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

Abstract

The invention relates to the technical field of communication, in particular to a positioning measurement method, device and equipment, a communication system and a storage medium, and the positioning measurement method comprises the steps that a first node receives a first message sent by a second node, the first message comprises first information, and the first information is used for requesting AI-assisted positioning measurement; the first node executes AI-assisted positioning measurement according to the first information; and the first node sends a second message to the second node, the second message comprising second information, and the second information being used for indicating the AI-assisted positioning measurement result. According to the method and the device, when the AI models are deployed on the distributed nodes, a plurality of AI models can be supported to be used on a plurality of different TRPs, and the reasoning result output by each AI model is obtained, so that AI-assisted positioning measurement is realized, the position of the terminal can be calculated according to the AI-assisted positioning result, and the accuracy of terminal positioning is improved.

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 distributed nodes.

[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] Receiving a first message sent by the second node, wherein the first message includes first information, and the first information is used to request artificial intelligence (AI)-assisted positioning measurement;

[0010] Performing AI-assisted positioning measurement according to the first information;

[0011] Send a second message to the second node, where the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0012] 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:

[0013] Sending a first message to the first node, where the first message includes first information, and the first information is used to request AI-assisted positioning measurement;

[0014] Receive a second message sent by the first node, wherein the second message includes second information, and the second information is used to indicate an AI-assisted positioning measurement result.

[0015] According to a third aspect of the embodiments of the present disclosure, a first device is provided, comprising:

[0016] a transceiver module, configured to receive a first message sent by a second device, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement;

[0017] a processing module, configured to perform AI-assisted positioning measurement according to the first information;

[0018] The transceiver module is further used to send a second message to the second device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0019] According to a fourth aspect of the embodiments of the present disclosure, a second device is provided, comprising:

[0020] a transceiver module, configured to send a first message to a first device, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement;

[0021] The transceiver module is further used to receive a second message sent by the first device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] According to an embodiment of the present disclosure, the second node can request AI-assisted positioning measurement by sending a first message to the first node. The first node can perform AI-assisted positioning measurement based on the first information included in the first message, and can return AI-assisted positioning measurement results to the second node by sending a second message to the second node. Accordingly, when the AI ​​model is deployed on a distributed node, it can support the use of multiple AI models on multiple different TRPs and obtain the inference results output by each AI model, thereby realizing AI-assisted positioning measurement, and then the terminal's position can be calculated based on the AI-assisted positioning results, which is conducive to improving the accuracy of terminal positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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.

[0029] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0030] FIG2 is an interactive schematic diagram illustrating a positioning measurement method according to an embodiment of the present disclosure.

[0031] FIG3 is a schematic diagram of an AI-assisted positioning architecture according to an embodiment of the present disclosure.

[0032] FIG4 is a schematic flowchart showing a positioning measurement method according to an embodiment of the present disclosure.

[0033] FIG5 is a schematic flowchart showing another positioning measurement method according to an embodiment of the present disclosure.

[0034] FIG6 is an interactive schematic diagram showing a positioning measurement method according to an embodiment of the present disclosure.

[0035] FIG7 is a schematic block diagram of a first device according to an embodiment of the present disclosure.

[0036] FIG8 is a schematic block diagram showing a second device according to an embodiment of the present disclosure.

[0037] FIG9 is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0038] FIG10 is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] Embodiments of the present disclosure provide a positioning measurement method, apparatus, device, communication system, and storage medium.

[0040] 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: receiving a first message sent by a second node, wherein the first message includes first information, and the first information is used to request artificial intelligence (AI)-assisted positioning measurement; performing AI-assisted positioning measurement according to the first information; and sending a second message to the second node, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0041] In the above embodiment, the second node can request AI-assisted positioning measurement by sending a first message to the first node. The first node can perform AI-assisted positioning measurement based on the first information included in the first message, and can return the AI-assisted positioning measurement result to the second node by sending a second message to the second node. Accordingly, when the AI ​​model is deployed on a distributed node, it can support the use of multiple AI models on multiple different TRPs and obtain the inference results output by each 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.

[0042] 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 result; an AI-assisted measurement indication; inference accuracy; or confidence.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: receiving a third message sent by the second node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; and sending a fourth message to the second node, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the third information includes at least one of the following: a type of requested AI model-related information; or a type of requested AI-related resource information.

[0045] In combination with some embodiments of the first aspect, in some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; and information on AI-related resources.

[0046] In combination with some embodiments of the first aspect. In some embodiments, the AI ​​model-related information includes at least one of the following: a cell identifier; a transmission reception point (TRP) identifier; an AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output latency of the AI ​​model; and nodes for AI model inference.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the information on AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0048] In the above embodiment, the second node can request AI model-related information and / or AI-related resource information by sending a third message to the first node, and the first node can return AI model-related information and / or AI-related resource information to the second node by sending a fourth message to the second node. Accordingly, when the AI ​​model is deployed on distributed nodes, the second node can obtain AI model-related information and / or AI-related resource information, and then decide whether to use AI-assisted positioning measurement when there is a terminal positioning requirement, which is conducive to improving positioning accuracy, improving resource utilization, and improving system performance.

[0049] In combination 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.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the first node comprises a distributed unit in an access network node, and the second node comprises a central unit in the access network node.

[0051] In combination with some embodiments of the first aspect, in some embodiments, multiple AI models are distributedly deployed on the first node.

[0052] 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: sending a first message to a first node, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement; receiving a second message sent by the first node, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0053] In the above embodiment, the second node can request AI-assisted positioning measurement by sending a first message to the first node. The first node can perform AI-assisted positioning measurement based on the first information included in the first message, and can return the AI-assisted positioning measurement result to the second node by sending a second message to the second node. Accordingly, when the AI ​​model is deployed on a distributed node, it can support the use of multiple AI models on multiple different TRPs and obtain the inference results output by each 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.

[0054] 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 result; an AI-assisted measurement indication; inference accuracy; or confidence.

[0055] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending a third message to the first node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; and receiving a fourth message sent by the first node, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0056] In conjunction with some embodiments of the second aspect, in some embodiments, the third information includes at least one of the following: the type of requested AI model-related information; and the type of requested AI-related resource information.

[0057] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; and information on AI-related resources.

[0058] In conjunction with some embodiments of the second aspect, in some embodiments, the AI ​​model-related information includes at least one of the following: a cell identifier; a TRP identifier; an AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output latency of the AI ​​model; and nodes of AI model inference.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, the information on AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0060] In the above embodiment, the second node can request AI model-related information and / or AI-related resource information by sending a third message to the first node, and the first node can return AI model-related information and / or AI-related resource information to the second node by sending a fourth message to the second node. Accordingly, when the AI ​​model is deployed on distributed nodes, the second node can obtain AI model-related information and / or AI-related resource information, and then decide whether to use AI-assisted positioning measurement when there is a terminal positioning requirement, which is conducive to improving positioning accuracy, improving resource utilization, and improving system performance.

[0061] 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.

[0062] In conjunction with some embodiments of the second aspect, in some embodiments, the first node comprises a distributed unit in an access network node, and the second node comprises a central unit in the access network node.

[0063] In conjunction with some embodiments of the second aspect, in some embodiments, multiple AI models are distributedly deployed on the first node.

[0064] In a third aspect, an embodiment of the present disclosure provides a first device, comprising:

[0065] a transceiver module, configured to receive a first message sent by a second device, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement;

[0066] a processing module, configured to perform AI-assisted positioning measurement according to the first information;

[0067] The transceiver module is further used to send a second message to the second device, wherein the second message includes second information, and the second 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, configured to send a first message to a first device, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement;

[0070] The transceiver module is further used to receive a second message sent by the first device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0086] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 the ordinal number 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0102] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0103] 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.

[0104] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0105] As shown in FIG1 , a communication system 100 includes an access network device 101 and a core network device 102 .

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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 .

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In some embodiments, in an artificial intelligence (AI)-assisted positioning architecture, multiple AI models can be distributedly deployed on the DU of an access network device. The multiple AI models can be the same or different.

[0114] In some embodiments, the core network device may include a location management function (LMF). The LMF may be used to locate the terminal.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] FIG2 is an interactive schematic diagram illustrating a positioning measurement method according to an embodiment of the present disclosure.

[0119] As shown in Figure 2, the positioning measurement method includes:

[0120] Step S201: The second node sends a third message to the first node.

[0121] In some embodiments, the first node comprises a node that performs AI-assisted positioning measurements, and the second node comprises a node that requests AI-assisted positioning measurements.

[0122] 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, multiple AI models are distributedly deployed on the first node, where the multiple AI models may be the same or different.

[0123] In some embodiments, the first node may include a DU in the access network device 101 , and the second node may include a CU in the access network device 101 .

[0124] In some embodiments, the first node receives a third message sent by the second node.

[0125] In some embodiments, the third message includes third information, where the third information is used to indicate AI-related request information.

[0126] In one possible implementation, the first node may include an access network device 101, the second node may include a core network device 102, and the third message may be an information request message based on the New Radio Positioning Protocol A (NR Positioning Protocol A, NRPPa). The information request message based on NRPPa may specifically include, but is not limited to, an NRPPa TRP information request message, an NRPPa AI information request message, and an NRPPa AI capability request message.

[0127] In another possible implementation, the first node may include a DU in the access network device 101, the second node may include a CU in the access network device 101, and the third message may be an information request message based on the F1 Application Proposal (F1AP). The F1AP-based information request message may specifically include, but is not limited to, an F1AP TRP information request message, an F1AP AI information request message, and an F1AP AI capability request message.

[0128] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; the type of AI-related resource information requested.

[0129] Step S202: The first node sends a fourth message to the second node.

[0130] In some embodiments, the second node receives the fourth message sent by the first node.

[0131] In some embodiments, the fourth message includes fourth information, where the fourth information is used to indicate AI-related feedback information.

[0132] In one possible implementation, the first node may include an access network device 101, the second node may include a core network device 102, and the fourth message may be an NRPPa-based information feedback message. The NRPPa-based information feedback message may specifically include, but is not limited to: NRPPa TRP information response information, NRPPa TRP information update information, NRPPa AI information feedback message, NRPPa AI capability feedback information, NRPPa AI information update message, NRPPa AI capability update message, etc.

[0133] In another possible implementation, the first node may include a DU in the access network device 101, the second node may include a CU in the access network device 101, and the fourth message may be an F1AP-based information feedback message. The F1AP-based information feedback message may specifically include but is not limited to: F1AP TRP information response information, F1AP TRP information update information, F1AP AI information feedback message, F1AP AI capability feedback information, F1AP AI information update message, F1AP AI capability update message, etc.

[0134] In some embodiments, the fourth information includes at least one of the following: AI model-related information; AI-related resource information. It should be noted that the "AI model-related information" referred to in this disclosure may also be described as AI model support information, AI model capability information, etc., and this disclosure is not limiting. The "AI-related resource information" referred to in this disclosure may also be described as AI-related resource status information, etc., and this disclosure is not limiting.

[0135] In one possible implementation, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0136] In one possible implementation, the information of the AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0137] Step S203: The second node sends a first message to the first node.

[0138] In some embodiments, the second node may determine whether to send the first message to the first node based on the fourth information.

[0139] In some embodiments, the first node receives a first message sent by the second node.

[0140] In some embodiments, the first message includes first information for requesting AI-assisted positioning measurement.

[0141] In one possible implementation, the first node may include an access network device 101, the second node may include a core network device 102, and the first message may be an NRPPa-based measurement request message. The NRPPa-based measurement request message may specifically include, but is not limited to, an NRPPa measurement request message, an NRPPa AI measurement request message, and the like.

[0142] In another possible implementation, the first node may include a DU in the access network device 101, the second node may include a CU in the access network device 101, and the first message may be an F1AP-based measurement request message. The F1AP-based measurement request message may specifically include, but is not limited to, an F1AP measurement request message, an F1AP AI measurement request message, and the like.

[0143] In some embodiments, the first information includes at least one of the following: an AI-assisted positioning measurement indication; and positioning measurement requirement information.

[0144] In one possible implementation, the positioning measurement information includes at least one of the following: a 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; and a traditional positioning measurement request. The traditional positioning measurement request specifically includes, but is not limited to, a gNB-RxTxTimeDiff measurement request, an uplink sounding reference signal received power (UL-SRS-RSRP) measurement request, an uplink angle of arrival (UL-AoA) measurement request, an uplink relative time of arrival (UL-RTOA) measurement request, multiple UL-AoA measurement requests, and an uplink sounding reference signal received path power (UL SRS-RSRPP) measurement request.

[0145] Step S204: The first node performs AI-assisted positioning measurement.

[0146] In some embodiments, the first node may perform AI-assisted positioning measurements based on the first information.

[0147] In some embodiments, the first node can perform positioning measurements on multiple TRPs based on the first information to obtain positioning measurement results; use the positioning measurement results obtained on each TRP as input data, input the corresponding AI model for reasoning, obtain the reasoning results output by the AI ​​model, and determine the reasoning results output by the AI ​​model as AI-assisted positioning measurement results.

[0148] Step S205: The first node sends a second message to the second node.

[0149] In some embodiments, the second node receives the second message sent by the first node.

[0150] In some embodiments, the second message includes second information, where the second information is used to indicate the AI-assisted positioning measurement result.

[0151] In one possible implementation, the first node may include an access network device 101, the second node may include a core network device 102, and the second message may be an NRPPa-based measurement feedback message. 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 measurement feedback message, and an NRPPa AI measurement report message.

[0152] In another possible implementation, the first node may include a DU in the access network device 101, the second node may include a CU in the access network device 101, and the second message may be an F1AP-based measurement feedback message. The F1AP-based measurement feedback message may specifically include, but is not limited to, an F1AP measurement response message, an F1AP measurement report message, an F1AP AI measurement feedback message, and an F1AP AI measurement report message.

[0153] In some embodiments, the second information includes at least one of the following: AI-assisted positioning measurement results; AI-assisted measurement indication; inference accuracy; and confidence level.

[0154] In some embodiments, the second node may locate the terminal according to the second information.

[0155] The communication method involved in the embodiments of the present disclosure may include at least one of steps S201 to 205. For example, step S201 may be implemented as an independent embodiment, step S203 may be implemented as an independent embodiment, step S204 may be implemented as an independent embodiment, steps S201+S202 may be implemented as an independent embodiment, and steps S203+S204+S205 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0156] In some embodiments, steps S201+S202 and steps S203+S204+S205 may be executed in an interchanged order or simultaneously.

[0157] In some embodiments, step S201 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0158] In some embodiments, step S202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0159] In some embodiments, step S203 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0160] In some embodiments, step S204 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0161] In some embodiments, step S205 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0162] In some embodiments, reference may be made to other optional embodiments described before or after the description corresponding to FIG. 2 .

[0163] 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.

[0164] 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.

[0165] In one possible implementation, FIG3 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 distributed nodes.

[0166] As shown in Figure 3, in a Central Unit-Distributed Unit (CU-DU) separation architecture, AI models can be deployed on distributed nodes such as gNB-DU1 and gNB-DU2. Each distributed node can have one or more Transmission and Receiving Points (TRPs). Multiple Transmission and Receiving Points (multi-TRPs) can correspond to N AI models on N base stations (N is a positive integer greater than 1). These N AI models can be the same or different. Each AI model can perform inference based on input information and output inference results.

[0167] 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.

[0168] However, there are still some technical problems. For example, when the AI ​​model is deployed on distributed nodes, how to implement AI-assisted positioning measurement, how to support the use of multiple AI models on multiple different TRPs to implement AI-assisted positioning measurement, etc.

[0169] 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.

[0170] In the embodiments of the present disclosure, the first node includes a node that performs AI-assisted positioning measurements, and the second node includes a node that requests AI-assisted positioning measurements. It should be noted that the terms "first node" and "second node" may refer to different nodes participating in the positioning measurement process and do not constitute a specific limitation on the present disclosure.

[0171] In some embodiments, the first node may include an access network node, and the second node may include a core network node. In one possible implementation, multiple AI models are distributedly deployed on the first node.

[0172] For example, as shown in Figure 3, 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). AI models such as AI model 0, AI model 1, ..., and AI model 17 are deployed on the NG-RAN node.

[0173] In some embodiments, the first node may include a distributed unit in an access network node, and the second node may include a central unit in the access network node. In one possible implementation, multiple AI models are distributedly deployed on the first node, where the multiple AI models may be the same or different.

[0174] For example, as shown in Figure 3, the first node can be a distributed node such as gNB-DU1 and gNB-DU2, and the second node can be a gNB-CU. gNB-DU1 is deployed with AI model 0 corresponding to TRP0 and AI model 1 corresponding to TRP1. gNB-DU2 is deployed with AI model 17 corresponding to TRP17. Other gNB-DUs are deployed with other AI models corresponding to other TRPs, which are not detailed here.

[0175] As shown in FIG4 , the positioning measurement method may include the following steps:

[0176] In step S401, a first message sent by a second node is received, where the first message includes first information, and the first information is used to request AI-assisted positioning measurement.

[0177] In step S402, AI-assisted positioning measurement is performed according to the first information.

[0178] For example, the first node may be an NG-RAN node, and the second node may be an LMF; after the LMF determines to use AI-assisted positioning measurement, it may send a measurement request message based on the New Radio Positioning Protocol A (NR Positioning Protocol A, NRPPa) to the NG-RAN node, wherein the measurement request message based on NRPPa may include first information, and the first information may be used to request AI-assisted positioning measurement; the NG-RAN node may receive the measurement request message based on NRPPa sent by the LMF, and may perform AI-assisted positioning measurement according to the instruction of the first information to obtain AI-assisted positioning measurement results.

[0179] The NRPPa-based measurement request message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa AI measurement request message, and the like.

[0180] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU. After the gNB-CU determines to use AI-assisted positioning measurement, it may send a measurement request message based on the F1 Application Proposal (F1AP) to the gNB-DU. The F1AP-based measurement request message may include first information, and the first information may be used to request the AI-assisted positioning measurement. The gNB-DU may receive the F1AP-based measurement request message sent by the gNB-CU and may perform the AI-assisted positioning measurement according to an instruction of the first information to obtain an AI-assisted positioning measurement result.

[0181] The F1AP-based measurement request message may specifically include but is not limited to: an F1AP measurement request message, an F1AP AI measurement request message, etc.

[0182] The first information may be, for example, AI-assisted measurement request information (AI assisted request).

[0183] It should be noted that in the above embodiment, communication between the NG-RAN node and the LMF is based on NRPPa, and communication between the gNB-CU and the gNB-CU is based on F1AP. This is only an exemplary description. Communication can also be based on other protocols as needed, and this disclosure does not limit it.

[0184] In some embodiments, the NG-RAN node may include a gNB-CU and a gNB-DU. In this case, after the LMF determines to use AI-assisted positioning measurement, it may send an NRPPa-based measurement request message to the gNB-CU, wherein the NRPPa-based measurement request message may include first information, and the first information may be used to request AI-assisted positioning measurement; the gNB-CU may receive the NRPPa-based measurement request message sent by the LMF, and may determine to use AI-assisted positioning measurement based on an instruction of the first information, and send an F1AP-based measurement request message to the gNB-DU, wherein the F1AP-based measurement request message may include the first information; the gNB-DU may receive the F1AP-based measurement request message sent by the gNB-CU, and may perform AI-assisted positioning measurement based on an instruction of the first information to obtain an AI-assisted positioning measurement result.

[0185] In some embodiments, in step S401, the first information includes at least one of the following: an AI-assisted positioning measurement indication; and positioning measurement requirement information. The AI-assisted positioning measurement indication is used to indicate a request to perform AI-assisted positioning measurement. The positioning measurement requirement information is used to indicate which second nodes need to perform positioning measurements and / or which positioning measurements the second nodes need to perform.

[0186] In one possible implementation, the positioning measurement information includes at least one of the following: a 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; a traditional positioning measurement request, specifically including but not limited to a gNB-RxTxTimeDiff measurement request, an uplink sounding reference signal received power (UL-SRS-RSRP) measurement request, an uplink angle of arrival (UL-AoA) measurement request, an uplink relative time of arrival (UL-RTOA) measurement request, a multiple UL-AoA measurement request, an uplink sounding reference signal reference signal received path power (UL SRS-RSRPP) measurement request, etc.

[0187] In some embodiments, in step S402, performing AI-assisted positioning measurement based on the first information may include: performing positioning measurement through multiple TRPs based on the first information to obtain positioning measurement results; using the positioning measurement results obtained by each TRP as input data, inputting the corresponding AI model for reasoning, obtaining the reasoning result output by the AI ​​model, and determining the reasoning result output by the AI ​​model as the AI-assisted positioning measurement result.

[0188] For example, as shown in Figure 3, after receiving the F1AP AI measurement request message from the gNB-CU, gNB-DU1 can perform positioning measurements on TRP0 and obtain positioning measurement results. Furthermore, gNB-DU1 can input the positioning measurement results obtained on TRP0 into the corresponding AI model 0 for inference, obtaining the inference results output by AI model 0. Similarly, gNB-DU1 can perform positioning measurements on other TRPs and input the positioning measurement results obtained on other TRPs into the corresponding AI models for inference, obtaining the inference results output by each AI model.

[0189] In one possible implementation, the gNB-DU can first process the positioning measurement results and then input the processed positioning measurement results into the corresponding AI model for inference. For example, the positioning measurement results can first be processed by singular value decomposition (SVD) and the eigenvalues ​​after SVD processing can then be input into the AI ​​model.

[0190] In step S403, a second message is sent to the second node, where the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0191] For example, the first node may be an NG-RAN node, and the second node may be an LMF; the NG-RAN node may send an NRPPa-based measurement feedback message to the LMF, wherein the NRPPa-based measurement feedback message may include second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0192] 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 measurement feedback message, an NRPPa AI measurement report message, and the like.

[0193] The second information may be, for example, an AI-assisted mean result.

[0194] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU; the gNB-DU may send an F1AP-based measurement feedback message to the gNB-CU, wherein the F1AP-based measurement feedback message may include second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0195] The F1AP-based measurement feedback message may specifically include but is not limited to: an F1AP measurement response message, an F1AP measurement report message, an F1AP AI measurement feedback message, an F1AP AI measurement report message, etc.

[0196] In some embodiments, in step S403, the second information includes at least one of the following: an AI-assisted positioning measurement result; an AI-assisted measurement indication; inference accuracy; and a confidence level. The AI-assisted measurement indication indicates that what the first node sends to the second node is an AI-assisted positioning measurement result, i.e., what the first node sends to the second node is an inference result of the AI ​​model on the positioning measurement result. The inference accuracy indicates the accuracy of the AI-assisted positioning measurement result.

[0197] 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.

[0198] Optionally, the second node may decide to use AI-assisted positioning measurement and may send a first message to the first node, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement.

[0199] Optionally, the second node may receive a second message sent by the first node, wherein the second message includes second information, and the second information is used to indicate an AI-assisted positioning measurement result.

[0200] Optionally, the second node may calculate the location of the terminal based on the second information. That is, the second node may locate the terminal based on the second information.

[0201] According to an embodiment of the present disclosure, the second node can request AI-assisted positioning measurement by sending a first message to the first node. The first node can perform AI-assisted positioning measurement based on the first information included in the first message, and can return AI-assisted positioning measurement results to the second node by sending a second message to the second node. Accordingly, when the AI ​​model is deployed on a distributed node, it can support the use of multiple AI models on multiple different TRPs and obtain the inference results output by each AI model, thereby realizing AI-assisted positioning measurement, and then the terminal's position can be calculated based on the AI-assisted positioning results, which is conducive to improving the accuracy of terminal positioning.

[0202] In some embodiments, before step S401, the method may further include: receiving a third message sent by the second node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; and sending a fourth message to the second node, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0203] For example, the first node may be an NG-RAN node, and the second node may be an LMF; the LMF may send an information request message based on NRPPa to the NG-RAN node, wherein the information request message based on NRPPa may include third information, and the third information may be used to indicate AI-related request information; the NG-RAN node may receive the information request message based on NRPPa sent by the LMF, and may send an information feedback message based on NRPPa to the LMF, wherein the information feedback message based on NRPPa includes fourth information, and the fourth information may be used to indicate AI-related feedback information.

[0204] Among them, the NRPPa-based information request message may specifically include but is not limited to: NRPPa TRP information request message, NRPPa AI information request message, NRPPa AI capability request information, etc.

[0205] The third information may be, for example, AI-related information request information.

[0206] Among them, the information feedback message based on NRPPa may specifically include but is not limited to: NRPPa TRP information response information, NRPPa TRP information update information, NRPPa AI information feedback message, NRPPa AI capability feedback information, NRPPa AI information update message, NRPPa AI capability update message, etc.

[0207] The fourth information may be, for example, AI support information (AI support info) and / or AI resource information (AI resource info).

[0208] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU; the gNB-CU may send an F1AP-based information request message to the gNB-DU, wherein the F1AP-based information request message may include third information, and the third information may be used to indicate AI-related request information; the gNB-DU may receive the F1AP-based information request message sent by the gNB-CU, and may send an F1AP-based information feedback message to the gNB-CU, wherein the F1AP-based information feedback message includes fourth information, and the fourth information may be used to indicate AI-related feedback information.

[0209] Among them, the F1AP-based information request message may specifically include but is not limited to: F1AP TRP information request message, F1AP AI information request message, F1AP AI capability request information, etc.

[0210] Among them, the F1AP-based information feedback message may specifically include but is not limited to: F1AP TRP information response information, F1AP TRP information update information, F1AP AI information feedback message, F1AP AI capability feedback information, F1AP AI information update message, F1AP AI capability update message, etc.

[0211] In some embodiments, the NG-RAN node may include a gNB-CU and a gNB-DU. In this case, after receiving an NRPPa-based information request message from the LMF, the gNB-CU may send an F1AP-based information request message to the gNB-DU. Furthermore, after receiving an F1AP-based information feedback message from the gNB-DU, the gNB-CU may send an NRPPa-based information feedback message to the LMF.

[0212] In some embodiments, the third information sent by the gNB-CU to the gNB-DU may be determined based on the third information sent by the LMF to the gNB-CU. Alternatively, the third information sent by the gNB-CU to the gNB-DU may be determined based on the gNB-CU implementation. For example, the gNB-CU may determine the third information to be sent to the gNB-DU based on Operation Administration and Maintenance (OAM) configuration information.

[0213] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; or the type of AI-related resource information requested. For example, the third information is a TRP information type request, where the TRP information type request is an AI model-related information type and / or an AI-related resource information type.

[0214] In some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; information about AI related resources.

[0215] It should be noted that the “AI model-related information” referred to in this disclosure can also be described as AI model support information, AI model capability information, etc., and this disclosure is not limited to this. The “AI-related resource information” referred to in this disclosure can also be described as AI-related resource status information, etc., and this disclosure is not limited to this.

[0216] In one possible implementation, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0217] The cell identifier can be used to indicate a specific cell. The TRP identifier can be used to indicate a specific TRP among multiple TRPs. The AI ​​model identifier can be used to indicate a specific AI model among multiple AI models distributedly deployed on the second node. The measurement values ​​supported by the AI ​​model can be used to indicate positioning methods and / or output values ​​that support AI-assisted measurement, such as UL-AoA, UL-SRS-RSRP, UL-RTOA, gNB-RxTxTimeDiff, Zenith Angles of Arrival (Z-AoA), UL-SRS-RSRPP, etc. The AI ​​model supported signals are used to indicate uplink signals applicable to the AI ​​model, such as SRS, pos-SRS (Positioning Sounding Reference Signal), or other uplink signals. The AI ​​model performance information is used to indicate historical performance information of the AI ​​model, such as accuracy and confidence. The node for AI model inference can be, for example, a gNB-CU, gNB-DU, a server directly connected to the base station, or an Operation Administration and Maintenance (OAM).

[0218] In one possible implementation, the information of the AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0219] Optionally, the second node may send a third message to the first node, where the third message includes third information, and the third information is used to indicate AI-related request information.

[0220] Optionally, the second node may receive a fourth message sent by the first node, where the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0221] Optionally, the second node may determine whether to use AI-assisted positioning measurement based on the fourth information.

[0222] In the above embodiment, the second node can request AI model-related information and / or AI-related resource information by sending a third message to the first node, and the first node can return AI model-related information and / or AI-related resource information to the second node by sending a fourth message to the second node. Accordingly, when the AI ​​model is deployed on distributed nodes, the second node can obtain AI model-related information and / or AI-related resource information, and then decide whether to use AI-assisted positioning measurement when there is a terminal positioning requirement, which is conducive to improving positioning accuracy, improving resource utilization, and improving system performance.

[0223] 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.

[0224] As shown in FIG5 , the positioning measurement method may include the following steps:

[0225] In step S501, a first message is sent to a first node, where the first message includes first information, and the first information is used to request AI-assisted positioning measurement.

[0226] Optionally, the first node may receive a first message sent by the second node, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement.

[0227] Optionally, the first node may perform AI-assisted positioning measurement based on the first message.

[0228] For example, the first node may be an NG-RAN node, and the second node may be an LMF; after the LMF determines to use AI-assisted positioning measurement, it may send an NRPPa-based measurement request message to the NG-RAN node, wherein the NRPPa-based measurement request message may include first information, and the first information may be used to request AI-assisted positioning measurement. Optionally, the NG-RAN node may receive the NRPPa-based measurement request message sent by the LMF. Optionally, the NG-RAN node may also perform AI-assisted positioning measurement according to the instruction of the first information to obtain AI-assisted positioning measurement results.

[0229] The NRPPa-based measurement request message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa AI measurement request message, and the like.

[0230] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU. After the gNB-CU determines to use AI-assisted positioning measurement, it may send an F1AP-based measurement request message to the gNB-DU. The F1AP-based measurement request message may include first information, and the first information may be used to request the AI-assisted positioning measurement. Optionally, the gNB-DU may receive the F1AP-based measurement request message sent by the gNB-CU. Optionally, the gNB-DU may also perform AI-assisted positioning measurement according to the instruction of the first information to obtain an AI-assisted positioning measurement result.

[0231] The F1AP-based measurement request message may specifically include but is not limited to: an F1AP measurement request message, an F1AP AI measurement request message, etc.

[0232] The first information may be, for example, AI-assisted measurement request information (AI assisted request) or the like.

[0233] It should be noted that in the above embodiment, communication between the NG-RAN node and the LMF is based on NRPPa, and communication between the gNB-CU and the gNB-CU is based on F1AP. This is only an exemplary description. Communication can also be based on other protocols as needed, and this disclosure does not limit it.

[0234] In some embodiments, the NG-RAN node may include a gNB-CU and a gNB-DU. In this case, after the LMF determines to use AI-assisted positioning measurement, it may send an NRPPa-based measurement request message to the gNB-CU, wherein the NRPPa-based measurement request message may include first information, and the first information may be used to request AI-assisted positioning measurement; the gNB-CU may receive the NRPPa-based measurement request message sent by the LMF, and may determine to use AI-assisted positioning measurement based on an instruction of the first information, and send an F1AP-based measurement request message to the gNB-DU, wherein the F1AP-based measurement request message may include the first information; the gNB-DU may receive the F1AP-based measurement request message sent by the gNB-CU, and may perform AI-assisted positioning measurement based on an instruction of the first information to obtain an AI-assisted positioning measurement result.

[0235] In some embodiments, in step S501, the first information includes at least one of the following: an AI-assisted positioning measurement indication; and positioning measurement requirement information. The AI-assisted positioning measurement indication is used to indicate a request to perform AI-assisted positioning measurement. The positioning measurement requirement information is used to indicate which second nodes need to perform positioning measurements and / or which positioning measurements the second nodes need to perform.

[0236] In one possible implementation, the positioning measurement information includes at least one of the following: a TRP identifier; an AI model identifier; a CIR measurement request; a PDP measurement request; a DP measurement request; a traditional positioning measurement request, specifically including but not limited to a gNB-RxTxTimeDiff measurement request, a UL-SRS-RSRP measurement request, a UL-AoA measurement request, a UL-RTOA measurement request, a Multiple UL-AoA measurement request, a UL SRS-RSRPP measurement request, etc.

[0237] Optionally, the first node can perform positioning measurements through multiple TRPs based on the first information to obtain positioning measurement results; use the positioning measurement results obtained by each TRP as input data, input the corresponding AI model for reasoning, obtain the reasoning results output by the AI ​​model, and determine the reasoning results output by the AI ​​model as AI-assisted positioning measurement results.

[0238] Optionally, the gNB-DU can first process the positioning measurement results and then input the processed positioning measurement results into the corresponding AI model for inference. For example, the positioning measurement results can first be processed by SVD, and then the eigenvalues ​​after SVD processing can be input into the AI ​​model.

[0239] In step S502, a second message sent by the first node is received, where the second message includes second information, and the second information is used to indicate an AI-assisted positioning measurement result.

[0240] Optionally, the first node may send a second message to the second node, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0241] 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 second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0242] 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 measurement feedback message, an NRPPa AI measurement report message, and the like.

[0243] The second information may be, for example, an AI-assisted mean result.

[0244] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU; the gNB-CU may receive an F1AP-based measurement feedback message sent by the gNB-DU, wherein the F1AP-based measurement feedback message may include second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0245] The F1AP-based measurement feedback message may specifically include but is not limited to: an F1AP measurement response message, an F1AP measurement report message, an F1AP AI measurement feedback message, an F1AP AI measurement report message, etc.

[0246] In some embodiments, in step S502, the second information includes at least one of the following: an AI-assisted positioning measurement result; an AI-assisted measurement indication; inference accuracy; and confidence. The AI-assisted measurement indication indicates that the first node is sending the second node an AI-assisted positioning measurement result, i.e., the first node is sending the second node an inference result of the AI ​​model on the positioning measurement result. The inference accuracy indicates the accuracy of the AI-assisted positioning measurement result.

[0247] 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.

[0248] According to an embodiment of the present disclosure, according to an embodiment of the present disclosure, the second node can request AI-assisted positioning measurement by sending a first message to the first node, and the first node can perform AI-assisted positioning measurement based on the first information included in the first message, and by sending a second message to the second node, the AI-assisted positioning measurement result can be returned to the second node. Accordingly, when the AI ​​model is deployed on a distributed node, it can support the use of multiple AI models on multiple different TRPs and obtain the inference results output by each AI model, thereby realizing AI-assisted positioning measurement, and then the position of the terminal can be calculated based on the AI-assisted positioning result, which is conducive to improving the accuracy of terminal positioning.

[0249] In some embodiments, the method further includes: calculating the location of the terminal based on the second information. That is, the second node can locate the terminal based on the second information.

[0250] In some embodiments, before step S501, the method may further include: sending a third message to the first node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; and receiving a fourth message sent by the first node, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0251] For example, the first node may be an NG-RAN node, and the second node may be an LMF; the LMF may send an information request message based on NRPPa to the NG-RAN node, wherein the information request message based on NRPPa may include third information, and the third information may be used to indicate AI-related request information; the NG-RAN node may receive the information request message based on NRPPa sent by the LMF, and may send an information feedback message based on NRPPa to the LMF, wherein the information feedback message based on NRPPa includes fourth information, and the fourth information may be used to indicate AI-related feedback information; the LMF may receive the fourth message sent by the NG-RAN node.

[0252] Among them, the NRPPa-based information request message may specifically include but is not limited to: NRPPa TRP information request message, NRPPa AI information request message, NRPPa AI capability request information, etc.

[0253] The third information may be, for example, AI-related information request information.

[0254] Among them, the information feedback message based on NRPPa may specifically include but is not limited to: NRPPa TRP information response information, NRPPa TRP information update information, NRPPa AI information feedback message, NRPPa AI capability feedback information, NRPPa AI information update message, NRPPa AI capability update message, etc.

[0255] The fourth information may be, for example, AI support information (AI support info) and / or AI resource information (AI resource info), etc.

[0256] For another example, the first node may be a gNB-DU, and the second node may be a gNB-CU; the gNB-CU may send an F1AP-based information request message to the gNB-DU, wherein the F1AP-based information request message may include third information, and the third information may be used to indicate AI-related request information; the gNB-DU may receive the F1AP-based information request message sent by the gNB-CU, and may send an F1AP-based information feedback message to the gNB-CU, wherein the F1AP-based information feedback message includes fourth information, and the fourth information may be used to indicate AI-related feedback information; the gNB-CU may receive the fourth message sent by the gNB-DU.

[0257] Among them, the F1AP-based information request message may specifically include but is not limited to: F1AP TRP information request message, F1AP AI information request message, F1AP AI capability request information, etc.

[0258] Among them, the F1AP-based information feedback message may specifically include but is not limited to: F1AP TRP information response information, F1AP TRP information update information, F1AP AI information feedback message, F1AP AI capability feedback information, F1AP AI information update message, F1AP AI capability update message, etc.

[0259] Optionally, the first node may receive a third message sent by the second node, wherein the third message includes third information, and the third information is used to indicate AI-related request information.

[0260] Optionally, the first node may send a fourth message to the second node, where the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0261] In some embodiments, the NG-RAN node may include a gNB-CU and a gNB-DU. In this case, after receiving an NRPPa-based information request message from the LMF, the gNB-CU may send an F1AP-based information request message to the gNB-DU. Furthermore, after receiving an F1AP-based information feedback message from the gNB-DU, the gNB-CU may send an NRPPa-based information feedback message to the LMF.

[0262] In some embodiments, the third information sent by the gNB-CU to the gNB-DU may be determined based on the third information sent by the LMF to the gNB-CU; alternatively, the third information sent by the gNB-CU to the gNB-DU may be determined based on the gNB-CU implementation. For example, the gNB-CU may determine the third information to be sent to the gNB-DU based on the configuration information of the OAM.

[0263] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; or the type of AI-related resource information requested. For example, the third information is a TRP information type request, where the TRP information type request is an AI model-related information type and / or an AI-related resource information type.

[0264] In some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; information about AI related resources.

[0265] It should be noted that the “AI model-related information” referred to in this disclosure can also be described as AI model support information, AI model capability information, etc., and this disclosure is not limited to this. The “AI-related resource information” referred to in this disclosure can also be described as AI-related resource status information, etc., and this disclosure is not limited to this.

[0266] In one possible implementation, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0267] The cell identifier can be used to indicate a specific cell. The TRP identifier can be used to indicate a specific TRP among multiple TRPs. The AI ​​model identifier can be used to indicate a specific AI model among multiple AI models distributedly deployed on the second node. The measurement values ​​supported by the AI ​​model can be used to indicate positioning methods and / or output values ​​that support AI-assisted measurement, such as UL-AoA, UL-SRS-RSRP, UL-RTOA, gNB-RxTxTimeDiff, Z-AoA, UL-SRS-RSRPP, etc. The signals supported by the AI ​​model are used to indicate uplink signals applicable to the AI ​​model, such as SRS, pos-SRS (positioning sounding reference signal), or other uplink signals. The performance information of the AI ​​model is used to indicate historical performance information of the AI ​​model, such as accuracy, confidence, etc. The node for AI model inference can be, for example, a gNB-CU, gNB-DU, a server directly connected to the base station, or OAM.

[0268] In one possible implementation, the information of the AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0269] In some embodiments, the method further includes: determining whether to use AI-assisted positioning measurement based on the fourth information.

[0270] In the above embodiment, the second node can request AI model-related information and / or AI-related resource information by sending a third message to the first node, and the first node can return AI model-related information and / or AI-related resource information to the second node by sending a fourth message to the second node. Accordingly, when the AI ​​model is deployed on distributed nodes, the second node can obtain AI model-related information and / or AI-related resource information, and then decide whether to use AI-assisted positioning measurement when there is a terminal positioning requirement, which is conducive to improving positioning accuracy, improving resource utilization, and improving system performance.

[0271] 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.

[0272] 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.

[0273] In some embodiments, the terms "component carrier (CC)", "cell", "frequency carrier", "carrier frequency" and the like can be used interchangeably.

[0274] 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.

[0275] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0276] 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.

[0277] As shown in Figure 6, the LMF can first request AI model-related information and / or AI-related resource information from the NG-RAN node to determine whether to use AI-assisted positioning measurement, and then request AI-assisted positioning measurement from the NG-RAN node to obtain AI-assisted positioning measurement results.

[0278] 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. Multiple AI models may be distributedly deployed on the gNB-DU, and the multiple AI models may be the same or different.

[0279] In step 601, the LMF may send an NRPPa-based information request message to the gNB-CU.

[0280] In some embodiments, the NRPPa-based information request message may specifically include but is not limited to: NRPPa TRP information request message, NRPPa AI information request message, NRPPa AI capability request information, etc.

[0281] In some embodiments, the NRPPa-based information request message may include third information, wherein the third information is used to indicate AI-related request information. The third information may be, for example, AI-related information request information.

[0282] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; or the type of AI-related resource information requested. For example, the third information is a TRP information type request, where the TRP information type request is an AI model-related information type and / or an AI-related resource information type.

[0283] Optionally, the gNB-CU may receive an NRPPa-based information request message sent by the LMF.

[0284] In step 602, the gNB-CU may send an F1AP-based information request message to the gNB-DU.

[0285] In some embodiments, the F1AP-based information request message may specifically include but is not limited to: F1AP TRP information request message, F1AP AI information request message, F1AP AI capability request information, etc.

[0286] In some embodiments, the F1AP-based information request message may include third information, wherein the third information is used to indicate AI-related request information. The third information may be, for example, AI-related information request information.

[0287] In some embodiments, the third information sent by the gNB-CU to the gNB-DU may be determined based on the third information sent by the LMF to the gNB-CU. Alternatively, the third information sent by the gNB-CU to the gNB-DU may be determined based on the gNB-CU implementation. For example, the gNB-CU may determine the third information to be sent to the gNB-DU based on Operation Administration and Maintenance (OAM) configuration information.

[0288] Optionally, the gNB-DU may receive an F1AP-based information request message sent by the gNB-CU.

[0289] In step 603, the gNB-DU may send an F1AP-based information feedback message to the gNB-CU.

[0290] In some embodiments, the F1AP-based information feedback message may specifically include but is not limited to: F1AP TRP information response information, F1AP TRP information update information, F1AP AI information feedback message, F1AP AI capability feedback information, F1AP AI information update message, F1AP AI capability update message, etc.

[0291] In some embodiments, the F1AP-based information feedback message includes fourth information, wherein the fourth information is used to indicate AI-related feedback information. The fourth information may be, for example, AI support information and / or AI resource information.

[0292] In some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; information about AI related resources.

[0293] In one possible implementation, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0294] The cell identifier can be used to indicate a specific cell. The TRP identifier can be used to indicate a specific TRP among multiple TRPs. The AI ​​model identifier can be used to indicate a specific AI model among multiple AI models distributedly deployed on the second node. The measurement values ​​supported by the AI ​​model can be used to indicate positioning methods and / or output values ​​that support AI-assisted measurement, such as UL-AoA, UL-SRS-RSRP, UL-RTOA, gNB-RxTxTimeDiff, Z-AoA, UL-SRS-RSRPP, etc. The signals supported by the AI ​​model are used to indicate uplink signals applicable to the AI ​​model, such as SRS, pos-SRS (positioning sounding reference signal), or other uplink signals. The performance information of the AI ​​model is used to indicate historical performance information of the AI ​​model, such as accuracy, confidence, etc. The node for AI model inference can be, for example, a gNB-CU, gNB-DU, a server directly connected to the base station, or OAM.

[0295] In one possible implementation, the information of the AI-related resources includes at least one of the following: a cell identifier; a TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0296] Optionally, the gNB-CU may receive the F1AP-based information feedback message sent by the gNB-DU.

[0297] In step 604, the gNB-CU may send an information feedback message based on NRPPa to the LMF.

[0298] In some embodiments, the NRPPa-based information feedback message may specifically include but is not limited to: NRPPa TRP information response information, NRPPa TRP information update information, NRPPa AI information feedback message, NRPPa AI capability feedback information, NRPPa AI information update message, NRPPa AI capability update message, etc.

[0299] In some embodiments, the NRPPa-based information feedback message may include fourth information, wherein the fourth information is used to indicate AI-related feedback information. The fourth information may be, for example, AI support information and / or AI resource information.

[0300] Optionally, the LMF may receive NRPPa-based information feedback messages sent by the gNB-CU.

[0301] In step 605, the LMF may determine whether to use AI-assisted positioning measurements.

[0302] In some embodiments, the LMF may determine to use AI-assisted positioning measurements based on the fourth information in the information feedback message based on NRPPa.

[0303] In step 606, the LMF may send a measurement request message based on NRPPa to the gNB-CU.

[0304] In some embodiments, the NRPPa-based measurement request message may specifically include but is not limited to: an NRPPa measurement request message, an NRPPa AI measurement request message, etc.

[0305] In some embodiments, the NRPPa-based measurement request message may include first information, wherein the first information is used to request AI-assisted positioning measurement. The first information may be, for example, AI-assisted measurement request information (AI assisted request).

[0306] Optionally, the gNB-CU may receive an NRPPa-based measurement request message sent by the LMF.

[0307] In step 607, the gNB-CU may send an F1AP-based measurement request message to the gNB-DU.

[0308] In some embodiments, the F1AP-based measurement request message may specifically include but is not limited to: an F1AP measurement request message, an F1AP AI measurement request message, etc.

[0309] In some embodiments, the F1AP-based measurement request message may include first information, wherein the first information is used to request AI-assisted positioning measurement. The first information may be, for example, AI-assisted measurement request information (AI assisted request).

[0310] Optionally, the gNB-DU may receive an F1AP-based measurement request message sent by the gNB-CU.

[0311] In step 608, the gNB-DU may perform AI-assisted positioning measurements.

[0312] In some embodiments, the gNB-DU may perform positioning measurements on multiple TRPs based on the first information to obtain positioning measurement results; use the positioning measurement results obtained on each TRP as input data, input the corresponding AI model for inference, obtain the inference results output by the AI ​​model, and determine the inference results output by the AI ​​model as the AI-assisted positioning measurement results.

[0313] In one possible implementation, the gNB-DU can first process the positioning measurement results and then input the processed positioning measurement results into the corresponding AI model for inference. For example, the positioning measurement results can first be processed by SVD, and the eigenvalues ​​after SVD processing can then be input into the AI ​​model.

[0314] In step 609, the gNB-DU may send an F1AP-based measurement feedback message to the gNB-CU.

[0315] In some embodiments, the F1AP-based measurement feedback message may specifically include but is not limited to: an F1AP measurement response message, an F1AP measurement report message, an F1AP AI measurement feedback message, an F1AP AI measurement report message, etc.

[0316] In some embodiments, the F1AP-based measurement feedback message may include second information, wherein the second information is used to indicate an AI-assisted positioning measurement result. The second information may be, for example, an AI-assisted mean result.

[0317] In some embodiments, the gNB-CU may receive F1AP-based measurement feedback messages sent by the gNB-DU.

[0318] In step 610, the gNB-CU may send a measurement feedback message based on NRPPa to the LMF.

[0319] In some embodiments, 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 measurement feedback message, an NRPPa AI measurement report message, and the like.

[0320] In some embodiments, the NRPPa-based measurement feedback message may include second information, wherein the second information is used to indicate an AI-assisted positioning measurement result. The second information may be, for example, an AI-assisted mean result.

[0321] In some embodiments, the LMF may receive NRPPa-based measurement feedback messages sent by the gNB-CU.

[0322] In some embodiments, the LMF may locate the terminal according to the second information in the measurement feedback message based on NRPPa.

[0323] Corresponding to the aforementioned embodiments of the positioning measurement method, the present disclosure also provides embodiments of a positioning measurement device.

[0324] FIG7 is a schematic block diagram of a first device according to an embodiment of the present disclosure. As shown in FIG7 , the first device 700 includes a transceiver module 701 and a processing module 702 .

[0325] In some embodiments, the transceiver module is configured to receive a first message sent by a second device, wherein the first message includes first information, and the first information is configured to request AI-assisted positioning measurement;

[0326] The processing module is configured to perform AI-assisted positioning measurement according to the first information;

[0327] The transceiver module is further used to send a second message to the second device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0328] In some embodiments, the second information includes at least one of the following: AI-assisted positioning measurement results; AI-assisted measurement indication; inference accuracy; confidence.

[0329] In some embodiments, the transceiver module is further configured to receive a third message sent by the second device, wherein the third message includes third information, and the third information is used to indicate AI-related request information;

[0330] The transceiver module is further used to send a fourth message to the second device, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0331] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; the type of AI-related resource information requested.

[0332] In some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; information about AI related resources.

[0333] In some embodiments, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0334] In some embodiments, the information of the AI-related resources includes at least one of the following: cell identifier; TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0335] FIG8 is a schematic block diagram of a second device according to an embodiment of the present disclosure. As shown in FIG8 , the second device 800 includes a transceiver module 801 .

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

[0337] The transceiver module is further used to receive a second message sent by the first device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

[0338] In some embodiments, the second information includes at least one of the following: AI-assisted positioning measurement results; AI-assisted measurement indication; inference accuracy; confidence.

[0339] In some embodiments, the transceiver module is further configured to send a third message to the first device, wherein the third message includes third information, and the third information is used to indicate AI-related request information;

[0340] The transceiver module is further used to receive a fourth message sent by the first device, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

[0341] In some embodiments, the third information includes at least one of the following: the type of AI model-related information requested; the type of AI-related resource information requested.

[0342] In some embodiments, the fourth information includes at least one of the following: information related to the AI ​​model; information about AI related resources.

[0343] In some embodiments, the AI ​​model-related information includes at least one of the following: cell identifier; TRP identifier; AI model identifier; measurement values ​​supported by the AI ​​model; signals supported by the AI ​​model; performance information of the AI ​​model; output delay of the AI ​​model; and nodes of AI model inference.

[0344] In some embodiments, the information of the AI-related resources includes at least one of the following: cell identifier; TRP identifier; available resources for AI computing power; available resources for AI storage; available resources for AI energy consumption; and available resources for AI measurement.

[0345] It should be noted that the modules included in the first device 700 and / or the second device 800 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] 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.

[0354] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0355] Figure 9 is a schematic diagram of the structure of a communication device 9100 proposed in an embodiment of the present disclosure. Communication device 9100 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 9100 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.

[0356] As shown in Figure 9, the communication device 9100 includes one or more processors 9101. The processor 9101 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 9101 is used to call instructions to enable the communication device 9100 to perform any of the above methods.

[0357] In some embodiments, the communication device 9100 further includes one or more memories 9102 for storing instructions. Optionally, all or part of the memories 9102 may be located outside the communication device 9100.

[0358] In some embodiments, the communication device 9100 further includes one or more transceivers 9103. When the communication device 9100 includes one or more transceivers 9103, the communication steps such as sending and receiving in the above method are performed by the transceiver 9103, and the other steps are performed by the processor 9101.

[0359] 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.

[0360] Optionally, the communication device 9100 further includes one or more interface circuits 9104, which are connected to the memory 9102. The interface circuits 9104 can be used to receive signals from the memory 9102 or other devices, and can be used to send signals to the memory 9102 or other devices. For example, the interface circuits 9104 can read instructions stored in the memory 9102 and send the instructions to the processor 9101.

[0361] The communication device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 9100 described in the present disclosure is not limited thereto, and the structure of the communication device 9100 may not be limited by FIG. 9 . 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.

[0362] FIG10 is a schematic diagram of the structure of a chip 10200 according to an embodiment of the present disclosure. If the communication device 9100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 10200 shown in FIG10 , but the present disclosure is not limited thereto.

[0363] Chip 10200 includes one or more processors 10201, and processor 10201 is used to call instructions to enable chip 10200 to execute any of the above methods.

[0364] In some embodiments, the chip 10200 further includes one or more interface circuits 10202, which are connected to the memory 10203. The interface circuit 10202 can be used to receive signals from the memory 10203 or other devices, and can be used to send signals to the memory.

[0365] 10203 or other devices to send signals. For example, the interface circuit 10202 can read the instructions stored in the memory 10203 and send the instructions to the processor 10201. Optionally, the terms interface circuit, interface, transceiver pin, transceiver, etc. can be used interchangeably.

[0366] In some embodiments, the chip 10200 further includes one or more memories 10203 for storing instructions. Alternatively, all or part of the memories 10203 may be outside the chip 10200.

[0367] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 9100, causes the communication device 9100 to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0368] The present disclosure also provides a program product, which, when executed by the communication device 9100, enables the communication device 9100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0369] 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: Receiving a first message sent by a second node, wherein the first message includes first information, and the first information is used to request artificial intelligence (AI)-assisted positioning measurement; Performing AI-assisted positioning measurement according to the first information; Send a second message to the second node, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

2. The method according to claim 1, characterized in that The second information includes at least one of the following: AI-assisted positioning measurements; AI-assisted measurement instructions; Inference accuracy; Confidence.

3. The method according to any one of claims 1 to 2, characterized in that The method further comprises: receiving a third message sent by the second node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; Send a fourth message to the second node, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

4. The method according to claim 3, characterized in that The third information includes at least one of the following: The type of AI model-related information requested; The type of AI-related resource information requested.

5. The method according to claim 4, characterized in that The fourth information includes at least one of the following: Information related to AI models; Information about AI-related resources.

6. The method according to claim 5, characterized in that The AI ​​model related information includes at least one of the following: Cell ID; Transmission receiving point TRP identifier; AI model identification; Measurements supported by the AI ​​model; Signals supported by AI models; Performance information of AI models; Output latency of AI models; Node for AI model reasoning.

7. The method according to claim 5, characterized in that The information of the AI-related resources includes at least one of the following: Cell ID; TRP logo; Available resources for AI computing power; Available resources for AI storage; Available resources for AI energy consumption; Available resources for AI measurement.

8. The method according to any one of claims 1 to 7, characterized in that The first node includes an access network node, and the second node includes a core network node.

9. The method according to any one of claims 1 to 7, characterized in that The first node comprises a distributed unit in an access network node, and the second node comprises a central unit in the access network node.

10. The method according to any one of claims 1 to 9, characterized in that Multiple AI models are distributedly deployed on the first node.

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

12. The method according to claim 11, characterized in that The second information includes at least one of the following: AI-assisted positioning measurements; AI-assisted measurement instructions; Inference accuracy; Confidence.

13. The method according to any one of claims 11 to 12, characterized in that The method further comprises: Sending a third message to the first node, wherein the third message includes third information, and the third information is used to indicate AI-related request information; A fourth message sent by the first node is received, wherein the fourth message includes fourth information, and the fourth information is used to indicate AI-related feedback information.

14. The method according to claim 13, characterized in that The third information includes at least one of the following: The type of AI model-related information requested; The type of AI-related resource information requested.

15. The method according to claim 14, characterized in that The fourth information includes at least one of the following: Information related to AI models; Information about AI-related resources.

16. The method according to claim 15, characterized in that The AI ​​model related information includes at least one of the following: Cell ID; TRP logo; AI model identification; Measurements supported by the AI ​​model; Signals supported by AI models; Performance information of AI models; Output latency of AI models; Node for AI model reasoning.

17. The method according to claim 15, characterized in that The information of the AI-related resources includes at least one of the following: Cell ID; TRP logo; Available resources for AI computing power; Available resources for AI storage; Available resources for AI energy consumption; Available resources for AI measurement.

18. The method according to any one of claims 11 to 17, characterized in that The first node includes an access network node, and the second node includes a core network node.

19. The method according to any one of claims 11 to 17, characterized in that The first node comprises a distributed unit in an access network node, and the second node comprises a central unit in the access network node.

20. The method according to any one of claims 11 to 19, characterized in that Multiple AI models are distributedly deployed on the first node.

21. A first device, characterized in that: The device comprises: A transceiver module, configured to receive a first message sent by a second device, wherein the first message includes first information, and the first information is used to request AI-assisted positioning measurement; A processing module, configured to perform AI-assisted positioning measurement according to the first information; The transceiver module is further used to send a second message to the second device, wherein the second message includes second information, and the second information is used to indicate the AI-assisted positioning measurement result.

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

23. 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 10.

24. 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 11-20.

25. 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 1-10 or 11-20.

26. 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 10, and the second device is configured to implement the positioning measurement method according to any one of claims 11 to 20.

27. 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 1-10 or 11-20.

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