second information includes at least one of the following: AI-assisted positioning measurement indication; Positioning measurement requirement information, used to request input information for the AI ​​model.

4. The method according to claim 1, characterized in that The determining to use AI-assisted positioning measurements includes: Determine the use of AI-assisted positioning measurements based on the implementation.

5. The method according to claim 1, characterized in that The method further comprises: Determine positioning measurement requirement information, wherein the positioning measurement requirement information is used to request input information of the AI ​​model; Perform positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model.

6. The method according to claim 5, characterized in that The method for determining the positioning measurement requirement information includes at least one of the following: Determine the positioning measurement requirement information according to second information in the second message sent by the second node; The positioning measurement requirement information is determined based on implementation.

7. The method according to claim 5, characterized in that The first node includes a first sub-node and a second sub-node; The determining of the positioning measurement requirement information includes: The first sub-node determines positioning measurement requirement information, and sends the positioning measurement requirement information to the second sub-node; The performing positioning measurement according to the positioning measurement requirement information to obtain input information of the AI ​​model includes: The second sub-node performs positioning measurement according to the positioning measurement requirement information, obtains input information of the AI ​​model, and sends the input information of the AI ​​model to the first sub-node.

8. The method according to any one of claims 5 to 7, characterized in that The positioning measurement requirement information includes at least one of the following: The requested transmission receiving point TRP identifier; AI model identification; Channel impulse response CIR measurement request; Power delay profile PDP measurement request; Delay profile DP measurement request; Traditional positioning measurement request.

9. The method according to any one of claims 5 to 7, characterized in that The input information of the AI ​​model includes at least one of the following: TRP logo; AI model identification; CIR measurement results; PDP measurement results; DP measurement results; Traditional positioning measurement results.

10. The method according to any one of claims 5 to 9, characterized in that An AI model is deployed on the first node, and the first information includes output information of the AI ​​model; the method further includes: Perform AI model reasoning according to the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

11. The method according to any one of claims 5 to 9, characterized in that An AI model is deployed on the second node, and the first information includes input information of the AI ​​model.

12. The method according to any one of claims 1 to 11, characterized in that The first node includes an access network node, and the second node includes a core network node.

13. The method according to any one of claims 7 to 11, characterized in that The first sub-node includes a centralized unit in an access network node, and the second sub-node includes a distributed unit in the access network node.

14. A positioning measurement method, characterized in that: Executed by the second node, the method includes: Receive a first message sent by a first node, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

15. The method according to claim 14, characterized in that The method further comprises: Send a second message to the first node, wherein the second message includes second information, and the second information is used to request AI-assisted positioning measurement.

16. The method according to claim 15, characterized in that The second information includes at least one of the following: AI-assisted positioning measurement indication; Positioning measurement requirement information, used to request input information for the AI ​​model.

17. The method according to claim 16, characterized in that The positioning measurement requirement information includes at least one of the following: The TRP identifier of the request; AI model identification; CIR measurement request; PDP measurement request; DP measurement request; Traditional positioning measurement request.

18. The method according to claim 16, characterized in that The input information of the AI ​​model includes at least one of the following: TRP logo; AI model identification; CIR measurement results; PDP measurement results; DP measurement results; Traditional positioning measurement results.

19. The method according to any one of claims 16 to 18, characterized in that An AI model is deployed on the first node, and the first information includes output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

20. The method according to any one of claims 16 to 18, characterized in that An AI model is deployed on the second node, and the first information includes input information of the AI ​​model; the method further includes: Perform AI model reasoning according to the input information of the AI ​​model to obtain output information of the AI ​​model, and the output information of the AI ​​model is used to locate the terminal.

21. The method according to any one of claims 14 to 20, characterized in that The first node includes an access network node, and the second node includes a core network node.

22. A first device, characterized in that: The device comprises: A processing module for determining positioning measurements using AI assistance; A transceiver module is used to send a first message to a second device, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

23. A second device, characterized in that: The device comprises: A transceiver module is used to receive a first message sent by a first device, wherein the first message includes first information, and the first information is used to indicate an AI-assisted positioning measurement result.

24. A first device, characterized in that: include: one or more processors; The first device is used to execute the positioning measurement method according to any one of claims 1 to 13.

25. A second device, characterized in that: include: one or more processors; The second device is used to execute the positioning measurement method according to any one of claims 14 to 21.

26. A communication device, characterized in that: include: one or more processors; The processor is used to call instructions so that the communication device executes the positioning measurement method described in any one of claims 1-13 or 14-21.

27. A communication system, characterized in that: The invention comprises a first device and a second device, wherein the first device is configured to implement the positioning measurement method according to any one of claims 1 to 13, and the second device is configured to implement the positioning measurement method according to any one of claims 14 to 21.

28. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device is enabled to execute the positioning measurement method according to any one of claims 1-13 or 14-21.

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