Information reporting method and device

Abstract

The invention discloses an information reporting method and device, and belongs to the technical field of wireless communication, and the information reporting method comprises the steps that a terminal sends first information to network side equipment, and the first information is used for representing that the terminal supports AI-based positioning.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and particularly relates to a method and device for information reporting. Background Art

[0002] Currently, Artificial Intelligence (AI) technology has been widely applied in various fields. Incorporating AI technology into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important research direction for wireless communication networks.

[0003] In the prior art, the positioning accuracy and reliability based on AI are relatively low. Summary of the Invention

[0004] Embodiments of this application provide a method and device for information reporting, which can realize the reporting of terminal capabilities information supporting AI positioning.

[0005] In a first aspect, a method for information reporting is provided, which is executed by a terminal. The method includes:

[0006] The terminal sends first information to a network-side device, and the first information is used to indicate that the terminal supports positioning based on Artificial Intelligence (AI).

[0007] In a second aspect, a method for information reporting is provided, which is executed by a network-side device. The method includes:

[0008] The network-side device receives the first information reported by the terminal, and the first information is used to indicate that the terminal supports positioning based on Artificial Intelligence (AI).

[0009] In a third aspect, an information reporting device is provided. The device includes:

[0010] A sending module, configured to send 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).

[0011] In a fourth aspect, an information reporting device is provided. The device includes:

[0012] A receiving module, configured to receive the first information reported by the terminal, where the first information is used to indicate that the terminal supports positioning based on Artificial Intelligence (AI).

[0013] In a fifth aspect, a terminal is provided. The terminal includes a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0014] In a sixth aspect, a terminal is provided, including a processor and a communication interface. The communication interface is configured to send 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).

[0015] In a seventh aspect, a network-side device is provided. The network-side device includes a processor and a memory. The memory stores a program or instructions that can run on the processor. When the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0016] In an eighth aspect, a network-side device is provided, including a processor and a communication interface. The communication interface is configured 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).

[0017] In a ninth aspect, a readable storage medium is provided. A program or instructions are stored on the readable storage medium. When the program or instructions are 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.

[0018] In a tenth aspect, a wireless communication system is provided, including a terminal and a network-side device. 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.

[0019] In an eleventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run a program or instructions to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0020] In a twelfth aspect, a computer program / program product is provided. The computer program / program product is stored in a storage medium. The program / program product 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.

[0021] In the embodiments of the present application, by introducing the terminal to report first information to the network-side device, the network-side device can learn about the terminal's ability to support AI-based positioning. Furthermore, the network-side device can perform network configurations and network operations related to AI-based positioning, improving the accuracy and reliability of the AI-based positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A block diagram of a wireless communication system to which the embodiments of the present application can be applied is shown;

[0023] Figure 2is a schematic diagram of a neural network in the prior art;

[0024] Figure 3 is a schematic diagram of a neuron in the prior art;

[0025] Figure 4 is one of the schematic flowcharts of the information reporting method provided by an embodiment of the present application;

[0026] Figure 5 is another schematic flowchart of the information reporting method provided by an embodiment of the present application;

[0027] Figure 6 is yet another schematic flowchart of the information reporting method provided by an embodiment of the present application;

[0028] Figure 7 is one of the schematic structural diagrams of the information reporting device provided by an embodiment of the present application;

[0029] Figure 8 is another schematic structural diagram of the information reporting device provided by an embodiment of the present application;

[0030] Figure 9 is the schematic structural diagram of the communication device provided by an embodiment of the present application;

[0031] Figure 10 is the schematic hardware structure diagram of the terminal provided by an embodiment of the present application;

[0032] Figure 11 is the schematic hardware structure diagram of the network - side device provided by an embodiment of the present application. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the present application.

[0034] The terms "first", "second", etc. in this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of this 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 multiple. In addition, "or" in this application means at least one of the connected objects. For example, "A or B" covers three scenarios, namely, Scenario 1: including A and not including B; Scenario 2: including B and not including A; Scenario 3: including both A and B. The character " / " generally indicates that the related objects before and after are in an "or" relationship.

[0035] The term "indication" in this application can be either a direct indication (or an explicit indication) or an indirect indication (or an implicit indication). Among them, a direct indication can be understood as that the sender clearly informs the receiver of specific information, operations to be performed, or request results, etc. in the sent indication; an indirect indication can be understood as that the receiver determines the corresponding information according to the indication sent by the sender, or makes a judgment and determines the operations to be performed or request results, etc. according to the judgment result.

[0036] It is worth pointing out that the technology described in the embodiments of this application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, and 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 this application are often used interchangeably, and the described technology can be used not only in the above-mentioned systems and radio technologies, but also in other systems and radio technologies. The following description describes the New Radio (NR) system for example purposes, and uses NR terms in most of the following descriptions, but these technologies can also be applied to systems other than the NR system, such as the 6th generation (6 thGeneration, 6G) communication system.

[0037] Figure 1A block diagram of a wireless communication system to which embodiments of the present application can be applied is shown. The wireless communication system includes a terminal 11 and a network-side device 12. Among them, the terminal can also be referred to as a User Equipment (UE). The terminal 11 can 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, a flight vehicle, a vehicle user equipment (VUE), a shipborne device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, TVs, washing machines, or furniture, etc.), a game console, a personal computer (PC), a teller machine, or a self-service machine, etc. Wearable devices include: smart watches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be referred to as 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 embodiments of the present application. The network-side device 12 can include an access network device or a core network device. Among them, the access network device can also be referred to as a Radio Access Network (RAN) device, a radio access network function, or a radio access network unit. The access network device can include a base station, a wireless local area network (WLAN) access point (AP), or a 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, Transmission Reception Point (TRP), or some other suitable term in the art. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments of this 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.

[0038] To facilitate a clearer understanding of the technical solutions provided in the embodiments of this application, some related knowledge is introduced as follows.

[0039] Regarding AI:

[0040] AI technology has been widely applied in various fields at present. Incorporating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is an important task for wireless communication networks. There are various implementation methods for the AI module, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. In the embodiments of this application, a neural network is used as an example for illustration, but the specific type of the AI module is not limited.

[0041] Figure 2 is a schematic diagram of a neural network in the prior art, as Figure 2 shown, a neural network is composed of neurons. Figure 3 is a schematic diagram of a neuron in the prior art, as Figure 3 shown. Among them, a1, a2,..., a KLet \(x\) be the input, \(w\) be the weights (multiplicative coefficients), \(b\) be the bias (additive coefficient), and \(\sigma(\cdot)\) be the activation function. Common activation functions include Sigmoid, tanh, ReLU (Rectified Linear Unit), etc.

[0042] The parameters of the neural network are optimized by gradient optimization algorithms. Gradient optimization algorithms are a class of algorithms that minimize or maximize 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 \(f(\cdot)\) is constructed. After having the model, the predicted output \(f(x)\) can be obtained according to 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 real situation.

[0043] Currently, common optimization algorithms are basically based on the error Back Propagation (BP) algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of signals and the backward propagation of errors. During forward propagation, the input samples are input from the input layer, and after being processed layer by layer through each hidden layer, they are transmitted to the output layer. If the actual output of the output layer does not match the expected output, it enters the backward propagation stage of errors. The error backpropagation is to transmit the output error back to the input layer layer by layer through the hidden layer in a certain form, and distribute the error to all units of each layer, so as to obtain the error signals of each layer of units. This error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer during the forward propagation of signals and the backward propagation of errors is carried out cyclically. The process of continuously adjusting the weights is also the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until a pre-set number of learning times is reached.

[0044] Common optimization algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (specifically Stochastic Gradient Descent with Momentum, named after the inventor), Adagrad (ADAptive GRADient descent), Adadelta, RMSprop (root mean square prop), Adam (Adaptive Moment Estimation), etc.

[0045] When these optimization algorithms perform backpropagation of errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained from the loss function, and then add the influence of the learning rate, previous gradient / derivative / partial derivative, etc., to obtain the gradient, which is then passed to the previous layer.

[0046] In the embodiments of the present application, the AI unit / AI model mentioned can 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 can 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 can be a processing method, algorithm, function, module, or unit for a specific data set. Or the AI unit / AI model can be a processing method, algorithm, function, module, or unit running on AI / ML-related hardware such as GPUs, NPUs, TPUs, ASICs, etc. The present invention does not make specific limitations in this regard. Optionally, the specific data set includes the input and / or output of the AI unit / AI model.

[0047] Optionally, the identifier of the AI unit / AI model can be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or the identifier of a specific data set associated with the AI unit / AI model, or the identifier of a specific scenario, environment, channel feature, device related to AI / ML, or the identifier of a function, feature, ability, or module related to AI / ML. The embodiments of the present application do not make specific limitations in this regard.

[0048] Next, in conjunction with the accompanying drawings, the information reporting method and device provided by the embodiments of the present application will be described in detail through some embodiments and their application scenarios.

[0049] Figure 4 is one of the schematic flowcharts of the information reporting method provided by an embodiment of this application. This method is applied to a terminal, such as Figure 4 shown, and this method includes:

[0050] 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).

[0051] Optionally, positioning based on AI refers to positioning based on an AI model or an AI function. An AI function may include one or more AI models, or an AI function may also be referred to as an AI model.

[0052] Optionally, the terminal reports, by sending the first information to the network-side device, the terminal capability of supporting positioning based on AI. After receiving the first information reported by the terminal, the network-side device learns that the terminal has the terminal capability of supporting positioning based on AI, and then may, based on the first information, perform operations related to positioning based on AI, where the operations related to positioning based on AI include at least one of the following: configuring positioning resources based on AI for the terminal; configuring a positioning method based on AI; AI function-related management operations.

[0053] Optionally, the positioning scenario based on AI may include at least one of the following:

[0054] Positioning scenario 1: UE-based positioning, with the AI model deployed on the UE side, and the positioning method includes direct AI / ML positioning or AI / ML-assisted positioning (Case 1: UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning);

[0055] Positioning scenario 2a: UE-assisted Location Management Function (LMF)-based positioning, with the AI model deployed on the UE side, and the positioning method includes AI / ML-assisted positioning (Case 2a: UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning);

[0056] Positioning scenario 2b: UE-assisted LMF-based positioning with the AI model deployed on the LMF side. The positioning method includes direct AI / ML positioning (Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning);

[0057] Positioning scenario 3a: NG-RAN node-assisted positioning with the AI model deployed on the gNB side. The positioning method includes AI / ML-assisted positioning (Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning);

[0058] Positioning scenario 3b: NG-RAN node-assisted positioning with the AI model deployed on the LMF side. The positioning method includes direct AI / ML positioning (Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning).

[0059] In the embodiments of the present application, by introducing the terminal to report the first information to the network-side device, the network-side device can learn about the terminal's ability to support AI-based positioning. Furthermore, the network-side device can perform network configurations and operations related to AI-based positioning, improving the accuracy and reliability of the AI-based positioning method.

[0060] Optionally, the first information includes at least one of the following:

[0061] 1) Capability information related to the downlink positioning reference signal (PRS) resources.

[0062] 2) Capability information related to the reporting of DL PRS measurements for UE-assisted Direct AI / ML positioning.

[0063] 3) Capability information related to the reporting of timing information measurements for UE-assisted AI / ML-assisted positioning.

[0064] 4) Capability information related to line of sight (LoS) and / or non-line of sight (NLOS) indication for terminal-assisted AI-assisted positioning.

[0065] 5) Capability information related to AI function performance supervision.

[0066] 6) Capability information related to AI function training.

[0067] Optionally, the capability information related to AI function training may include: Whether the terminal supports AI function fine-tuning or retraining (Support of model fine-tuning or retraining [for Case 1 or 2a]).

[0068] 7) Capability information related to PRS measurement.

[0069] Optionally, the capability information related to the downlink positioning reference signal (DL PRS) resource described in 1) may include at least one of the following:

[0070] a) Capability information related to the DL PRS resource for direct AI positioning (DL PRS Resource for Direct AI / ML positioning), including at least one of the following:

[0071] (1) The maximum number of DL PRS resource sets per transmission and reception point (TRP) per positioning frequency layer supported by the terminal (Max number of DL PRS Resource Sets per TRP per frequency layer supported by UE); Optional value sets (Values) are, for example, {1, 2}.

[0072] (2) The maximum number of TRPs across all positioning frequency layers supported by the terminal (Max number of TRPs across all positioning frequency layers per UE); Optional value sets are, for example, {4, 6, 12, 16, 24, 32, 64, 128, 256}.

[0073] (3) The maximum number of positioning frequency layers supported by the terminal (Max number of positioning frequency layers UE supports); Optional value sets are, for example, {1, 2, 3, 4}.

[0074] b) DL PRS Resource for Direct AI / ML positioning on a band, including at least one of the following:

[0075] (1) Max number of DL PRS Resources per DL PRS Resource Set; the optional value set is, for example, {1, 2, 4, 8, 16, 32, 64}.

[0076] (2) Max 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}.

[0077] c) DL PRS Resource for Direct AI / ML positioning on a band combination, including at least one of the following:

[0078] (1) Max number of DL PRS Resources supported by 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}.

[0079] (2) The maximum number of DL PRS resources supported by the UE across all positioning frequency layers, TRPs, and DL PRS resource sets for frequency range 2 (FR2) (Max number of DL PRS Resources supported by 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}.

[0080] (3) The maximum number of DL PRS resources supported by the UE across all positioning frequency layers, TRPs, and DL PRS resource sets for FR1 in the frequency range of FR1 and FR2 (Max 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}.

[0081] (4) The maximum number of DL PRS resources supported by the UE across all positioning frequency layers, TRPs, and DL PRS resource sets for FR2 in the frequency range of FR1 and FR2 (Max number of DL PRS Resources supported by 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}.

[0082] d) DL PRS resource - related capability information for AI - assisted positioning (DL PRS Resource for AI / ML - assisted positioning), including at least one of the following:

[0083] (1) Max number of DL PRS Resource Sets per TRP per frequency layer supported by UE; The optional value set is, for example, {1, 2}.

[0084] (2) Max 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}.

[0085] (3) Max number of positioning frequency layers UE supports; The optional value set is, for example, {1, 2, 3, 4}.

[0086] e) DL PRS Resource for AI / ML assisted positioning on a band, including at least one of the following:

[0087] (1) Max number of DL PRS Resources per DL PRS Resource Set; The optional value set is, for example, {1, 2, 4, 8, 16, 32, 64}.

[0088] (2) Max 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}.

[0089] f) DL PRS Resource for AI / ML-assisted positioning on a band combination, including at least one of the following:

[0090] (1) Max number of DL PRS Resources supported by 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}.

[0091] (2) Max number of DL PRS Resources supported by 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}.

[0092] (3) Max 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}.

[0093] (4) Maximum number of DL PRS Resources supported by 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}.

[0094] Optionally, the DL PRS measurement reporting related capability information for UE-assisted direct AI positioning described in (2) may include at least one of the following:

[0095] a) Whether to support reporting DL PRS reference signal received power (RSRP) measurement; The optional value set is, for example, {0, 1}.

[0096] 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}.

[0097] Optionally, the UE reports DL PRS RSRP measurements. The DL PRS RSRP measurements can be used by network-side devices for RSRP fingerprint positioning, and can also be used for model supervision or model selection, etc. The specific role of the DL PRS RSRP measurements in this application is not limited.

[0098] c) Support reporting channel measurement type to LMF; The optional value set is, for example, {path delay, path power, path phase} or {Channel Impulse Response (CIR), Power Delay profile (PDP), Delay Profile (DP)}.

[0099] 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.

[0100] d) Support reporting mode of channel measurement to LMF; The optional value set is, for example, {report each path, tap or sample each channel}.

[0101] Optionally, the channel measurement reporting mode is used to indicate whether the terminal reports each path or each channel tap or sample.

[0102] e) Max number of DL channel measurements per TRP; The optional value set is, for example, {1, 2, 3, 4}.

[0103] f) First path reporting for UE-assisted Direct AI / ML positioning.

[0104] Optionally, the first path reporting for UE-assisted Direct AI / ML positioning includes at least one of the following:

[0105] (1) Support of RS path timing reporting for the first path.

[0106] (2) Support of RSRPP reporting for the first path.

[0107] (3) Support of RS path phase reporting for the first path.

[0108] g) Additional path reporting for UE-assisted Direct AI / ML positioning.

[0109] Optionally, additional path reporting for UE-assisted Direct AI / ML positioning is supported, including at least one of the following:

[0110] (1) Maximal number of additional paths per TRP; optional value sets are, for example, {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0111] (2) Maximal number of additional paths per reference signal resource per TRP.

[0112] (3) Support of additional detected path timing reporting for K>2 additional paths in UE-assisted Direct AI / ML positioning.

[0113] (4) Support of RSRPP reporting for additional paths.

[0114] (5) Support of RS path phase reporting for additional paths.

[0115] h) Support for reporting multiple paths information for UE-assisted Direct AI positioning (Multiple paths reporting for UE-assisted Direct AI / ML positioning).

[0116] Optionally, support for reporting multiple paths information for UE-assisted Direct AI positioning includes at least one of the following:

[0117] (1) The maximum number of paths per TRP supported for reporting; the optional value set is, for example, {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0118] (2) The maximum number of paths per reference signal resource per TRP supported for reporting.

[0119] (3) Whether support for reporting multiple detected path timing information for UE-assisted Direct AI positioning.

[0120] (4) Whether support for reporting RSRPP for multiple paths.

[0121] (5) Whether support for reporting RS path phase for multiple paths.

[0122] i) Support for reporting multiple time-domain channel samples or taps information for UE-assisted Direct AI positioning (Multiple channel samples or taps reporting for UE-assisted Direct AI / ML positioning).

[0123] Optionally, support for reporting multiple time-domain channel samples or taps information for UE-assisted Direct AI positioning includes at least one of the following:

[0124] (1) The maximal number of channel taps per TRP for which reporting is supported; an example of the set of optional values is {2, 4, 6, 8, 16, 32, 64, 128, 256}.

[0125] (2) The maximal number of channel taps per reference signal resource per TRP for which reporting is supported.

[0126] (3) Support of multiple detected tap timing reporting for UE-assisted Direct AI / ML positioning.

[0127] (4) Support of channel tap power reporting for multiple taps.

[0128] (5) Support of channel tap phase reporting for multiple taps.

[0129] j) Support of reporting time resolution indication information for channel multipaths and / or time resolution indication information for channel taps for UE-assisted direct AI positioning. For example, the time resolution indication information may be a parameter k related to the time resolution, and the time resolution is k*T, where T is defined by the protocol.

[0130] Optionally, the capability information related to the timing information measurement reporting for UE-assisted AI / ML-assisted positioning in (3) (Timing Measurement Reporting for UE-assisted AI / ML-assisted positioning [for Case2a]) may include at least one of the following:

[0131] a) Support of reporting the time of arrival (TOA).

[0132] b) Support of reporting the reference signal time difference (RSTD).

[0133] c) Support reporting TOA type to LMF; The optional value set is, for example, {soft, hard}.

[0134] d) Support reporting RSTD type to LMF; The optional value set is, for example, {soft, hard}.

[0135] e) Maximum number of TOA per TRP supported for reporting (DL TOA measurement per TRPs); The optional value set is, for example, {1, 2, 3, 4}.

[0136] f) Maximum number of DL RSTD per TRP supported for reporting (DL RSTD measurement per pair of TRPs); The optional value set is, for example, {1, 2, 3, 4}.

[0137] g) Support reporting soft information type to LMF; The optional value set is, for example, {Type 1, Type 2}. Soft information of the measured quantity describes the accuracy, uncertainty, or probability distribution of the measured quantity relative to hard information. Soft information can have different expression forms, such as confidence intervals, probability distributions, etc., corresponding to different soft information types here, such as Type 1 or Type 2.

[0138] Optionally, the line of sight (OS) and / or non-line of sight (NLOS) indication related capability information for UE-assisted AI-assisted positioning in 4) may include at least one of the following:

[0139] a) Whether to support reporting LOS or NLOS indication;

[0140] b) Support reporting LOS / NLOS indication type to LMF; The optional value set is, for example, {soft, hard}.

[0141] c) Support the reported LOS or NLOS indicator granularity; for example, the LOS or NLOS indicator granularity is 0.1. For example, the value of the LOS / NLOS indicator ranges from 0 to 1, with a granularity of 0.1.

[0142] d) Whether to support the reception of assistance data including the LOS or NLOS indicator (Support reception of the assistance data containing the LOS / NLOS indicator), where the assistance data includes at least one of the following:

[0143] (1) LOS or NLOS indicator type

[0144] (2) LOS or NLOS indicator granularity

[0145] Optionally, the AI function performance monitoring related capability information in 5) may include at least one of the following:

[0146] a) Model monitoring metric reporting for UE-assisted Direct AI / ML positioning [for Case2b]

[0147] Optionally, the AI function performance monitoring metric related capability information for UE-assisted direct AI positioning may include at least one of the following:

[0148] (1) Support of model monitoring metric reporting to LMF for UE-assisted direct AI positioning

[0149] (2) The statistical distribution type or parameters supported for reporting the UE-assisted direct AI positioning. For example, the statistical distribution type is Gaussian distribution, with a mean of x and a variance of y.

[0150] (3) Support model monitoring metric type to LMF for UE-assisted direct AI positioning; the optional value set is, for example, {distribution of SINR, distribution of RSRP, scenario ID}.

[0151] b) Model monitoring metric reporting for UE-assisted AI / ML assisted positioning [for Case 2a].

[0152] Optionally, the model monitoring metric reporting related capabilities information for UE-assisted AI / ML assisted positioning may include at least one of the following:

[0153] (1) Support of model monitoring metric reporting to LMF for UE-assisted AI / ML assisted positioning.

[0154] (2) Statistical distribution type or parameters supported for reporting of UE-assisted AI / ML assisted positioning.

[0155] (3) Support model monitoring metric type to LMF for UE-assisted AI / ML assisted positioning; the optional value set is, for example, {distribution of SINR, distribution of RSRP, scenario ID}. For example, the model 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.

[0156] c) Model performance indicator reporting for UE-assisted AI / ML assisted positioning [for Case 2a].

[0157] Optionally, the AI function performance indication related capability information for terminal-assisted AI-assisted positioning includes at least one of the following:

[0158] (1) Whether it supports reporting the performance indication at the AI function level (Support of model performanceindicator reporting to LMF).

[0159] It should be noted that the supervision metrics are used for AI function supervision, and the supervision metrics are used to determine at least one of the effectiveness, reliability, and accuracy of the AI function. The performance indication is used to indicate at least one of the effectiveness, reliability, and accuracy of the AI function.

[0160] (2) Whether it supports reporting the performance indication at the model level (Support of model-level performanceindicator).

[0161] (3) The AI function performance supervision capabilities supported for reporting, including model-level supervision (Model-level monitoring) and / or functionality-level supervision (Functionality-levelmonitoring).

[0162] (4) The type of performance indication supported for reporting; the optional value set is, for example, {soft, hard}.

[0163] (5) The granularity of the performance indication at the AI function level supported for reporting (Model performance indicatorgranularity).

[0164] (6) The granularity of the performance indication at the model level supported for reporting.

[0165] d) The maximum number of samples supported for obtaining model supervision metric information and / or model performance indication information.

[0166] Optionally, the PRS measurement related capability information in (7) may include at least one of the following:

[0167] a) Whether it supports PRS measurement in the RRC_INACTIVE state for terminal-assisted direct AI positioning (Support of PRSmeasurement in RRC_INACTIVE state for UE-assisted Direct AI / ML positioning).

[0168] b) Support of PRS measurement in RRC_INACTIVE state for UE-assisted AI / ML assisted positioning.

[0169] c) Support of PRS measurement in RRC_INACTIVE state for UE-based direct AI / ML positioning.

[0170] In the information reporting method provided in the embodiments of the present application, the terminal reports to the network-side device the terminal capabilities supporting AI-based positioning (such as Case1, Case2a, and Case 2b); the implementation of the AI-based positioning method is supported.

[0171] The first information reported by the terminal relates to the time-domain channel state information and intermediate feature 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 reporting-related capability information for UE-assisted direct AI positioning; timing information measurement reporting-related capability information for UE-assisted AI-assisted positioning; line-of-sight LOS and / or non-line-of-sight NLOS indication-related capability information for UE-assisted AI-assisted positioning; AI function performance supervision-related capability information; AI function training-related capability information; PRS measurement-related capability information.

[0172] Figure 5 FIG. 2 is a second schematic flowchart of the information reporting method provided in the embodiments of the present application. This method is applied to a network-side device, such as Figure 5 As shown, the method includes:

[0173] Step 501, the network-side device receives the first information reported by the terminal, and the first information is used to characterize that the terminal supports AI-based positioning.

[0174] Optionally, AI-based positioning refers to positioning based on an AI model or an AI function. An AI function may include one or more AI models, or an AI function may also be referred to as an AI model.

[0175] Optionally, the terminal reports the terminal capabilities supporting AI-based positioning by sending the first information to the network-side device. After receiving the first information reported by the terminal, the network-side device learns that the terminal has the terminal capabilities supporting AI-based positioning.

[0176] In an embodiment of the present application, by introducing the terminal to report first information to the network-side device, after receiving the first information, the network-side device learns about the terminal capability of supporting AI-based positioning. Furthermore, the network-side device can perform network configurations and network operations related to AI-based positioning, improving the accuracy and reliability of the AI-based positioning method.

[0177] Optionally, the information reporting method provided by the embodiment of the present application further includes:

[0178] Based on the first information, the network-side device performs operations related to AI-based positioning, where the operations related to AI-based positioning include at least one of the following:

[0179] 1) Configure AI-based positioning resources for the terminal.

[0180] 2) Configure AI-based positioning methods.

[0181] 3) AI function-related management operations.

[0182] After receiving the first information reported by the terminal, based on the first information, the network-side device can configure and request the terminal to support the AI-based positioning method.

[0183] For example, the network-side device configures the downlink positioning reference signal according to 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 according to the terminal's acquisition and processing capabilities of the measurement quantities; for yet another example, the network-side device requests the terminal to report specified information related to model performance supervision according to the terminal's model supervision capability, etc.

[0184] Optionally, the first information includes at least one of the following:

[0185] Downlink positioning reference signal (DL PRS) resource-related capability information;

[0186] DL PRS measurement reporting-related capability information for terminal-assisted direct AI positioning;

[0187] Timing information measurement reporting-related capability information for terminal-assisted AI-assisted positioning;

[0188] Line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information for terminal-assisted AI-assisted positioning;

[0189] AI function performance supervision-related capability information;

[0190] AI function training-related capability information;

[0191] PRS measurement-related capability information.

[0192] Optionally, the downlink positioning reference signal DL PRS resource-related capability information includes at least one of the following:

[0193] The DL PRS resource-related capability information for direct AI positioning includes at least one of the following: the maximum number of DL PRS resource sets of each transmission and reception point 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;

[0194] The DL PRS resource-related capability information for direct AI positioning in a frequency band includes: 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;

[0195] The DL PRS resource-related capability information for direct AI positioning in 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;

[0196] The DL PRS resource-related capability information for AI-assisted positioning includes at least one of the following: the maximum number of DL PRS resource sets of 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;

[0197] The DL PRS resource-related capability information for AI-assisted positioning in a frequency band includes: 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;

[0198] AI-assisted positioning DL PRS resource-related capability information on a frequency band combination, including 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 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.

[0199] Optionally, the DL PRS measurement reporting-related capability information for terminal-assisted direct AI positioning includes at least one of the following:

[0200] Whether it supports reporting the DL PRS reference signal received power (RSRP) measurement;

[0201] The maximum number of DL PRS RSRP measurements on different PRS resources in the same TRP that it supports reporting;

[0202] The type of channel measurement that it supports reporting;

[0203] The channel measurement reporting mode that it supports reporting;

[0204] The maximum number of DL channel measurements on different PRS resources in the same TRP that it supports reporting;

[0205] Whether it supports reporting the first path information for terminal-assisted direct AI positioning;

[0206] Whether it supports reporting the additional path information for terminal-assisted direct AI positioning;

[0207] Whether it supports reporting multiple path information for terminal-assisted direct AI positioning;

[0208] Whether it supports reporting multiple sampling points of the time-domain channel or multiple tap information of the time-domain channel for terminal-assisted direct AI positioning;

[0209] Whether it supports reporting the time resolution indication information of the channel multipath and / or the time resolution indication information of the channel taps for terminal-assisted direct AI positioning.

[0210] Optionally, the information on whether it supports reporting the first path information for terminal-assisted direct AI positioning includes at least one of the following:

[0211] Whether it supports reporting the reference signal (RS) path timing information of the first path;

[0212] Whether to support reporting the reference signal received path power (RSRPP) of the first path;

[0213] Whether to support reporting the RS path phase of the first path.

[0214] Optionally, the additional path information that supports reporting terminal-assisted direct AI positioning includes at least one of the following:

[0215] The maximum number of additional paths on each TRP that supports reporting;

[0216] The maximum number of additional paths on each reference signal resource on each TRP that supports reporting;

[0217] Whether to support reporting the additional detected path timing information of multiple additional paths;

[0218] Whether to support reporting the RSRPP of the additional path;

[0219] Whether to support reporting the RS path phase of the additional path.

[0220] Optionally, the multiple path information that supports reporting terminal-assisted direct AI positioning includes at least one of the following:

[0221] The maximum number of paths on each TRP that supports reporting;

[0222] The maximum number of paths on each reference signal resource on each TRP that supports reporting;

[0223] Whether to support reporting multiple detected path timing information;

[0224] Whether to support reporting the RSRPP of multiple paths;

[0225] Whether to support reporting the RS path phase of multiple paths.

[0226] Optionally, the multiple sampling points of the time-domain channel or the multiple tap information of the time-domain channel that supports reporting terminal-assisted direct AI positioning includes at least one of the following:

[0227] The maximum number of channel taps on each TRP that supports reporting;

[0228] The maximum number of channel taps on each reference signal resource on each TRP that supports reporting;

[0229] Whether to support reporting multiple detected tap timing information;

[0230] Whether to support reporting the channel tap power of multiple taps;

[0231] Whether it supports reporting the channel tap phase of multiple taps.

[0232] Optionally, the relevant capability information for reporting the timing information measurement of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0233] Whether it supports reporting the Time of Arrival (TOA);

[0234] Whether it supports reporting the Reference Signal Time Difference (RSTD);

[0235] The type of TOA supported for reporting;

[0236] The type of RSTD supported for reporting;

[0237] The maximum number of TOAs for each TRP supported for reporting;

[0238] The maximum number of DL RSTDs for each TRP supported for reporting;

[0239] The type of soft information supported for reporting.

[0240] Optionally, the relevant capability information for the Line of Sight (LOS) and / or Non-Line of Sight (NLOS) indication of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0241] Whether it supports reporting the LOS or NLOS indication;

[0242] The granularity of the LOS or NLOS indication supported for reporting;

[0243] Whether it supports receiving the auxiliary data including the LOS or NLOS indication, and the auxiliary data includes at least one of the following: the type of LOS or NLOS indication; the granularity of the LOS or NLOS indication.

[0244] Optionally, the relevant capability information for the AI function performance supervision includes at least one of the following:

[0245] The relevant capability information for the AI function performance supervision metrics of the terminal-assisted direct AI positioning;

[0246] The relevant capability information for the AI function performance supervision metrics of the terminal-assisted AI-assisted positioning;

[0247] The relevant capability information for the AI function performance indication of the terminal-assisted AI-assisted positioning;

[0248] The maximum number of samples supported for obtaining the model supervision metric information and / or the model performance indication information.

[0249] Optionally, the relevant capability information for the AI function performance supervision metrics of the terminal-assisted direct AI positioning includes at least one of the following:

[0250] Whether to support reporting the AI function performance supervision indicators for terminal-assisted direct AI positioning;

[0251] The statistical distribution type or parameters of terminal-assisted direct AI positioning that support reporting;

[0252] The type of AI function performance supervision indicators for terminal-assisted direct AI positioning that support reporting.

[0253] Optionally, the AI function performance supervision indicator-related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0254] Whether to support reporting the AI function performance supervision indicators for terminal-assisted AI-assisted positioning;

[0255] The statistical distribution type or parameters of terminal-assisted AI-assisted positioning that support reporting;

[0256] The type of AI function performance supervision indicators for terminal-assisted AI-assisted positioning that support reporting.

[0257] Optionally, the AI function performance indication-related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0258] Whether to support reporting the performance indication at the AI function level;

[0259] Whether to support reporting the performance indication at the model level;

[0260] The performance supervision capabilities that support reporting, including model-level supervision and / or function-level supervision;

[0261] The type of performance indication that support reporting;

[0262] The granularity of the performance indication at the level that support reporting;

[0263] The granularity of the performance indication at the model level that support reporting.

[0264] Optionally, the AI function training-related capability information includes: whether to support the terminal to perform AI function fine-tuning or retraining.

[0265] Optionally, the PRS measurement-related capability information includes at least one of the following:

[0266] Whether to support the PRS measurement in the inactive state of terminal-assisted direct AI positioning;

[0267] Whether to support the PRS measurement in the inactive state of terminal-assisted AI-assisted positioning;

[0268] Whether to support the PRS measurement in the inactive state of direct AI positioning based on the terminal.

[0269] Figure 6 This is the third schematic flow chart of the information reporting method provided by the embodiments of the present application. This method is executed in cooperation by a terminal and a network-side device. As Figure 6 shown, this method includes step 601 and step 602, where:

[0270] Step 601: The terminal sends first information to the network-side device, and the first information is used to indicate that the terminal supports AI-based positioning.

[0271] Step 602: The network-side device receives the first information reported by the terminal and learns that the terminal supports AI-based positioning.

[0272] Optionally, AI-based positioning refers to positioning based on an AI model or an AI function. An AI function may include one or more AI models, or an AI function may also be referred to as an AI model.

[0273] Optionally, the terminal reports the terminal capability of supporting AI-based positioning to the network-side device by sending the first information. 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.

[0274] Optionally, based on the first information, the network-side device performs operations related to AI-based positioning. Among them, the operations related to AI-based positioning include at least one of the following:

[0275] 1) Configure AI-based positioning resources for the terminal.

[0276] 2) Configure an AI-based positioning method.

[0277] 3) AI function-related management operations.

[0278] In the embodiments of the present application, by introducing the terminal to report the first information to the network-side device, the network-side device learns the terminal capability of the terminal supporting AI-based positioning. Furthermore, the network-side device can perform network configurations and network operations related to AI-based positioning, improving the accuracy and reliability of the AI-based positioning method.

[0279] For the information reporting method provided by the embodiments of the present application, the execution subject may be an information reporting device. In the embodiments of the present application, taking the information reporting device executing the information reporting method as an example, the information reporting device provided by the embodiments of the present application is described.

[0280] Figure 7 This is one of the schematic structural diagrams of the information reporting device provided by the embodiments of the present application. As Figure 7 shown, the information reporting device 700 is applied to a terminal. The information reporting device 700 includes:

[0281] A sending module 701, configured to send 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).

[0282] In an embodiment of this application, by introducing the reporting of the first information to the network-side device, the network-side device can learn about the terminal capability that the terminal supports positioning based on AI. Furthermore, the network-side device can perform network configurations and network operations related to positioning based on AI, improving the accuracy and reliability of the positioning method based on AI.

[0283] Optionally, the first information includes at least one of the following:

[0284] Capability information related to downlink positioning reference signal (DL PRS) resources;

[0285] Capability information related to reporting DL PRS measurements for terminal-assisted direct AI positioning;

[0286] Capability information related to reporting timing information measurements for terminal-assisted AI-assisted positioning;

[0287] Capability information related to indicating line of sight (LOS) and / or non-line of sight (NLOS) for terminal-assisted AI-assisted positioning;

[0288] Capability information related to AI function performance supervision;

[0289] Capability information related to AI function training;

[0290] Capability information related to PRS measurements.

[0291] Optionally, the capability information related to downlink positioning reference signal (DL PRS) resources includes at least one of the following:

[0292] Capability information related to DL PRS resources for direct AI positioning, including at least one of: the maximum number of DL PRS resource sets of each transmission and reception point (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;

[0293] Capability information related to DL PRS resources for direct AI positioning in 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;

[0294] DL PRS resource related capability information for direct AI positioning on a frequency band combination, including 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.

[0295] DL PRS resource related capability information for AI-assisted positioning, including at least one of the maximum number of DL PRS resource sets of 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.

[0296] 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.

[0297] DL PRS resource related capability information for AI-assisted positioning on a frequency band combination, including 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 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.

[0298] Optionally, the DL PRS measurement reporting related capability information assisted by the terminal for direct AI positioning includes at least one of the following:

[0299] Whether it supports reporting the DL PRS reference signal received power (RSRP) measurement quantity;

[0300] The maximum number of DL PRS RSRP measurement quantities on different PRS resources in the same TRP supported for reporting.

[0301] Channel measurement quantity types supported for reporting

[0302] Channel measurement reporting modes supported for reporting

[0303] Maximum number of DL channel measurement quantities on different PRS resources in the same TRP supported for reporting

[0304] First path information of terminal-assisted direct AI positioning supported for reporting

[0305] Additional path information of terminal-assisted direct AI positioning supported for reporting

[0306] Multiple path information of terminal-assisted direct AI positioning supported for reporting

[0307] Multiple sampling points of the time domain channel or multiple tap information of the time domain channel of terminal-assisted direct AI positioning supported for reporting

[0308] Time resolution indication information of the channel multipath and / or time resolution indication information of the channel tap of terminal-assisted direct AI positioning supported for reporting

[0309] Optionally, the first path information of the terminal-assisted direct AI positioning supported for reporting includes at least one of the following:

[0310] Whether to support reporting of reference signal RS path timing information for the first path

[0311] Whether to support reporting of reference signal received path power RSRPP for the first path

[0312] Whether to support reporting of the RS path phase for the first path

[0313] Optionally, the additional path information of the terminal-assisted direct AI positioning supported for reporting includes at least one of the following:

[0314] Maximum number of additional paths on each TRP supported for reporting

[0315] Maximum number of additional paths on each reference signal resource on each TRP supported for reporting

[0316] Whether to support reporting of additional detected path timing information for multiple additional paths

[0317] Whether to support reporting of RSRPP for additional paths

[0318] Whether to support reporting of the RS path phase for additional paths

[0319] Optionally, the multiple path information supporting the reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0320] The maximum number of paths on each TRP supported for reporting;

[0321] The maximum number of paths for each reference signal resource on each TRP supported for reporting;

[0322] Whether reporting of multiple detected path timing information is supported;

[0323] Whether reporting of RSRPP for multiple paths is supported;

[0324] Whether reporting of the RS path phase for multiple paths is supported.

[0325] Optionally, the multiple sampling points of the time-domain channel or the multiple tap information of the time-domain channel supporting the reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0326] The maximum number of channel taps on each TRP supported for reporting;

[0327] The maximum number of channel taps for each reference signal resource on each TRP supported for reporting;

[0328] Whether reporting of multiple detected tap timing information is supported;

[0329] Whether reporting of the channel tap power for multiple taps is supported;

[0330] Whether reporting of the channel tap phase for multiple taps is supported.

[0331] Optionally, the capability information related to the measurement and reporting of the timing information for terminal-assisted AI-assisted positioning includes at least one of the following:

[0332] Whether reporting of the time of arrival TOA is supported;

[0333] Whether reporting of the reference signal time difference RSTD is supported;

[0334] The type of TOA supported for reporting;

[0335] The type of RSTD supported for reporting;

[0336] The maximum number of TOAs for each TRP supported for reporting;

[0337] The maximum number of DL RSTDs for each TRP supported for reporting;

[0338] The type of soft information supported for reporting.

[0339] Optionally, the line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0340] Whether to support reporting LOS or NLOS indication;

[0341] The granularity of the supported LOS or NLOS indication for reporting;

[0342] Whether to support receiving auxiliary data including LOS or NLOS indication, where the auxiliary data includes at least one of the following: LOS or NLOS indication type; LOS or NLOS indication granularity.

[0343] Optionally, the AI function performance supervision related capability information includes at least one of the following:

[0344] The AI function performance supervision metric related capability information for the terminal-assisted direct AI positioning;

[0345] The AI function performance supervision metric related capability information for the terminal-assisted AI-assisted positioning;

[0346] The AI function performance indication related capability information for the terminal-assisted AI-assisted positioning;

[0347] The maximum number of samples supported for obtaining model supervision metric information and / or model performance indication information.

[0348] Optionally, the AI function performance supervision metric related capability information for the terminal-assisted direct AI positioning includes at least one of the following:

[0349] Whether to support reporting the AI function performance supervision metric for the terminal-assisted direct AI positioning;

[0350] The statistical distribution type or parameter supported for reporting the terminal-assisted direct AI positioning;

[0351] The type of the AI function performance supervision metric supported for reporting the terminal-assisted direct AI positioning.

[0352] Optionally, the AI function performance supervision metric related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0353] Whether to support reporting the AI function performance supervision metric for the terminal-assisted AI-assisted positioning;

[0354] The statistical distribution type or parameter supported for reporting the terminal-assisted AI-assisted positioning;

[0355] The type of the AI function performance supervision metric supported for reporting the terminal-assisted AI-assisted positioning.

[0356] Optionally, the AI function performance indication related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0357] Whether it supports reporting the performance indication at the AI function level;

[0358] Whether it supports reporting the performance indication at the model level;

[0359] The supported AI function performance supervision capabilities, including model-level supervision and / or function-level supervision;

[0360] The supported types of performance indications;

[0361] The granularity of the performance indication level supported for reporting;

[0362] The granularity of the performance indication at the model level supported for reporting.

[0363] Optionally, the AI function training related capability information includes: whether it supports the terminal to perform fine-tuning or retraining of the AI function.

[0364] Optionally, the PRS measurement related capability information includes at least one of the following:

[0365] Whether it supports the PRS measurement in the inactive state of the direct AI positioning assisted by the terminal;

[0366] Whether it supports the PRS measurement in the inactive state of the AI-assisted positioning assisted by the terminal;

[0367] Whether it supports the PRS measurement in the inactive state of the direct AI positioning based on the terminal.

[0368] The information reporting device 700 in the embodiments of the present application may 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 a chip. The electronic device may be a terminal or other devices other than the terminal. Exemplarily, the terminal may include, but is not limited to, the types of the terminal 11 listed above, and other devices may be a server, a Network Attached Storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0369] The information reporting device 700 provided in the embodiments of the present application can implement Figure 4 each process implemented by the method embodiment shown and achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0370] Figure 8 This is the second structural schematic diagram of the information reporting device provided in the embodiments of the present application, as shown in Figure 8As shown, the information reporting device 800 is applied to a network-side device. The information reporting device 800 includes:

[0371] A receiving module 801, configured 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).

[0372] In an embodiment of this application, by receiving the first information reported by the terminal, the terminal capability of supporting positioning based on AI is obtained. Furthermore, the network-side device can perform network configurations and network operations related to positioning based on AI, improving the accuracy and reliability of the positioning method based on AI.

[0373] Optionally, the device further includes:

[0374] An execution module, configured to perform operations related to positioning based on AI based on the first information, where the operations related to positioning based on AI include at least one of the following:

[0375] Configure positioning resources based on AI for the terminal;

[0376] Configure a positioning method based on AI;

[0377] AI function-related management operations.

[0378] Optionally, the first information includes at least one of the following:

[0379] Downlink positioning reference signal (DL PRS) resource-related capability information;

[0380] DL PRS measurement report-related capability information for terminal-assisted direct AI positioning;

[0381] Timing information measurement report-related capability information for terminal-assisted AI-assisted positioning;

[0382] Line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information for terminal-assisted AI-assisted positioning;

[0383] AI function performance supervision-related capability information;

[0384] AI function training-related capability information;

[0385] PRS measurement-related capability information.

[0386] Optionally, the downlink positioning reference signal (DL PRS) resource-related capability information includes at least one of the following:

[0387] Capability information related to DL PRS resources for direct AI positioning, including at least one of: the maximum number of DL PRS resource sets of each transmission and reception point (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.

[0388] Capability information related to DL PRS resources 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.

[0389] Capability information related to DL PRS resources for direct AI positioning on a frequency band combination, including at least one of: 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.

[0390] Capability information related to DL PRS resources for AI-assisted positioning, including at least one of: the maximum number of DL PRS resource sets of 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.

[0391] Capability information related to DL PRS resources 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.

[0392] AI-assisted positioning DL PRS resource-related capability information on a frequency band combination, including 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 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.

[0393] Optionally, the terminal-assisted direct AI positioning DL PRS measurement reporting-related capability information includes at least one of the following:

[0394] Whether it supports reporting the DL PRS reference signal received power (RSRP) measurement;

[0395] The maximum number of DL PRS RSRP measurements on different PRS resources in the same TRP that support reporting;

[0396] The type of channel measurement supported for reporting;

[0397] The channel measurement reporting mode supported for reporting;

[0398] The maximum number of DL channel measurements on different PRS resources in the same TRP that support reporting;

[0399] Support for reporting the first path information of the terminal-assisted direct AI positioning;

[0400] Support for reporting the additional path information of the terminal-assisted direct AI positioning;

[0401] Support for reporting the multiple path information of the terminal-assisted direct AI positioning;

[0402] Support for reporting multiple sampling points of the time-domain channel or multiple tap information of the time-domain channel of the terminal-assisted direct AI positioning;

[0403] Support for reporting the time resolution indication information of the channel multipath and / or the time resolution indication information of the channel taps of the terminal-assisted direct AI positioning.

[0404] Optionally, the support for reporting the first path information of the terminal-assisted direct AI positioning includes at least one of the following:

[0405] Whether it supports reporting the reference signal (RS) path timing information of the first path;

[0406] Whether to support the reporting of the reference signal received path power (RSRPP) of the first path;

[0407] Whether to support the reporting of the RS path phase of the first path.

[0408] Optionally, the additional path information supporting the reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0409] The maximum number of additional paths on each TRP supported for reporting;

[0410] The maximum number of additional paths on each reference signal resource on each TRP supported for reporting;

[0411] Whether to support the reporting of the additional detected path timing information for multiple additional paths;

[0412] Whether to support the reporting of the RSRPP of the additional path;

[0413] Whether to support the reporting of the RS path phase of the additional path.

[0414] Optionally, the multiple path information supporting the reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0415] The maximum number of paths on each TRP supported for reporting;

[0416] The maximum number of paths on each reference signal resource on each TRP supported for reporting;

[0417] Whether to support the reporting of multiple detected path timing information;

[0418] Whether to support the reporting of the RSRPP of multiple paths;

[0419] Whether to support the reporting of the RS path phase of multiple paths.

[0420] Optionally, the multiple sampling points of the time-domain channel or the multiple tap information of the time-domain channel supporting the reporting of terminal-assisted direct AI positioning includes at least one of the following:

[0421] The maximum number of channel taps on each TRP supported for reporting;

[0422] The maximum number of channel taps on each reference signal resource on each TRP supported for reporting;

[0423] Whether to support the reporting of multiple detected tap timing information;

[0424] Whether to support the reporting of the channel tap power of multiple taps;

[0425] Whether it supports reporting the channel tap phase of multiple taps.

[0426] Optionally, the capability information related to the measurement and reporting of the timing information of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0427] Whether it supports reporting the Time of Arrival (TOA);

[0428] Whether it supports reporting the Reference Signal Time Difference (RSTD);

[0429] The type of TOA supported for reporting;

[0430] The type of RSTD supported for reporting;

[0431] The maximum number of TOAs of each TRP supported for reporting;

[0432] The maximum number of DL RSTDs of each TRP supported for reporting;

[0433] The type of soft information supported for reporting.

[0434] Optionally, the capability information related to the Line of Sight (LOS) and / or Non-Line of Sight (NLOS) indication of the terminal-assisted AI-assisted positioning includes at least one of the following:

[0435] Whether it supports reporting the LOS or NLOS indication;

[0436] The granularity of the LOS or NLOS indication supported for reporting;

[0437] Whether it supports receiving auxiliary data including the LOS or NLOS indication, and the auxiliary data includes at least one of the following: the type of LOS or NLOS indication; the granularity of the LOS or NLOS indication.

[0438] Optionally, the capability information related to the AI function performance supervision includes at least one of the following:

[0439] The capability information related to the AI function performance supervision metrics of the terminal-assisted direct AI positioning;

[0440] The capability information related to the AI function performance supervision metrics of the terminal-assisted AI-assisted positioning;

[0441] The capability information related to the AI function performance indication of the terminal-assisted AI-assisted positioning;

[0442] The maximum number of samples supported for obtaining model supervision metric information and / or model performance indication information.

[0443] Optionally, the capability information related to the AI function performance supervision metrics of the terminal-assisted direct AI positioning includes at least one of the following:

[0444] Whether to support reporting AI function performance supervision metrics for terminal-assisted direct AI positioning;

[0445] Statistical distribution types or parameters of terminal-assisted direct AI positioning supported for reporting;

[0446] Types of AI function performance supervision metrics for terminal-assisted direct AI positioning supported for reporting.

[0447] Optionally, the AI function performance supervision metric-related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0448] Whether to support reporting AI function performance supervision metrics for terminal-assisted AI-assisted positioning;

[0449] Statistical distribution types or parameters of terminal-assisted AI-assisted positioning supported for reporting;

[0450] Types of AI function performance supervision metrics for terminal-assisted AI-assisted positioning supported for reporting.

[0451] Optionally, the AI function performance indication-related capability information for the terminal-assisted AI-assisted positioning includes at least one of the following:

[0452] Whether to support reporting performance indications at the AI function level;

[0453] Whether to support reporting performance indications at the model level;

[0454] Supported reporting AI function performance supervision capabilities, including model-level supervision and / or function-level supervision;

[0455] Types of supported reporting performance indications;

[0456] Granularity of supported reporting level of performance indications;

[0457] Granularity of supported reporting model-level performance indications.

[0458] Optionally, the AI function training-related capability information includes: whether to support the terminal for AI function fine-tuning or retraining.

[0459] Optionally, the PRS measurement-related capability information includes at least one of the following:

[0460] Whether to support PRS measurements in the inactive state for terminal-assisted direct AI positioning;

[0461] Whether to support PRS measurements in the inactive state for terminal-assisted AI-assisted positioning;

[0462] Whether to support PRS measurements in the inactive state for direct AI positioning based on the terminal.

[0463] The information reporting device 800 in the embodiments of the present application may 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 a chip. The electronic device may be a network-side device or other devices other than network-side devices. Exemplarily, the network-side device may include, but is not limited to, the types of the network-side device 12 listed above.

[0464] The information reporting device 800 provided in the embodiments of the present application can implement Figure 5 each process implemented by the method embodiment shown and achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0465] The embodiments of the present application also provide a communication device. Figure 9 is a schematic structural diagram of the communication device provided in the embodiments of the present application. As Figure 9 shown, the communication device 900 includes a processor 901 and a memory 902. A program or instruction that can run on the processor 901 is stored on the memory 902. For example, when the communication device 900 is a terminal, when the program or instruction is executed by the processor 901, it implements the above Figure 4 each step of the method embodiment shown and can achieve the same technical effect. When the communication device 900 is a network-side device, when the program or instruction is executed by the processor 901, it implements the above Figure 5 each step of the method embodiment shown and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0466] The embodiments of the present application also provide a terminal, including a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement the steps in the method embodiment as Figure 4 shown. This terminal embodiment corresponds to the above terminal-side method embodiment. Each implementation process and implementation manner of the above method embodiment can be applied to this terminal embodiment and can achieve the same technical effect.

[0467] The embodiments of the present application also provide a terminal. Figure 10 is a schematic hardware structure diagram of the terminal provided in the embodiments of the present application. As Figure 10 shown, the terminal 1000 includes, but is not limited to, at least some components such as 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.

[0468] Those skilled in the art can understand that the terminal 1000 may further include a power source (such as a battery) for powering each component. The power source can be logically connected to the processor 1010 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 10 The terminal structure shown in Figure 10 does not limit the terminal. The terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0469] It should be understood that in the embodiments of the present application, the input unit 1004 may include a Graphics Processing Unit (GPU) 10041 and a microphone 10042. The graphics processing unit 10041 processes the image data of static pictures or videos 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 can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 1007 includes at least one of a touch panel 10071 and other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 can include two parts: a touch detection device and a touch controller. The 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 a joystick, which will not be elaborated here.

[0470] In the embodiments of the present application, after the radio frequency unit 1001 receives downlink data from a network-side device, it can be transmitted to the processor 1010 for processing; in addition, the radio frequency unit 1001 can send uplink data to the network-side device. Generally, the radio frequency unit 1001 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0471] 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. Among them, the first storage area may store an operating system, application programs 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 (DDR SDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct rambus random access memory (DRRAM). The memory 1009 in the embodiments of the present application includes, but is not limited to, these and any other suitable types of memories.

[0472] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor 1010 either.

[0473] Among them, the radio frequency unit 1001 is used to send a first piece of information to a network-side device, and the first piece of information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

[0474] It can be understood that the implementation processes of the implementation manners mentioned in this embodiment can refer to Figure 4 the relevant descriptions of the method embodiment shown, and achieve the same or corresponding technical effects. To avoid repetition, they will not be elaborated here.

[0475] An embodiment of the present application further provides a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run programs or instructions to implement the steps of the method embodiment as Figure 5 shown. This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment. Each implementation process and implementation manner of the above method embodiment can be applied to this network-side device embodiment, and the same technical effects can be achieved.

[0476] An embodiment of the present application further provides a network-side device. Figure 11 It is a schematic diagram of the hardware structure of the network-side device provided by the embodiment of the present application. As Figure 11 shown, 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. After processing the received information, the radio frequency device 112 sends it out through the antenna 111.

[0477] In the above embodiments, the method executed by the network-side device can be implemented in the baseband device 113, and the baseband device 113 includes a baseband processor.

[0478] The baseband device 113 may include, for example, at least one baseband board, and multiple chips are provided on the baseband board. As Figure 11 shown, 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 and execute the operations of the network device shown in the above method embodiment.

[0479] The network-side device may further include a network interface 116, and this interface is, for example, a Common Public Radio Interface (CPRI).

[0480] Specifically, the network-side device 1100 of the embodiment of the present application further includes: instructions or programs stored on the memory 115 and executable on the processor 114. The processor 114 calls the instructions or programs in the memory 115 to execute Figure 5 the methods executed by the modules shown, and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0481] The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above-mentioned embodiment of the information reporting method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0482] Wherein, 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 disc, etc. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0483] The embodiments of the present application further provide a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run a program or instruction to implement each process of the above-mentioned embodiment of the information reporting method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0484] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, a system chip, a chip system or a system-on-chip, etc.

[0485] The embodiments of the present application further provide 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 each process of the above-mentioned embodiment of the information reporting method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0486] The embodiments of the present application further provide a wireless communication system, including: a terminal and a network-side device. The terminal can be used to execute the steps of the method embodiment as Figure 4 shown, and the network-side device can be used to execute the steps of the method embodiment as Figure 5 shown.

[0487] It should be noted that in this text, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising that element. In addition, it should be noted 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 reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0488] From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions for causing a terminal or a network-side device to execute the methods described in various embodiments of the present application.

[0489] The embodiments of the present application have been described above with reference to the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms of embodiments without departing from the spirit and scope protected by the claims of the present application. These embodiments are all within the protection scope of the present application.

Claims

1. An information reporting method, characterized in that, Including: 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).

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 reporting-related capability information for terminal-assisted direct AI positioning; Timing information measurement reporting-related capability information for terminal-assisted AI-assisted positioning; Line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information for terminal-assisted AI-assisted positioning; AI function performance supervision-related capability information; AI function training-related capability information; PRS measurement-related 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 of each transmission and reception point (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 in 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; DL PRS resource-related capability information for direct AI positioning in 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; DL PRS resource-related capability information for AI-assisted positioning, including at least one of the maximum number of DL PRS resource sets of 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 in 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; AI-assisted positioning DL PRS resource-related capability information on a frequency band combination, including 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 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.

4. The information reporting method according to claim 2 or 3, characterized in that, The terminal-assisted direct AI positioning DL PRS measurement reporting-related capability information, including at least one of the following: Whether it supports reporting the DL PRS reference signal received power (RSRP) measurement quantity; The maximum number of DL PRS RSRP measurement quantities on different PRS resources in the same TRP supported for reporting; The type of channel measurement quantity supported for reporting; The channel measurement reporting mode supported for reporting; The maximum number of DL channel measurement quantities on different PRS resources in the same TRP supported for reporting; Whether it supports reporting the first path information for terminal-assisted direct AI positioning; Whether it supports reporting the additional path information for terminal-assisted direct AI positioning; Whether it supports reporting the multi-path information for terminal-assisted direct AI positioning; Whether it supports reporting the multiple sampling points of the time domain channel or the multiple tap information of the time domain channel for terminal-assisted direct AI positioning; Whether it supports reporting the time resolution indication information of the channel multi-path and / or the time resolution indication information of the channel taps for terminal-assisted direct AI positioning.

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

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

7. The information reporting method according to claim 4, wherein The said support for reporting the multi-path information for terminal-assisted direct AI positioning, including at least one of the following: The maximum number of paths on each TRP supported for reporting; The maximum number of paths on each reference signal resource on each TRP supported for reporting; Whether it supports reporting the multiple detected path timing information; Whether it supports reporting the RSRPP of multiple paths; Whether it supports reporting the RS path phase of multiple paths.

8. The information reporting method according to claim 4, characterized in that, The multiple sampling points of the time-domain channel or the information of multiple taps of the time-domain channel that support reporting terminal-assisted direct AI positioning include at least one of the following: The maximum number of channel taps supported for reporting on each TRP; The maximum number of channel taps of each reference signal resource supported for reporting on each TRP; Whether reporting of multiple detected tap timing information is supported; Whether reporting of channel tap power of multiple taps is supported; Whether reporting of channel tap phase of multiple taps is supported.

9. The information reporting method according to any one of claims 2 to 8, characterized in that The relevant capability information for reporting the timing information measurement of terminal-assisted AI-assisted positioning includes at least one of the following: Whether reporting of time of arrival (TOA) is supported; Whether reporting of reference signal time difference (RSTD) is supported; The type of TOA supported for reporting; The type of RSTD supported for reporting; The maximum number of TOAs supported for reporting on each TRP; The maximum number of DL RSTDs supported for reporting on each TRP; The type of soft information supported for reporting.

10. The information reporting method according to any one of claims 2 to 9, characterized in that The relevant capability information for line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication of terminal-assisted AI-assisted positioning includes at least one of the following: Whether reporting of LOS or NLOS indication is supported; The granularity of the LOS or NLOS indication supported for reporting; Whether receiving of auxiliary data including LOS or NLOS indication is supported, where the auxiliary data includes at least one of the following: type of LOS or NLOS indication; granularity of LOS or NLOS indication.

11. The information reporting method according to any one of claims 2 to 10, characterized in that, The relevant capability information for AI function performance supervision includes at least one of the following: The relevant capability information for AI function performance supervision metrics of terminal-assisted direct AI positioning; The relevant capability information for AI function performance supervision metrics of terminal-assisted AI-assisted positioning; The relevant capability information for AI function performance indication of terminal-assisted AI-assisted positioning; The maximum number of samples supported for obtaining model supervision metric information and / or model performance indication information.

12. The information reporting method according to claim 11, wherein The relevant capability information for AI function performance supervision metrics of terminal-assisted direct AI positioning includes at least one of the following: Whether reporting of AI function performance supervision metrics of terminal-assisted direct AI positioning is supported; The type of statistical distribution or parameters supported for reporting of terminal-assisted direct AI positioning; The type of AI function performance supervision metrics of terminal-assisted direct AI positioning supported for reporting.

13. The information reporting method according to claim 11, wherein The relevant capability information for AI function performance supervision metrics of terminal-assisted AI-assisted positioning includes at least one of the following: Whether reporting of AI function performance supervision metrics of terminal-assisted AI-assisted positioning is supported; The type of statistical distribution or parameters supported for reporting of terminal-assisted AI-assisted positioning; The type of AI function performance supervision metrics of terminal-assisted AI-assisted positioning supported for reporting.

14. The information reporting method according to claim 11, wherein The relevant capability information for AI function performance indication of terminal-assisted AI-assisted positioning includes at least one of the following: Whether reporting of performance indication at the AI function level is supported; Whether reporting of performance indication at the model level is supported; The AI function performance supervision capabilities supported for reporting, including model-level supervision and / or function-level supervision; The type of performance indication supported for reporting; The granularity of the performance indication at the AI function level supported for reporting; The granularity of the performance indication at the model level supported for reporting.

15. The information reporting method according to any one of claims 2 to 14, characterized in that, The AI function training related capability information includes: whether the terminal is supported to perform AI function fine-tuning or retraining.

16. The information reporting method according to any one of claims 2 to 15, characterized in that The PRS measurement related capability information includes at least one of the following: Whether the non-active state PRS measurement of terminal-assisted direct AI positioning is supported; Whether the non-active state PRS measurement of terminal-assisted AI-assisted positioning is supported; Whether the non-active state PRS measurement of direct AI positioning based on the terminal is supported.

17. A method for information reporting, characterized in that, It includes: The network-side device receives the first information reported by the terminal, and 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 includes: The network-side device performs AI-based positioning related operations based on the first information, where the AI-based positioning related operations include at least one of the following: Configure AI-based positioning resources for the terminal; Configure AI-based positioning methods; AI function related management operations.

19. The information reporting method according to claim 17 or 18, characterized in that The first information includes at least one of the following: Downlink positioning reference signal (DL PRS) resource related capability information; DL PRS measurement reporting related capability information of terminal-assisted direct AI positioning; Timing information measurement reporting related capability information of terminal-assisted AI-assisted positioning; Line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication related capability information of terminal-assisted AI-assisted positioning; AI function performance supervision related capability information; AI function training related capability information; PRS measurement related 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: Direct AI positioning DL PRS resource related capability information, including at least one of the maximum number of DL PRS resource sets of each transmission and reception point (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; Direct AI positioning DL PRS resource related capability information in 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; Direct AI positioning DL PRS resource related capability information in 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; AI-assisted positioning DL PRS resource-related capability information, including at least one of: the maximum number of DL PRS resource sets of 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. AI-assisted positioning DL PRS resource-related capability information in 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. AI-assisted positioning DL PRS resource-related capability information in a frequency band combination, including 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 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.

21. The information reporting method according to claim 19 or 20, characterized in that, The terminal-assisted AI-assisted positioning timing information measurement and reporting-related capability information, including at least one of the following: Whether it supports reporting Time of Arrival (TOA). Whether it supports reporting Reference Signal Time Difference (RSTD). The supported TOA types for reporting. The supported RSTD types for reporting. The maximum number of TOAs of each TRP supported for reporting. The maximum number of DL RSTDs of each TRP supported for reporting. The supported soft information types for reporting.

22. The information reporting method according to any one of claims 19 to 21, characterized in that, The terminal-assisted AI-assisted positioning Line-of-Sight (LOS) and / or Non-Line-of-Sight (NLOS) indication-related capability information, including at least one of the following: Whether it supports reporting LOS or NLOS indication. The granularity of the supported LOS or NLOS indication for reporting. Whether it supports receiving auxiliary data including LOS or NLOS indication, where the auxiliary 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, characterized in that, The AI function performance supervision-related capability information, including at least one of the following: The AI function performance supervision metric-related capability information for terminal-assisted direct AI positioning. The AI function performance supervision metric-related capability information for terminal-assisted AI-assisted positioning. The AI function performance indication-related capability information for terminal-assisted AI-assisted positioning. The maximum number of samples supported for obtaining model supervision metric information and / or model performance indication information.

24. The information reporting method according to any one of claims 19 to 23, characterized in that, The AI function training-related capability information, including: whether it supports the terminal to perform AI function fine-tuning or retraining.

25. The information reporting method according to any one of claims 19 to 24, characterized in that, The PRS measurement-related capability information, including at least one of the following: Whether it supports PRS measurement in the inactive state for terminal-assisted direct AI positioning. Whether to support PRS measurements in the inactive state for terminal-assisted AI-assisted positioning; Whether to support PRS measurements in the inactive state for direct AI positioning based on the terminal.

26. An information reporting device, characterized in that, Including: A sending module, configured to send 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).

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 reporting-related capability information for direct AI positioning assisted by the terminal; Timing information measurement reporting-related capability information for AI-assisted positioning assisted by the terminal; Line-of-sight (LOS) and / or non-line-of-sight (NLOS) indication-related capability information for AI-assisted positioning assisted by the terminal; AI function performance supervision-related capability information; AI function training-related capability information; PRS measurement-related capability information.

28. An information reporting device, characterized in that, Including: A receiving module, configured to receive the first information reported by the terminal, where the first information is used to indicate that the terminal supports positioning based on artificial intelligence (AI).

29. The information reporting device according to claim 28, wherein The apparatus further includes: An execution module, configured to 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: Configure AI-based positioning resources for the terminal; Configure an AI-based positioning method; AI function-related management operations.

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

31. A terminal, characterized in that, Including a processor and a memory, where the memory stores a program or instruction that can 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, characterized in that, Including a processor and a memory, where the memory stores a program or instruction that can 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 17 to 25 are implemented.

33. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the information reporting method according to any one of claims 1 to 16 is implemented, or the steps of the information reporting method according to any one of claims 17 to 25 are implemented.

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