Information reporting method and apparatus

Abstract

The present application relates to the technical field of wireless communications, and discloses an information reporting method and apparatus. The information reporting method of embodiments of the present application comprises: a terminal sends first information to a network side device, wherein the first information is used for representing that the terminal supports positioning based on artificial intelligence (AI).

Description

Information reporting method and device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on December 27, 2023, with application number 202311831519.X and invention name “Information Reporting Method and Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application belongs to the field of wireless communication technology, and specifically relates to an information reporting method and device. Background Art

[0004] Currently, artificial intelligence (AI) technology has been widely used in various fields. Integrating AI technology into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important research direction of wireless communication networks.

[0005] In existing technologies, the accuracy and reliability of AI-based positioning are low. Summary of the Invention

[0006] The embodiments of the present application provide an information reporting method and apparatus that can implement reporting of terminal capability information that supports AI positioning.

[0007] In a first aspect, an information reporting method is provided, which is executed by a terminal, and the method includes:

[0008] The terminal sends first information to the network side device, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0009] In a second aspect, an information reporting method is provided, which is performed by a network-side device, and the method includes:

[0010] The network side device receives first information reported by the terminal, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0011] In a third aspect, an information reporting device is provided, the device comprising:

[0012] The sending module is used to send first information to the network side device, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence AI.

[0013] In a fourth aspect, an information reporting device is provided, the device comprising:

[0014] The receiving module is used to receive first information reported by the terminal, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0015] In a fifth aspect, a terminal is provided, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0016] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the communication interface is used to send first information to a network side device, and the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

[0017] In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0018] In the eighth aspect, a network side device is provided, including a processor and a communication interface, wherein the communication interface is used to receive first information reported by a terminal, and the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

[0019] In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0020] In the tenth aspect, a wireless communication system is provided, comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.

[0021] In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0022] In the twelfth aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0023] In an embodiment of the present application, by introducing the terminal to report the first information to the network side device, the network side device is informed of the terminal's terminal capability of supporting AI-based positioning, and then the network side device can perform AI-based positioning-related network configuration and network operations, thereby improving the accuracy and reliability of the AI-based positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG1 shows a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0025] FIG2 is a schematic diagram of a neural network in the prior art;

[0026] FIG3 is a schematic diagram of a neuron in the prior art;

[0027] FIG4 is a flow chart of one of the information reporting methods provided in an embodiment of the present application;

[0028] FIG5 is a second flow chart of the information reporting method provided in an embodiment of the present application;

[0029] FIG6 is a third flow chart of the information reporting method provided in an embodiment of the present application;

[0030] FIG7 is a schematic diagram of a structure of an information reporting device according to an embodiment of the present application;

[0031] FIG8 is a second structural diagram of the information reporting device provided in an embodiment of the present application;

[0032] FIG9 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0033] FIG10 is a schematic diagram of the hardware structure of a terminal provided in an embodiment of the present application;

[0034] FIG11 is a schematic diagram of the hardware structure of the network side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0036] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0037] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0038] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. thGeneration, 6G) communication system.

[0039] FIG1 shows a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal may also be referred to as user equipment (UE), and the terminal 11 may be a mobile phone, a tablet personal computer, a laptop computer, a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device, an aircraft (flight vehicle), a vehicle user equipment (VUE), a ship-borne device, a pedestrian user equipment (PUE), a smart home (a household appliance with wireless communication capabilities, such as a refrigerator, a television, a washing machine, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side device. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AP) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0040] In order to facilitate a clearer understanding of the technical solutions provided by the embodiments of the present application, some relevant knowledge is first introduced as follows.

[0041] About AI:

[0042] AI technology is currently widely used in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for wireless communication networks. AI modules can be implemented in a variety of ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example for illustration, but does not limit the specific type of AI module.

[0043] FIG2 is a schematic diagram of a neural network in the prior art. As shown in FIG2 , a neural network is composed of neurons. FIG3 is a schematic diagram of a neuron in the prior art. As shown in FIG3 , a1, a2, …, a K is the input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, Tanh, ReLU (Rectified Linear Unit), etc.

[0044] The parameters of the neural network are optimized using a gradient optimization algorithm. A gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (also called a loss function), which is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f(.) is constructed. With the model, the predicted output f(x) can be obtained based on the input x, and the difference between the predicted value and the true value (f(x)-Y) can be calculated, which is the loss function. The goal is to find the appropriate W and b to minimize the value of the above loss function. The smaller the loss value, the closer the model is to the actual situation.

[0045] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0046] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), Adagrad (ADAptive GRADient descent, adaptive gradient descent), Adadelta, RMSprop (root mean square prop, root mean square error reduction), Adam (Adaptive Moment Estimation, adaptive momentum estimation), etc.

[0047] When these optimization algorithms backpropagate errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained by the loss function, add the influence of the learning rate, the previous gradient / derivative / partial derivative, etc., obtain the gradient, and pass the gradient to the previous layer.

[0048] The AI ​​unit / AI model described in the embodiments of this application may also be referred to as an AI unit, an AI model, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, a neural network function, etc., or the AI ​​unit / AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a GPU, NPU, TPU, ASIC, etc., and the present invention does not make specific limitations on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit / AI model.

[0049] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This embodiment of the application does not specifically limit this.

[0050] The information reporting method and device provided in the embodiments of the present application are described in detail below with reference to some embodiments and their application scenarios in conjunction with the accompanying drawings.

[0051] FIG4 is a flow chart of an information reporting method according to an embodiment of the present application. The method is applied to a terminal. As shown in FIG4 , the method includes:

[0052] Step 401: The terminal sends first information to a network-side device, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0053] Optionally, AI-based positioning refers to positioning based on an AI model or an AI function. The AI ​​function may include one or more AI models, or the AI ​​function may also be referred to as an AI model.

[0054] Optionally, the terminal sends first information to the network-side device to report that the terminal has the terminal capability to support AI-based positioning. After receiving the first information reported by the terminal, the network-side device learns that the terminal has the terminal capability to support AI-based positioning, and can then perform AI-based positioning-related operations based on the first information, where the AI-based positioning-related operations include at least one of the following: configuring AI-based positioning resources for the terminal; configuring an AI-based positioning method; and AI function-related management operations.

[0055] Optionally, the AI-based positioning scenario may include at least one of the following:

[0056] Positioning scenario 1: UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning;

[0057] Positioning scenario 2a: UE-assisted / LMF-based positioning with UE-side model (AI / ML assisted positioning). The AI ​​model is deployed on the UE side. Positioning methods include AI / ML assisted positioning.

[0058] Positioning scenario 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning;

[0059] Positioning scenario 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning.

[0060] Positioning scenario 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning.

[0061] In an embodiment of the present application, by introducing the terminal to report the first information to the network side device, the network side device is informed of the terminal's terminal capability of supporting AI-based positioning, and then the network side device can perform AI-based positioning-related network configuration and network operations, thereby improving the accuracy and reliability of the AI-based positioning method.

[0062] Optionally, the first information includes at least one of the following:

[0063] 1) Downlink positioning reference signal (PRS) resource-related capability information.

[0064] 2) DL PRS measurement and reporting related capability information for UE-assisted Direct AI / ML positioning.

[0065] 3) Terminal-assisted AI / ML-assisted positioning (UE-assisted AI / ML-assisted positioning) timing information measurement and reporting related capability information.

[0066] 4) Line of sight (OS) and / or non-line of sight (NLOS) indication related capability information of terminal-assisted AI-assisted positioning.

[0067] 5) AI functional performance supervision-related capability information.

[0068] 6) AI functional training related capability information.

[0069] Optionally, the AI ​​function training-related capability information may include: whether the terminal supports AI function fine-tuning or retraining (Support of model fine-tuning or retraining [for Case 1 or 2a]).

[0070] 7) PRS measurement related capability information.

[0071] Optionally, the downlink positioning reference signal DL PRS resource-related capability information in 1) may include at least one of the following:

[0072] a) DL PRS resource capability information for direct AI / ML positioning, including at least one of the following:

[0073] (1) Maximum number of DL PRS Resource Sets per TRP per frequency layer supported by the UE; the optional value set (Values) is, for example, {1, 2}.

[0074] (2) Maximum number of TRPs across all positioning frequency layers per UE; the optional value set is, for example, {4, 6, 12, 16, 24, 32, 64, 128, 256}.

[0075] (3) Maximum number of positioning frequency layers supported by the terminal (Max number of positioning frequency layers UE supports); the optional value set is, for example, {1, 2, 3, 4}.

[0076] b) DL PRS resource capability information for direct AI / ML positioning on a band, including at least one of the following:

[0077] (1) Maximum number of DL PRS resources per DL PRS Resource Set; the optional value set is, for example, {1, 2, 4, 8, 16, 32, 64}.

[0078] (2) Maximum number of DL PRS resources per positioning frequency layer; the optional value set is, for example, {6, 24, 32, 64, 96, 128, 256, 512, 1024}.

[0079] c) DL PRS resource capability information for direct AI / ML positioning on a band combination, including at least one of the following:

[0080] (1) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR1-only; the optional value set is, for example, {6, 24, 64, 128, 192, 256, 512, 1024, 2048}.

[0081] (2) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR2-only; the optional value set is, for example, {24, 64, 96, 128, 192, 256, 512, 1024, 2048}.

[0082] (3) Maximum number of DL PRS Resources supported by UE across all frequency layers, TRPs and DL PRS Resource Sets for FR1 in FR1 / FR2 mixed operation; the optional value set is, for example, {6, 24, 64, 128, 192, 256, 512, 1024, 2048}.

[0083] (4) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR2 in FR1 / FR2 mixed operation; the optional value set is, for example, {24, 64, 96, 128, 192, 256, 512, 1024, 2048}.

[0084] d) DL PRS resource capability information for AI / ML assisted positioning, including at least one of the following:

[0085] (1) Maximum number of DL PRS Resource Sets per TRP per frequency layer supported by the terminal; the optional value set is, for example, {1, 2}.

[0086] (2) Maximum number of TRPs across all positioning frequency layers per UE; the optional value set is, for example, {4, 6, 12, 16, 24, 32, 64, 128, 256}.

[0087] (3) Maximum number of positioning frequency layers supported by the terminal (Max number of positioning frequency layers UE supports); the optional value set is, for example, {1, 2, 3, 4}.

[0088] e) DL PRS resource capability information for AI / ML assisted positioning on a band, including at least one of the following:

[0089] (1) Maximum number of DL PRS resources per DL PRS Resource Set; the optional value set is, for example, {1, 2, 4, 8, 16, 32, 64}.

[0090] (2) Maximum number of DL PRS resources per positioning frequency layer; the optional value set is, for example, {6, 24, 32, 64, 96, 128, 256, 512, 1024}.

[0091] f) DL PRS resource capability information for AI / ML-assisted positioning on a band combination, including at least one of the following:

[0092] (1) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR1-only. The optional value set is, for example, {6, 24, 64, 128, 192, 256, 512, 1024, 2048}.

[0093] (2) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR2-only. The optional value set is, for example, {24, 64, 96, 128, 192, 256, 512, 1024, 2048}.

[0094] (3) Maximum number of DL PRS Resources supported by UE across all frequency layers, TRPs and DL PRS Resource Sets for FR1 in FR1 / FR2 mixed operation; the optional value set is, for example, {6, 24, 64, 128, 192, 256, 512, 1024, 2048}.

[0095] (4) Maximum number of DL PRS Resources supported by the UE across all frequency layers, TRPs and DL PRS Resource Sets for FR2 in FR1 / FR2 mixed operation; the optional value set is, for example, {24, 64, 96, 128, 192, 256, 512, 1024, 2048}.

[0096] Optionally, the DL PRS measurement reporting related capability information for UE-assisted direct AI positioning (DL PRS Measurement Reporting for UE-assisted Direct AI / ML positioning [for Case 2b]) in 2) may include at least one of the following:

[0097] a) Whether to support reporting of DL PRS reference signal received power RSRP measurement (Support DL PRS-RSRP measurements); the optional value set is, for example, {0, 1}.

[0098] b) Maximum number of DL PRS RSRP measurements on different PRS resources from the same TRP supported by the UE; the optional value set is, for example, {1, 2, 3, 4, 5, 6, 7, 8}.

[0099] Optionally, the terminal reports the DL PRS RSRP measurement amount. The DL PRS RSRP measurement amount can be used by the network side device to perform RSRP fingerprint positioning, and can also be used for model supervision or model selection, etc. This application does not limit the specific role of the DL PRS RSRP measurement amount.

[0100] c) Support reporting channel measurement type to LMF; optional value sets include {path delay, path power, path phase} or {channel impulse response (Channel Impulse Response, CIR), power delay profile (Power Delay Profile, PDP), delay profile (Delay Profile, DP)}.

[0101] Among them, CIR can include path delay, path power and path phase; PDP can include path delay and path power; DP only includes path delay.

[0102] d) Support reporting mode of channel measurement to LMF; the optional value set is, for example, {path-by-path reporting, channel tap-by-channel or sample-by-channel reporting}.

[0103] Optionally, the channel measurement reporting mode is used to instruct the terminal to report each path, or each channel tap or sample.

[0104] e) The maximum number of DL channel measurements on different PRS resources in the same TRP that can be reported (Max number of DL channel measurements per TRP); the optional value set is, for example, {1, 2, 3, 4}.

[0105] f) Supports reporting of first path information for UE-assisted Direct AI / ML positioning.

[0106] Optionally, the first path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0107] (1) Whether the reference signal (RS) path timing information reporting for the first path is supported.

[0108] (2) Whether the Reference Signal Received Path Power (RSRPP) reporting for the first path is supported.

[0109] (3) Whether RS ​​path phase reporting for the first path is supported.

[0110] g) Support for reporting additional path information for UE-assisted Direct AI / ML positioning.

[0111] Optionally, support reporting of additional path information for terminal-assisted direct AI positioning, including at least one of the following:

[0112] (1) Maximum number of additional paths per TRP that can be reported; the optional value set is, for example, {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0113] (2) The maximum number of additional paths per reference signal resource supported for reporting on each TRP.

[0114] (3) Whether to support reporting of additional detected path timing information for multiple additional paths (Support of additional detected path timing reporting for K>2additional paths UE-assisted Direct AI / ML positioning).

[0115] (4) Support of RSRPP reporting for additional paths.

[0116] (5) Whether RS ​​path phase reporting for additional paths is supported.

[0117] h) Support for reporting multiple paths information for UE-assisted Direct AI / ML positioning.

[0118] Optionally, multiple path information for terminal-assisted direct AI positioning can be reported, including at least one of the following:

[0119] (1) Maximum number of paths per TRP that can be reported; the optional value set is, for example, {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0120] (2) The maximum number of paths supported for each reference signal resource on each TRP that can be reported.

[0121] (3) Whether to support reporting of multiple detected path timing information (Support of multiple detected path timing reporting for UE-assisted Direct AI / ML positioning).

[0122] (4) Support of RSRPP reporting for multiple paths.

[0123] (5) Whether RS ​​path phase reporting for multiple paths is supported.

[0124] i) Support reporting of multiple channel samples or taps for UE-assisted Direct AI / ML positioning.

[0125] Optionally, supporting reporting of multiple sampling points or multiple tap information of the time domain channel for terminal-assisted direct AI positioning includes at least one of the following:

[0126] (1) Maximum number of channel taps per TRP that can be reported; the optional value set is, for example, {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0127] (2) The maximum number of channel taps per reference signal resource per TRP that can be reported.

[0128] (3) Whether multiple detected tap timing information reporting is supported (Support of multiple detected tap timing reporting for UE-assisted Direct AI / ML positioning).

[0129] (4) Whether channel tap power reporting for multiple taps is supported.

[0130] (5) Whether channel tap phase reporting for multiple taps is supported.

[0131] j) Support reporting of time resolution indication information of channel multipath and / or channel taps for terminal-assisted direct AI positioning. For example, the time resolution indication information may be a parameter k related to time resolution, where the time resolution is k*T, where T is a protocol specification.

[0132] Optionally, the timing information measurement and reporting related capability information for terminal-assisted AI-assisted positioning (Timing Measurement Reporting for UE-assisted AI / ML-assisted positioning [for Case 2a]) in 3) may include at least one of the following:

[0133] a) Whether Time of Arrival (TOA) reporting is supported.

[0134] b) Whether reporting of reference signal time difference (RSTD) is supported.

[0135] c) Support reporting TOA type to LMF; the optional value set is, for example, {soft, hard}.

[0136] d) Support reporting RSTD type to LMF; the optional value set is, for example, {soft, hard}.

[0137] e) The maximum number of TOAs supported for reporting per TRP (DL TOA measurement per TRPs); the optional value set is, for example, {1, 2, 3, 4}.

[0138] f) Maximum number of DL RSTD measurements per pair of TRPs supported for reporting; the optional value set is, for example, {1, 2, 3, 4}.

[0139] g) Support reporting soft information type to LMF; the optional value set is, for example, {Type 1, Type 2}. Soft information, as opposed to hard information, describes the accuracy, uncertainty, or probability distribution of a measurement. Soft information can be expressed in different forms, such as confidence intervals and probability distributions, corresponding to different soft information types, such as Type 1 or Type 2.

[0140] Optionally, the line of sight (OS) and / or non-line of sight (NLOS) indication-related capability information of the terminal-assisted AI-assisted positioning in 4) may include at least one of the following:

[0141] a) Whether it supports reporting LOS or NLOS indication;

[0142] b) Support reporting LOS or NLOS indicator type to LMF; the optional value set is, for example, {soft, hard}.

[0143] c) Supporting reported LOS or NLOS indicator granularity (LOS / NLOS indicator granularity); for example, the LOS or NLOS indicator granularity is 0.1. For example, the LOS / NLOS indicator value ranges from 0 to 1, with a granularity of 0.1.

[0144] d) Whether the system supports reception of assistance data containing the LOS / NLOS indicator, where the assistance data includes at least one of the following:

[0145] (1)LOS or NLOS indicator type;

[0146] (2)LOS or NLOS indicator granularity.

[0147] Optionally, the AI ​​function performance supervision-related capability information in 5) may include at least one of the following:

[0148] a) Model monitoring metric reporting for UE-assisted Direct AI / ML positioning [for Case 2b]

[0149] Optionally, the capability information related to the AI ​​function performance supervision indicator of terminal-assisted direct AI positioning may include at least one of the following:

[0150] (1) Whether support is provided for reporting the AI ​​function performance monitoring indicators for terminal-assisted direct AI positioning (Support of model monitoring metric reporting to LMF).

[0151] (2) The statistical distribution type or parameters of the terminal-assisted direct AI positioning that supports reporting. For example, the statistical distribution type is Gaussian distribution, the mean is x, and the variance is y.

[0152] (3) Support model monitoring metric type to LMF for reporting terminal-assisted direct AI positioning; the optional value set is, for example, {distribution of SINR, distribution of RSRP, scenario ID}.

[0153] b) Model monitoring metric reporting for UE-assisted AI / ML assisted positioning [for Case 2a]

[0154] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted AI-assisted positioning may include at least one of the following:

[0155] (1) Whether support for reporting the AI ​​function performance monitoring indicators of terminal-assisted AI-assisted positioning (Support of model monitoring metric reporting to LMF) is supported.

[0156] (2) Supports reporting of statistical distribution types or parameters of terminal-assisted AI-assisted positioning.

[0157] (3) Support model monitoring metric type to LMF for reporting terminal-assisted AI-assisted positioning; the optional value set is, for example, {distribution of SINR, distribution of RSRP, scenario ID}. For example, the performance monitoring metric type may include at least one of the following: information related to model inference error (such as model inference error, statistical information of model inference error); statistical information of SINR; statistical information of RSRP.

[0158] c) Model performance indicator reporting for UE-assisted AI / ML assisted positioning [for Case 2a]

[0159] Optionally, the AI ​​function performance indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0160] (1) Whether support for reporting AI function-level performance indicators (Support of model performance indicator reporting to LMF) is supported.

[0161] It should be noted that supervision indicators are indicators used to monitor AI functions and are used to determine at least one of the effectiveness, reliability, and accuracy of AI functions. Performance indicators are used to indicate at least one of the effectiveness, reliability, and accuracy of AI functions.

[0162] (2) Whether the reporting of model-level performance indicators is supported.

[0163] (3) Support for reporting AI functional performance monitoring capability, including model-level monitoring and / or functional-level monitoring.

[0164] (4) Support performance indicator type for reporting; the optional value set is, for example, {soft, hard}.

[0165] (5) Model performance indicator granularity for the AI ​​function level that can be reported.

[0166] (6) Support for reporting model-level performance indicator granularity.

[0167] d) The maximum number of samples supported for obtaining model supervision indicator information and / or model performance indicator information.

[0168] Optionally, the PRS measurement-related capability information in 7) may include at least one of the following:

[0169] a) Whether PRS measurement in RRC_INACTIVE state for UE-assisted Direct AI / ML positioning is supported.

[0170] b) Whether PRS measurement in RRC_INACTIVE state for UE-assisted AI / ML assisted positioning is supported.

[0171] c) Whether PRS measurement in RRC_INACTIVE state for UE-based direct AI / ML positioning is supported.

[0172] In the information reporting method provided in the embodiment of the present application, the terminal reports the terminal capability of supporting AI-based positioning (such as Case 1, Case 2a and Case 2b) to the network side device; and supports the implementation of the AI-based positioning method.

[0173] The first information reported by the terminal involves time domain channel state information and intermediate characteristic quantities measured and reported by the terminal. The first information may include at least one of the following: DL PRS resource-related capability information; DL PRS measurement and reporting capability information for terminal-assisted direct AI positioning; timing information measurement and reporting capability information for terminal-assisted AI-assisted positioning; line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication capability information for terminal-assisted AI-assisted positioning; AI function performance supervision capability information; AI function training capability information; and PRS measurement capability information.

[0174] FIG5 is a second flow chart of an information reporting method provided in an embodiment of the present application. The method is applied to a network-side device. As shown in FIG5 , the method includes:

[0175] Step 501: A network-side device receives first information reported by a terminal, where the first information is used to indicate that the terminal supports artificial intelligence (AI)-based positioning.

[0176] Optionally, AI-based positioning refers to positioning based on an AI model or an AI function. The AI ​​function may include one or more AI models, or the AI ​​function may also be referred to as an AI model.

[0177] Optionally, the terminal sends first information to the network side device to report that the terminal has the terminal capability of supporting AI-based positioning. After receiving the first information reported by the terminal, the network side device learns that the terminal has the terminal capability of supporting AI-based positioning.

[0178] In an embodiment of the present application, by introducing a terminal to report first information to a network side device, after receiving the first information, the network side device learns that the terminal supports the terminal capability of AI-based positioning, and then the network side device can perform AI-based positioning-related network configuration and network operations, thereby improving the accuracy and reliability of the AI-based positioning method.

[0179] Optionally, the information reporting method provided in the embodiment of the present application further includes:

[0180] The network-side device performs an AI-based positioning-related operation based on the first information, where the AI-based positioning-related operation includes at least one of the following:

[0181] 1) Configuring AI-based positioning resources for the terminal.

[0182] 2) Configure AI-based positioning methods.

[0183] 3) Management operations related to AI functions.

[0184] After receiving the first information reported by the terminal, the network side device can configure and request the terminal to support the AI-based positioning method based on the first information.

[0185] For example, the network-side device configures the downlink positioning reference signal based on the terminal's measurement capability of the positioning reference signal; for another example, the network-side device requests the terminal to report specified measurement quantities based on the terminal's ability to obtain and process measurement quantities; for another example, the network-side device requests the terminal to report specified model performance supervision-related information based on the terminal's model supervision capability.

[0186] Optionally, the first information includes at least one of the following:

[0187] Downlink Positioning Reference Signal (DL PRS) resource-related capability information;

[0188] DL PRS measurement and reporting capabilities for terminal-assisted direct AI positioning;

[0189] Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information;

[0190] Terminal-assisted AI-assisted positioning (LOS) and / or NLOS indication capability information;

[0191] AI functional performance monitoring related capability information;

[0192] AI functional training related capability information;

[0193] PRS measures relevant capability information.

[0194] Optionally, the downlink positioning reference signal DL PRS resource-related capability information includes at least one of the following:

[0195] DL PRS resource-related capability information for direct AI positioning, including at least one of the maximum number of DL PRS resource sets for each transmitting and receiving node TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0196] DL PRS resource-related capability information for direct AI positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0197] The DL PRS resource-related capability information for direct AI positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 1 FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 2 FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR2 in the frequency range of FR1 and FR2;

[0198] DL PRS resource-related capability information for AI-assisted positioning, including at least one of the maximum number of DL PRS resource sets for each TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0199] DL PRS resource-related capability information for AI-assisted positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0200] The DL PRS resource-related capability information for AI-assisted positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2.

[0201] Optionally, the DL PRS measurement and reporting related capability information of the terminal-assisted direct AI positioning includes at least one of the following:

[0202] Whether to support reporting of DL PRS reference signal received power RSRP measurement;

[0203] The maximum number of DL PRS RSRP measurements supported for reporting on different PRS resources in the same TRP;

[0204] Channel measurement types supported for reporting;

[0205] Supports reporting channel measurement reporting mode;

[0206] The maximum number of DL channel measurements on different PRS resources in the same TRP that can be reported;

[0207] Supports reporting the first path information of terminal-assisted direct AI positioning;

[0208] Supports reporting of additional path information for terminal-assisted direct AI positioning;

[0209] Supports reporting of multiple path information for terminal-assisted direct AI positioning;

[0210] Supports reporting of multiple sampling points or multiple taps of the time domain channel for terminal-assisted direct AI positioning;

[0211] Support reporting of time resolution indication information of channel multipath and / or time resolution indication information of channel taps for terminal-assisted direct AI positioning.

[0212] Optionally, the first path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0213] Whether the reference signal (RS) path timing information of the first path is supported for reporting;

[0214] Whether the reference signal received path power (RSRPP) of the first path is supported for reporting;

[0215] Whether to support RS path phase reporting of the first path.

[0216] Optionally, the additional path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0217] The maximum number of additional paths supported for each TRP;

[0218] The maximum number of additional paths per reference signal resource per TRP that can be reported;

[0219] Whether to support reporting of additional detected path timing information for multiple additional paths;

[0220] Whether to support reporting of RSRPP for additional paths;

[0221] Whether to support reporting of RS path phase of additional paths.

[0222] Optionally, the multiple path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0223] The maximum number of paths supported for reporting on each TRP;

[0224] The maximum number of paths supported for each reference signal resource on each TRP that can be reported;

[0225] Whether it supports reporting of timing information of multiple detected paths;

[0226] Whether to support reporting of multiple paths of RSRPP;

[0227] Whether to support reporting of RS path phases of multiple paths.

[0228] Optionally, the supporting reporting of multiple sampling points or multiple tap information of the time domain channel for terminal-assisted direct AI positioning includes at least one of the following:

[0229] The maximum number of channel taps supported for reporting on each TRP;

[0230] The maximum number of channel taps per reference signal resource per TRP that can be reported;

[0231] Whether to support reporting of multiple detected tap timing information;

[0232] Whether to support multi-tap channel tap power reporting;

[0233] Whether to support multi-tap channel tap phase reporting.

[0234] Optionally, the timing information measurement and reporting related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0235] Whether to support reporting of arrival time TOA;

[0236] Whether to support reporting of reference signal time difference RSTD;

[0237] Supported reporting TOA types;

[0238] RSTD types supported for reporting;

[0239] The maximum number of TOAs supported for each TRP to be reported;

[0240] The maximum number of DL RSTDs supported for reporting per TRP;

[0241] Supported soft information types for reporting.

[0242] Optionally, the line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0243] Whether to support reporting of LOS or NLOS indication;

[0244] Supports reported LOS or NLOS indication granularity;

[0245] Whether receiving assistance data including LOS or NLOS indication is supported, where the assistance data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

[0246] Optionally, the AI ​​function performance supervision-related capability information includes at least one of the following:

[0247] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted direct AI positioning;

[0248] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted AI-assisted positioning;

[0249] AI function performance indicator related capability information of terminal-assisted AI-assisted positioning;

[0250] The maximum number of samples supported for obtaining model supervision metric information and / or model performance indicators.

[0251] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted direct AI positioning includes at least one of the following:

[0252] Whether support is provided for reporting of AI function performance monitoring indicators for terminal-assisted direct AI positioning;

[0253] Supports reporting of statistical distribution types or parameters of terminal-assisted direct AI positioning;

[0254] Supports reporting of AI function performance monitoring indicator types for terminal-assisted direct AI positioning.

[0255] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0256] Whether support is provided for reporting AI function performance monitoring indicators for terminal-assisted AI-assisted positioning;

[0257] Supports reporting of statistical distribution types or parameters of terminal-assisted AI-assisted positioning;

[0258] Supports reporting of AI function performance monitoring indicator types for terminal-assisted AI-assisted positioning.

[0259] Optionally, the AI ​​function performance indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0260] Whether it supports reporting AI function-level performance indicators;

[0261] Whether to support reporting of model-level performance indicators;

[0262] Support for reporting AI function performance supervision capabilities, including model-level supervision and / or function-level supervision;

[0263] Supported reporting performance indicator types;

[0264] The granularity of performance indicators at the level supported for reporting;

[0265] Supports model-level performance indicator granularity for reporting.

[0266] Optionally, the AI ​​function training-related capability information includes: whether the terminal supports AI function fine-tuning or retraining.

[0267] Optionally, the PRS measurement-related capability information includes at least one of the following:

[0268] Whether to support inactive PRS measurement for terminal-assisted direct AI positioning;

[0269] Whether to support inactive PRS measurement for terminal-assisted AI-assisted positioning;

[0270] Whether to support inactive PRS measurement based on terminal-based direct AI positioning.

[0271] FIG6 is a third flow chart of the information reporting method provided in an embodiment of the present application. The method is executed by the terminal and the network side device in cooperation. As shown in FIG6 , the method includes steps 601 and 602, wherein:

[0272] Step 601: The terminal sends first information to a network-side device, where the first information is used to indicate that the terminal supports AI-based positioning.

[0273] Step 602: The network-side device receives the first information reported by the terminal and learns that the terminal supports AI-based positioning.

[0274] Optionally, AI-based positioning refers to positioning based on an AI model or an AI function. The AI ​​function may include one or more AI models, or the AI ​​function may also be referred to as an AI model.

[0275] Optionally, the terminal sends first information to the network side device to report that the terminal has the terminal capability of supporting AI-based positioning. After receiving the first information reported by the terminal, the network side device learns that the terminal has the terminal capability of supporting AI-based positioning.

[0276] Optionally, the network-side device performs an AI-based positioning-related operation based on the first information, where the AI-based positioning-related operation includes at least one of the following:

[0277] 1) Configuring AI-based positioning resources for the terminal.

[0278] 2) Configure AI-based positioning methods.

[0279] 3) Management operations related to AI functions.

[0280] In an embodiment of the present application, by introducing the terminal to report the first information to the network side device, the network side device is informed of the terminal's terminal capability of supporting AI-based positioning, and then the network side device can perform AI-based positioning-related network configuration and network operations, thereby improving the accuracy and reliability of the AI-based positioning method.

[0281] The information reporting method provided in the embodiment of the present application can be executed by an information reporting device. In the embodiment of the present application, the information reporting device performing the information reporting method is taken as an example to illustrate the information reporting device provided in the embodiment of the present application.

[0282] FIG7 is a schematic diagram of a structure of an information reporting device according to an embodiment of the present application. As shown in FIG7 , an information reporting device 700 is applied to a terminal. The information reporting device 700 includes:

[0283] The sending module 701 is used to send first information to the network side device, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0284] In an embodiment of the present application, by introducing the reporting of the first information to the network side device, the network side device is informed of the terminal capability of the terminal supporting AI-based positioning, and then the network side device can perform AI-based positioning-related network configuration and network operations, thereby improving the accuracy and reliability of the AI-based positioning method.

[0285] Optionally, the first information includes at least one of the following:

[0286] Downlink Positioning Reference Signal (DL PRS) resource-related capability information;

[0287] DL PRS measurement and reporting capabilities for terminal-assisted direct AI positioning;

[0288] Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information;

[0289] Terminal-assisted AI-assisted positioning (LOS) and / or NLOS indication capability information;

[0290] AI functional performance monitoring related capability information;

[0291] AI functional training related capability information;

[0292] PRS measures relevant capability information.

[0293] Optionally, the downlink positioning reference signal DL PRS resource-related capability information includes at least one of the following:

[0294] DL PRS resource-related capability information for direct AI positioning, including at least one of the maximum number of DL PRS resource sets for each transmitting and receiving node TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0295] DL PRS resource-related capability information for direct AI positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0296] The DL PRS resource-related capability information for direct AI positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 1 FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 2 FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR2 in the frequency range of FR1 and FR2;

[0297] DL PRS resource-related capability information for AI-assisted positioning, including at least one of the maximum number of DL PRS resource sets for each TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0298] DL PRS resource-related capability information for AI-assisted positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0299] The DL PRS resource-related capability information for AI-assisted positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2.

[0300] Optionally, the DL PRS measurement and reporting related capability information of the terminal-assisted direct AI positioning includes at least one of the following:

[0301] Whether to support reporting of DL PRS reference signal received power RSRP measurement;

[0302] The maximum number of DL PRS RSRP measurements supported for reporting on different PRS resources in the same TRP;

[0303] Channel measurement types supported for reporting;

[0304] Supports reporting channel measurement reporting mode;

[0305] The maximum number of DL channel measurements on different PRS resources in the same TRP that can be reported;

[0306] Supports reporting the first path information of terminal-assisted direct AI positioning;

[0307] Supports reporting of additional path information for terminal-assisted direct AI positioning;

[0308] Supports reporting of multiple path information for terminal-assisted direct AI positioning;

[0309] Supports reporting of multiple sampling points or multiple taps of the time domain channel for terminal-assisted direct AI positioning;

[0310] Support reporting of time resolution indication information of channel multipath and / or time resolution indication information of channel taps for terminal-assisted direct AI positioning.

[0311] Optionally, the first path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0312] Whether the reference signal (RS) path timing information of the first path is supported for reporting;

[0313] Whether the reference signal received path power (RSRPP) of the first path is supported for reporting;

[0314] Whether to support RS path phase reporting of the first path.

[0315] Optionally, the additional path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0316] The maximum number of additional paths supported for each TRP;

[0317] The maximum number of additional paths per reference signal resource per TRP that can be reported;

[0318] Whether to support reporting of additional detected path timing information for multiple additional paths;

[0319] Whether to support reporting of RSRPP for additional paths;

[0320] Whether to support reporting of RS path phase of additional paths.

[0321] Optionally, the multiple path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0322] The maximum number of paths supported for reporting on each TRP;

[0323] The maximum number of paths supported for each reference signal resource on each TRP that can be reported;

[0324] Whether it supports reporting of timing information of multiple detected paths;

[0325] Whether to support reporting of multiple paths of RSRPP;

[0326] Whether to support reporting of RS path phases of multiple paths.

[0327] Optionally, the supporting reporting of multiple sampling points or multiple tap information of the time domain channel for terminal-assisted direct AI positioning includes at least one of the following:

[0328] The maximum number of channel taps supported for reporting on each TRP;

[0329] The maximum number of channel taps per reference signal resource per TRP that can be reported;

[0330] Whether to support reporting of multiple detected tap timing information;

[0331] Whether to support multi-tap channel tap power reporting;

[0332] Whether to support multi-tap channel tap phase reporting.

[0333] Optionally, the timing information measurement and reporting related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0334] Whether to support reporting of arrival time TOA;

[0335] Whether to support reporting of reference signal time difference RSTD;

[0336] Supported reporting TOA types;

[0337] RSTD types supported for reporting;

[0338] The maximum number of TOAs supported for each TRP to be reported;

[0339] The maximum number of DL RSTDs supported for reporting per TRP;

[0340] Supported soft information types for reporting.

[0341] Optionally, the line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0342] Whether to support reporting of LOS or NLOS indication;

[0343] Supports reported LOS or NLOS indication granularity;

[0344] Whether receiving assistance data including LOS or NLOS indication is supported, where the assistance data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

[0345] Optionally, the AI ​​function performance supervision-related capability information includes at least one of the following:

[0346] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted direct AI positioning;

[0347] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted AI-assisted positioning;

[0348] AI function performance indicator related capability information of terminal-assisted AI-assisted positioning;

[0349] The maximum number of samples supported for obtaining model supervision metric information and / or model performance indicators.

[0350] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted direct AI positioning includes at least one of the following:

[0351] Whether support is provided for reporting of AI function performance monitoring indicators for terminal-assisted direct AI positioning;

[0352] Supports reporting of statistical distribution types or parameters of terminal-assisted direct AI positioning;

[0353] Supports reporting of AI function performance monitoring indicator types for terminal-assisted direct AI positioning.

[0354] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0355] Whether support is provided for reporting AI function performance monitoring indicators for terminal-assisted AI-assisted positioning;

[0356] Supports reporting of statistical distribution types or parameters of terminal-assisted AI-assisted positioning;

[0357] Supports reporting of AI function performance monitoring indicator types for terminal-assisted AI-assisted positioning.

[0358] Optionally, the AI ​​function performance indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0359] Whether it supports reporting AI function-level performance indicators;

[0360] Whether to support reporting of model-level performance indicators;

[0361] Support for reporting AI function performance supervision capabilities, including model-level supervision and / or function-level supervision;

[0362] Supported reporting performance indicator types;

[0363] The granularity of performance indicators at the level supported for reporting;

[0364] Supports model-level performance indicator granularity for reporting.

[0365] Optionally, the AI ​​function training-related capability information includes: whether the terminal supports AI function fine-tuning or retraining.

[0366] Optionally, the PRS measurement-related capability information includes at least one of the following:

[0367] Whether to support inactive PRS measurement for terminal-assisted direct AI positioning;

[0368] Whether to support inactive PRS measurement for terminal-assisted AI-assisted positioning;

[0369] Whether to support inactive PRS measurement based on terminal-based direct AI positioning.

[0370] The information reporting device 700 in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal, or it can be other devices other than a terminal. For example, the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

[0371] The information reporting device 700 provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0372] FIG8 is a second structural diagram of an information reporting device provided in an embodiment of the present application. As shown in FIG8 , the information reporting device 800 is applied to a network-side device. The information reporting device 800 includes:

[0373] The receiving module 801 is used to receive first information reported by a terminal, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0374] In an embodiment of the present application, by receiving the first information reported by the terminal, the terminal capability of supporting AI-based positioning is learned, and then the network side device can perform AI-based positioning-related network configuration and network operations to improve the accuracy and reliability of the AI-based positioning method.

[0375] Optionally, the device further comprises:

[0376] an execution module, configured to perform an AI-based positioning-related operation based on the first information, wherein the AI-based positioning-related operation includes at least one of the following:

[0377] Configuring AI-based positioning resources for the terminal;

[0378] Configure AI-based positioning methods;

[0379] Management operations related to AI functions.

[0380] Optionally, the first information includes at least one of the following:

[0381] Downlink Positioning Reference Signal (DL PRS) resource-related capability information;

[0382] DL PRS measurement and reporting capabilities for terminal-assisted direct AI positioning;

[0383] Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information;

[0384] Terminal-assisted AI-assisted positioning (LOS) and / or NLOS indication capability information;

[0385] AI functional performance monitoring related capability information;

[0386] AI functional training related capability information;

[0387] PRS measures relevant capability information.

[0388] Optionally, the downlink positioning reference signal DL PRS resource-related capability information includes at least one of the following:

[0389] DL PRS resource-related capability information for direct AI positioning, including at least one of the maximum number of DL PRS resource sets for each transmitting and receiving node TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0390] DL PRS resource-related capability information for direct AI positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0391] The DL PRS resource-related capability information for direct AI positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 1 FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 2 FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs, and DL PRS resource sets for FR2 in the frequency range of FR1 and FR2;

[0392] DL PRS resource-related capability information for AI-assisted positioning, including at least one of the maximum number of DL PRS resource sets for each TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal;

[0393] DL PRS resource-related capability information for AI-assisted positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer;

[0394] The DL PRS resource-related capability information for AI-assisted positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2.

[0395] Optionally, the DL PRS measurement and reporting related capability information of the terminal-assisted direct AI positioning includes at least one of the following:

[0396] Whether to support reporting of DL PRS reference signal received power RSRP measurement;

[0397] The maximum number of DL PRS RSRP measurements supported for reporting on different PRS resources in the same TRP;

[0398] Channel measurement types supported for reporting;

[0399] Supports reporting channel measurement reporting mode;

[0400] The maximum number of DL channel measurements on different PRS resources in the same TRP that can be reported;

[0401] Supports reporting the first path information of terminal-assisted direct AI positioning;

[0402] Supports reporting of additional path information for terminal-assisted direct AI positioning;

[0403] Supports reporting of multiple path information for terminal-assisted direct AI positioning;

[0404] Supports reporting of multiple sampling points or multiple taps of the time domain channel for terminal-assisted direct AI positioning;

[0405] Support reporting of time resolution indication information of channel multipath and / or time resolution indication information of channel taps for terminal-assisted direct AI positioning.

[0406] Optionally, the first path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0407] Whether the reference signal (RS) path timing information of the first path is supported for reporting;

[0408] Whether the reference signal received path power (RSRPP) of the first path is supported for reporting;

[0409] Whether to support RS path phase reporting of the first path.

[0410] Optionally, the additional path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0411] The maximum number of additional paths supported for each TRP;

[0412] The maximum number of additional paths per reference signal resource per TRP that can be reported;

[0413] Whether to support reporting of additional detected path timing information for multiple additional paths;

[0414] Whether to support reporting of RSRPP for additional paths;

[0415] Whether to support reporting of RS path phase of additional paths.

[0416] Optionally, the multiple path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0417] The maximum number of paths supported for reporting on each TRP;

[0418] The maximum number of paths supported for each reference signal resource on each TRP that can be reported;

[0419] Whether it supports reporting of timing information of multiple detected paths;

[0420] Whether to support reporting of multiple paths of RSRPP;

[0421] Whether to support reporting of RS path phases of multiple paths.

[0422] Optionally, the supporting reporting of multiple sampling points or multiple tap information of the time domain channel for terminal-assisted direct AI positioning includes at least one of the following:

[0423] The maximum number of channel taps supported for reporting on each TRP;

[0424] The maximum number of channel taps per reference signal resource per TRP that can be reported;

[0425] Whether to support reporting of multiple detected tap timing information;

[0426] Whether to support multi-tap channel tap power reporting;

[0427] Whether to support multi-tap channel tap phase reporting.

[0428] Optionally, the timing information measurement and reporting related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0429] Whether to support reporting of arrival time TOA;

[0430] Whether to support reporting of reference signal time difference RSTD;

[0431] Supported reporting TOA types;

[0432] RSTD types supported for reporting;

[0433] The maximum number of TOAs supported for each TRP to be reported;

[0434] The maximum number of DL RSTDs supported for reporting per TRP;

[0435] Supported soft information types for reporting.

[0436] Optionally, the line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0437] Whether to support reporting of LOS or NLOS indication;

[0438] Supports reported LOS or NLOS indication granularity;

[0439] Whether receiving assistance data including LOS or NLOS indication is supported, where the assistance data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

[0440] Optionally, the AI ​​function performance supervision-related capability information includes at least one of the following:

[0441] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted direct AI positioning;

[0442] Capability information related to the AI ​​function performance monitoring indicators of terminal-assisted AI-assisted positioning;

[0443] AI function performance indicator related capability information of terminal-assisted AI-assisted positioning;

[0444] The maximum number of samples supported for obtaining model supervision metric information and / or model performance indicators.

[0445] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted direct AI positioning includes at least one of the following:

[0446] Whether support is provided for reporting of AI function performance monitoring indicators for terminal-assisted direct AI positioning;

[0447] Supports reporting of statistical distribution types or parameters of terminal-assisted direct AI positioning;

[0448] Supports reporting of AI function performance monitoring indicator types for terminal-assisted direct AI positioning.

[0449] Optionally, the capability information related to the AI ​​function performance supervision indicator of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0450] Whether support is provided for reporting AI function performance monitoring indicators for terminal-assisted AI-assisted positioning;

[0451] Supports reporting of statistical distribution types or parameters of terminal-assisted AI-assisted positioning;

[0452] Supports reporting of AI function performance monitoring indicator types for terminal-assisted AI-assisted positioning.

[0453] Optionally, the AI ​​function performance indication-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0454] Whether it supports reporting AI function-level performance indicators;

[0455] Whether to support reporting of model-level performance indicators;

[0456] Support for reporting AI function performance supervision capabilities, including model-level supervision and / or function-level supervision;

[0457] Supported reporting performance indicator types;

[0458] The granularity of performance indicators at the level supported for reporting;

[0459] Supports model-level performance indicator granularity for reporting.

[0460] Optionally, the AI ​​function training-related capability information includes: whether the terminal supports AI function fine-tuning or retraining.

[0461] Optionally, the PRS measurement-related capability information includes at least one of the following:

[0462] Whether to support inactive PRS measurement for terminal-assisted direct AI positioning;

[0463] Whether to support inactive PRS measurement for terminal-assisted AI-assisted positioning;

[0464] Whether to support inactive PRS measurement based on terminal-based direct AI positioning.

[0465] The information reporting device 800 in the embodiments of the present application can be an electronic device, such as an electronic device with an operating system, or a component of an electronic device, such as an integrated circuit or chip. The electronic device can be a network-side device or other device other than a network-side device. For example, the network-side device can include, but is not limited to, the types of network-side devices 12 listed above.

[0466] The information reporting device 800 provided in the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0467] The embodiment of the present application also provides a communication device. FIG9 is a schematic diagram of the structure of the communication device provided by the embodiment of the present application. As shown in FIG9 , the communication device 900 includes a processor 901 and a memory 902. The memory 902 stores a program or instruction that can be run on the processor 901. For example, when the communication device 900 is a terminal, the program or instruction is executed by the processor 901 to implement the various steps of the method embodiment shown in FIG4 above, and can achieve the same technical effect. When the communication device 900 is a network-side device, the program or instruction is executed by the processor 901 to implement the various steps of the method embodiment shown in FIG5 above, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0468] The present application also provides a terminal including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps in the method embodiment shown in FIG4 . This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment are applicable to this terminal embodiment and can achieve the same technical effects.

[0469] The present application also provides a terminal. Figure 10 is a schematic diagram of the hardware structure of the terminal provided in the present application. As shown in Figure 10, the terminal 1000 includes, but is not limited to, at least some of the components including a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, and a processor 1010.

[0470] Those skilled in the art will appreciate that the terminal 1000 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1010 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG10 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0471] It should be understood that in an embodiment of the present application, the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042, and the graphics processing unit 10041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include two parts: a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and an operating stick, which will not be repeated here.

[0472] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1001 may transmit the data to the processor 1010 for processing. Furthermore, the RF unit 1001 may send uplink data to the network-side device. Typically, the RF unit 1001 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0473] The memory 1009 can be used to store software programs or instructions and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1009 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0474] Processor 1010 may include one or more processing units. Optionally, processor 1010 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 1010.

[0475] Among them, the radio frequency unit 1001 is used to send first information to the network side device, and the first information is used to indicate that the terminal supports positioning based on artificial intelligence AI.

[0476] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment shown in Figure 4, and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0477] The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG5 . This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this network-side device embodiment and can achieve the same technical effects.

[0478] The embodiment of the present application also provides a network-side device. Figure 11 is a schematic diagram of the hardware structure of the network-side device provided in the embodiment of the present application. As shown in Figure 11, the network-side device 1100 includes: an antenna 111, a radio frequency device 112, a baseband device 113, a processor 114, and a memory 115. The antenna 111 is connected to the radio frequency device 112. In the uplink direction, the radio frequency device 112 receives information through the antenna 111 and sends the received information to the baseband device 113 for processing. In the downlink direction, the baseband device 113 processes the information to be sent and sends it to the radio frequency device 112. The radio frequency device 112 processes the received information and sends it out through the antenna 111.

[0479] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 113 , which includes a baseband processor.

[0480] The baseband device 113 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 11, one of the chips is, for example, a baseband processor, which is connected to the memory 115 through a bus interface to call the program in the memory 115 to execute the network device operations shown in the above method embodiment.

[0481] The network side device may further include a network interface 116, which is, for example, a Common Public Radio Interface (CPRI).

[0482] Specifically, the network side device 1100 of the embodiment of the present application also includes: instructions or programs stored in the memory 115 and executable on the processor 114. The processor 114 calls the instructions or programs in the memory 115 to execute the methods executed by the modules shown in FIG5 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0483] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned information reporting method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0484] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0485] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned information reporting method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0486] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0487] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the above-mentioned information reporting method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0488] An embodiment of the present application also provides a wireless communication system, including: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the method embodiment shown in Figure 4, and the network-side device can be used to execute the steps of the method embodiment shown in Figure 5.

[0489] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0490] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0491] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A method for reporting information, wherein: include: The terminal sends first information to the network side device, where the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

2. The information reporting method according to claim 1, wherein: The first information includes at least one of the following: Downlink positioning reference signal DL PRS resource related capability information; DL PRS measurement and reporting related capability information for terminal-assisted direct AI positioning; Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information; Terminal-assisted AI-assisted positioning line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information; AI functional performance supervision related capability information; AI functional training related capability information; PRS measures relevant capability information.

3. The information reporting method according to claim 2, wherein: The downlink positioning reference signal DL PRS resource related capability information includes at least one of the following: DL PRS resource-related capability information for direct AI positioning, including: at least one of the maximum number of DL PRS resource sets for each transmission and reception node TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal; DL PRS resource-related capability information for direct AI positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer; The DL PRS resource-related capability information for direct AI positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for frequency range 1FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for frequency range 2FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2; The DL PRS resource-related capability information for AI-assisted positioning includes: at least one of the maximum number of DL PRS resource sets for each TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal; DL PRS resource-related capability information for AI-assisted positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer; The DL PRS resource-related capability information for AI-assisted positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2.

4. The information reporting method according to claim 2 or 3, wherein: The DL PRS measurement and reporting related capability information of the terminal-assisted direct AI positioning includes at least one of the following: Whether to support reporting of DL PRS reference signal received power RSRP measurement quantity; The maximum number of DL PRS RSRP measurements on different PRS resources in the same TRP that can be reported; Channel measurement types supported for reporting; Supports reporting of channel measurement reporting modes; The maximum number of DL channel measurements on different PRS resources in the same TRP that can be reported; Supports reporting of the first path information of terminal-assisted direct AI positioning; Supports reporting of additional path information for terminal-assisted direct AI positioning; Supports reporting of multiple path information for terminal-assisted direct AI positioning; Support reporting of multiple sampling points or multiple tap information of the time domain channel for terminal-assisted direct AI positioning; Support reporting of time resolution indication information of channel multipath and / or time resolution indication information of channel taps for terminal-assisted direct AI positioning.

5. The information reporting method according to claim 4, wherein: The first path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following: Whether to support reporting of reference signal RS path timing information of the first path; Whether to support reporting of the reference signal received path power (RSRPP) of the first path; Whether to support RS path phase reporting of the first path.

6. The information reporting method according to claim 4, wherein: The additional path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following: The maximum number of additional paths supported for reporting on each TRP; The maximum number of additional paths per reference signal resource per TRP that can be reported; Whether to support reporting of additional detected path timing information for multiple additional paths; Whether to support reporting of RSRPP of additional paths; Whether to support reporting of RS path phase of additional paths.

7. The information reporting method according to claim 4, wherein: The multiple path information supporting reporting of terminal-assisted direct AI positioning includes at least one of the following: The maximum number of paths supported for reporting on each TRP; The maximum number of paths per reference signal resource per TRP that can be reported; Whether it supports reporting of multiple detected path timing information; Whether to support reporting of multiple paths of RSRPP; Whether to support reporting of RS path phases of multiple paths.

8. The information reporting method according to claim 4, wherein: The supporting reporting of multiple sampling points of the time domain channel or multiple tap information of the time domain channel for terminal-assisted direct AI positioning includes at least one of the following: The maximum number of channel taps supported for reporting per TRP; The maximum number of channel taps per reference signal resource per TRP that can be reported; Whether to support reporting of multiple detected tap timing information; Whether to support multi-tap channel tap power reporting; Whether to support multi-tap channel tap phase reporting.

9. The information reporting method according to any one of claims 2 to 8, wherein: The timing information measurement and reporting related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following: Whether to support reporting of arrival time TOA; Whether to support reporting of reference signal time difference RSTD; TOA types supported for reporting; Supported RSTD types to be reported; The maximum number of TOAs supported for each TRP to be reported; The maximum number of DL RSTDs supported for reporting per TRP; Supported soft information types to be reported.

10. The information reporting method according to any one of claims 2 to 9, wherein: The terminal-assisted AI-assisted positioning line-of-sight LOS and / or non-line-of-sight NLOS indication-related capability information includes at least one of the following: Whether to support reporting of LOS or NLOS indication; Supports reported LOS or NLOS indication granularity; Whether receiving assistance data including LOS or NLOS indication is supported, wherein the assistance data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

11. The information reporting method according to any one of claims 2 to 10, wherein: The AI ​​function performance supervision-related capability information includes at least one of the following: Capability information related to the AI ​​function performance supervision indicators of terminal-assisted direct AI positioning; Capability information related to the AI ​​function performance supervision indicators of terminal-assisted AI-assisted positioning; AI function performance indicator related capability information of terminal-assisted AI-assisted positioning; The maximum number of samples supported for obtaining model supervision metric information and / or model performance indicator information.

12. The information reporting method according to claim 11, wherein: The AI ​​function performance supervision indicator related capability information of the terminal-assisted direct AI positioning includes at least one of the following: Whether to support reporting of AI function performance supervision indicators for terminal-assisted direct AI positioning; Supports reporting of statistical distribution types or parameters of terminal-assisted direct AI positioning; Supports reporting of AI function performance supervision indicator types for terminal-assisted direct AI positioning.

13. The information reporting method according to claim 11, wherein: The AI ​​function performance supervision indicator-related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following: Whether to support reporting of AI function performance supervision indicators for terminal-assisted AI-assisted positioning; Supports reporting of statistical distribution types or parameters of terminal-assisted AI-assisted positioning; Supports reporting of AI function performance supervision indicator types for terminal-assisted AI-assisted positioning.

14. The information reporting method according to claim 11, wherein: The AI ​​function performance indication related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following: Whether to support reporting of AI function-level performance indicators; Whether to support reporting of model-level performance indicators; Supports reporting of AI function performance supervision capabilities, including model-level supervision and / or function-level supervision; Supports reporting of performance indication types; Supports reporting of AI function-level performance indicator granularity; Supports reporting of model-level performance indicators granularity.

15. The information reporting method according to any one of claims 2 to 14, wherein: The AI ​​function training-related capability information includes: whether the terminal supports AI function fine-tuning or retraining.

16. The information reporting method according to any one of claims 2 to 15, wherein: The PRS measurement related capability information includes at least one of the following: Whether to support inactive PRS measurement for terminal-assisted direct AI positioning; Whether to support inactive PRS measurement for terminal-assisted AI-assisted positioning; Whether to support inactive PRS measurement based on terminal-based direct AI positioning.

17. A method for reporting information, wherein: include: The network side device receives first information reported by the terminal, where the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

18. The information reporting method according to claim 17, wherein: The method further comprises: The network-side device performs an AI-based positioning-related operation based on the first information, wherein the AI-based positioning-related operation includes at least one of the following: Configuring AI-based positioning resources for the terminal; Configure AI-based positioning methods; Management operations related to AI functions.

19. The information reporting method according to claim 17 or 18, wherein: The first information includes at least one of the following: Downlink positioning reference signal DL PRS resource related capability information; DL PRS measurement and reporting related capability information for terminal-assisted direct AI positioning; Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information; Terminal-assisted AI-assisted positioning line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information; AI functional performance supervision related capability information; AI functional training related capability information; PRS measures relevant capability information.

20. The information reporting method according to claim 19, wherein: The downlink positioning reference signal DL PRS resource related capability information includes at least one of the following: DL PRS resource-related capability information for direct AI positioning, including: at least one of the maximum number of DL PRS resource sets for each transmission and reception node TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal; DL PRS resource-related capability information for direct AI positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer; The DL PRS resource-related capability information for direct AI positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for frequency range 1FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for frequency range 2FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2; The DL PRS resource-related capability information for AI-assisted positioning includes: at least one of the maximum number of DL PRS resource sets for each TRP on each positioning frequency layer supported by the terminal, the maximum number of TRPs on all positioning frequency layers supported by the terminal, and the maximum number of positioning frequency layers supported by the terminal; DL PRS resource-related capability information for AI-assisted positioning on a frequency band, including: the maximum number of DL PRS resources in each DL PRS resource set and / or the maximum number of DL PRS resources on each positioning frequency layer; The DL PRS resource-related capability information for AI-assisted positioning on a frequency band combination includes at least one of the following: the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR1; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets for FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR1 in the frequency range of FR1 and FR2; the maximum number of DL PRS resources supported by the terminal on all positioning frequency layers, TRPs and DL PRS resource sets of FR2 in the frequency range of FR1 and FR2.

21. The information reporting method according to claim 19 or 20, wherein: The timing information measurement and reporting related capability information of the terminal-assisted AI-assisted positioning includes at least one of the following: Whether to support reporting of arrival time TOA; Whether to support reporting of reference signal time difference RSTD; TOA types supported for reporting; Supported RSTD types to be reported; The maximum number of TOAs supported for each TRP to be reported; The maximum number of DL RSTDs supported for reporting per TRP; Supported soft information types to be reported.

22. The information reporting method according to any one of claims 19 to 21, wherein: The terminal-assisted AI-assisted positioning line-of-sight LOS and / or non-line-of-sight NLOS indication-related capability information includes at least one of the following: Whether to support reporting of LOS or NLOS indication; Supports reported LOS or NLOS indication granularity; Whether receiving assistance data including LOS or NLOS indication is supported, wherein the assistance data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

23. The information reporting method according to any one of claims 19 to 22, wherein: The AI ​​function performance supervision-related capability information includes at least one of the following: Capability information related to the AI ​​function performance supervision indicators of terminal-assisted direct AI positioning; Capability information related to the AI ​​function performance supervision indicators of terminal-assisted AI-assisted positioning; AI function performance indicator related capability information of terminal-assisted AI-assisted positioning; The maximum number of samples supported for obtaining model supervision metric information and / or model performance indicator information.

24. The information reporting method according to any one of claims 19 to 23, wherein: The AI ​​function training-related capability information includes: whether the terminal supports AI function fine-tuning or retraining.

25. The information reporting method according to any one of claims 19 to 24, wherein: The PRS measurement related capability information includes at least one of the following: Whether to support inactive PRS measurement for terminal-assisted direct AI positioning; Whether to support inactive PRS measurement for terminal-assisted AI-assisted positioning; Whether to support inactive PRS measurement based on terminal-based direct AI positioning.

26. An information reporting device, wherein: include: The sending module is used to send first information to the network side device, where the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

27. The information reporting device according to claim 26, wherein: The first information includes at least one of the following: Downlink positioning reference signal DL PRS resource related capability information; DL PRS measurement and reporting related capability information for terminal-assisted direct AI positioning; Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information; Terminal-assisted AI-assisted positioning line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information; AI functional performance supervision related capability information; AI functional training related capability information; PRS measures relevant capability information.

28. An information reporting device, wherein: include: The receiving module is used to receive first information reported by the terminal, where the first information is used to characterize that the terminal supports positioning based on artificial intelligence AI.

29. The information reporting device according to claim 28, wherein: The device also includes: An execution module is configured to perform an AI-based positioning-related operation based on the first information, wherein the AI-based positioning-related operation includes at least one of the following: Configuring AI-based positioning resources for the terminal; Configure AI-based positioning methods; Management operations related to AI functions.

30. The information reporting device according to claim 28 or 29, wherein: The first information includes at least one of the following: Downlink positioning reference signal DL PRS resource related capability information; DL PRS measurement and reporting related capability information for terminal-assisted direct AI positioning; Terminal-assisted AI-assisted positioning timing information measurement and reporting related capability information; Terminal-assisted AI-assisted positioning line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information; AI functional performance supervision related capability information; AI functional training related capability information; PRS measures relevant capability information.

31. A terminal, wherein: It includes a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information reporting method according to any one of claims 1 to 16 are implemented.

32. A network side device, wherein: It includes a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information reporting method as described in any one of claims 17 to 25 are implemented.

33. A readable storage medium, wherein: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the information reporting method as described in any one of claims 1 to 16, or implements the steps of the information reporting method as described in any one of claims 17 to 25.

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