Positioning method, first network element, second network element, and communication system

Abstract

The present disclosure relates to a positioning method, a first network element, a second network element, and a communication system. The positioning method comprises: receiving first information from a second network element, wherein the second network element is deployed with an artificial intelligence (AI) model, and the first information indicates the capability of the second network element to support positioning based on the AI model. According to the embodiments of the present disclosure, it can be made clear that a second network element deployed with an AI model sends to a first network element the capability of positioning based on the AI model, thereby realizing positioning based on AI model capability.

Description

Positioning method, first network element, second network element and communication system Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a positioning method, a first network element, a second network element, and a communication system. Background Art

[0002] Artificial Intelligence (AI) is gradually being applied in the communications field, for example, in 5G New Radio (NR) communications scenarios and even in 6G communications scenarios.

[0003] Summary of the Invention

[0004] In order to achieve positioning based on AI models, the terminal and / or access network equipment needs to send corresponding information to the core network equipment to indicate the capabilities of the AI ​​models deployed by each.

[0005] The embodiments of the present disclosure provide a positioning method, a first network element, a second network element, and a communication system.

[0006] According to a first aspect of an embodiment of the present disclosure, a positioning method is proposed, which is applied to a first network element. The method includes: receiving first information from a second network element, wherein the second network element is deployed with an artificial intelligence (AI) model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0007] According to a second aspect of an embodiment of the present disclosure, a positioning method is proposed, which is applied to a second network element. The method includes: sending first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0008] According to a third aspect of an embodiment of the present disclosure, a positioning method is proposed, which includes: a second network element sends first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model; the first network element receives the first information.

[0009] According to the fourth aspect of an embodiment of the present disclosure, a first network element is proposed, comprising: a transceiver module for receiving first information from a second network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0010] According to the fifth aspect of an embodiment of the present disclosure, a second network element is proposed, including: a transceiver module, used to send first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0011] According to a sixth aspect of an embodiment of the present disclosure, a first network element is proposed, comprising: one or more processors; wherein the processor is used to execute the positioning method of the first aspect.

[0012] According to a seventh aspect of an embodiment of the present disclosure, a second network element is proposed, comprising: one or more processors; wherein the processor is used to execute the positioning method of the second aspect.

[0013] According to an eighth aspect of an embodiment of the present disclosure, a communication system is provided, including a first network element and a second network element, wherein the first network element is configured to implement the positioning method of the first aspect, and the second network element is configured to implement the positioning method of the second aspect.

[0014] According to a ninth aspect of an embodiment of the present disclosure, a storage medium is provided, wherein the storage medium stores instructions. When the instructions are executed on a communication device, the communication device executes the positioning method of any one of the first and second aspects.

[0015] Through the embodiments of the present disclosure, the second network element deployed with the AI ​​model can clearly send the capability of performing positioning based on the AI ​​model to the first network element, thereby realizing positioning based on the AI ​​model capability. 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] FIG1A is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0018] FIG1B is a schematic diagram showing interaction between a terminal and a server for positioning according to an embodiment of the present disclosure.

[0019] FIG1C is a schematic diagram illustrating interaction between a terminal and a server for positioning according to an embodiment of the present disclosure.

[0020] FIG2A is an interactive schematic diagram of a positioning method according to an embodiment of the present disclosure.

[0021] FIG2B is an interactive schematic diagram of a positioning method according to situation A) according to an embodiment of the present disclosure.

[0022] FIG2C is an interactive schematic diagram of a positioning method for situation -B) according to an embodiment of the present disclosure.

[0023] FIG3A is a flow chart of a positioning method according to an embodiment of the present disclosure.

[0024] FIG3B is a flow chart of a positioning method according to an embodiment of the present disclosure.

[0025] FIG4 is a flow chart of a positioning method according to an embodiment of the present disclosure.

[0026] FIG5 is an interactive schematic diagram of a positioning method according to an embodiment of the present disclosure.

[0027] FIG6A is a schematic structural diagram of a first network element proposed in an embodiment of the present disclosure.

[0028] FIG6B is a schematic structural diagram of a second network element proposed in an embodiment of the present disclosure.

[0029] FIG7A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0030] FIG7B is a schematic diagram of the structure of the chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The embodiments of the present disclosure provide a positioning method, a first network element, a second network element, and a communication system.

[0032] In a first aspect, an embodiment of the present disclosure proposes receiving first information from a second network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0033] In the above embodiment, by sending the first information to the first network element, the capability of the AI ​​model deployed on the second network element is transmitted to the first network element, so that the first network element and the second network element can perform AI model positioning based on the capability of the AI ​​model.

[0034] In combination with some embodiments of the first aspect, in some embodiments, the second network element is a terminal, and the first information includes at least one of the following: direct positioning capability based on the AI ​​model; indirect positioning capability based on the AI ​​model; reference signal measurement capability; AI functions supported by the terminal; AI models supported by the terminal; support for AI model transmission; no support for AI model transmission; support for AI model switching; support for AI function switching; support for fallback from AI positioning to non-AI positioning; support for AI model update; support for AI model activation; support for AI model deactivation.

[0035] In the above embodiment, when the second network element is a terminal, the corresponding capabilities of the AI ​​model deployed on the terminal are clarified. This allows the terminal to report the capabilities of the clarified AI model to the first network element, thereby achieving positioning based on the AI ​​model.

[0036] In combination with some embodiments of the first aspect, in some embodiments, the reference signal measurement positioning capability includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; flight time TOA; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal received arrival time difference.

[0037] In the above embodiment, the specific content of the reference signal measurement and positioning capability is clarified, so that the terminal can perform PRS measurement and positioning based on the content, and further realize positioning based on the AI ​​model.

[0038] In combination with some embodiments of the first aspect, in some embodiments, the first information also includes at least one of the following: auxiliary data support capability information; input information; output information; processing capability information; reference signal capability information; generalization capability information; deployment scenario; applicable conditions; monitoring information; storage capability information; computing capability information.

[0039] In the above embodiment, the definition of additional capabilities for a specific AI model is clarified, thereby enabling capability reporting of a specific AI model.

[0040] In combination with some embodiments of the first aspect, in some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning; support for auxiliary data for model training; support for auxiliary data for model monitoring.

[0041] In the above embodiment, the specific content of the auxiliary data support capability is clarified, so that the terminal can determine whether the terminal has the auxiliary data support capability based on the content, and then realize positioning based on the AI ​​model.

[0042] In combination with some embodiments of the first aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal receive and transmit time difference; flight time TOA.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the output information includes at least one of the following: location information of the terminal; an indication of line-of-sight propagation or non-line-of-sight propagation; time of flight TOA; and measurement information of a predicted reference signal.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the processing capability information includes at least one of the following: processing time; activation time; switching time; and update time.

[0045] In combination with some embodiments of the first aspect, in some embodiments, the reference signal capability information includes at least one of the following: a maximum number of reference signals; a reference signal resource; a reference signal resource set; and a frequency layer.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the deployment scenario includes at least one of the following: an indoor scenario; an outdoor scenario; an urban scenario; a hotspot scenario.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the applicable condition includes at least one of the following: signal to interference plus noise ratio SINR; reference signal received power RSRP; time error group TEG; adaptation scenario.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring; terminal support model performance monitoring used performance indicators; terminal support reporting model performance monitoring results; terminal support reporting model performance monitoring indicators; terminal support reporting performance indicator types.

[0049] In combination with some embodiments of the first aspect, in some embodiments, the storage capacity information includes at least one of the following: the maximum storage capacity for storing AI models; the storage capacity corresponding to each AI function; and the storage capacity corresponding to each AI model.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0051] In combination with some embodiments of the first aspect, in some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side; the maximum computing capability of the terminal for each AI function; the maximum computing capability of the terminal for each AI model.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the first information is obtained based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0053] In the above embodiment, by reusing the existing protocol to send the first information, information transmission resources can be saved compared to specifying a new communication protocol for sending.

[0054] In combination with some embodiments of the first aspect, in some embodiments, the second network element is an access network device, and the first information includes at least one of the following: the ability to support AI model positioning; supported AI functions; supported AI models; input information of each AI function or each AI model; output information of each AI function or each AI model; and the ability to support measurement coordination between transmission and receiving nodes TRP.

[0055] In the above embodiment, when the second network element is an access network device, the corresponding capabilities of the AI ​​model deployed on the access network device are clarified. This allows the access network device to report the capabilities of the specified AI model to the first network element, thereby achieving positioning based on the AI ​​model.

[0056] In combination with some embodiments of the first aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal received power RSRP; access network device received arrival time difference.

[0057] In combination with some embodiments of the first aspect, in some embodiments, the output information includes at least one of the following: terminal location information, line-of-sight propagation or non-line-of-sight propagation indication; time of flight TOA; and measurement information of a reference signal predicted based on an AI model.

[0058] With reference to some embodiments of the first aspect, in some embodiments, the first information is obtained based on the New Radio Positioning Protocol NRPPa.

[0059] In the above embodiment, by reusing the existing protocol to send the first information, information transmission resources can be saved compared to specifying a new communication protocol for sending.

[0060] In a second aspect, an embodiment of the present disclosure proposes a positioning method, which is applied to a second network element. The method includes: sending first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0061] In combination with some embodiments of the second aspect, in some embodiments, the second network element is an access network device, and the first information includes at least one of the following: direct positioning capability based on the AI ​​model; indirect positioning capability based on the AI ​​model; reference signal measurement capability; AI functions supported by the terminal; AI models supported by the terminal; support for AI model transmission; no support for AI model transmission; support for AI model switching; support for AI function switching; support for fallback from AI positioning to non-AI positioning; support for AI model update; support for AI model activation; support for AI model deactivation.

[0062] In combination with some embodiments of the second aspect, in some embodiments, the reference signal measurement positioning capability includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristics DP; flight time TOA; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal received arrival time difference.

[0063] In combination with some embodiments of the second aspect, in some embodiments, the first information also includes at least one of the following: auxiliary data support capability information; input information; output information; processing capability information; reference signal capability information; generalization capability information; deployment scenario; applicable conditions; monitoring information; storage capability information; computing capability information.

[0064] In combination with some embodiments of the second aspect, in some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning; support for auxiliary data for model training; support for auxiliary data for model monitoring.

[0065] In combination with some embodiments of the second aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; time of flight TOA.

[0066] In combination with some embodiments of the second aspect, in some embodiments, the output information includes at least one of the following: location information of the terminal; an indication of line-of-sight propagation or non-line-of-sight propagation; time of flight TOA; and measurement information of a predicted reference signal.

[0067] In combination with some embodiments of the second aspect, in some embodiments, the processing capability information includes at least one of the following: processing time; activation time; switching time; update time.

[0068] In combination with some embodiments of the second aspect, in some embodiments, the reference signal capability information includes at least one of the following: a maximum number of reference signals; a reference signal resource; a reference signal resource set; and a frequency layer.

[0069] In conjunction with some embodiments of the second aspect, in some embodiments, the deployment scenario includes at least one of the following: an indoor scenario;

[0070] Outdoor scenes; urban scenes; hot spots.

[0071] In combination with some embodiments of the second aspect, in some embodiments, the applicable condition includes at least one of the following: signal to interference plus noise ratio SINR; reference signal received power RSRP; time error group TEG; adaptation scenario.

[0072] In combination with some embodiments of the second aspect, in some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring; terminal support model performance indicators used for performance monitoring; terminal support reporting model performance monitoring results; terminal support reporting model performance indicators; terminal support reporting performance indicator types.

[0073] In combination with some embodiments of the second aspect, in some embodiments, the storage capacity information includes at least one of the following: the maximum storage capacity for storing AI models; the storage capacity corresponding to each AI function; and the storage capacity corresponding to each AI model.

[0074] In combination with some embodiments of the second aspect, in some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0075] In combination with some embodiments of the second aspect, in some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side; the maximum computing capability of the terminal for each AI function; the maximum computing capability of the terminal for each AI model.

[0076] In combination with some embodiments of the second aspect, in some embodiments, the first information is sent by the second network element based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0077] In combination with some embodiments of the second aspect, in some embodiments, the second network element is an access network device, and the first information includes at least one of the following: the ability to support AI model positioning; supported AI functions; supported AI models; input information of each AI function or each AI model; output information of each AI function or each AI model; the ability to support measurement coordination between transmission and receiving nodes TRP.

[0078] In combination with some embodiments of the second aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal received power RSRP; access network device received arrival time difference.

[0079] In combination with some embodiments of the second aspect, in some embodiments, the output information includes at least one of the following: terminal location information, line-of-sight propagation or non-line-of-sight propagation indication; time of flight TOA; measurement information of a reference signal predicted based on an AI model.

[0080] In combination with some embodiments of the second aspect, in some embodiments, the first information is sent by the second network element based on the New Radio Positioning Protocol NRPPa.

[0081] In a third aspect, an embodiment of the present disclosure proposes a positioning method, which includes: a second network element sends first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model; the first network element receives the first information.

[0082] In a fourth aspect, an embodiment of the present disclosure proposes a first network element, comprising: a transceiver module for receiving first information from a second network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0083] In combination with some embodiments of the fourth aspect, in some embodiments, when the second network element is an access network device, the first information includes at least one of the following: direct positioning capability based on the AI ​​model; indirect positioning capability based on the AI ​​model; reference signal measurement capability; AI functions supported by the terminal; AI models supported by the terminal; support for AI model transmission; no support for AI model transmission; support for AI model switching; support for AI function switching; support for fallback from AI positioning to non-AI positioning; support for AI model update; support for AI model activation; support for AI model deactivation.

[0084] In combination with some embodiments of the fourth aspect, in some embodiments, the reference signal measurement positioning capability includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristics DP; flight time TOA; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal received arrival time difference.

[0085] In combination with some embodiments of the fourth aspect, in some embodiments, the first information also includes at least one of the following: auxiliary data support capability information; input information; output information; processing capability information; reference signal capability information; generalization capability information; deployment scenario; applicable conditions; monitoring information; storage capability information; and computing capability information.

[0086] In combination with some embodiments of the fourth aspect, in some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning; support for auxiliary data for model training; support for auxiliary data for model monitoring.

[0087] In combination with some embodiments of the fourth aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; time of flight TOA.

[0088] In combination with some embodiments of the fourth aspect, in some embodiments, the output information includes at least one of the following: location information of the terminal; an indication of line-of-sight propagation or non-line-of-sight propagation; time of flight TOA; and measurement information of a predicted reference signal.

[0089] In combination with some embodiments of the fourth aspect, in some embodiments, the processing capability information includes at least one of the following: processing time; activation time; switching time; update time.

[0090] In combination with some embodiments of the fourth aspect, in some embodiments, the reference signal capability information includes at least one of the following: maximum number of reference signals; reference signal resources; reference signal resource set; frequency layer.

[0091] In combination with some embodiments of the fourth aspect, in some embodiments, the deployment scenario includes at least one of the following: indoor scene; outdoor scene; urban scene; hotspot scene.

[0092] In combination with some embodiments of the fourth aspect, in some embodiments, the applicable condition includes at least one of the following: signal to interference plus noise ratio SINR; reference signal received power RSRP; time error group TEG; adaptation scenario.

[0093] In combination with some embodiments of the fourth aspect, in some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring; terminal support model performance indicators used for performance monitoring; terminal support reporting of model performance monitoring results; terminal support reporting of model performance indicators; terminal support reporting of performance indicator types.

[0094] In combination with some embodiments of the fourth aspect, in some embodiments, the storage capacity information includes at least one of the following: the maximum storage capacity for storing AI models; the storage capacity corresponding to each AI function; and the storage capacity corresponding to each AI model.

[0095] In combination with some embodiments of the fourth aspect, in some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0096] In combination with some embodiments of the fourth aspect, in some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side; the maximum computing capability of the terminal for each AI function; the maximum computing capability of the terminal for each AI model.

[0097] In combination with some embodiments of the fourth aspect, in some embodiments, the first information is obtained based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0098] In combination with some embodiments of the fourth aspect, in some embodiments, the second network element is an access network device, and the first information includes at least one of the following: the ability to support AI model positioning; supported AI functions; supported AI models; input information of each AI function or each AI model; output information of each AI function or each AI model; and the ability to support measurement coordination between transmission and receiving nodes TRP.

[0099] In combination with some embodiments of the fourth aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal received power RSRP; access network device received arrival time difference.

[0100] In combination with some embodiments of the fourth aspect, in some embodiments, the output information includes at least one of the following: terminal location information, line-of-sight propagation or non-line-of-sight propagation indication; time of flight TOA; measurement information of the reference signal predicted based on the AI ​​model.

[0101] In combination with some embodiments of the fourth aspect, in some embodiments, the first information is obtained based on the New Radio Positioning Protocol NRPPa.

[0102] In the fifth aspect, an embodiment of the present disclosure proposes a second network element, including: a transceiver module, used to send first information to a first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0103] In combination with some embodiments of the fifth aspect, in some embodiments, when the second network element is a terminal, the first information includes at least one of the following: direct positioning capability based on the AI ​​model; indirect positioning capability based on the AI ​​model; reference signal measurement capability; AI functions supported by the terminal; AI models supported by the terminal; support for AI model transmission; no support for AI model transmission; support for AI model switching; support for AI function switching; support for fallback from AI positioning to non-AI positioning; support for AI model update; support for AI model activation; support for AI model deactivation.

[0104] In combination with some embodiments of the fifth aspect, in some embodiments, the reference signal measurement positioning capability includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristics DP; flight time TOA; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal received arrival time difference.

[0105] In combination with some embodiments of the fifth aspect, in some embodiments, the first information also includes at least one of the following: auxiliary data support capability information; input information; output information; processing capability information; reference signal capability information; generalization capability information; deployment scenario; applicable conditions; monitoring information; storage capability information; computing capability information.

[0106] In combination with some embodiments of the fifth aspect, in some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning; support for auxiliary data for model training; support for auxiliary data for model monitoring.

[0107] In combination with some embodiments of the fifth aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal time difference RSTD; reference signal received path power RSRPP; reference signal received power RSRP; terminal receive and transmit time difference; flight time TOA.

[0108] In combination with some embodiments of the fifth aspect, in some embodiments, the output information includes at least one of the following: location information of the terminal; an indication of line-of-sight propagation or non-line-of-sight propagation; time of flight TOA; and measurement information of a predicted reference signal.

[0109] In combination with some embodiments of the fifth aspect, in some embodiments, the processing capability information includes at least one of the following: processing time; activation time; switching time; update time.

[0110] In combination with some embodiments of the fifth aspect, in some embodiments, the reference signal capability information includes at least one of the following: maximum number of reference signals; reference signal resources; reference signal resource set; frequency layer.

[0111] In combination with some embodiments of the fifth aspect, in some embodiments, the deployment scenario includes at least one of the following: indoor scene; outdoor scene; urban scene; hotspot scene.

[0112] In combination with some embodiments of the fifth aspect, in some embodiments, the applicable conditions include at least one of the following: signal to interference plus noise ratio SINR; reference signal received power RSRP; time error group TEG; adaptation scenario.

[0113] In combination with some embodiments of the fifth aspect, in some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring; terminal support model performance indicators used for performance monitoring; terminal support reporting of model performance monitoring results; terminal support reporting of model performance monitoring indicators; terminal support reporting of performance indicator types.

[0114] In combination with some embodiments of the fifth aspect, in some embodiments, the storage capacity information includes at least one of the following: the maximum storage capacity for storing AI models; the storage capacity corresponding to each AI function; and the storage capacity corresponding to each AI model.

[0115] In combination with some embodiments of the fifth aspect, in some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0116] In combination with some embodiments of the fifth aspect, in some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side; the maximum computing capability of the terminal for each AI function; the maximum computing capability of the terminal for each AI model.

[0117] In combination with some embodiments of the fifth aspect, in some embodiments, the transceiver module is further used to send the first information based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0118] In combination with some embodiments of the fifth aspect, in some embodiments, the second network element is an access network device, and the first information includes at least one of the following: the ability to support AI model positioning; supported AI functions; supported AI models; input information of each AI function or each AI model; output information of each AI function or each AI model; the ability to support measurement coordination between transmission and receiving nodes TRP.

[0119] In combination with some embodiments of the fifth aspect, in some embodiments, the input information includes at least one of the following: channel impulse response CIR; power delay spectrum PDP; delay characteristic DP; reference signal received power RSRP; access network device received arrival time difference.

[0120] In combination with some embodiments of the fifth aspect, in some embodiments, the output information includes at least one of the following: terminal location information, line-of-sight propagation or non-line-of-sight propagation indication; time of flight TOA; and measurement information of a reference signal predicted based on an AI model.

[0121] In combination with some embodiments of the fifth aspect, in some embodiments, the transceiver module is further used to send the first information based on the New Radio Positioning Protocol NRPPa.

[0122] In a sixth aspect, an embodiment of the present disclosure proposes a first network element, comprising: one or more processors; wherein the processor is used to execute the positioning method of the first aspect.

[0123] In a seventh aspect, the present disclosure implements a second network element, comprising: one or more processors; wherein the processor is used to execute the positioning method of the second aspect.

[0124] In an eighth aspect, the present disclosure implements a communication system, comprising a first network element and a second network element, wherein the first network element is configured to implement the positioning method of the first aspect, and the second network element is configured to implement the positioning method of the second aspect.

[0125] In a ninth aspect, the present disclosure implements a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the positioning method of any one of the first and second aspects.

[0126] It is understandable that the above-mentioned terminal, access network device, first network element, second network element, core network device, communication system, storage medium, program product, computer program, chip or 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.

[0127] In some embodiments, the terms "positioning method" and "information processing method" and "communication method" are interchangeable; "positioning device" and "information processing device" and "communication device" are interchangeable; and "information processing system" and "communication system" are interchangeable.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0133] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In some embodiments, terms such as "time / frequency" and "time / frequency domain" refer to the time domain and / or the frequency domain.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0148] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0149] 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.

[0150] FIG1A is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0151] As shown in FIG1A , a communication system 100 includes a first network element (node) 101 and a second network element (node) 102 .

[0152] In some embodiments, the first network element 101 includes a module in a core network device for implementing a specified function, but the name is not limited thereto, such as a module for implementing a location management function (LMF).

[0153] In some embodiments, the core network device may be a single device including a first network element, a second network element, etc., or may be a plurality of devices or a group of devices, each including all or part of the first network element, the second network element, etc. The network element may be virtual or physical. The core network may include, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0154] In some embodiments, the second network element includes, for example, a terminal or an access network device.

[0155] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0156] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0157] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0158] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0159] 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.

[0160] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1A , or a portion thereof, but are not limited thereto. The entities shown in FIG1A are illustrative only. The communication system may include all or part of the entities shown in FIG1A , or may include other entities other than those shown in FIG1A . The number and form of the entities may be 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.

[0161] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0162] The embodiments of the present disclosure also provide a positioning method, which implements AI-based positioning by defining AI-based positioning-related capabilities supported by the terminal and / or AI-based positioning-related capabilities supported by the access network device.

[0163] In some embodiments, artificial intelligence (AI) is gradually being applied in the communications field. For example, in 5G New Radio (NR) communication scenarios, and even in 6G communication scenarios. For example, in 5G or 6G communication scenarios, positioning based on AI or machine learning (ML) can be implemented. In other words, AI functions are applied to terminal positioning, and AI technology is used to achieve direct or indirect positioning of the terminal.

[0164] In some embodiments, Figure 1B is a schematic diagram illustrating the interaction between a terminal and a server for positioning according to an embodiment of the present disclosure. As shown in Figure 1B, the server requests positioning from the terminal based on the Long-Term Positioning Protocol (LPP). The terminal (also referred to as the Target in the figure) responds to the request from the server (also referred to as the Server in the figure) by sending positioning information to the server.

[0165] It can be understood that the server in Figure 1B can be understood as a module in the core network device for implementing the positioning management function, such as a positioning management function (LMF) module.

[0166] In some embodiments, Figure 1C is a schematic diagram illustrating interaction between a terminal and a server for positioning according to an embodiment of the present disclosure. As shown in Figure 1C, the terminal (also referred to as the Target in the figure) can send positioning information to the server (also referred to as the Server in the figure) based on LPP.

[0167] Based on the above positioning process, it can be understood that the terminal needs to provide relevant AI capabilities to the server.

[0168] It is understandable that when the AI ​​function is deployed on the terminal side, if positioning based on the AI ​​function is to be achieved, the relevant AI capabilities need to be reported to the server.

[0169] It is also understandable that in addition to being deployed on the terminal, the AI ​​function can also be deployed on the access network device side. When the AI ​​function is deployed on the access network device side, the access network device also needs to provide the server with AI positioning-related capabilities supported by the access network device.

[0170] However, for AI functions deployed on different communication nodes, how each node should report AI positioning-related capabilities to the server needs to be clarified. Based on this, the embodiments of the present disclosure provide a positioning method.

[0171] FIG2A is an interactive schematic diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG2A , the embodiment of the present disclosure relates to a positioning method, which includes:

[0172] Step S2101: The second network element 102 sends first information to the first network element 101.

[0173] In some embodiments, the first network element 101 obtains the first information.

[0174] In some embodiments, the first network element 101 receives first information from the second network element 102 .

[0175] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0176] 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.

[0177] In some embodiments, the second network element 102 is deployed with an artificial intelligence (AI) model.

[0178] In some embodiments, "AI model", "artificial intelligence model", "machine learning (ML) model", "ML model", "AI", "AI function", "ML function", "artificial intelligence function", and "machine learning function" can be replaced with each other.

[0179] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0180] In some embodiments, the first information indicates that the second network element 102 supports the capability of positioning based on an AI model.

[0181] Optionally, the first information is used by the first network element 101 for positioning. For example, the first information is used by the first network element 101 for AI-based positioning.

[0182] It is understandable that the second network element 102 can also be deployed with corresponding AI functions to replace the AI ​​model to achieve the same effect. For example, the second network element 102 can be deployed with a function for AI positioning to achieve AI positioning.

[0183] In some embodiments, the second network element 102 includes any one of the following: a terminal, or an access network device.

[0184] In some embodiments, the first network element 101 may be a unit in a core network device that implements a positioning function, for example, the first network element 101 may be a LMF.

[0185] Based on this, the second network element 102 sends the first information to the first network element 101, which may include any of the following situations:

[0186] -A) The terminal sends the first information to the LMF.

[0187] -B) The access network device sends the first information to the LMF.

[0188] Step S2102: The first network element 101 performs positioning based on the first information.

[0189] In some embodiments, the LMF performs AI-based positioning of the terminal based on the first information sent by the access network device.

[0190] In some embodiments, the LMF performs AI-based positioning of the terminal based on the first information sent by the terminal.

[0191] The positioning method involved in the embodiments of the present disclosure may include at least one of steps S2101 and S2102. For example, step S2101 may be implemented as an independent embodiment, step S2102 may be implemented as an independent embodiment, and step S2101 + step S2102 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0192] In some embodiments, steps S2101 and S2102 may be executed in an interchanged order or simultaneously.

[0193] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0194] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0195] FIG2B is an interactive schematic diagram of a positioning method according to a situation -A) in accordance with an embodiment of the present disclosure. As shown in FIG2B , an embodiment of the present disclosure relates to a positioning method, the method comprising:

[0196] Step S2201: The terminal sends first information to the LMF.

[0197] The optional implementation of step S2201 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0198] In some embodiments, for -A) situation,

[0199] The terminal sends the first information to the LMF based on the Long Term Evolution Positioning Protocol (LPP) and / or based on the Sidelink Positioning Protocol (SLPP).

[0200] 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.

[0201] In some embodiments, for case -A), the first information includes at least one of the following:

[0202] Direct positioning capability based on AI model, indirect positioning capability based on AI model, Positioning Reference Signal (PRS) measurement capability, AI functions supported by the terminal, AI models supported by the terminal, support for AI model transmission, no support for AI model transmission, support for AI model switching, support for AI function switching, support for fallback from AI positioning to non-AI positioning, no support for fallback from AI positioning to non-AI positioning, support for AI model update, support for AI model activation (active), support for AI model deactivation (deactive).

[0203] In some embodiments, the first information includes the direct positioning capability of the AI ​​model, which can be understood as the terminal's ability to perform positioning based on the AI ​​model, and the terminal's ability to directly obtain location information based on the AI ​​model. For example, the terminal can obtain the location coordinates of the target based on the AI ​​model.

[0204] In some embodiments, the first information includes the indirect positioning capability of the AI ​​model, which can be understood as the terminal's ability to perform positioning based on the AI ​​model. The terminal cannot directly obtain location information based on the AI ​​model, but can obtain intermediate parameters that can calculate location information based on the AI ​​model. Among them, the intermediate parameters can be, for example, at least one of the following: flight time or reference signal time difference (RSTD). For example: based on the AI ​​model, the flight time of the target is obtained, and the position coordinates of the target are further obtained based on the flight time.

[0205] In some embodiments, the reference signal measurement capability includes a positioning reference signal (PRS) measurement capability.

[0206] In some embodiments, the measurement capability of the reference signal includes at least one of the following: channel impulse response (CIR), power delay profile (PDP), delay particularity (DP) DP, time of aero (TOA), reference signal time difference (RSTD), reference signal path power (RSRPP), reference signal received power (RSRP), or terminal received arrival time difference (UE Rx-Tx time difference).

[0207] It can be understood that, for the measurement capability of the PRS, it is possible to define any one of the above-mentioned contents as the measurement capability of the PRS.

[0208] For example, it is assumed that the measurement capability of the PRS is described by measuring the CIR.

[0209] It is also possible to define the measurement capability of PRS as measuring multiple of the above contents.

[0210] For example, it is assumed that the measurement capability of the PRS is defined by measuring CIR and PDP.

[0211] In some embodiments, the first information includes the AI ​​function supported by the terminal, which can be understood as the AI ​​function that the terminal can normally use in terms of the terminal's ability to perform positioning based on the AI ​​model.

[0212] Exemplarily, if the terminal can normally use the function based on AI positioning, it can be considered that the terminal supports the function based on AI positioning.

[0213] In some embodiments, the first information includes an AI model supported by the terminal, which can be understood as an AI model that can be normally used by the terminal for the terminal's ability to perform positioning based on the AI ​​model.

[0214] Exemplarily, if the terminal can normally use the model based on AI positioning, it can be considered that the terminal supports the model based on AI positioning.

[0215] In some embodiments, the first information includes support for AI model transmission, which can be understood as the terminal's ability to perform positioning based on the AI ​​model. In the communication system, the terminal can normally receive or send the AI ​​model.

[0216] Exemplarily, the terminal can receive an AI model sent from an access network device, or the terminal can send an AI model to the access network device, that is, it can be considered that the terminal supports AI model transmission.

[0217] In some embodiments, the first information includes that AI model transmission is not supported, which can be understood as indicating that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal cannot normally receive or send the AI ​​model in the communication system. For example, the terminal supports receiving the AI ​​model sent to the terminal by the LMF.

[0218] For example, the terminal cannot receive the AI ​​model sent from the access network device, or the terminal cannot send the AI ​​model to the access network device, that is, it can be considered that the terminal does not support AI model transmission.

[0219] In some embodiments, the terms "delivery", "transfer", "transmission", etc. can be used interchangeably.

[0220] In some embodiments, the first information includes support for AI model switching, which can be understood as the terminal having the ability to switch AI models for positioning based on the AI ​​model. For example, the terminal supports switching AI models according to the instructions of the LMF.

[0221] Exemplarily, according to the instruction of LMF, the terminal can switch from the AI ​​model identified as 1# to the AI ​​model identified as 2#, that is, it can be considered that the terminal supports AI model switching.

[0222] It can be understood that when the first information does not include content related to supporting AI model switching, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal does not support the switching of the AI ​​model.

[0223] In some embodiments, the first information includes support for AI function switching, which can be understood as the terminal having the ability to switch the AI ​​function for the terminal's ability to perform positioning based on the AI ​​model. For example, the terminal supports switching the AI ​​function according to the instruction of the LMF.

[0224] Exemplarily, the LMF may instruct the terminal to switch from the AI ​​function of calculating the terminal position to the AI ​​function of determining multipath NLOS / LOS.

[0225] Exemplarily, according to the instruction of the LMF, the terminal can switch from the first AI function to the second AI function, that is, it can be considered that the terminal supports the switching of the AI ​​function.

[0226] It can be understood that when the first information does not include content related to supporting AI function switching, in some embodiments, it can be understood that the terminal does not support the switching of AI functions in terms of the terminal's ability to perform positioning based on the AI ​​model.

[0227] In some embodiments, the first information includes support for fallback from AI positioning to non-AI positioning, which can be understood as the terminal having the ability to perform positioning based on the AI ​​model, and having the function of falling back from the AI ​​positioning function to non-AI positioning. For example, the terminal supports fallback from AI positioning to non-AI positioning according to the instruction of the LMF.

[0228] Exemplarily, the LMF may instruct the terminal to fall back from the AI-based positioning function to the non-AI-based positioning function.

[0229] In some embodiments, the first information includes support for AI model update, which can be understood as the terminal having the capability of AI model update.

[0230] Exemplarily, the terminal can update the AI ​​model from the first version to the second version (for example, the performance of the AI ​​model corresponding to the second version is better than or equal to the performance of the AI ​​model corresponding to the first version), which can be understood as the terminal having the function of supporting AI model updates.

[0231] Exemplarily, the terminal can update certain parameters of the AI ​​model, which can be understood as the terminal having a function of supporting AI model update.

[0232] It can be understood that when the first information does not include content related to supporting AI function switching, in some embodiments, it can be understood that the terminal does not support AI model updates with respect to the terminal's ability to perform positioning based on the AI ​​model.

[0233] In some embodiments, the first information includes support for AI model activation, which can be understood as the terminal having the ability to activate the AI ​​model for positioning based on the AI ​​model. For example, the terminal supports AI model activation according to the instruction of the LMF.

[0234] Exemplarily, according to the instruction of LMF, the terminal can change the AI ​​model from an inactive state to an active state, that is, it can be considered that the terminal supports AI model activation.

[0235] It can be understood that when the first information does not include support for AI model activation, in some embodiments, it can be understood that the terminal does not support AI model activation for the terminal's ability to perform positioning based on the AI ​​model.

[0236] In some embodiments, the first information includes support for AI deactivation, which can be understood as indicating that the terminal has the ability to deactivate the AI ​​model for positioning based on the AI ​​model. For example, the terminal supports deactivating the AI ​​model according to the instruction of the LMF.

[0237] Exemplarily, the LMF may instruct the terminal to activate the capability of the AI ​​model for positioning.

[0238] Exemplarily, the terminal can change the AI ​​model from an activated state to an inactivated state, and it can be considered that the terminal supports deactivation of the AI ​​model.

[0239] It can be understood that when the first information does not include support for AI model deactivation, in some embodiments, it can be understood that the terminal does not support AI model deactivation with respect to the terminal's ability to perform positioning based on the AI ​​model.

[0240] Optionally, the content included in the above-mentioned first information can be considered as a description of the general functional information of the AI ​​model.

[0241] In some embodiments, the first information includes at least one of the following: assistance data support capability information, input information, output information, AI process capability information, reference signal capability information, generalization capability information, deployment scenario, applicable conditions, monitoring information, storage capability information, or calculation capability information.

[0242] Optionally, the reference signal capability information includes, for example, positioning reference signal capability information.

[0243] It can be understood that the reference signal capability information can be determined based on the usage of the AI ​​model. For example, if the AI ​​model is used for CSI prediction, the reference signal capability information is the reference signal capability information used for CSI prediction, or if the AI ​​model is used for positioning, the reference signal capability information is the reference signal capability information used for positioning (such as positioning reference signal capability information).

[0244] In some embodiments, the terms "monitor" and "supervisory" can be used interchangeably.

[0245] In some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0246] It is understandable that for some specified AI models or some specified AI functions, additional information can be defined in the first information to obtain the specified AI model or specified AI function.

[0247] 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.

[0248] For example, when the designated AI function is a monitoring function, in addition to defining the general functional information description of the AI ​​model through the first information, it is also necessary to define monitoring information for its monitoring function in the first information.

[0249] In some embodiments, the first information includes auxiliary data support capability information, which can be understood as, for the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal also has the ability to support auxiliary data.

[0250] In some embodiments, the assistance data support capability information includes at least one of the following:

[0251] Supports auxiliary data for model fine-tuning, auxiliary data for model training, or auxiliary data for model monitoring.

[0252] It can be understood that when the first information does not include auxiliary data support capability information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal does not have the ability to support auxiliary data except for the general capabilities included in the first information.

[0253] Exemplarily, the terminal supports auxiliary data indicating that the terminal supports receiving auxiliary information sent by the LMF, for example, the terminal supports receiving auxiliary information sent by the LMF for model training and model performance monitoring.

[0254] In some embodiments, the first information includes input information, which can be understood as, for the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal also has the ability to support input information.

[0255] In some embodiments, the input information includes at least one of the following: CIR, PDP, DP, RSTD, RSRPP, RSRP, and the terminal's receive and transmit time difference or TOA. In some embodiments, the first information includes output information, which can be understood as indicating that, in addition to the general capabilities included in the first information, the terminal also has the ability to support output information for its ability to perform positioning based on the AI ​​model.

[0256] In some embodiments, the output information includes at least one of the following: location information of the terminal, line of sight or non-line of sight (Los / Nlos) indication, TOA, and measurement information of a predicted reference signal.

[0257] It can be understood that when the first information does not include output information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal does not have the ability to support output information except for the general capabilities included in the first information.

[0258] In some embodiments, the first information includes processing capability information, which can be understood as indicating that, with respect to the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal has the ability to support information processing. In some embodiments, the processing capability information includes at least one of the following: processing time, activation time, switch time, or update time.

[0259] Exemplarily, the terminal supports processing capability information indicating that the terminal supports receiving processing capability information sent by the LMF, for example, the terminal supports receiving processing information such as switching time for model switching or activation time for model activation sent by the LMF.

[0260] It can be understood that when the first information does not include processing information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal does not have the ability to support information processing except for the general capabilities included in the first information.

[0261] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0262] In some embodiments, the first information includes positioning reference signal capability information, which can be understood as, for the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal has the ability to target positioning reference signal-related restrictions.

[0263] In some embodiments, the reference signal capability information includes at least one of the following: a maximum number of reference signals (TRP); a reference signal resource (PRS resource); a reference signal resource set (PRS resource set) or a frequency layer (frequency layer).

[0264] Optionally, when the reference signal capability information is positioning reference signal capability information, in some embodiments, the reference signal capability information includes at least one of the following: maximum number of positioning reference signals (max number of TRP); positioning reference signal resources (PRS resource); positioning reference signal resource set (PRS resource set) or frequency layer (frequency layer).

[0265] In some embodiments, the reference signal resources may be, for example, reference signal resources corresponding to each set, reference signal resources corresponding to each transmission reception point (TRP), or reference signal resources corresponding to each layer.

[0266] Optionally, in the case where the reference signal resource is a positioning reference signal resource, the positioning reference signal resource may be, for example, a positioning reference signal resource corresponding to each set (per set), a positioning reference signal resource corresponding to each transmission reception point (TRP), or a positioning reference signal resource corresponding to each layer (per layer).

[0267] In some embodiments, the reference signal resource set may be, for example, a reference signal resource set corresponding to each TRP or a reference signal resource set corresponding to each frequency layer.

[0268] Optionally, when the reference signal resource set is a positioning reference signal resource set, the positioning reference signal resource set may be, for example, a positioning reference signal resource set corresponding to each TRP or a positioning reference signal resource set corresponding to each frequency layer.

[0269] Exemplarily, the terminal supports positioning reference signal capability information indicating that the terminal supports receiving positioning reference signal capability information sent by LMF, for example, the terminal supports receiving positioning reference signal capability information such as the maximum number of supportable positioning reference signals sent by LMF.

[0270] It can be understood that when the first information does not include reference signal capability information, in some embodiments, it can be understood that with respect to the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal does not have the ability to have reference signal-related restrictions.

[0271] For example, taking the positioning reference signal capability information as an example, when the first information does not include the positioning reference signal capability information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal does not have the ability to restrict the positioning reference signal.

[0272] In some embodiments, the generalization capability information may include, for example, support for AI model generalization or non-support for AI model generalization.

[0273] In some embodiments, the first information includes generalization capability information, which can be understood as, for the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal has the ability to generalize the AI ​​model.

[0274] Exemplarily, when the generalization information indicates support for AI model generalization, the first information includes generalization capability information, which can be understood as the terminal's ability to support AI model generalization. When the generalization information indicates support for AI model generalization, the first information includes generalization capability information, which can be understood as the terminal's ability to not support AI model generalization.

[0275] In some embodiments, the generalization capability of the AI ​​model means that the AI ​​model can be applied to multiple scenarios or multiple functions.

[0276] In some embodiments, the first information includes a deployment scenario, which can be understood as, with respect to the terminal's ability to perform positioning based on an AI model, in addition to the general capabilities included in the first information, the terminal has restricted capabilities related to the deployment scenario.

[0277] In some embodiments, the deployment scenario includes at least one of the following: an indoor scenario, an outdoor scenario, an urban scenario, or a hotspot scenario.

[0278] Exemplarily, the first information includes an indoor scene in the deployment scenario, which can be understood as the terminal supporting the deployment of the AI ​​model in an indoor scene.

[0279] It is understood that if the first information does not include a deployment scenario, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal does not have capabilities related to deployment scenario restrictions. For example, if the first information does not include outdoor scenarios, it can be understood that the terminal does not support the deployment of the AI ​​model in outdoor scenarios.

[0280] In some embodiments, the first information includes applicable conditions, which can be understood as, for the terminal's ability to perform positioning based on the AI ​​model, in addition to the general capabilities included in the first information, the terminal has restricted capabilities for usage conditions.

[0281] In some embodiments, the applicable conditions include at least one of the following: signal-to-noise and interference ratio (SINR), reference signal received power (RSRP), timing error group (TEG), or adaptation scenario.

[0282] In some embodiments, the terminal can only use a specified SINR and / or a specified SINR range for AI model-based positioning.

[0283] Exemplarily, the first information may define a restriction based on an applicable condition represented by the SINR, for example, the applicable condition is that the SINR satisfies a certain threshold.

[0284] In some embodiments, the terminal can only use RSRP and / or specify an RSRP range for positioning based on the AI ​​model.

[0285] For example, the first information may define a restriction based on an applicable condition represented by RSRP, for example, the usage condition is that RSRP meets a certain threshold.

[0286] In some embodiments, the terminal can only use TEG and / or specify a TEG range for AI model-based positioning.

[0287] For example, the first information may define a restriction based on an applicable condition represented by the TEG, for example, the usage condition is that the TEG satisfies a certain threshold.

[0288] It can be understood that when the first information does not include a deployment scenario, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on an AI model, the terminal has no restrictions on applicable conditions except for the general capabilities included in the first information.

[0289] In some embodiments, the first information includes monitoring information, which can be understood as indicating that, in addition to the general capabilities included in the first information, the terminal has monitoring capabilities for the AI ​​model in terms of its ability to perform positioning based on the AI ​​model. For example, the terminal supports monitoring of the AI ​​model according to instructions from the LMF.

[0290] For example, according to the instructions of LMF, the terminal can perform performance monitoring on the AI ​​model identified as 1#.

[0291] In some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring, performance indicators used in terminal support model performance monitoring, terminal support reporting model performance monitoring results, terminal support reporting model performance monitoring indicators, or terminal support reporting performance indicator types.

[0292] It can be understood that when the first information does not include monitoring information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal does not have the ability to monitor the AI ​​model except for the general capabilities included in the first information.

[0293] In some embodiments, the first information includes storage capability information, which can be understood as indicating that, in addition to the general capabilities included in the first information, the terminal has storage capability limitations with respect to its ability to perform positioning based on the AI ​​model. For example, the terminal supports limiting storage capability based on instructions from the LMF.

[0294] For example, based on the instructions of the LMF, the terminal can clearly identify the space that can be used to store the AI ​​model.

[0295] In some embodiments, the storage capacity information includes at least one of the following: a maximum storage capacity for storing an AI model, a storage capacity corresponding to each AI function, or a storage capacity corresponding to each AI model.

[0296] It can be understood that when the first information does not include storage capacity information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal has no relevant restrictions on storage capacity except for the general capabilities included in the first information.

[0297] In some embodiments, the first information includes computing capability information, which can be understood as indicating that, in addition to the general capabilities included in the first information, the terminal has limitations on computing capability with respect to its ability to perform positioning based on the AI ​​model. For example, the terminal supports limiting computing capability based on instructions from the LMF.

[0298] For example, according to the instructions of the LMF, the terminal can clearly understand the computing power corresponding to each AI function.

[0299] In some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side, the maximum computing capability of the terminal for each AI function, or the maximum computing capability of the terminal for each AI model.

[0300] Optionally, the computing capability of the terminal may be represented by floating point calculation (flop).

[0301] Exemplarily, the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side may be represented by the maximum floating-point computing capability of the terminal for the AI ​​function deployed on the terminal side.

[0302] It can be understood that when the first information does not include computing capability information, in some embodiments, it can be understood that, with respect to the terminal's ability to perform positioning based on the AI ​​model, the terminal has no relevant restrictions on computing capability except for the general capabilities included in the first information.

[0303] Step S2202: LMF performs positioning based on the first information.

[0304] In some embodiments, the LMF determines whether the terminal has direct positioning capability based on the AI ​​model based on the first information sent by the terminal, and then decides whether to configure the terminal to use the direct positioning mode.

[0305] Exemplarily, the first information includes: the terminal is able to support direct positioning capability based on the AI ​​model, and LMF configures the terminal to perform AI-based direct positioning mode.

[0306] Exemplarily, the first information includes: the terminal cannot support the direct positioning capability based on the AI ​​model, and the LMF configures the terminal to use the indirect positioning mode based on AI.

[0307] In some embodiments, the LMF determines the reference signal measurement capability supported by the terminal based on the first information sent by the terminal, and instructs the terminal to perform positioning based on the supported reference signal measurement capability based on the reference signal measurement capability, or instructs the terminal to obtain a measurement quantity based on the reference signal.

[0308] Exemplarily, the first information includes: if the terminal supports the measurement capability of CIR and TOA, the LMF instructs the terminal to perform positioning based on CIR and / or TOA, or instructs the terminal to measure the reference signal to obtain CIR and / or TOA, or instructs the terminal to report CIR and / or TOA.

[0309] In some embodiments, the LMF determines whether the terminal has the ability to support AI model transmission based on the first information, transmits the AI ​​model to the terminal, and instructs the terminal to perform positioning based on the transmitted AI model.

[0310] Exemplarily, the first information includes: the terminal supports the transmission capability of the 1#AI model used for positioning, the LMF transmits the 1#AI model to the terminal, and instructs the terminal to perform positioning based on the 1#AI model.

[0311] Exemplarily, the first information includes: if the terminal supports the ability of auxiliary data, the LMF can send auxiliary information to the terminal, such as auxiliary information sent by the LMF for model training and model performance monitoring.

[0312] It should be noted that the above-mentioned embodiment description of LMF positioning based on the first information is only an illustrative example of part of the terminal capabilities. It can be understood that LMF can make corresponding instructions or operations based on all the content related to the terminal capabilities mentioned in the first information, thereby instructing the terminal to perform positioning based on the corresponding instructions or operations of LMF.

[0313] The optional implementation of step S2202 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0314] In some embodiments, steps S2201 and S2202 may be performed in an interchangeable order or simultaneously.

[0315] In some embodiments, step S2202 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0316] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2B .

[0317] FIG2C is an interactive schematic diagram of a positioning method according to a situation B) in accordance with an embodiment of the present disclosure. As shown in FIG2C , an embodiment of the present disclosure relates to a positioning method, the method comprising:

[0318] Step S2301: The access network device sends first information to the LMF.

[0319] The optional implementation of step S2301 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0320] In some embodiments, for case -B), the access network device (e.g., gNB) sends first information to the LMF based on the new radio positioning protocol (NR positioning protocol A, NRPPa).

[0321] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0322] In some embodiments, terms such as "synchronization signal (SS)", "synchronization signal block (SSB)", "reference signal (RS)", "pilot", and "pilot signal" can be used interchangeably.

[0323] In some embodiments, for the -B) situation, the first information includes at least one of the following: the ability to support AI model positioning, supported AI functions, supported AI models, input information of each AI function or each AI model, output information of each AI function or each AI model, supported AI function deployment information, or the ability to support measurement coordination between TRPs.

[0324] It is understandable that the input information of each AI function or each AI model is: the input information of each AI function or the input information of each AI model. For example, the access network device supports the capability of supporting AI model positioning according to the LMF indication, and performs positioning based on the AI ​​model. Exemplarily, according to the LMF indication, the access network has the capability of AI model positioning, and the access network device performs positioning based on the AI ​​model based on the LMF indication.

[0325] It can be understood that the output information of each AI function or each AI model is: the output information of each AI function or the output information of each AI model.

[0326] It can be understood that for the capabilities of supporting AI model positioning, supported AI functions, related embodiments of supported AI models and optional embodiments, please refer to the relevant exemplary descriptions in the -A) situation. To avoid repetitive introduction, they will not be described here one by one.

[0327] It should be noted that, since in case A) the terminal sends the first information to the LMF, and in case B) the access network device sends the first information to the LMF, in the relevant exemplary description, the phrase "the terminal supports receiving the LMF" in case B) should read "the access network device supports receiving the LMF."

[0328] In some embodiments, the first information includes supported AI function deployment information, which can be understood as, for the access network device's ability to perform positioning based on the AI ​​model, the access network device has the ability to restrict the deployment conditions for the supported AI models.

[0329] Optionally, the supported AI function deployment information includes: supporting AI deployment in a centralized unit (CU) or supporting AI deployment in a distributed unit (DU). For example, the access network device supports AI model deployment in a CU.

[0330] Exemplarily, according to the LMF indication, the access network device has the ability to support the deployment of the AI ​​model in the CU, and the access network device deploys the available AI model in the CU based on this capability. In some embodiments, the first information includes the ability to support measurement coordination between TRPs, which can be understood as the ability of the access network device to perform positioning based on the AI ​​model. The access network device has the ability to coordinate measurements between TRPs. For example, the access network device supports the measurement coordination capability between TRPs indicated by the LMF, and performs positioning measurement coordination based on the AI ​​model between TRPs.

[0331] Exemplarily, according to the LMF indication, the access network device has the measurement coordination capability between TRPs, and the access network device performs measurement coordination related to AI model-based positioning between TRPs (for example, between terminals) based on the LMF indication.

[0332] It can be understood that the first information does not include the ability to support measurement coordination between TRPs. In some embodiments, it can be understood that with respect to the ability of the access network device to perform positioning based on the AI ​​model, the access network device does not have the ability to coordinate measurements between TRPs.

[0333] In some embodiments, the input information (ie, the input information of each AI function or each AI model) includes at least one of the following: CIR, PDP, DP, RSRP, or access network device received arrival time difference.

[0334] In some embodiments, the output information (i.e., the output information of each AI function, or each AI model) includes at least one of the following: terminal location information, line-of-sight or non-line-of-sight propagation indication, time-of-flight TOA, or measurement information of a reference signal predicted based on the AI ​​model.

[0335] Step S2302: LMF performs positioning based on the first information.

[0336] In some embodiments, the LMF determines whether the access network device has the ability to support AI model-based positioning based on the first information sent by the access network, and then decides whether to instruct the access network device to use the AI ​​model-based positioning mode.

[0337] Exemplarily, the first information includes: the access network device supports the ability of AI model-based positioning, and LMF instructs the access network device to use AI model-based positioning.

[0338] In some embodiments, the LMF determines the AI ​​functions supported by the access network device based on the first information sent by the access network.

[0339] Exemplarily, the first information includes: the access network device supports the AI ​​positioning function, and the LMF instructs the access network device to use the positioning mode based on the AI ​​function.

[0340] In some embodiments, the LMF determines the AI ​​function supported by the access network device based on the first information sent by the access network device, and then instructs the access network device to perform positioning based on the supported AI function.

[0341] Exemplarily, the first information includes that the access network device supports the function of direct positioning based on AI, and LMF instructs the access network device to use the mode of direct positioning based on AI based on this function.

[0342] In some embodiments, the LMF determines the AI ​​model supported by the access network device based on the first information sent by the access network device, and then instructs the access network device to perform positioning based on the supported AI model.

[0343] Exemplarily, the first information includes that the access network device supports a model based on AI direct positioning, and LMF instructs the access network device to use a mode based on AI direct positioning based on the model.

[0344] The optional implementation of step S2302 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0345] In some embodiments, steps S2301 and S2302 may be executed in an interchanged order or simultaneously.

[0346] In some embodiments, step S2302 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0347] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2C .

[0348] FIG3A is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a positioning method, which includes:

[0349] Step S3101, obtain first information.

[0350] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A, step S2201 in Figure 2B and step S2301 in Figure 2C, as well as other related parts in the embodiments involved in Figures 2A, 2B and 2C, which will not be repeated here.

[0351] In some embodiments, the first network element 101 receives the first information sent by the second network element 102, but is not limited thereto. The first information sent by other entities may also be received.

[0352] In some embodiments, the first network element 101 obtains first information specified by a protocol.

[0353] In some embodiments, the first network element 101 obtains the first information from an upper layer(s).

[0354] In some embodiments, the first network element 101 performs processing to obtain the first information.

[0355] Step S3102: perform positioning based on the first information.

[0356] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A, step S2202 in Figure 2B and step S2302 in Figure 2C, as well as other related parts in the embodiments involved in Figures 2A, 2B and 2C, which will not be repeated here.

[0357] The positioning method involved in the embodiments of the present disclosure may include at least one of steps S3101 and S3102. For example, step S3101 may be implemented as an independent embodiment, step S3102 may be implemented as an independent embodiment, and step S3102 + step S3102 may be implemented as independent embodiments, but the present invention is not limited thereto.

[0358] In some embodiments, step S3101 and step S3102 may be executed in an interchanged order or simultaneously.

[0359] In some embodiments, step S3102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0360] FIG3B is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a positioning method, which includes:

[0361] Step S3201, obtain first information.

[0362] The optional implementation of step S3201 can be found in step S2101 of Figure 2A, step S2201 of Figure 2B, step S2301 of Figure 2C and the optional implementation of Figure 3A, as well as other related parts in the embodiments involved in Figures 2A, 2B, 2C and 3A, which will not be repeated here.

[0363] In some embodiments, first information is received from a second network element, wherein the second network element is deployed with an AI model, and the first information indicates that the second network element supports the capability of positioning based on the AI ​​model.

[0364] In some embodiments, when the second network element is a terminal, the first information includes at least one of the following: direct positioning capability based on the AI ​​model, indirect positioning capability based on the AI ​​model, reference signal measurement capability, AI function supported by the terminal, AI model supported by the terminal, support for AI model transmission, no support for AI model transmission, support for AI model switching, support for AI function switching, support for fallback from AI positioning to non-AI positioning, support for AI model update, support for AI model activation, and support for AI model deactivation.

[0365] In some embodiments, the reference signal measurement positioning capability includes at least one of the following: CIR, PDP, DP, TOA, RSTD, RSRPP, RSRP, or terminal received arrival time difference.

[0366] In some embodiments, the first information also includes at least one of the following: auxiliary data support capability information, input information, output information, processing capability information, positioning reference signal capability information, generalization capability information, deployment scenario, applicable conditions, monitoring information, storage capability information or computing capability information.

[0367] In some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning, support for auxiliary data for model training, or support for auxiliary data for model monitoring.

[0368] In some embodiments, the input information includes at least one of the following: CIR, PDP, DP, RSTD, RSRPP, RSRP, or TOA.

[0369] In some embodiments, the output information includes at least one of the following: location information of the terminal, an indication of line-of-sight propagation or non-line-of-sight propagation, and measurement information of TOA or a predicted reference signal.

[0370] In some embodiments, the processing capability information includes at least one of the following: processing time, activation time, switching time, and update time.

[0371] In some embodiments, the positioning reference signal capability information includes at least one of the following: a maximum number of positioning reference signals, a positioning reference signal resource, a positioning reference signal resource set, or a frequency layer.

[0372] In some embodiments, the deployment scenario includes at least one of the following: an indoor scenario, an outdoor scenario, an urban scenario, or a hotspot scenario.

[0373] In some embodiments, the applicable condition includes at least one of the following: SINR, RSRP, TEG, or adaptation scenario.

[0374] In some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring, performance indicators used in terminal support model performance monitoring, terminal support reporting model performance monitoring results, terminal support reporting model performance monitoring indicators, or terminal support reporting performance indicator types.

[0375] In some embodiments, the storage capacity information includes at least one of the following: a maximum storage capacity for storing an AI model, a storage capacity corresponding to each AI function, or a storage capacity corresponding to each AI model.

[0376] In some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0377] In some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side, the maximum computing capability of the terminal for each AI function, or the maximum computing capability of the terminal for each AI model.

[0378] In some embodiments, the first information is obtained based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0379] In some embodiments, when the second network element is an access network device, the first information includes at least one of the following: the ability to support AI model positioning, supported AI functions, supported AI models, input information of each AI function, or each AI model, output information of each AI function, or each AI model, or the ability to support measurement coordination between transmission and receiving nodes TRP.

[0380] In some embodiments, the input information includes at least one of the following: CIR, PDP, DP, RSRP, or access network device received arrival time difference.

[0381] In some embodiments, the output information includes at least one of the following: location information of the terminal, an indication of line-of-sight propagation or non-line-of-sight propagation, TOA, or measurement information of a reference signal predicted based on an AI model.

[0382] In some embodiments, the first information is obtained based on NRPPa.

[0383] FIG4 is a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in FIG4 , the embodiment of the present disclosure relates to a positioning method, which includes:

[0384] Step S4101, sending the first information.

[0385] The optional implementation of step S4101 can be found in step S2101 of Figure 2A, step S2201 of Figure 2B, step S2301 of Figure 2C and the optional implementation of Figure 3A, as well as other related parts in the embodiments involved in Figures 2A, 2B, 2C and 3A, which will not be repeated here.

[0386] In some embodiments, the second network element sends first information to the first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model.

[0387] In some embodiments, the second network element is an access network device, and the first information includes at least one of the following: direct positioning capability based on the AI ​​model, indirect positioning capability based on the AI ​​model, reference signal measurement capability, AI function supported by the terminal, AI model supported by the terminal, support for AI model transmission, no support for AI model transmission, support for AI model switching, support for AI function switching, support for fallback from AI positioning to non-AI positioning, support for AI model update, support for AI model activation, or support for AI model deactivation.

[0388] In some embodiments, the reference signal measurement positioning capability includes at least one of the following: CIR, PDP, DP, TOA, RSTD, RSRPP, RSRP, or terminal received arrival time difference.

[0389] In some embodiments, the first information also includes at least one of the following: auxiliary data support capability information, input information, output information, processing capability information, positioning reference signal capability information, generalization capability information, deployment scenario, applicable conditions, monitoring information, storage capability information or computing capability information.

[0390] In some embodiments, the auxiliary data support capability information includes at least one of the following: support for auxiliary data for model fine-tuning, support for auxiliary data for model training, or support for auxiliary data for model monitoring.

[0391] In some embodiments, the input information includes at least one of the following: CIR, PDP, DP, RSTD, RSRPP, RSRP, or TOA.

[0392] In some embodiments, the output information includes at least one of the following: location information of the terminal, an indication of line-of-sight propagation or non-line-of-sight propagation, and measurement information of TOA or a predicted reference signal.

[0393] In some embodiments, the processing capability information includes at least one of the following: processing time, activation time, switching time, or update time.

[0394] In some embodiments, the positioning reference signal capability information includes at least one of the following: a maximum number of positioning reference signals, a positioning reference signal resource, a positioning reference signal resource set, or a frequency layer.

[0395] In some embodiments, the deployment scenario includes at least one of the following: an indoor scenario, an outdoor scenario, an urban scenario, or a hotspot scenario.

[0396] In some embodiments, the applicable condition includes at least one of the following: SINR, RSRP, TEG, or adaptation scenario.

[0397] In some embodiments, the monitoring information includes at least one of the following: terminal support model performance monitoring, performance indicators used in terminal support model performance monitoring, terminal support reporting model performance monitoring results, terminal support reporting model performance indicators, or terminal support reporting performance indicator types.

[0398] In some embodiments, the storage capacity information includes at least one of the following: a maximum storage capacity for storing an AI model, a storage capacity corresponding to each AI function, or a storage capacity corresponding to each AI model.

[0399] In some embodiments, the first information corresponds to a specified AI model or a specified AI function.

[0400] In some embodiments, the computing capability information includes at least one of the following: the maximum computing capability of the terminal for the AI ​​function deployed on the terminal side, the maximum computing capability of the terminal for each AI function, or the maximum computing capability of the terminal for each AI model.

[0401] In some embodiments, the first information is obtained based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

[0402] In some embodiments, the second network element is an access network device, and the first information includes at least one of the following: the ability to support AI model positioning, supported AI functions, supported AI models, each AI function, or input information of each AI model, each AI function, or output information of each AI model, or the ability to support measurement coordination between transmission and receiving nodes TRP.

[0403] In some embodiments, the input information includes at least one of the following: CIR, PDP, DP, RSRP, or access network device received arrival time difference.

[0404] In some embodiments, the output information includes at least one of the following: location information of the terminal, an indication of line-of-sight propagation or non-line-of-sight propagation, TOA, and measurement information of a reference signal predicted based on an AI model.

[0405] In some embodiments, the first information is obtained based on the New Radio Positioning Protocol NRPPa.

[0406] FIG5 is an interactive diagram of a positioning method according to an embodiment of the present disclosure. As shown in FIG5 , an embodiment of the present disclosure relates to a positioning method, and the method includes:

[0407] Step S5101: The second network element 102 sends first information to the first network element 101.

[0408] In some embodiments, the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the capability of positioning based on the AI ​​model.

[0409] The optional implementation of step S5101 can be found in the optional implementation of step S2101 in Figure 2A, step S3101 in Figure 3A, step S3201 in Figure 3B, and step S4101 in Figure 4, as well as other related parts in the embodiments involved in Figures 2, 3A, 3B and 4, which will not be repeated here.

[0410] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, terminal side, network device side, etc., which will not be repeated here.

[0411] In some embodiments, a method for AI positioning capability is defined, where the AI ​​positioning capability includes: AI positioning capability supported by the terminal and AI positioning capability supported by the core network device.

[0412] In some embodiments, the AI ​​positioning capabilities supported by the terminal include at least one of the following:

[0413] Direct positioning capability based on AI, or indirect positioning capability based on AI.

[0414] Optionally, the AI-based direct positioning capability can be understood as directly obtaining the terminal's location information through AI.

[0415] Optionally, the AI-based indirect positioning capability can be understood as obtaining intermediate quantities of the positioning terminal through AI, and determining the terminal's location information (UE location) through the intermediate quantities. For example, flight time, RSTD and other measurement results are obtained through AI.

[0416] In some embodiments, the AI ​​positioning capabilities supported by the terminal further include at least one of the following:

[0417] PRS measurement capability, AI function information, AI model information, whether model delivery / transfer is supported, whether AI function switching is supported, whether AI model switching is supported, or whether fallback from AI positioning to non-AI positioning is supported.

[0418] Optionally, the PRS measurement capability includes at least one of the following:

[0419] The ability to support CIR measurement, PDP measurement, DP measurement, TOA measurement, RSTD measurement, RSRPP measurement, RSRP measurement, terminal Rx-Tx time difference measurement, etc.

[0420] Optionally, the AI ​​function may be understood as a function of obtaining corresponding information through AI, and the corresponding information may include at least one of the following:

[0421] Terminal location information, TOA information, RSTD information, or Los / Nlos indication information.

[0422] Optionally, the model information may be, for example, identification information of the model.

[0423] In some embodiments, each AI model or each AI function also includes the following capabilities:

[0424] Support of assistance data: support capability, input capability, output capability, AI processing capability, generalization capability, deployment scenario, applicable conditions, monitoring capability, storage capability, or computing capability.

[0425] Optionally, the auxiliary data support capability includes at least one of the following: data for model fine tuning, data for model training, or data for model monitoring.

[0426] Optionally, the input capability includes at least one of the following:

[0427] The capability of inputting CIR, the capability of inputting PDP, the capability of inputting DP, or the capability of inputting other measurement results (legacy measurement) (eg, inputting RSTD or RSRP).

[0428] Optionally, the output capability includes at least one of the following:

[0429] The ability to output TOA, the ability to output Loss / Nlos indication, the ability to output location information, and the ability to output other AI-based function prediction values ​​(legacy measurements predicted by AI).

[0430] Optionally, the AI ​​processing capability includes at least one of the following:

[0431] Time-related processing capabilities, or PRS-related restriction capabilities.

[0432] Optionally, the time-related processing capability includes at least one of the following: AI processing time capability, AI restart time (reuse the response time), activation time (activate time) or switching time (switch time).

[0433] Optionally, the PRS-related restriction capability includes at least one of the following:

[0434] Maximum number of TRPs (max number of TRPs), PRS resources, for example, resources corresponding to each set, resources corresponding to each TRP, resources corresponding to each layer (PRS resource (per set / TRP / layer)), PRS resource set, for example, PRS resource set corresponding to each TRP or PRS resource set corresponding to each frequency layer (PRS resource set (per TRP / frequency layer)), frequency layer.

[0435] Optionally, the generalization capability refers to whether the capability of generalization is supported (supported or not).

[0436] Optionally, the deployment scenario includes at least one of the following: an indoor scenario or an outdoor scenario, etc.

[0437] Optionally, the applicable conditions include at least one of the following: may include deployment scenarios, SINR-related applicable conditions, RSRP-related applicable conditions, or TEG error-related applicable conditions.

[0438] Optionally, the monitoring capability includes at least one of the following: the terminal's ability to perform AI model monitoring (UE monitor), the terminal's ability to not perform AI model monitoring but to provide corresponding monitoring indicators (UE provides the metrics and the detailed metrics). Monitoring indicators include, for example, indirect monitoring indicators or LMF corresponding calculation indicators.

[0439] Optionally, the computing capability includes at least one of the following: the maximum floating point number corresponding to the terminal (max flops for UE), the maximum floating point number required for each AI model or the floating point number required for each AI function (max flops for AI positioning, the required flops for each AI model / functionality).

[0440] Optionally, the storage capacity includes at least one of the following: the storage capacity corresponding to each AI function, the storage capacity corresponding to each AI model, the storage capacity requirement corresponding to each AI function, or the storage capacity requirement corresponding to each AI model.

[0441] In some embodiments, the AI ​​positioning capabilities supported by the terminal are sent to the LMF via LPP or SLPP messages.

[0442] In some embodiments, the AI ​​positioning capabilities supported by the terminal are sent to other terminals via SLPP.

[0443] In some embodiments, the AI ​​positioning capabilities supported by the access network device include at least one of the following:

[0444] Support of AI positioning capabilities, supported AI functionality or models.

[0445] In some embodiments, for each AI function or AI model, the following is also included:

[0446] Input capability, and output capability.

[0447] Optionally, input capabilities include:

[0448] The ability to input CIR, the ability to input PDP, and the ability to input other measurement information (legacy measurement).

[0449] Other measurement information includes, for example, the Rx-Tx time difference or RSRP of the access network equipment (gNB).

[0450] Optionally, output capabilities include:

[0451] The ability to output Loss / Nlos indication, the ability to output TOA, and the ability to output AI-based prediction information (the legacy measurement predicted by AI).

[0452] In some embodiments, the AI ​​positioning capabilities supported by the access network device also include: whether the measurement coordination between TRPs is supported (Whether supports the measurement coordination between TRPs) and whether the AI ​​model deployment capability is supported.

[0453] In some embodiments, whether the AI ​​model deployment capability is supported includes at least one of the following:

[0454] Whether the AI ​​model can be deployed in CU or DU.

[0455] In some embodiments, the AI ​​positioning supported by the access network device is sent to the LMF via an NRPPa message.

[0456] In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments.

[0457] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0458] 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.

[0459] 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.

[0460] Figure 6A is a structural diagram of the first network element proposed in an embodiment of the present disclosure. As shown in Figure 6A, the first network element 6100 may include: a transceiver module 6101. In some embodiments, the above-mentioned transceiver module 6101 is used to receive first information from a second network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model. Optionally, the above-mentioned transceiver module 6101 is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2101, but not limited to this) performed by the first network element 101 in any of the above methods, which will not be repeated here.

[0461] In some embodiments, the processing module 6102 is used to perform positioning based on the first information. Optionally, the processing module 6102 is used to perform at least one of the processing steps (such as step S2102, but not limited thereto) performed by the first network element 101 in any of the above methods, which will not be repeated here.

[0462] Figure 6B is a structural diagram of the second network element proposed in an embodiment of the present disclosure. As shown in Figure 6B, the second network element 6200 may include: a transceiver module 6201. In some embodiments, the above-mentioned transceiver module 6201 is used to send first information to the first network element, wherein the second network element is deployed with an artificial intelligence AI model, and the first information indicates that the second network element supports the ability to perform positioning based on the AI ​​model. Optionally, the above-mentioned transceiver module 6201 is used to execute at least one of the communication steps such as sending and / or receiving (for example, step S2101, but not limited to this) performed by the second network element 102 in any of the above methods, which will not be repeated here.

[0463] In some embodiments, the processing module 6202 is used to process at least one of the communication steps such as sending and / or receiving (such as step S2102, but not limited thereto) based on the first information, which will not be repeated here.

[0464] 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.

[0465] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 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 7100 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.

[0466] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 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 7100 is used to perform any of the above methods. Optionally, one or more processors 7101 are used to call instructions to enable the communication device 7100 to perform any of the above methods.

[0467] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2102, 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, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be interchangeable, the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be interchangeable, and the terms receiver, receiving unit, receiver, and receiving circuit may be interchangeable.

[0468] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memories 7103 may be located outside the communication device 7100. In alternative embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuits 7104 are connected to the memories 7103 and may be configured to receive data from the memories 7103 or other devices, or to send data to the memories 7103 or other devices. For example, the interface circuits 7104 may read data stored in the memories 7103 and send the data to the processor 7101.

[0469] The communication device 7100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A. 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.

[0470] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0471] The chip 7200 includes one or more processors 7201. The chip 7200 is configured to execute any of the above methods.

[0472] In some embodiments, chip 7200 further includes one or more interface circuits 7202. Alternatively, terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 7200 further includes one or more memories 7203 for storing data. Alternatively, all or part of memory 7203 may be located external to chip 7200. Optionally, interface circuit 7202 is connected to memory 7203 and may be used to receive data from memory 7203 or other devices, or may be used to send data to memory 7203 or other devices. For example, interface circuit 7202 may read data stored in memory 7203 and send the data to processor 7201.

[0473] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (e.g., but not limited to, step S2101) in the above method, such as sending and / or receiving. For example, the interface circuit 7202 performs the communication steps (e.g., sending and / or receiving) in the above method, which means that the interface circuit 7202 performs data exchange between the processor 7201, the chip 7200, the memory 7203, or the transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (e.g., but not limited to, step S2102).

[0474] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0475] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes 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.

[0476] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0477] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A positioning method, characterized in that, Applied to a first network element, the method includes: Receiving first information from a second network element, where the second network element deploys an artificial intelligence (AI) model, and the first information represents the ability of the second network element to support positioning based on the AI model.

2. The method according to claim 1, wherein The second network element is a terminal, and the first information includes at least one of the following: Direct positioning ability based on the AI model; Indirect positioning ability based on the AI model; Reference signal measurement ability; AI functions supported by the terminal; AI models supported by the terminal; Support for AI model transmission; Do not support AI model transmission; Support for AI model switching; Support for AI function switching; Support for fallback from AI positioning to non-AI positioning; Support for AI model update; Support for AI model activation; Support for AI model deactivation.

3. The method according to claim 2, wherein The reference signal measurement positioning ability includes at least one of the following: Channel impulse response (CIR); Power delay profile (PDP); Delay profile (DP); Time of arrival (TOA); Reference signal time difference (RSTD); Received reference signal path power (RSRPP); Received reference signal power (RSRP); Terminal received time difference of arrival.

4. The method according to claim 2 or 3, characterized in that The first information further includes at least one of the following: Auxiliary data support ability information; Input information; Output information; Processing ability information; Reference signal ability information; Generalization ability information; Deployment scenario; Applicable conditions; Monitoring information; Storage ability information; Computing ability information.

5. The method according to claim 4, wherein The auxiliary data support ability information includes at least one of the following: Support for auxiliary data for model fine-tuning; Support for auxiliary data for model training; Support for auxiliary data for model monitoring.

6. The method according to claim 4, wherein The input information includes at least one of the following: Channel impulse response (CIR); Power delay profile (PDP); Delay profile (DP); Reference signal time difference (RSTD); Received reference signal path power (RSRPP); Received reference signal power (RSRP); Terminal received transmit time difference; Time of arrival (TOA).

7. The method according to claim 4, characterized in that, The output information includes at least one of the following: Location information of the terminal; Line-of-sight or non-line-of-sight propagation indication; Time of arrival (TOA); Measured information of the predicted reference signal.

8. The method according to claim 4, wherein The processing ability information includes at least one of the following: Processing time; Activation time; Switching time; Update time.

9. The method according to claim 4, wherein The reference signal ability information includes at least one of the following: Maximum number of reference signals; Reference signal resources; Reference signal resource set; Frequency layer.

10. The method according to claim 4, wherein The deployment scenario includes at least one of the following: Indoor scenario; Outdoor scenario; Urban scenario; Hotspot scenario.

11. The method according to claim 4, wherein The applicable conditions include at least one of the following: Signal-to-interference-plus-noise ratio (SINR); Received reference signal power (RSRP); Time error group (TEG); Adaptive scenario.

12. The method according to claim 4, characterized in that, The monitoring information includes at least one of the following: Terminal supports model performance monitoring; Performance metrics used for terminal-supported model performance monitoring; Terminal supports reporting model performance monitoring results; Terminal supports reporting model performance monitoring metrics; Type of performance metrics supported by the terminal for reporting.

13. The method according to claim 4, wherein The storage ability information includes at least one of the following: Maximum storage capacity for storing AI models; Storage capacity corresponding to each AI function; Storage capacity corresponding to each AI model.

14. The method according to claim 4, wherein The first information corresponds to a specified AI model or a specified AI function.

15. The method according to claim 4, wherein The computing power information includes at least one of the following: The maximum computing power of the terminal for deploying AI functions on the terminal side; The maximum computing power of the terminal for each AI function; The maximum computing power of the terminal for each AI model.

16. The method according to claim 1, characterized in that The first information is obtained based on any one of the following: Long-Term Evolution Positioning Protocol (LPP), or Sidelink Positioning Protocol (SLPP).

17. The method according to claim 1, wherein The second network element is an access network device, and the first information includes at least one of the following: The ability to support AI model positioning; Supported AI functions; Supported AI models; The input information of each AI function or each AI model; The output information of each AI function or each AI model; The ability to support measurement coordination between Transmission and Reception Points (TRPs).

18. The method according to claim 17, wherein The input information includes at least one of the following: Channel Impulse Response (CIR); Power Delay Profile (PDP); Delay Profile (DP); Reference Signal Received Power (RSRP); The time difference of arrival received by the access network device.

19. The method according to claim 17, wherein The output information includes at least one of the following: The location information of the terminal; Line-of-sight or non-line-of-sight propagation indication; Time of Arrival (TOA); Measurement information of the reference signal predicted based on the AI model.

20. The method according to claim 1, wherein The first information is obtained based on the New Radio Positioning Protocol (NRPPa).

21. A positioning method, characterized in that, Applied to the second network element, the method includes: Sending the first information to the first network element, wherein the second network element deploys an Artificial Intelligence (AI) model, and the first information represents the ability of the second network element to support positioning based on the AI model.

22. The method according to claim 21, wherein The second network element is an access network device, and the first information includes at least one of the following: The direct positioning ability based on the AI model; The indirect positioning ability based on the AI model; Reference signal measurement ability; AI functions supported by the terminal; AI models supported by the terminal; Support for AI model transmission; Do not support AI model transmission; Support for AI model switching; Support for AI function switching; Support for falling back from AI positioning to non-AI positioning; Support for AI model update; Support for AI model activation; Support for AI model deactivation.

23. The method according to claim 22, wherein The reference signal measurement positioning ability includes at least one of the following: Channel Impulse Response (CIR); Power Delay Profile (PDP); Delay Profile (DP); Time of Arrival (TOA); Reference Signal Time Difference (RSTD); Reference Signal Received Path Power (RSRPP); Reference Signal Received Power (RSRP); The time difference of arrival received by the terminal.

24. The method according to claim 22 or 23, characterized in that, The first information further includes at least one of the following: Auxiliary data support ability information; Input information; Output information; Processing ability information; Reference signal ability information; Generalization ability information; Deployment scenario; Applicable conditions; Monitoring information; Storage ability information; Computing power information.

25. The method according to claim 24, wherein The auxiliary data support ability information includes at least one of the following: Support for auxiliary data for model fine-tuning; Support for auxiliary data for model training; Support for auxiliary data for model monitoring.

26. The method according to claim 24, characterized in that, The input information includes at least one of the following: Channel Impulse Response (CIR); Power Delay Profile (PDP); Delay Profile (DP P); Reference Signal Time Difference (RSTD); Reference signal received path power RSRPP; Reference signal received power RSRP; Time of arrival TOA.

27. The method according to claim 24, characterized in that, The output information includes at least one of the following: Location information of the terminal; Line-of-sight propagation or non-line-of-sight propagation indication; Time of arrival TOA; Measured information of the predicted reference signal.

28. The method according to claim 24, wherein The processing capability information includes at least one of the following: Processing time; Activation time; Handover time; Update time.

29. The method according to claim 24, wherein The reference signal capability information includes at least one of the following: maximum number of reference signals; Reference signal resources; Reference signal resource set; Frequency layer.

30. The method according to claim 24, wherein The deployment scenario includes at least one of the following: Indoor scenario; Outdoor scenario; Urban scenario; Hotspot scenario.

31. The method according to claim 24, wherein The applicable conditions include at least one of the following: Signal-to-interference-plus-noise ratio SINR; Reference signal received power RSRP; Time error group TEG; Adaptive scenario.

32. The method according to claim 24, wherein The monitoring information includes at least one of the following: Terminal support for model performance monitoring; Performance metrics used for terminal support for model performance monitoring; Terminal support for reporting model performance monitoring results; Terminal support for reporting model performance metrics; Types of performance metrics supported for reporting by the terminal.

33. The method according to claim 24, wherein, The storage capability information includes at least one of the following: Maximum storage capability for storing AI models; Storage capability corresponding to each AI function; Storage capability corresponding to each AI model.

34. The method according to claim 24, wherein The first information corresponds to a specified AI model or a specified AI function.

35. The method according to claim 33, wherein The computing capability information includes at least one of the following: Maximum computing capability of the terminal for AI functions deployed on the terminal side; Maximum computing capability of the terminal for each AI function; Maximum computing capability of the terminal for each AI model.

36. The method according to claim 21, wherein The first information is sent by the second network element based on any one of the following: Long Term Evolution Positioning Protocol LPP, or Sidelink Positioning Protocol SLPP.

37. The method according to claim 21, wherein The second network element is an access network device, and the first information includes at least one of the following: Capability to support AI model positioning; Supported AI functions; Supported AI models; Input information for each AI function or each AI model; Output information for each AI function or each AI model; Capability to support measurement coordination between transmission and reception points TRP.

38. The method according to claim 37, wherein The input information includes at least one of the following: Channel impulse response CIR; Power delay profile PDP; Delay profile DP; Reference signal received power RSRP; Time difference of arrival received by the access network device.

39. The method according to claim 37, wherein The output information includes at least one of the following: Location information of the terminal; Line-of-sight propagation or non-line-of-sight propagation indication; Time of arrival TOA; Measured information of the reference signal predicted based on the AI model.

40. The method according to claim 21, wherein The first information is sent by the second network element based on the New Radio Positioning Protocol NRPPa.

41. A positioning method, characterized in that, The method includes: The second network element sends first information to the first network element, wherein the second network element deploys an artificial intelligence AI model, and the first information indicates the capability of the second network element to support positioning based on the AI model; The first network element receives the first information.

42. A first network element, characterized in that, Includes: A transceiver module, configured to receive first information from a second network element, where the second network element is deployed with an artificial intelligence (AI) model, and the first information indicates the ability of the second network element to support positioning based on the AI model.

43. A second network element, characterized in that, Comprising: A transceiver module, configured to send first information to a first network element, where the second network element is deployed with an artificial intelligence (AI) model, and the first information indicates the ability of the second network element to support positioning based on the AI model.

44. A first network element, characterized in that, Comprising: One or more processors; Wherein, the processor is configured to execute the positioning method according to any one of claims 1 to 20.

45. A second network element, characterized in that, Comprising: One or more processors; Wherein, the processor is configured to execute the positioning method according to any one of claims 21 to 40.

46. A communication system, characterized in that, Comprising a first network element and a second network element, where the first network element is configured to implement the positioning method according to any one of claims 1 to 20, and the second network element is configured to implement the positioning method according to any one of claims 21 to 40.

47. A storage medium storing instructions, characterized in that, When the instruction runs on a communication device, the communication device is caused to execute the positioning method according to any one of claims 1 to 20 or 21 to 40.

Images