Positioning method and device and storage medium

Abstract

The invention relates to a positioning method and device and a storage medium. The method executed by a first node comprises the following steps: determining positioning measurement data of a first application scene; a first positioning AI model corresponding to the first application scene is determined, and the first positioning AI model comprises a common layer universal for different application scenes and a first adaptive layer special for the first application scene; and determining positioning position information according to the positioning measurement data and the first positioning AI model. Therefore, different application scenes can share the common layer, the storage overhead of the positioning AI model of different application scenes can be reduced, and AI-based high-precision terminal positioning is realized.

Description

Positioning method, device and storage medium Technical Field

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

[0002] With the continuous development of artificial intelligence (AI) technology in recent years, AI-based solutions have been widely used in the field of wireless communication technology.

[0003] Summary of the Invention

[0004] The embodiments of the present disclosure provide a positioning method, device, and storage medium for solving the problem of large storage overhead when setting corresponding positioning AI models for different application scenarios.

[0005] The embodiments of the present disclosure provide a positioning method, device, and storage medium.

[0006] According to a first aspect of an embodiment of the present disclosure, a positioning method is proposed. The method is executed by a first node, including: determining positioning measurement data of a first application scenario; determining a first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; and determining positioning position information based on the positioning measurement data and the first positioning AI model.

[0007] In the above embodiment, the first node determines the first positioning AI model corresponding to the first application scenario of the positioning measurement data. The first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario. Then, the first node can locate the terminal based on the first positioning AI model and the positioning measurement data. Since different application scenarios can use the common layer, the storage overhead of the positioning AI models of different application scenarios can be reduced, thereby achieving high-precision terminal positioning based on AI.

[0008] According to a second aspect of an embodiment of the present disclosure, a positioning method is proposed, which is executed by a second node, including: sending positioning information or positioning measurement data of a first application scenario to a first node, wherein the first application scenario is used by the first node to determine a first positioning AI model, the positioning information is used by the first node to determine the positioning measurement data, and the positioning measurement data and the first positioning AI model are used by the first node to determine positioning position information.

[0009] In the above embodiment, the second node can provide the first node with positioning information or positioning measurement data of the first application scenario, so as to determine the first positioning AI model corresponding to the first application scenario based on the positioning measurement data of the first application scenario at the first node, and locate the terminal based on the positioning measurement data and the first positioning AI model. Since different application scenarios can use a common layer, the storage overhead of the positioning AI models of different application scenarios can be reduced, thereby achieving high-precision terminal positioning based on AI.

[0010] According to a third aspect of an embodiment of the present disclosure, a first node is provided, including: a processing module for determining positioning measurement data of a first application scenario; the processing module is also used to determine a first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; the processing module is also used to determine positioning position information based on the positioning measurement data and the first positioning AI model.

[0011] According to a fourth aspect of an embodiment of the present disclosure, a second node is provided, including: a transceiver module, used to send positioning information or positioning measurement data of a first application scenario to a first node, wherein the first application scenario is used for the first node to determine a first positioning AI model, the positioning information is used for the first node to determine the positioning measurement data, and the positioning measurement data and the first positioning AI model are used for the first node to determine positioning position information.

[0012] According to a fifth aspect of an embodiment of the present disclosure, a communication device is provided, comprising: one or more processors; wherein the communication device is used to execute the method described in the first aspect above.

[0013] According to a sixth aspect of an embodiment of the present disclosure, a communication device is provided, comprising: one or more processors; wherein the communication device is used to execute the method described in the second aspect above.

[0014] According to the seventh aspect of an embodiment of the present disclosure, a communication system is provided, characterized in that it includes a first node and a second node, wherein the first node is configured to implement the method described in the first aspect above, and the second node is configured to implement the method described in the second aspect above.

[0015] According to an eighth aspect of an embodiment of the present disclosure, a storage medium is provided, which stores instructions, and is characterized in that when the instructions are executed on a communication device, the communication device executes the method described in the first and second aspects above. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0017] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure;

[0018] FIG2 is a flow chart of a positioning method provided by an embodiment of the present disclosure;

[0019] FIG3 is a schematic diagram of an implementation of an AI-based positioning technology provided by an embodiment of the present disclosure;

[0020] FIG4 is a schematic diagram of an application mode of the AI-based positioning technology provided by an embodiment of the present disclosure;

[0021] FIG5A is an interactive flow chart of a method for determining positioning measurement data provided by an embodiment of the present disclosure;

[0022] FIG5B is an interactive flow chart of another method for determining positioning measurement data provided by an embodiment of the present disclosure;

[0023] FIG6A is a flowchart of a method for determining a positioning AI model provided by an embodiment of the present disclosure;

[0024] FIG6B is a flowchart of another method for determining a positioning AI model provided by an embodiment of the present disclosure;

[0025] FIG7 is an interactive flow chart of a positioning method provided by an embodiment of the present disclosure;

[0026] FIG8 is a schematic diagram of a model application of a positioning method provided by an embodiment of the present disclosure;

[0027] FIG9A is a structural diagram of a first node provided by an embodiment of the present disclosure;

[0028] FIG9B is a structural diagram of a second node provided by an embodiment of the present disclosure;

[0029] FIG10A is a structural diagram of a communication device provided by an embodiment of the present disclosure;

[0030] FIG10B is a schematic structural diagram of a chip provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The embodiments of the present disclosure provide a positioning method, device, and storage medium.

[0032] In a first aspect, an embodiment of the present disclosure proposes a positioning method, which is executed by a first node, including: determining positioning measurement data of a first application scenario; determining a first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; determining positioning position information based on the positioning measurement data and the first positioning AI model.

[0033] In the above embodiment, the first node determines the first positioning AI model corresponding to the first application scenario of the positioning measurement data. The first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario. The terminal can then be positioned based on the first positioning AI model and the positioning measurement data. Since different application scenarios can use the common layer, the storage overhead of the positioning AI models of different application scenarios can be reduced, thereby achieving high-precision terminal positioning based on AI.

[0034] In combination with some embodiments of the first aspect, in some embodiments, the first node determines the positioning measurement data of the first application scenario, including: receiving the positioning measurement data of the first application scenario sent by the second node, and determining the positioning measurement data; or receiving the positioning information of the first application scenario sent by the second node, and determining the positioning measurement data based on the positioning information.

[0035] In the above embodiment, the first node may obtain the positioning measurement data of the first application scenario from the second node, so as to locate the terminal in combination with the positioning measurement data of the first application scenario.

[0036] In combination with some embodiments of the first aspect, in some embodiments, the first node determines the first positioning AI model corresponding to the first application scenario, including: determining a positioning AI model group composed of positioning AI models corresponding to different application scenarios, wherein the positioning AI model group includes a common layer common to different application scenarios and an adaptive layer dedicated to different application scenarios; according to the first application scenario, determining the first positioning AI model corresponding to the first application scenario in the positioning AI model group.

[0037] In the above embodiment, the first node can determine the first positioning AI model corresponding to the first application scenario from the positioning AI model group composed of positioning AI models corresponding to different application scenarios. The positioning AI model group includes a common layer common to different application scenarios and an adaptive layer dedicated to different application scenarios. It can reduce the storage overhead of positioning AI models for different application scenarios and realize high-precision terminal positioning based on AI.

[0038] In combination with some embodiments of the first aspect, in some embodiments, the first node determines a positioning AI model group composed of positioning AI models corresponding to different application scenarios, including: obtaining a training data set and an initial positioning AI model, wherein the training data set includes training sample data for different application scenarios; training the initial positioning AI model according to the training data set to determine an intermediate positioning AI model, wherein the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios; training the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario; determining the positioning AI model group according to the positioning AI models corresponding to different application scenarios.

[0039] In the above embodiment, the first node can train the initial positioning AI model based on the training data set to determine the positioning AI model group. When using the positioning AI model group to locate the terminal, it can effectively reduce the storage overhead of the positioning AI model for different application scenarios in the communication device.

[0040] In combination with some embodiments of the first aspect, in some embodiments, the first node trains the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario, including: fixing the common layer of the intermediate positioning AI model, adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determining the positioning AI model corresponding to each application scenario.

[0041] In the above embodiment, the first node trains the intermediate positioning AI model, fixes the common layer of the intermediate positioning AI model, adjusts the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determines the positioning AI model corresponding to each application scenario. Then, based on the positioning AI models corresponding to different application scenarios, the positioning AI model group can be determined. When the positioning AI model group is used to locate the terminal, the storage overhead of the positioning AI models for different application scenarios in the communication equipment can be effectively reduced.

[0042] In combination with some embodiments of the first aspect, in some embodiments, the training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

[0043] In the above embodiment, the initial positioning AI model may be trained based on the sample positioning measurement data and the sample terminal location information, or the initial positioning AI model may be trained based on the sample positioning measurement data and the sample positioning intermediate parameters.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the first node determines a positioning AI model group composed of positioning AI models corresponding to different application scenarios, including: receiving indication information sent by the second node or the third node, wherein the indication information is used to indicate the positioning AI model group; and determining the positioning AI model group according to the indication information.

[0045] In the above embodiment, the first node can obtain the positioning AI model group from the second node or the third node to use the positioning AI model group to realize the positioning of the terminal. Since the positioning AI model group has a common layer for different application scenarios, the storage overhead of the positioning AI models of different application scenarios can be reduced, and high-precision terminal positioning based on AI can be achieved.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the first node determines the positioning position information based on the positioning measurement data and the first positioning AI model, including: processing the positioning measurement data through the common layer and the first adaptive layer to determine the positioning position information.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the above method also includes: the first node sends a positioning intermediate parameter to the third node, wherein the positioning position information is the positioning intermediate parameter; or determines the terminal position information based on the positioning intermediate parameter, and sends the terminal position information to the third node, wherein the positioning position information is the positioning intermediate parameter; or sends the terminal position information to the third node, wherein the positioning position information is the terminal position information.

[0048] In the above embodiment, after determining the positioning position information as the positioning intermediate parameter or the terminal location information, the first node can report it to the third node, or the first node can determine the terminal location information based on the positioning position information as the positioning intermediate parameter, and then report it to the third node.

[0049] In combination with some embodiments of the first aspect, in some embodiments, the first node, the second node, and the positioning measurement data are selected from one of the following: the first node is a terminal, the second node is an access network device, the positioning information is a positioning reference signal PRS, and the positioning measurement data is channel measurement data based on PRS; the first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on PRS; the first node is an access network device, the second node is a terminal, the positioning information is a sounding reference signal-positioning SRS-Pos, and the positioning measurement data is channel measurement data based on SRS-Pos; the first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on SRS-Pos.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the first node is a terminal, the second node is an access network device, and the third node is a core network device; or the first node is an access network device, the second node is a terminal, and the third node is a core network device.

[0051] In combination with some embodiments of the first aspect, in some embodiments, different application scenarios include at least one of the following: different channel scenarios; different channel parameter configurations; different network coverage ranges.

[0052] In the second aspect, an embodiment of the present disclosure proposes a positioning method, which is executed by a second node, including: sending positioning information or positioning measurement data of a first application scenario to a first node, wherein the first application scenario is used by the first node to determine a first positioning AI model, the positioning information is used by the first node to determine the positioning measurement data, and the positioning measurement data and the first positioning AI model are used by the first node to determine the positioning position information.

[0053] In the above embodiment, the second node can provide the first node with positioning information or positioning measurement data of the first application scenario, so as to determine the first positioning AI model corresponding to the first application scenario based on the positioning measurement data of the first application scenario at the first node, and locate the terminal based on the positioning measurement data and the first positioning AI model. Since different application scenarios can use a common layer, the storage overhead of the positioning AI models of different application scenarios can be reduced, thereby achieving high-precision terminal positioning based on AI.

[0054] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: the second node sends indication information to the first node, wherein the indication information is used to indicate a positioning AI model group composed of positioning AI models corresponding to different application scenarios.

[0055] In the above embodiment, the second node can provide the first node with a positioning AI model group composed of positioning AI models corresponding to different application scenarios, so that the terminal can be positioned at the first node using the positioning AI model group for different application scenarios.

[0056] In combination with some embodiments of the second aspect, in some embodiments, the above method also includes: the second node determines the positioning AI model group.

[0057] In combination with some embodiments of the second aspect, in some embodiments, the second node determines the positioning AI model group, including: obtaining a training data set and an initial positioning AI model, wherein the training data set includes training sample data for different application scenarios; training the initial positioning AI model according to the training data set to determine an intermediate positioning AI model, wherein the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios; training the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario; and determining the positioning AI model group according to the positioning AI models corresponding to different application scenarios.

[0058] In the above embodiment, the second node can use the training data set to train the initial positioning AI model to obtain a positioning AI model group, so that when the positioning AI model group is used to locate the terminal, the storage overhead of the positioning AI model for different application scenarios in the communication device can be effectively reduced.

[0059] In combination with some embodiments of the second aspect, in some embodiments, the second node trains the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario, including: fixing the common layer of the intermediate positioning AI model, adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determining the positioning AI model corresponding to each application scenario.

[0060] In the above embodiment, the second node trains the intermediate positioning AI model, fixes the common layer of the intermediate positioning AI model, adjusts the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determines the positioning AI model corresponding to each application scenario. Then, according to the positioning AI models corresponding to different application scenarios, the positioning AI model group can be determined, so that when the positioning AI model group is used to locate the terminal, the storage overhead of the positioning AI models for different application scenarios in the communication equipment can be effectively reduced.

[0061] In combination with some embodiments of the second aspect, in some embodiments, the training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

[0062] In combination with some embodiments of the second aspect, in some embodiments, the first node, the second node, and the positioning measurement data are selected from one of the following: the first node is a terminal, the second node is an access network device, the positioning information is PRS, and the positioning measurement data is channel measurement data based on PRS; the first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on PRS; the first node is an access network device, the second node is a terminal, the positioning information is SRS-Pos, and the positioning measurement data is channel measurement data based on SRS-Pos; the first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on SRS-Pos.

[0063] In combination with some embodiments of the second aspect, in some embodiments, different application scenarios include at least one of the following: different channel scenarios; different channel parameter configurations; different network coverage ranges.

[0064] In a third aspect, an embodiment of the present disclosure proposes a first node, which includes at least one of a transceiver module and a processing module; wherein the first node is used to execute an optional implementation method of the first aspect.

[0065] In a fourth aspect, an embodiment of the present disclosure proposes a second node, wherein the above-mentioned first node includes at least one of a transceiver module and a processing module; wherein the above-mentioned second node is used to execute the optional implementation method of the second aspect.

[0066] In a fifth aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; wherein the communication device is used to execute the optional implementation method of the first aspect.

[0067] In a sixth aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; wherein the communication device is used to execute the optional implementation method of the second aspect.

[0068] In the seventh aspect, an embodiment of the present disclosure proposes a communication system, which includes: a first node and a second node; wherein the first node is configured to execute the method described in the optional implementation manner of the first aspect, and the second node is configured to execute the method described in the optional implementation manner of the second aspect.

[0069] In an eighth aspect, an embodiment of the present disclosure proposes a storage medium, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

[0070] In a ninth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation of the first and second aspects.

[0071] In a tenth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first and second aspects.

[0072] In an eleventh aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0073] It is understandable that the first node, the second node, the communication device, the communication system, the storage medium, the program product, the computer program, the chip, or the chip system 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.

[0074] The embodiments of the present disclosure provide a positioning method, apparatus, and storage medium. In some embodiments, the terms positioning method, information processing method, communication method, etc. can be used interchangeably.

[0075] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0076] 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.

[0077] 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.

[0078] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0079] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0080] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0081] 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.

[0082] 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.

[0083] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0084] 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.

[0085] In some embodiments, terms such as "time / frequency" and "time / frequency domain" refer to the time domain and / or the frequency domain.

[0086] 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.

[0087] 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.

[0088] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0089] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0095] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0096] 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.

[0097] FIG1 is an architecture diagram of a communication system provided by an embodiment of the present disclosure.

[0098] As shown in FIG1 , a communication system 100 includes a terminal 101 , an access network device 102 , and a core network device 103 .

[0099] In some embodiments, the terminal 101 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.

[0100] In some embodiments, the terminal may also be referred to as a terminal device, a user device, etc., and the names are interchangeable, and the embodiments of the present disclosure do not impose specific limitations on this.

[0101] In some embodiments, the access network device 102 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.

[0102] In some embodiments, the access network device 102 may also be a satellite.

[0103] In some embodiments, the core network device 103 may be a device including multiple network elements such as a first network element and a second network element, or may be multiple devices or a group of devices, each including multiple network functions. A network element may also be referred to as a network function, which 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).

[0104] In some embodiments, the first network element is, for example, an access and mobility management function (AMF).

[0105] In some embodiments, the first network element is, for example, a location management function (LMF).

[0106] In some embodiments, the second network element is, for example, a unified data management (UDM).

[0107] In some embodiments, the first network element is used for access control and mobility management of the terminal accessing the operator network, including, for example, mobility status management, allocation of user temporary identity, authentication and authorization of users, etc., and its name is not limited thereto.

[0108] In some embodiments, the first network element is used to provide positioning services for terminals and other devices.

[0109] In some embodiments, the second network element is used to manage subscription information of the terminal.

[0110] 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.

[0111] 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.

[0112] The embodiments of the present disclosure may be applied to long term evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, ultra mobile broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, ultra-wideband (UWB), and the like. band, UWB), Bluetooth (registered trademark), public land mobile network (PLMN) network, device-to-device (D2D) system, machine-to-machine (M2M) system, Internet of Things (IoT) system, vehicle-to-everything (V2X), systems using other communication methods, and next-generation systems based on them. In addition, multiple systems can also be combined (for example, a combination of LTE or LTE-A and 5G) for application.

[0113] In recent years, high-precision positioning technology has become a hot research topic to meet the demand for location-based services in various commercial service scenarios and industrial IoT scenarios. It facilitates services such as indoor navigation, augmented and virtual reality, and autonomous driving. Implementing high-precision positioning services based on wireless communication network infrastructure is a key area of ​​mobile communication research and a key component of wireless communication standardization research throughout history.

[0114] The 3rd Generation Partnership Project (3GPP) introduced a variety of methods, such as time measurement and angle-based methods, in the NR release-16 standard to achieve high-precision positioning in indoor and outdoor scenarios. With the continuous increase in related business needs, 3GPP launched a project in release-17 to enhance positioning accuracy for commercial scenarios and industrial internet of things (IIOT) scenarios, with the goal of achieving high-precision positioning at the decimeter level, and is committed to meeting the high-precision location service needs of the consumer and enterprise markets. However, the positioning algorithms in related technologies, such as the time difference of arrival (TDOA) and multi-round trip time (multi-RTT) methods specified in the 3GPP standard, are difficult to meet the very strict positioning accuracy requirements in scenarios such as IIoT.

[0115] With the continuous development of artificial intelligence technology in recent years, AI-based solutions have been widely used in the field of wireless communication technology. Deep neural network models can effectively complete complex data processing and feature modeling processes, and have now been applied to solve many key problems in wireless communications. Terminal positioning based on AI is an important application case of artificial intelligence technology in the field of communications, and is also one of the research directions of 3GPP standardization. The technical solution based on AI positioning can use the positioning AI model (deep neural network model) to model the mapping relationship between channel measurement data and terminal location coordinates. The main application process of the terminal positioning solution based on deep learning is: after using training data to complete the network model training (model training) of the AI ​​model used for terminal positioning, the trained positioning AI model is stored, and the trained positioning AI model is used to complete the terminal positioning work in the actual system, that is, model inference (model inference). Compared with traditional positioning methods, it can achieve higher positioning accuracy.

[0116] In related technologies, AI-based positioning solutions can model the mapping relationship between channel measurement data and the terminal's location information based on a positioning AI model. This can then be combined with the positioning AI model to predict the location information based on the actual channel measurement data. However, due to the significant differences in the characteristic distribution of channel measurement data across different application scenarios, a single positioning AI model cannot effectively predict location information based on channel measurement data across different application scenarios.

[0117] Therefore, related technologies usually use data from different application scenarios to train and store positioning AI models for different application scenarios. However, the number of parameters of positioning AI models is usually large, and the training and storage of positioning AI models has the problem of high storage overhead.

[0118] Based on this, in an embodiment of the present disclosure, a positioning method, device, and storage medium are provided. The method executed by the first node includes: determining positioning measurement data for a first application scenario; determining a first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; and determining positioning location information based on the positioning measurement data and the first positioning AI model. Thus, different application scenarios can use a common layer, which can reduce the storage overhead of positioning AI models for different application scenarios and achieve high-precision terminal positioning based on AI.

[0119] FIG2 is a schematic diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2 , the embodiment of the present disclosure relates to a positioning method, which includes:

[0120] S201: The first node determines positioning measurement data of a first application scenario, and determines a first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario.

[0121] In some embodiments, the first node determines the positioning measurement data of the first application scenario by itself, or the first node receives the positioning measurement data of the first application scenario sent by other nodes and determines the positioning measurement data of the first application scenario based on the data.

[0122] In some embodiments, as shown in FIG3 , the positioning AI model includes two types of positioning, namely direct positioning based on the positioning AI model as shown in FIG3 , that is, the positioning AI model directly determines the position of the terminal or other network element to be positioned based on the measurement data used for positioning; and indirect positioning assisted by the positioning AI model as shown in FIG3 , that is, the positioning AI model generates intermediate positioning parameters based on the measurement data used for positioning, and the network element performing positioning uses the intermediate positioning parameters generated by the positioning AI model for positioning. Specifically, for direct positioning based on the positioning AI model: the input of the positioning AI model is the channel measurement data used for positioning, such as positioning measurement data, and the output is terminal location information, such as terminal location coordinates. For indirect positioning assisted by the positioning AI model: the input of the positioning AI model is the channel measurement data used for positioning, such as positioning measurement data, and the output is positioning intermediate parameters. The positioning intermediate parameters may include time of arrival (TOA), angle of arrival (AOA), etc. Based on the positioning intermediate parameters, the terminal location information, such as terminal location coordinates, can be calculated using traditional positioning methods.

[0123] In the embodiments of the present disclosure, in the direct positioning and indirect positioning methods, the input of the positioning AI model is positioning measurement data.

[0124] In some embodiments, the first node is a terminal, an access network device, or a core network device.

[0125] In some embodiments, the first node receives positioning information of the first application scenario sent by the second node, and determines positioning measurement data based on the positioning information; or receives positioning information of the first application scenario sent by the second node, and determines positioning measurement data based on the positioning information.

[0126] In some embodiments, as shown in FIG4 , the terminal positioning solution based on the positioning AI model includes five application modes.

[0127] Mode 1: Direct or indirect positioning on the terminal side: The terminal calculates the measurement value used for positioning, such as the channel impulse response (CIR), based on the positioning reference signal (PRS) sent by the access network equipment (base station, BS). The CIR is input into the positioning AI model to directly obtain the terminal's location coordinates. Alternatively, the terminal inputs the measurement value used for positioning into the AI ​​model to obtain intermediate positioning parameters such as ToA and AoA, and then uses traditional positioning methods such as TDOA to obtain the terminal's location coordinates. After obtaining the positioning result, i.e., the location coordinates, the terminal reports the result to the core network equipment (such as LMF).

[0128] Mode 2: Terminal-assisted indirect positioning on the LMF side: The terminal calculates the measurement value used for positioning, such as CIR, based on the PRS sent by the BS, inputs the positioning AI model to obtain intermediate positioning parameters such as ToA and AoA, and reports the intermediate positioning parameters to the LMF. The LMF uses traditional positioning methods such as TDOA to obtain the terminal position coordinates.

[0129] Mode 3: Terminal-assisted direct positioning on the LMF side: The terminal calculates the measurement value used for positioning, such as CIR, based on the PRS sent by the BS, and reports the measurement value used for positioning to the LMF. The LMF inputs the received measurement value into the positioning AI model to obtain the terminal location coordinates.

[0130] Mode 4: BS-assisted indirect positioning on the LMF side: The BS calculates measurement values ​​for positioning, such as CIR, based on the SRS-Pos (sounding reference signal-position) sent by the terminal. The BS inputs the measurement values ​​for positioning into the positioning AI model to obtain intermediate positioning parameters such as ToA and AoA, and reports the intermediate positioning parameters to the LMF. The LMF uses traditional positioning methods such as TDOA to obtain the terminal position coordinates.

[0131] Mode 5: BS-assisted direct positioning on the LMF side: The BS calculates and obtains measurement values ​​used for positioning, such as CIR, based on the SRS sent by the terminal, and reports the measurement values ​​used for positioning to the LMF. The LMF inputs the positioning measurement values ​​into the positioning AI model to directly obtain the terminal location coordinates.

[0132] In the above five modes, data acquisition and model inference may be implemented on different devices, and the AI ​​model may be deployed on the terminal, BS or LMF respectively. For application modes 1, 2, and 4, the channel measurement data required to be input by the AI ​​model is obtained by the terminal or BS based on the positioning reference signal measurement. The AI ​​model is also deployed on the terminal or BS. Therefore, the terminal or BS can directly input the channel measurement data it obtains into the AI ​​model to obtain the positioning result, and does not involve the reporting and transmission of the channel measurement data used for positioning. For modes 3 and 5, the channel measurement data used for AI positioning needs to be first obtained by the terminal or BS based on the reference channel calculation, and then reported to the LMF. The AI ​​model is input on the LMF side and the positioning result is output.

[0133] In some embodiments, the first node, the second node, and the positioning measurement data are selected from one of the following: the first node is a terminal, the second node is an access network device, the positioning information is PRS, and the positioning measurement data is channel measurement data based on PRS; the first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on PRS; the first node is an access network device, the second node is a terminal, the positioning information is SRS-Pos, and the positioning measurement data is channel measurement data based on SRS-Pos; the first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on SRS-Pos.

[0134] In some embodiments, the positioning measurement data is a channel impulse response (CIR) calculated by the first node based on a received reference signal.

[0135] Exemplarily, when the first node is a terminal, the positioning measurement data is a CIR (eg, a downlink CIR) calculated by the terminal based on a positioning reference signal (PRS) sent by an access network device.

[0136] Exemplarily, when the first node is an access network device, the positioning measurement data is a CIR (eg, an uplink CIR) calculated by the access network device based on a sounding reference signal (SRS) sent by the terminal.

[0137] Exemplarily, when the first node is an access network device, the positioning measurement data is a CIR (eg, uplink CIR) calculated by the access network device based on a sounding reference signal-position (SRS-Pos) sent by the terminal for positioning.

[0138] In some embodiments, the positioning measurement data is received by the first node from other nodes.

[0139] Exemplarily, when the first node is a core network device, the positioning measurement data is PRS-based channel measurement data received by the core network device from the terminal.

[0140] Exemplarily, when the first node is a core network device, the positioning measurement data is SRS (or SRS-Pos)-based channel measurement data received by the core network device from the access network device.

[0141] In an embodiment of the present disclosure, when the first node determines the positioning measurement data of the first application scenario, it can determine the first positioning AI model corresponding to the first application scenario based on the first application scenario.

[0142] It can be understood that positioning AI models corresponding to different application scenarios can be deployed at the first node, or the first node can obtain positioning AI models corresponding to different application scenarios from other nodes. When the first node determines the first application scenario of the positioning measurement data, it can determine the first positioning AI model corresponding to the first application scenario, and then realize positioning based on the positioning measurement data and the first positioning AI model.

[0143] In some embodiments, the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario.

[0144] It is understandable that different positioning AI models can correspond to different application scenarios, wherein the positioning AI models corresponding to different application scenarios can have a common layer structure, such as a common layer common to different application scenarios, and the positioning AI models corresponding to different application scenarios can also include their own unique layer structure, such as an adaptive layer dedicated to each application scenario. Therefore, when storing positioning AI models for different application scenarios, only the common layers common to different application scenarios and the adaptive layers dedicated to different application scenarios can be stored, which can reduce the storage space occupied by storing the positioning AI models for different application scenarios and save storage overhead.

[0145] In an embodiment of the present disclosure, the first node can store a common layer common to different application scenarios and an adaptive layer dedicated to different application scenarios. When the first node determines the positioning measurement data of the first application scenario, it can determine the first positioning AI model corresponding to the first application scenario. The first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario.

[0146] It is understood that the first positioning AI model corresponding to the first application scenario is obtained by training the initial AI model using the sample positioning measurement data in the first application scenario. Of course, in the process of training the initial AI model using the sample positioning measurement data in the first application scenario to obtain the first positioning AI model, other data may also be combined, such as sample positioning measurement data from other application scenarios. This is not specifically limited in the present embodiment.

[0147] In some embodiments, different application scenarios include at least one of the following: different channel scenarios; different channel parameter configurations; different network coverage ranges.

[0148] In the embodiment of the present disclosure, different application scenarios include at least one of different channel scenarios, different channel parameter configurations, and different network coverage ranges.

[0149] In some embodiments, the first node determines the first positioning AI model corresponding to the first application scenario, including: determining a positioning AI model group composed of positioning AI models corresponding to different application scenarios, wherein the positioning AI model group includes a common layer common to different application scenarios and an adaptive layer dedicated to different application scenarios; according to the first application scenario, determining the first positioning AI model corresponding to the first application scenario in the positioning AI model group.

[0150] In an embodiment of the present disclosure, the first node can determine a positioning AI model group composed of positioning AI models corresponding to different application scenarios. The positioning AI model group includes a common layer common to different application scenarios and an adaptive layer dedicated to different application scenarios. Then, when the first node determines the first application scenario of the positioning measurement data, it can determine the first positioning AI model corresponding to the first application scenario in the positioning AI model group according to the first application scenario, and determine the first positioning AI model including a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario.

[0151] In some embodiments, the positioning AI model group is trained at the first node and stored at the first node, or the positioning AI model group is obtained by the first node from other nodes.

[0152] In some embodiments, the first node determines a positioning AI model group composed of positioning AI models corresponding to different application scenarios, including: obtaining a training data set and an initial positioning AI model, wherein the training data set includes training sample data for different application scenarios; training the initial positioning AI model according to the training data set to determine an intermediate positioning AI model, wherein the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios; training the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario; determining the positioning AI model group according to the positioning AI models corresponding to different application scenarios.

[0153] In the embodiment of the present disclosure, the positioning AI model group is obtained through model training at the first node.

[0154] The first node may first obtain a training data set and an initial positioning AI model. The training data set includes training sample data for different application scenarios. The initial AI model may adopt a neural network model or a machine learning model. The first node may then train the initial positioning AI model based on the training data set to determine an intermediate positioning AI model.

[0155] In some embodiments, the training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

[0156] In a possible implementation, the training sample data includes sample positioning measurement data and sample terminal location information.

[0157] Among them, the first node trains the initial positioning AI model according to the training data set to determine the intermediate positioning AI model. The training data set includes training sample data for different application scenarios. When the training sample data includes sample positioning measurement data and sample positioning location information, and the sample positioning location information includes sample terminal location information, the sample positioning measurement data can be input into the initial positioning AI model to obtain predicted terminal location information, and then the parameters of the initial positioning AI model are adjusted according to the predicted terminal location information and the sample terminal location information until the predicted terminal location information and the sample terminal location information meet specific conditions. For example, the loss value calculated according to the predicted terminal location information and the sample terminal location information is less than a specific value. At this time, the initial positioning AI model after parameter adjustment is determined to be the intermediate positioning AI model.

[0158] In another possible implementation, the training sample data includes sample positioning measurement data and sample positioning intermediate parameters.

[0159] Among them, the first node trains the initial positioning AI model according to the training data set to determine the intermediate positioning AI model. The training data set includes training sample data for different application scenarios. When the training sample data includes sample positioning measurement data and sample positioning position information, and the sample positioning position information includes sample positioning intermediate parameters, the sample positioning measurement data can be input into the initial positioning AI model to obtain predicted positioning intermediate parameters, and then the parameters of the initial positioning AI model are adjusted according to the predicted positioning intermediate parameters and the sample positioning intermediate parameters until the predicted positioning intermediate parameters and the sample positioning intermediate parameters meet specific conditions, for example, the loss value calculated according to the predicted positioning intermediate parameters and the sample positioning intermediate parameters is less than a specific value. At this time, the initial positioning AI model after parameter adjustment is determined to be the intermediate positioning AI model.

[0160] In the disclosed embodiments, the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios. In other words, both the common layer and the intermediate adaptive layer of the intermediate positioning AI model can be common to different scenarios. When determining the AI ​​model corresponding to each application scenario, the intermediate positioning AI model can be adjusted, especially the intermediate adaptive layer, to obtain an adaptive layer dedicated to each scenario, while the common layer can be used.

[0161] In an embodiment of the present disclosure, after determining the intermediate positioning AI model, the first node can train the intermediate positioning AI model based on the training sample data of each application scenario, determine the positioning AI model corresponding to each application scenario, and then determine the positioning AI model group based on the positioning AI models corresponding to different application scenarios.

[0162] In some embodiments, the first node trains the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario, including: fixing the common layer of the intermediate positioning AI model, adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determining the positioning AI model corresponding to each application scenario.

[0163] In an embodiment of the present disclosure, after determining the intermediate positioning AI model, the first node can train the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario.

[0164] Among them, the first node trains the intermediate positioning AI model according to the training sample data of each application scenario, and can fix the common layer of the intermediate positioning AI model, that is, keep the parameters of the common layer of the intermediate positioning AI model unchanged, and adjust the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, that is, adjust the parameters of the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario.

[0165] In a possible implementation, the training sample data includes sample positioning measurement data and sample terminal location information.

[0166] Among them, taking a certain application scenario as an example, the first node trains the intermediate positioning AI model according to the sample positioning measurement data and sample terminal location information of the application scenario, and can keep the parameters of the common layer of the intermediate positioning AI model unchanged, and input the sample positioning measurement data of the application scenario into the corresponding intermediate positioning AI model to obtain the predicted terminal location information, and then adjust the parameters of the intermediate adaptive layer of the intermediate positioning AI model according to the predicted terminal location information and the sample terminal location information until the predicted terminal location information and the sample terminal location information meet specific conditions, such as the loss value calculated according to the predicted terminal location information and the sample terminal location information is less than a specific value. At this time, it is determined that the intermediate positioning AI model after parameter adjustment is the positioning AI model corresponding to the application scenario.

[0167] In a possible implementation, the training sample data includes sample positioning measurement data and sample positioning intermediate parameters.

[0168] Among them, taking a certain application scenario as an example, the first node trains the intermediate positioning AI model according to the sample positioning measurement data and sample positioning intermediate parameters of the application scenario, and can keep the parameters of the common layer of the intermediate positioning AI model unchanged, and input the sample positioning measurement data of the application scenario into the corresponding intermediate positioning AI model to obtain the predicted positioning intermediate parameters, and then adjust the parameters of the intermediate adaptive layer of the intermediate positioning AI model according to the predicted positioning intermediate parameters and the sample positioning intermediate parameters until the predicted positioning intermediate parameters and the sample positioning intermediate parameters meet specific conditions, for example, the loss value calculated according to the predicted positioning intermediate parameters and the sample positioning intermediate parameters is less than a specific value. At this time, it is determined that the intermediate positioning AI model after parameter adjustment is the positioning AI model corresponding to the application scenario.

[0169] In some embodiments, the first node determines a positioning AI model group composed of positioning AI models corresponding to different application scenarios, including: receiving indication information sent by the second node or the third node, wherein the indication information is used to indicate the positioning AI model group; and determining the positioning AI model group according to the indication information.

[0170] In an embodiment of the present disclosure, a first node may receive indication information sent by a second node, the indication information being used to indicate a positioning AI model group, so that the first node may determine the positioning AI model group based on the indication information. In some embodiments, the first node is a terminal and the second node is an access network device; or the first node is an access network device and the second node is a terminal; or the first node is a core network device and the second node is an access network device; or the first node is a core network device and the second node is a terminal.

[0171] In an embodiment of the present disclosure, a first node may receive indication information sent by a third node, the indication information being used to indicate a positioning AI model group, so that the first node may determine the positioning AI model group based on the indication information. In some embodiments, the first node is a terminal and the third node is an access network device; or the first node is an access network device and the third node is a terminal; or the first node is a core network device and the third node is an access network device; or the first node is a core network device and the third node is a terminal; or the first node is at least one of a terminal, an access network device, and a core network device, and the second node is a server or a dedicated device.

[0172] S202: The first node determines positioning position information based on positioning measurement data and a first positioning AI model.

[0173] In the embodiment of the present disclosure, when the first node determines the positioning measurement data and the first positioning AI model, it can determine the positioning position information according to the positioning measurement data and the first positioning AI model.

[0174] In some embodiments, referring to the two types and five application modes of the above-mentioned positioning AI models, the first node determines the positioning position information based on the positioning measurement data and the first positioning AI model, and the positioning position information is the terminal position information or the positioning intermediate parameter.

[0175] In some embodiments, the first node determines the positioning location information based on the positioning measurement data and the first positioning AI model, including: processing the positioning measurement data through the common layer and the first adaptive layer to determine the positioning location information.

[0176] In one possible implementation, the layer structure of the first positioning AI model is a common layer + a first adaptive layer. When the first node determines the positioning measurement data and the first positioning AI model, it can process the positioning measurement data through the common layer and the first adaptive layer of the first positioning AI model to determine the positioning position information.

[0177] In another possible implementation, the layer structure of the first positioning AI model is a first adaptive layer + a common layer. When the first node determines the positioning measurement data and the first positioning AI model, it can process the positioning measurement data through the first adaptive layer and the common layer of the first positioning AI model to determine the positioning position information.

[0178] In another possible implementation, the shared layer includes multiple sub-shared layers, and the layer structure of the first positioning AI model is a partial sub-shared layer + a first adaptive layer + another partial sub-shared layer. When the first node determines the positioning measurement data and the first positioning AI model, it can process the positioning measurement data through the partial sub-shared layer, the first adaptive layer and another partial sub-shared layer of the first positioning AI model to determine the positioning position information.

[0179] In another possible implementation, the first adaptive layer includes multiple first sub-adaptive layers, and the layer structure of the first positioning AI model is part of the first adaptive layer + a common layer + another part of the first adaptive layer. When the first node determines the positioning measurement data and the first positioning AI model, it can process the positioning measurement data through part of the first sub-adaptive layer, the common layer and another part of the first sub-adaptive layer of the first positioning AI model to determine the positioning position information.

[0180] In another possible implementation, the common layer includes multiple sub-common layers, the first adaptive layer includes multiple first sub-adaptive layers, and the layer structure of the first positioning AI model is a cross-arrangement of multiple sub-common layers and multiple first sub-adaptive layers, or can also be any arrangement of multiple sub-common layers and multiple first sub-adaptive layers. When the first node determines the positioning measurement data and the first positioning AI model, it can process the positioning measurement data through the multiple sub-common layers and multiple first sub-adaptive layers of the first positioning AI model to determine the positioning position information.

[0181] In some embodiments, the first node sends the positioning intermediate parameters to the third node, wherein the positioning position information is the positioning intermediate parameters; or the first node determines the terminal position information based on the positioning intermediate parameters and sends the terminal position information to the third node, wherein the positioning position information is the positioning intermediate parameters; or the first node sends the terminal position information to the third node, wherein the positioning position information is the terminal position information.

[0182] In the embodiment of the present disclosure, when the positioning position information is an intermediate positioning parameter, the first node may send the intermediate positioning parameter to the third node after determining the intermediate positioning parameter.

[0183] In the embodiment of the present disclosure, when the positioning position information is a positioning intermediate parameter, after determining the positioning intermediate parameter, the first node can determine the terminal position information according to the positioning intermediate parameter, and then send the terminal position information to the third node.

[0184] In the embodiment of the present disclosure, when the positioning location information is terminal location information, the first node may send the terminal location information to the third node after determining the terminal location information.

[0185] In some embodiments, the first node is a terminal, the second node is an access network device, and the third node is a core network device; or the first node is an access network device, the second node is a terminal, and the third node is a core network device.

[0186] 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", "code element", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0187] In some embodiments, terms such as "uplink", "uplink", "physical uplink" can be interchangeable with each other, and terms such as "downlink", "downlink", "physical downlink" can be interchangeable with each other, and terms such as "side", "sidelink", "side communication", "sidelink communication", "direct connection", "direct link", "direct communication", "direct link communication" can be interchangeable with each other.

[0188] In some embodiments, the terms "downlink control information (DCI)", "downlink (DL) assignment", "DL DCI", "uplink (UL) grant", "UL DCI" and the like may be used interchangeably.

[0189] In some embodiments, terms such as "synchronization signal (SS)", "synchronization signal block (SSB)", "reference signal (RS)", "pilot", and "pilot signal" can be used interchangeably.

[0190] 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.

[0191] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0192] In some embodiments, terms such as "common layer", "universal layer", "public layer", and "shared layer" can be used interchangeably; terms such as "adaptive layer", "dedicated layer", "unique layer", and "exclusive layer" can be used interchangeably.

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

[0194] By implementing the embodiments of the present disclosure, the first node determines a first positioning AI model corresponding to a first application scenario of the positioning measurement data. The first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario. Then, the first node can perform terminal positioning based on the first positioning AI model and the positioning measurement data. Since different application scenarios can use the common layer, the storage overhead of the positioning AI models of different application scenarios can be reduced, thereby achieving high-precision terminal positioning based on AI.

[0195] FIG5A is an interactive diagram of a method for determining positioning measurement data according to an embodiment of the present disclosure. As shown in FIG5A , an embodiment of the present disclosure relates to a method for determining positioning measurement data, the method comprising:

[0196] S501A: The second node sends positioning measurement data of a first application scenario to the first node.

[0197] Among them, the optional implementation of S501A can refer to the optional implementation of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0198] In some embodiments, the first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on PRS; or the first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on SRS-Pos.

[0199] By implementing the embodiments of the present disclosure, the first node can determine the positioning measurement data of the first application scenario to determine the first positioning AI model of the first application scenario, and then determine the positioning position information based on the positioning measurement data and the first positioning AI model.

[0200] FIG5B is an interactive diagram of a method for determining positioning measurement data according to an embodiment of the present disclosure. As shown in FIG5B , an embodiment of the present disclosure relates to a method for determining positioning measurement data, the method comprising:

[0201] S501B: The second node sends positioning information of the first application scenario to the first node.

[0202] S502B: The first node determines positioning measurement data according to the positioning information.

[0203] Among them, the optional implementation of S501B and S502B can refer to the optional implementation of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0204] In some embodiments, the first node is a terminal, the second node is an access network device, the positioning information is a positioning reference signal PRS, and the positioning measurement data is channel measurement data based on PRS; or the first node is an access network device, the second node is a terminal, the positioning information is a sounding reference signal-positioning SRS-Pos, and the positioning measurement data is channel measurement data based on SRS-Pos.

[0205] By implementing the embodiments of the present disclosure, the first node can determine the positioning measurement data of the first application scenario to determine the first positioning AI model of the first application scenario, and then determine the positioning position information based on the positioning measurement data and the first positioning AI model.

[0206] FIG6A is a schematic diagram of a method for determining a positioning AI model according to an embodiment of the present disclosure. As shown in FIG6A , an embodiment of the present disclosure relates to a method for determining a positioning AI model, the method comprising:

[0207] S601A, the first node obtains a training data set and an initial positioning AI model, wherein the training data set includes training sample data for different application scenarios.

[0208] S602A, the first node trains the initial positioning AI model according to the training data set to determine the intermediate positioning AI model, wherein the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios.

[0209] S603A, the first node trains the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario.

[0210] S604A, the first node determines a positioning AI model group based on the positioning AI models corresponding to different application scenarios.

[0211] Among them, the optional implementation methods of S601A to S604A can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0212] In some embodiments, the first node is at least one of a terminal, an access network device, and a core network device.

[0213] In some embodiments, different application scenarios include at least one of the following: different channel scenarios; different channel parameter configurations; different network coverage ranges.

[0214] In some embodiments, the training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

[0215] In some embodiments, the first node trains the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario, including: fixing the common layer of the intermediate positioning AI model, adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determining the positioning AI model corresponding to each application scenario.

[0216] By implementing the embodiments of the present disclosure, the first node can determine a positioning AI model group composed of positioning AI models corresponding to different application scenarios, so that when determining the first application scenario of the positioning measurement data, it can determine the first positioning AI model corresponding to the first application scenario, and then determine the positioning position information based on the positioning measurement data and the first positioning AI model.

[0217] FIG6B is an interactive diagram of a method for determining a positioning AI model according to an embodiment of the present disclosure. As shown in FIG6B , an embodiment of the present disclosure relates to a method for determining a positioning AI model, the method comprising:

[0218] S601B, the second node or the third node obtains a training data set and an initial positioning AI model, wherein the training data set includes training sample data for different application scenarios.

[0219] S602B, the second node or the third node trains the initial positioning AI model according to the training data set to determine the intermediate positioning AI model, wherein the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios.

[0220] S603B, the second node or the third node trains the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario.

[0221] S604B, the second node or the third node determines the positioning AI model group according to the positioning AI models corresponding to different application scenarios.

[0222] S605B, the second node sends indication information to the first node, where the indication information is used to indicate the positioning AI model group.

[0223] S606B: The first node determines the positioning AI model group according to the instruction information.

[0224] Among them, the optional implementation methods of S601B to S606B can refer to the optional implementation methods of S201 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0225] In some embodiments, the first node is a terminal, the second node or the third node is an access network device, or the first node is an access network device, the second node or the third node is a terminal, or the first node is a core network device, the second node or the third node is a terminal, or the first node is a terminal, the second node or the third node is a core network device, or the first node is a core network device, the second node or the third node is an access network device, or the first node is an access network device, the second node or the third node is a core network device, or the first node is at least one of a terminal, an access network device and a core network device, and the second node is a server or a dedicated device.

[0226] In some embodiments, different application scenarios include at least one of the following: different channel scenarios; different channel parameter configurations; different network coverage ranges.

[0227] In some embodiments, the training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

[0228] In some embodiments, the second node trains the intermediate positioning AI model based on the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario, including: fixing the common layer of the intermediate positioning AI model, adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario, and determining the positioning AI model corresponding to each application scenario.

[0229] By implementing the embodiments of the present disclosure, the first node can determine a positioning AI model group composed of positioning AI models corresponding to different application scenarios, so that when determining the first application scenario of the positioning measurement data, it can determine the first positioning AI model corresponding to the first application scenario, and then determine the positioning position information based on the positioning measurement data and the first positioning AI model.

[0230] FIG7 is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG7 , the embodiment of the present disclosure relates to a positioning method, which includes:

[0231] S701: The first node determines positioning position information based on positioning measurement data and a first positioning AI model.

[0232] S702A: The first node sends an intermediate positioning parameter to the third node, where the positioning position information is the intermediate positioning parameter.

[0233] S703A: The third node determines the terminal location information according to the intermediate positioning parameters.

[0234] S702B: The first node sends the terminal location information to the third node, where the positioning location information is the terminal location information.

[0235] S702C: The first node determines the terminal location information according to the intermediate positioning parameters, where the positioning location information is the intermediate positioning parameters.

[0236] S703C: The first node sends the terminal location information to the third node, where the positioning location information is an intermediate positioning parameter.

[0237] Among them, the optional implementation methods of S701 to S703C can refer to the optional implementation method of S202 in Figure 2 and other related parts in the embodiment involved in Figure 2, and will not be repeated here.

[0238] In some embodiments, the first node determines the positioning location information based on the positioning measurement data and the first positioning AI model. For the relevant description, please refer to the relevant description in the above embodiments, which will not be repeated here.

[0239] In some embodiments, the first node is a terminal and the third node is a core network device; or the first node is an access network device and the third node is a core network device.

[0240] The communication method involved in the embodiments of the present disclosure may include at least one of S701 to S703C. For example, S701+S702A can be implemented as an independent embodiment, S701+S702A+S703A can be implemented as an independent embodiment, S701+S702B can be implemented as an independent embodiment, S701+S702C can be implemented as an independent embodiment, and S701+S702C+S703C can be implemented as an independent embodiment, but are not limited thereto.

[0241] In some embodiments, S703A, S702B, S702C, and S703C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0242] In some embodiments, S702B, S702C, and S703C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0243] In some embodiments, S702A, S702C, and S703C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0244] In some embodiments, S702A, S702B, and S703C are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0245] In some embodiments, S702A and S702B are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0246] By implementing the embodiments of the present disclosure, after determining the positioning position information, if the positioning position information is an intermediate positioning parameter, the first node can send it to the third node to determine the intermediate positioning parameter at the third node; or if the positioning position information is terminal position information, the first node can send it to the third node to determine the terminal position information at the third node; or if the positioning position information is an intermediate positioning parameter, the first node can determine the terminal position information based on the intermediate positioning parameter and send it to the third node to determine the terminal position information at the third node.

[0247] To facilitate understanding of the embodiments of the present disclosure, an exemplary embodiment is provided.

[0248] It is understandable that the main application process of the high-precision positioning solution based on deep learning in the relevant technology is: in the model training stage, after completing the AI ​​network model training (Model Training) for high-precision positioning using training data, the trained positioning AI model is deployed on the terminal, BS or LMF, and then the target positioning work in the actual system is completed in the model inference stage, that is, model inference (Model Inference). However, due to different channel scenarios, different channel parameter configurations or different base station coverage ranges (collectively referred to as different model application scenarios), the characteristic distribution of the channel measurement data used for positioning may vary greatly. Therefore, the relevant technology uses data from different application scenarios to train and store different AI positioning models for different application scenarios.

[0249] To ensure positioning accuracy in various application scenarios, multiple positioning AI models need to be trained and deployed and stored in terminals, base stations, or LMFs. However, the number of parameters in positioning AI models is usually large, and actual communication equipment needs to allocate a large amount of storage space for multiple positioning AI models in different application scenarios. Compared with base stations and LMFs, terminal storage resources are more scarce, and the storage space for positioning AI model applications is also more limited. Therefore, the positioning AI model training and application solutions in related technologies have certain problems with high model storage overhead.

[0250] The above problems have not been solved. In order to reduce the storage overhead of the positioning AI model, a training and application method for sharing part of the positioning AI model is proposed to realize the sharing of some layers in the positioning AI model in different application scenarios, that is, part of the positioning AI model is shared layers, and the other parts are adaptive layers dedicated to different application scenarios.

[0251] The model training process is as follows:

[0252] a. In the case that there are N application scenarios in the actual system application, obtain the model training data set Data for each application scenario i , i = 1, 2, ... N (see the training data set in the above embodiment);

[0253] b. Use data sets from N application scenarios i , i = 1, 2, ... N composed of a mixed data set (see the training data set in the above embodiment) to train a positioning AI model (see the intermediate positioning AI model in the above embodiment);

[0254] c. Separate the shared layers in the positioning AI model and fix its model parameters, and use the model training data Data under N application scenarios respectively i , i=1,2,…N fine-tune the parameters of each adaptive layer to obtain the adaptive layer model parameters in N application scenarios i , i = 1, 2, ..., N (see the positioning AI model group composed of positioning AI models corresponding to different application scenarios in the above embodiments).

[0255] In an exemplary embodiment, as shown in FIG8 , the model application structure, the specific model application process is as follows: the channel measurement data used for positioning is first processed by the shared layers, and then processed by the adaptive layers in the application scenario corresponding to the input data. i ,i=1,2,…,N, and output the positioning result, that is, the target position coordinates.

[0256] In the disclosed embodiments, for application scenarios where high-precision positioning is achieved based on AI models, and to address the high storage overhead caused by the need for terminals, base stations, or localized positioning modules (LMFs) to store multiple positioning AI models in multiple application scenarios, a low-storage-overhead positioning AI model training and application method is proposed. This method, specifically, a model training and application method that is shared by some of the positioning AI models, can effectively reduce the storage overhead of positioning AI models in actual communication devices, thereby promoting the application of AI-based high-precision positioning solutions in actual communication systems.

[0257] The present disclosure also provides an apparatus for implementing any of the above methods. For example, a device is provided that includes units or modules for implementing each step performed by the first node in any of the above methods. For another example, another device is provided that includes units or modules for implementing each step performed by the second node in any of the above methods.

[0258] 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.

[0259] 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.

[0260] FIG9A is a schematic diagram of the structure of a first node proposed in an embodiment of the present disclosure. As shown in FIG9A , the first node 10 may include at least one of a transceiver module 11 and a processing module 12 .

[0261] In some embodiments, the above-mentioned processing module 12 is used to determine the positioning measurement data of the first application scenario; the processing module 12 is also used to determine the first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; the processing module 12 is also used to determine the positioning position information based on the positioning measurement data and the first positioning AI model.

[0262] Optionally, the transceiver module 11 is configured to execute at least one of the communication steps (e.g., S501A, S501B, S605B, S702A, S702B, S703C, but not limited thereto) such as sending and / or receiving performed by the first node 10 in any of the above methods, which are not described in detail here. Optionally, the processing module 12 is configured to execute at least one of the other steps (e.g., S201-S202, S502B, S601A-S604A, S604B, S701, S702C, but not limited thereto) performed by the first node 10 in any of the above methods, which are not described in detail here.

[0263] FIG9B is a schematic diagram of the structure of the second node proposed in an embodiment of the present disclosure. As shown in FIG9B , the second node 20 may include at least one of a transceiver module 21 and a processing module 22 .

[0264] In some embodiments, the above-mentioned transceiver module 21 is used to send positioning information or positioning measurement data of a first application scenario to the first node, wherein the first application scenario is used for the first node to determine a first positioning AI model, the positioning information is used for the first node to determine the positioning measurement data, and the positioning measurement data and the first positioning AI model are used for the first node to determine the positioning position information.

[0265] Optionally, the transceiver module 21 is configured to execute at least one of the communication steps (e.g., S501A, S501B, and S605B, but not limited thereto) such as sending and / or receiving performed by the second node 20 in any of the above methods, which are not described in detail here. Optionally, the processing module 22 is configured to execute at least one of the other steps (e.g., S601B to S604B, but not limited thereto) performed by the second node 20 in any of the above methods, which are not described in detail here.

[0266] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0267] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0268] Figure 10A is a schematic diagram of the structure of a communication device 8100 proposed in an embodiment of the present disclosure. Communication device 8100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, 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 8100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0269] As shown in Figure 10A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol 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. Optionally, the communication device 8100 is used to perform any of the above methods. Optionally, one or more processors 8101 are used to call instructions to enable the communication device 8100 to perform any of the above methods.

[0270] In some embodiments, the communication device 8100 further includes one or more transceivers 8102. When the communication device 8100 includes one or more transceivers 8102, the transceiver 8102 performs at least one of the communication steps (e.g., S501A, S501B, S605B, S702A, S702B, S703C, but not limited thereto) of transmitting and / or receiving in the above method, and the processor 8101 performs at least one of the other steps (e.g., S201-S202, S502B, S601A-S604A, S604B, S701, S702C, S601B-S604B, but not limited thereto). In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, terms such as transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface can be replaced with each other, terms such as transmitter, transmitting unit, transmitter, and transmitting circuit can be replaced with each other, and terms such as receiver, receiving unit, receiver, and receiving circuit can be replaced with each other.

[0271] In some embodiments, the communication device 8100 further includes one or more memories 8103 for storing data. Alternatively, all or part of the memories 8103 may be located outside the communication device 8100. In alternative embodiments, the communication device 8100 may include one or more interface circuits 8104. Optionally, the interface circuits 8104 are connected to the memory 8102 and may be configured to receive data from the memory 8102 or other devices, or to send data to the memory 8102 or other devices. For example, the interface circuits 8104 may read data stored in the memory 8102 and send the data to the processor 8101.

[0272] The communication device 8100 described in the above embodiment may be a first node or a second node, but the scope of the communication device 8100 described in the present disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG. 10A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0273] FIG10B is a schematic diagram of the structure of the chip 8200 proposed in an embodiment of the present disclosure. If the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 8200 shown in FIG10B , but the present disclosure is not limited thereto.

[0274] The chip 8200 includes one or more processors 8201. The chip 8200 is configured to execute any of the above methods.

[0275] In some embodiments, chip 8200 further includes one or more interface circuits 8202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 8200 further includes one or more memories 8203 for storing data. Alternatively, all or part of memory 8203 may be located external to chip 8200. Optionally, interface circuit 8202 is connected to memory 8203 and may be used to receive data from memory 8203 or other devices, or may be used to send data to memory 8203 or other devices. For example, interface circuit 8202 may read data stored in memory 8203 and send the data to processor 8201.

[0276] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., S501A, S501B, S605B, S702A, S702B, S703C, but not limited thereto). The interface circuit 8202 performing the communication steps such as sending and / or receiving in the above method, for example, means that the interface circuit 8202 performs data exchange between the processor 8201, the chip 8200, the memory 8203, or the transceiver device. In some embodiments, the processor 8201 performs at least one of the other steps (e.g., S201-S202, S502B, S601A-S604A, S604B, S701, S702C, S601B-S604B, but not limited thereto).

[0277] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 8100, causes the communication device 8100 to execute any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

[0278] The present disclosure also provides a program product, which, when executed by the communication device 8100, enables the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0279] 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.

[0280] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0281] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0282] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that, The method is executed by a first node and includes: Determining positioning measurement data for a first application scenario; Determining a first positioning AI model corresponding to the first application scenario, where the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer specific to the first application scenario; Determining positioning position information based on the positioning measurement data and the first positioning AI model.

2. The method according to claim 1, characterized in that, The determining of the positioning measurement data for the first application scenario includes: Determining the positioning measurement data for the first application scenario received from a second node as the positioning measurement data; or Receiving positioning information for the first application scenario sent by the second node and determining the positioning measurement data based on the positioning information.

3. The method according to claim 1 or 2, characterized in that, The determining of the first positioning AI model corresponding to the first application scenario includes: Determining a positioning AI model group composed of positioning AI models corresponding to different application scenarios, where the positioning AI model group includes a common layer common to different application scenarios and adaptive layers specific to different application scenarios respectively; Determining the first positioning AI model corresponding to the first application scenario in the positioning AI model group based on the first application scenario.

4. The method according to claim 3, wherein The determining of the positioning AI model group composed of positioning AI models corresponding to different application scenarios includes: Obtaining a training data set and an initial positioning AI model, where the training data set includes training sample data for different application scenarios; Training the initial positioning AI model according to the training data set to determine an intermediate positioning AI model, where the intermediate positioning AI model includes a common layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios; Training the intermediate positioning AI model according to the training sample data for each application scenario to determine the positioning AI model corresponding to each application scenario; Determining the positioning AI model group based on the positioning AI models corresponding to different application scenarios.

5. The method according to claim 4, characterized in that, The training of the intermediate positioning AI model according to the training sample data for each application scenario to determine the positioning AI model corresponding to each application scenario includes: Fixing the common layer of the intermediate positioning AI model and adjusting the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data for each application scenario to determine the positioning AI model corresponding to each application scenario.

6. The method according to claim 4 or 5, wherein The training sample data includes sample positioning measurement data and sample positioning position information, where the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

7. The method according to claim 3, wherein The determining of the positioning AI model group composed of positioning AI models corresponding to different application scenarios includes: Receiving indication information sent by the second node or the third node, where the indication information is used to indicate the positioning AI model group; Determining the positioning AI model group based on the indication information.

8. The method according to any one of claims 1 to 7, characterized in that, The determining of the positioning position information based on the positioning measurement data and the first positioning AI model includes: Process the positioning measurement data through the shared layer and the first adaptive layer to determine the positioning location information.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: Sending positioning intermediate parameters to a third node, where the positioning location information is the positioning intermediate parameters; or Determining terminal location information based on the positioning intermediate parameters and sending the terminal location information to a third node, where the positioning location information is the positioning intermediate parameters; or Sending terminal location information to a third node, where the positioning location information is the terminal location information.

10. The method according to claim 2, characterized in that, The first node, the second node, and the positioning measurement data are selected from one of the following: The first node is a terminal, the second node is an access network device, the positioning information is a positioning reference signal (PRS), and the positioning measurement data is channel measurement data based on the PRS; The first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on the PRS; The first node is an access network device, the second node is a terminal, the positioning information is a sounding reference signal - positioning SRS - Pos, and the positioning measurement data is channel measurement data based on the SRS - Pos; The first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on the SRS - Pos.

11. The method according to claim 7 or 9, wherein The first node is a terminal, the second node is an access network device, and the third node is a core network device; or The first node is an access network device, the second node is a terminal, and the third node is a core network device.

12. The method according to any one of claims 1 to 11, characterized in that, The different application scenarios include at least one of the following: Different channel scenarios; Different channel parameter configurations; Different network coverage ranges.

13. A positioning method, characterized in that, The method is executed by a second node and includes: Sending positioning information or positioning measurement data of a first application scenario to a first node, where the first application scenario is used by the first node to determine a first positioning AI model, the positioning information is used by the first node to determine positioning measurement data, and the positioning measurement data and the first positioning AI model are used by the first node to determine positioning location information.

14. The method according to claim 13, wherein The method further includes: Sending indication information to the first node, where the indication information is used to indicate a positioning AI model group formed by positioning AI models corresponding to different application scenarios.

15. The method according to claim 14, wherein The method further includes: Determining the positioning AI model group.

16. The method according to claim 15, wherein The determining the positioning AI model group includes: Obtaining a training data set and an initial positioning AI model, where the training data set includes training sample data of different application scenarios; Training the initial positioning AI model according to the training data set to determine an intermediate positioning AI model, where the intermediate positioning AI model includes a shared layer common to different application scenarios and an intermediate adaptive layer common to different application scenarios; Training the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario. Determine the group of positioning AI models according to the positioning AI models corresponding to different application scenarios.

17. The method according to claim 16, wherein Training the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario includes: Fix the common layer of the intermediate positioning AI model, and adjust the intermediate adaptive layer of the intermediate positioning AI model according to the training sample data of each application scenario to determine the positioning AI model corresponding to each application scenario.

18. The method according to claim 16 or 17, wherein The training sample data includes sample positioning measurement data and sample positioning position information, wherein the sample positioning position information includes sample terminal position information or sample positioning intermediate parameters.

19. The method according to any one of claims 13 to 18, characterized in that, The first node, the second node, and the positioning measurement data are selected from one of the following: The first node is a terminal, the second node is an access network device, the positioning information is PRS, and the positioning measurement data is channel measurement data based on PRS; The first node is a core network device, the second node is a terminal, and the positioning measurement data is channel measurement data based on PRS; The first node is an access network device, the second node is a terminal, the positioning information is SRS-Pos, and the positioning measurement data is channel measurement data based on SRS-Pos; The first node is a core network device, the second node is an access network device, and the positioning measurement data is channel measurement data based on SRS-Pos.

20. The method according to any one of claims 13 to 19, characterized in that The different application scenarios include at least one of the following: Different channel scenarios; Different channel parameter configurations; Different network coverage ranges.

21. A first node, characterized in that, Including: A processing module for determining the positioning measurement data of the first application scenario; The processing module is further configured to determine the first positioning AI model corresponding to the first application scenario, wherein the first positioning AI model includes a common layer common to different application scenarios and a first adaptive layer dedicated to the first application scenario; The processing module is further configured to determine the positioning position information according to the positioning measurement data and the first positioning AI model.

22. A second node, characterized in that, Including: A transceiver module for sending the positioning information or positioning measurement data of the first application scenario to the first node, wherein the first application scenario is used for the first node to determine the first positioning AI model, the positioning information is used for the first node to determine the positioning measurement data, and the positioning measurement data and the first positioning AI model are used for the first node to determine the positioning position information.

23. A communication device, characterized in that, Including: One or more processors; Wherein, the communication device is configured to execute the method according to any one of claims 1 to 12.

24. A communication device, characterized in that, Including: One or more processors; Wherein, the communication device is configured to execute the method according to any one of claims 13 to 20.

25. A communication system, characterized in that, Including a first node and a second node, wherein the first node is configured to implement the method according to any one of claims 1 to 12, and the second node is configured to implement the method according to any one of claims 13 to 20.

26. A storage medium storing instructions, characterized in that, When the instruction runs on a communication device, the communication device is caused to execute the method according to any one of claims 1 to 12, 13 to 20.

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