WO2025036223A1 - Information reporting method, information receiving method, and device - Google Patents

Abstract

The present application belongs to the technical field of communications. Disclosed are an information reporting method, an information receiving method, and a device. The information reporting method in the embodiments of the present application comprises: a first device sending first information to a second device, wherein the first information is used for indicating at least one type of the following information: information related to an AI function; information related to an AI model; and device capability information of the first device that is related to at least one of the AI function and the AI model.

Description

Information reporting method, information receiving method and device

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No. 202311018685.8 filed in China on August 11, 2023, the entire contents of which are incorporated herein by reference.

Technical Field

The present application belongs to the field of communication technology, and specifically relates to an information reporting method, an information receiving method and a device.

Background Art

Artificial Intelligence (AI) has been widely used in various fields. Integrating AI into wireless communication networks and significantly improving technical indicators such as throughput, latency and user capacity are important tasks for future wireless communication networks.

At present, AI models can be used for business processing in many scenarios, such as positioning based on AI models. In the process of using AI models, it is usually necessary for terminals and network side devices to interact to achieve business processing based on AI models. Therefore, how to make the terminal side and the network side have a consistent understanding of AI model-related information is a problem that technicians in this field need to solve.

Summary of the invention

The embodiments of the present application provide an information reporting method, an information receiving method and a device, which can solve the problem of how to make the terminal side and the network side have a consistent understanding of AI model-related information.

In a first aspect, an information reporting method is provided, comprising:

The first device sends first information to the second device, where the first information is used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information of the first device related to at least one of the AI function and the AI model.

In a second aspect, a method for receiving information is provided, comprising:

The second device receives first information sent by the first device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; and device capability information of the first device related to at least one of the AI function and the AI model.

In a third aspect, an information reporting device is provided, including:

A sending module is used to send first information to a second device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; and device capability information related to the first device and at least one of the AI function and the AI model.

In a fourth aspect, an information receiving device is provided, comprising:

A receiving module is used to receive first information sent by a first device, where the first information is used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information related to the first device and at least one of the AI function and the AI model.

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

In a sixth aspect, a first device is provided, comprising a processor and a communication interface, wherein the processor is used to..., and the communication interface is used to send first information to a second device, the first information being used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information of the first device related to at least one of the AI function and the AI model.

In the seventh aspect, a second device is provided, which includes a processor and a memory, wherein the memory stores a program or instruction that can be executed on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the second aspect are implemented.

In an eighth aspect, a second device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive first information sent by a first device, wherein the first information is used to indicate at least one type of information: Information: information related to the AI function; information related to the AI model; device capability information related to the first device and at least one of the AI function and the AI model.

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

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

In the eleventh aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.

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

In an embodiment of the present application, a first device sends first information to a second device, and the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; device capability information of the first device related to at least one of the AI function and the AI model, so that the first device and the second device have a consistent understanding of the AI model, relevant information of the AI function and device capability information, so that the second device can assist in the life cycle management of the first device model, and better support operations such as reasoning of the first device model, thereby improving the accuracy and reliability of the model reasoning results, and thereby improving the business processing performance of the AI model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1a is a schematic diagram of the architecture of a wireless communication system provided by an embodiment of the present application;

FIG1b is a schematic diagram of a neuron provided in an embodiment of the present application;

FIG2 is a flow chart of one of the information reporting methods provided in the embodiment of the present application;

3 is a schematic diagram of a first information coding structure of an information reporting method provided in an embodiment of the present application;

FIG4 is a second flow chart of the information reporting method provided in an embodiment of the present application;

FIG5 is a schematic diagram of a structure of an information reporting device provided in an embodiment of the present application;

FIG6 is a second structural diagram of the information reporting device provided in an embodiment of the present application;

FIG7 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application;

FIG8 is a schematic diagram of the structure of a terminal according to an embodiment of the present application;

FIG9 is a schematic diagram of a structure of a network side device according to an embodiment of the present application;

FIG. 10 is a second schematic diagram of the structure of the network side device according to an embodiment of the present application.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of this application.

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

The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, operations to be performed, or request results in the sent indication; an indirect indication can be understood as the receiver determining the corresponding information according to the indication sent by the sender, or making a judgment and determining the operation to be performed or the request result according to the judgment result.

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

FIG1a shows a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network side device 12 . Among them, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (Ultra-mobile Personal Computer, UMPC), a mobile Internet device (Mobile Internet Device, MID), an augmented reality (Augmented Reality, AR), a virtual reality (Virtual Reality, VR) device, a robot, a wearable device (Wearable Device), a flight vehicle (flight vehicle), a vehicle user equipment (VUE), a shipborne equipment, a pedestrian terminal (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines or furniture, etc.), a game console, a personal computer (Personal Computer, PC), a teller machine or a self-service machine and other terminal side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AP) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc. Among them, the base station can be called Node B (Node B, NB), Evolved Node B (Evolved Node B, eNB), the next generation Node B (the next generation Node B, gNB), New Radio Node B (New Radio Node B, NR Node B), access point, Relay Base Station (Relay Base Station, RBS), Serving Base Station (Serving Base Station, SBS), Base Transceiver Station (Base Transceiver Station, BTS), radio base station, radio transceiver, base The base station is not limited to specific technical terms as long as the same technical effect is achieved. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.

The core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (Mobility Management Entity, MME), access mobility management function (Access and Mobility Management Function, AMF), session management function (Session Management Function, SMF), user plane function (User Plane Function, UPF), policy control function (Policy Control Function, PCF), policy and charging rules function unit (Policy and Charging Rules Function, PCRF), edge application service discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home user server (Home Subscriber Server, HSS), centralized network configuration (CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), binding support function (Binding Support Function, BSF), application function (Application Function, AF), etc. Yes, in the embodiments of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.

First, the nouns and application scenarios involved in the embodiments of the present application are introduced:

1. The AI unit/AI model described in the embodiments of the present application may also be referred to as an AI unit, an AI model, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI characteristic, a machine learning model, a neural network, a neural network function, a neural network function, etc., or the AI unit/AI model may also refer to a processing unit capable of implementing specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI unit/AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI unit/AI model may be a processing method, algorithm, function, module or unit running on AI/ML related hardware such as a graphics processing unit (GPU), a neural network processing unit (NPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), etc., and the present invention does not specifically limit this. Optionally, the specific data set includes the input and/or output of the AI unit/AI model.

Optionally, the identifier of the AI unit/AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI unit/AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI/ML, or an identifier of a function, feature, capability, or module related to the AI/ML, which is not specifically limited in the embodiments of the present application.

2. There are many ways to implement AI models, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application uses neural networks as an example for illustration, but does not limit the specific type of AI models.

Optionally, the neural network is composed of neurons, and a schematic diagram of neurons is shown in FIG1b. Where a ₁ , a ₂ , … a _K are inputs, w ₁ , w ₂ , … w _K are weights (multiplicative coefficients), (w ₁ , w ₂ , … w _K ) can be denoted as W, b is a bias (additive coefficient), and σ(.) is an activation function. Common activation functions include Sigmoid, tanh, Rectified Linear Unit (ReLU), etc.

Among them, z=a ₁ w ₁ +…+a _k w _k +…+a _K w _K +b.

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

At present, the common optimization algorithms are basically based on the BP (error Back Propagation) algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of the signal and the back propagation of the error. During the forward propagation, the input sample is transmitted from the input layer, processed by each hidden layer layer by layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the back propagation stage of the error. Error back propagation is to propagate the output error layer by layer through the hidden layer to the input layer in some form, and distribute the error to all units in each layer, so as to obtain the error signal of each layer unit, and this error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer of the signal forward propagation and error back propagation is repeated. The process of continuous adjustment of weights is 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 the pre-set number of learning times is reached.

Common optimization algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), Adaptive Gradient Descent (Adagrad), root mean square prop (RMSprop), Adaptive Moment Estimation (Adam), etc.

When the error is back-propagated, these optimization algorithms obtain the derivative/partial derivative of the current neuron based on the error/loss obtained by the loss function, and add the influence of the learning rate, the previous gradient/derivative/partial derivative, etc. to obtain the gradient. Pass it to the upper layer.

The information reporting method provided in the embodiment of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

Please refer to FIG. 2 , an embodiment of the present application provides an information reporting method, the execution subject of this embodiment is a first device, and the method includes:

Step 101: A first device sends first information to a second device, where the first information is used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information related to the first device and at least one of the AI function and the AI model.

Optionally, the first device may be a terminal or a network side device. When the first device is a terminal, the second device is a network side device, such as a core network device, for example, the core network device is a location management function (LMF); when the first device is a network side device, the second device is a different network side device, for example, the first device is an access network device, and the second device is a core network device, for example, the core network device is a location management function LMF.

Specifically, the first device sends information related to at least one of the AI model and the AI function, or device capability information related to at least one of the AI model and the AI function to the second device.

In the method of this embodiment, a first device sends first information to a second device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; device capability information of the first device related to at least one of the AI function and the AI model, so that the first device and the second device have a common understanding of the AI model, information related to the AI function and device capability information, so that the second device can assist the first device in performing life cycle management of the model, and better support operations such as reasoning of the first device model, thereby improving the accuracy and reliability of the model reasoning results, and thereby improving the business processing performance of the AI model.

Optionally, when the first information is used to indicate information related to the AI function, the first information includes at least one of the following:

AI function identifier, dataset identifier included in the AI function, model input data type supported by the AI function, model output data type supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, PRS configuration information supported by the AI function, inference delay range supported by the AI function, Reference Signal Received Power (RSRP) range supported by the AI function, RSRP distribution supported by the AI function, Signal to Interference plus Noise Ratio (SINR) range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, Transmission and Reception Point (TRP) identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function.

Optionally, the scenario of this embodiment is that the AI model comes from a network-side device or a non-network-side device.

Specifically, the AI function identifier is such as the ID of the positioning function based on the AI model;

Optionally, an AI function may include one or more dataset IDs, and a dataset ID may correspond to one or more AI models;

Optionally, the inference latency range supported by the AI function may include at least one of the following: maximum latency, minimum latency;

The RSRP range supported by the AI function may, for example, indicate a minimum RSRP value supported by the AI function;

The first information may include a distribution type and at least one of the parameters of the RSRP distribution, for example, the distribution type is a Gaussian distribution, and the parameters include a mean and a variance; optionally, the distribution type may be agreed upon by protocol.

The SINR range supported by the AI function, for example, may indicate the minimum SINR value supported by the AI function;

The first information may include a distribution type and at least one of parameters of the SINR distribution, for example, the distribution type is Gaussian distribution, and the parameters include mean and variance; optionally, the distribution type may be agreed upon by protocol.

Among them, implicit features can refer to features that have no actual physical meaning.

The area identifier supported by the AI function means that the AI function can be used in the area corresponding to the area identifier, for example, the AI function can be used when the terminal is in the area corresponding to the area identifier, such as the positioning function, such as the area ID includes at least one;

The TRP ID supported by the AI function means that the TRP corresponding to the TRP ID can use the AI function, such as the TRP ID can be one or more;

The cell identifier supported by the AI function means that the cell corresponding to the cell identifier can use the AI function, for example, the terminal can use the AI function when it is in the cell corresponding to the cell identifier, such as the positioning function, which can be one or more, including at least one of the following: a global cell ID and a physical cell ID;

Optionally, the PRS identifier and cell identifier may be used to determine a specific TRP.

The reasoning accuracy supported by the AI function, for example, 90% of the terminal positioning errors are less than or equal to 1 meter.

In one embodiment, the AI function includes at least N AI models, and optionally, the AI model network is not visible, for example, the model structure and parameter network of the AI model are not visible;

Optionally, the type of model input data includes at least one of the following:

Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

Specifically, the time domain channel impulse response is used to represent the multipath delay, power and phase information of the channel. The multipath delay, power and phase are used to represent the changes of the signal delay, signal power and signal phase caused by different paths of the channel respectively.

Among them, the delay power spectrum is used to represent the multipath delay and power information of the channel; the delay spectrum is used to represent the delay information of the channel.

Optionally, the type of model output data includes at least one of the following:

Time of Arrival (TOA); Reference Signal Time Difference (RSTD); First indication information; Angle of Arrival (AOA); Angle of Departure (AOD); Position coordinates; The first indication information is used to indicate whether the first device and the second device are in line-of-sight (LOS) or non-line-of-sight (NLOS).

Specifically, when the first indication information indicates "1", for example, it indicates that the terminal is in line of sight from the TRP, and "0" indicates that the terminal is in non-line of sight from the TRP; the first indication information can also be a decimal between 0 and 1, such as 0.9, which indicates line of sight when it is greater than a certain threshold, and non-line of sight when it is less than or equal to the threshold.

Optionally, the PRS configuration information includes at least one of the following:

Signal bandwidth; comb structure, time domain multipath resolution.

Specifically, a comb-like Comb structure, such as Comb 2, 4 or 6, etc.

Time domain multipath resolution refers to the resolution of two adjacent paths in a multipath scenario, that is, the minimum interval between the two paths that can be identified.

Optionally, the time domain multipath resolution is related to the PRS bandwidth, for example, the time domain multipath resolution is proportional to the inverse of the bandwidth a×1/B, where B represents the bandwidth and a represents a constant.

Optionally, the reasoning accuracy includes at least one of the following:

The highest inference accuracy corresponding to each of the different types of model output data;

For any type of the model output data, the highest inference accuracy corresponding to each of the different types of model input data.

Specifically, it can be achieved by at least one of the following methods:

1) The highest reasoning accuracy for different types of model output data supported by the AI function; for example, when the model output data is location coordinates, the supported highest reasoning accuracy is 90% and the terminal positioning error is 0.5 meters; or the average positioning error of the terminal is 0.5 meters; when the model output data is TOA, the supported highest reasoning accuracy is 95% and the terminal positioning error is 0.5 meters; or the average positioning error of the terminal is 0.5. Optionally, the highest reasoning accuracy can also be the reasoning accuracy of the position estimated by TOA based on the model output.

2) Given the type of model output data, the highest inference accuracy for different input data supported by the AI function; for example, when the model output data is position coordinates, the highest inference accuracy corresponding to the model input data when it is channel impulse response, power delay spectrum, delay spectrum or RSRP.

In the above implementation, the first device reports information related to the AI function to the second device, so that the first device and the second device can have a common understanding of the relevant information of the AI function, so that the second device can assist the life cycle management of the first device model, such as activation or deactivation management of the model, and better support the reasoning of the first device model. For example, auxiliary information can be sent to the first device to help the first device perform operations such as model reasoning, improve the accuracy and reliability of the model reasoning results, and thereby improve the business processing performance of the AI model, such as positioning performance.

Optionally, all or part of the first information is reported through the capability of the first device.

Optionally, the first information also includes: a prerequisite feature group for supporting the AI function group).

Optionally, the first information also includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

For example, the terminal capability is positioning capability, and the feature group supporting the positioning capability corresponds to the feature group serial number (index is 13-1, 13-1a, 13-2, 13-2a, etc.). For feature group 13-2 (DL PRS resources for DL AOD), the prerequisite feature group is, for example, 13-1 (DLPRS processing capability).

Optionally, when the first information is used to indicate AI model related information, the first information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model.

The above information is similar to the information related to the AI function in the aforementioned embodiment and will not be repeated here.

In the above implementation, the first device reports AI model-related information to the second device, so that the first device and the second device can have a common understanding of the relevant information of the AI model, so that the second device can assist the first device model lifecycle management, and better support the reasoning and other operations of the first device model, thereby improving the accuracy and reliability of the model reasoning results, and thereby improving the business processing performance of the AI model, such as positioning performance.

Optionally, the first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information.

Specifically, the information can be indicated by a model identifier (ID). For example, the first information is a relatively long code (which can be a fixed length or a variable length). Codes at different positions represent different meanings. As shown in FIG3 , bits 1-10 represent the global AI model ID, bits 11-14 represent the data set ID, bits 15-20 represent the model output type, and so on.

Optionally, all or part of the information included in the first information is reported via device capabilities;

Optionally, the encoding length of the first information and the meaning of each field may be agreed upon by a protocol.

In the above implementation, different parameters are represented by encoding at different positions of the first information, and the implementation is simple.

Optionally, model information can be reported in the following ways:

Method 1 (reporting the association information between models):

After the first device sends the first information of the first AI model to the second device, the method further includes:

If the first information of the second AI model has at least one parameter that is the same as that of the first AI model, the first device sends the first information of the second AI model to the second device, and the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model.

Method 2 (feature-centric reporting):

In a case where the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes:

The AI model identifier corresponding to the same parameters.

Specifically, some information of multiple AI models may be the same. For example, multiple AI models have the same SINR range. If the information of each AI model is reported separately, this part of the information may be reported repeatedly. To solve this problem, the above method 1 and method 2 can be used:

For method 1, if the terminal reports the information of model 1, and the model input type and output type of model 2 are the same as those of model 1, then when the terminal reports the information of model 2, it no longer reports the same information as model 1, but sends the association information between model 2 and model 1, for example, adding an indication associated with model 1 in the first information, indicating that the default information is the same as the corresponding information of model 1, and the first information may also include: the difference information between model 2 and model 1, such as TRP identification, inference accuracy, etc.

For method 2, for example, the AI models contained in the dataset ID 1 include model 1, model 2, and model Type 3, the model input data is the channel impulse response, and the AI model IDs included include model 1, model 4, model 5, and so on.

For example, the second model uses defaults at the same parameter position, which is used to indicate that the corresponding parameters of the previous model are continued to be used, and the format is as follows: [xxx][xxx][default][default]; each [] represents a parameter, and [xxx][xxx] represents the difference information from the previous model; or the corresponding parameters of the Nth model (indicated by the model ID) are continued to be used; the format is as follows: [xxx][xxx][default][default], model ID, the model ID is the ID of the associated model, that is, the associated information.

In the above implementation, by reporting the association information between models, or reporting the model information based on features, signaling overhead and resources can be saved.

Optionally, for the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group that supports the first parameter.

Optionally, for the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the AI function-related information and the AI model-related information in the first information may be divided into two types of information, that is, the first information includes at least one of the following: second information and third information, the second information is used to indicate the AI function-related information, and the third information is used to indicate the AI model-related information;

The second information includes at least one of the following:

AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function;

The third information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model.

The above information is similar to the information related to the AI function in the aforementioned embodiment and will not be repeated here.

Optionally, the second information is reported through the capability of the first device, and the third information is carried through the second information through Radio Resource Control (RRC) signaling or Media Access Control (MAC)-Control Element (CE) or Long Term Evolution Positioning Protocol (LTE Positioning Protocol, LPP) signaling.

Optionally, the device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following: a support capability for model deployment; a measurement capability for model input data;

The support capability for model deployment includes at least one of the following:

The maximum supported model computational complexity;

The maximum number of model parameters supported;

The maximum number of models supported;

Model storage capabilities;

Computational capacity;

whether to support receiving the model from the second device;

whether to support deployment of the model from the second device;

Compilation capabilities for AI models;

The ability to measure model input data includes at least one of the following:

The data types supported for measurement;

The maximum number of paths supported for measurement;

The maximum number of additional paths supported for measurement;

Maximum number of channel taps supported for measurement;

The maximum number of TRPs supported for measurement;

A PRS that supports the maximum number of TRPs measured within a preset measurement time window;

Supported preprocessing methods for model input data.

Specifically, support capabilities for model deployment, such as support capabilities related to computing power and storage;

For example, the maximum AI model computational complexity supported by the terminal is 20 million floating point operations per second. Floating-point Operations per Second, MFLOPs);

For example, the maximum model parameter amount supported by the terminal is 200KB. Optionally, the model parameter amount can be divided into levels based on different parameter scales, such as A: <100k, B: 100k~1M, C: >1M. Correspondingly, the maximum model parameter amount supported by the terminal can be expressed by level, for example, the maximum model parameter amount supported by the terminal is level B.

Optionally, the data types supported for measurement are consistent with the types of model input data, including at least one of the following: time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

The maximum number of additional paths refers to the number of paths other than the primary path, for example;

The maximum number of TRPs supported for measurement, for example, refers to the maximum number of TRPs for one positioning;

Among them, the preset measurement time window refers to the measurement time window for measuring the PRS of the maximum TRP number TRP, that is, the PRS of the maximum TRP number TRP needs to be configured within the measurement time window.

Optionally, the preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization.

Optionally, the truncation process involves, for example, at least one of the following information: truncation length, position, and other information.

Optionally, the FFT information includes, for example, a window length of the FFT.

Optionally, the scenario of this embodiment may be that the AI model of the first device comes from the second device.

In the above implementation, the first device reports device capability information to the second device, so that the first device and the second device can have a common understanding of the device capability information of the first device, so that the second device can assist in the life cycle management of the first device model, and better support the reasoning and other operations of the first device model, thereby improving the accuracy and reliability of the model reasoning results, and thereby improving the business processing performance of the AI model, such as positioning performance.

For example, for AI positioning, the terminal side models are divided into two types:

Case 1: AI/ML Direct Positioning Model

The model input data includes, for example, the following types: time domain channel impulse response CIR, delay power spectrum PDP or delay spectrum DP;

The type of model output data is position;

Case 2: AI/ML assisted positioning model

The model input data includes, for example, the following types: time domain channel impulse response CIR, delay power spectrum PDP or delay spectrum DP;

Model output data includes, for example, the following types: TOA, RSTD, or LoS/NLoS, etc.;

For example, the model source at the terminal includes the following cases:

a) Model transfer from NW side;

b) Model delivery from non-NW side;

Specifically, for a), the terminal side model comes completely from the network NW side, so the NW side has all the information of the terminal side model, and the terminal can report the device capability information in the first information to the network side device; for b), the terminal side model comes completely from the non-NW side, so the NW side has no information of the UE side model, and the terminal can report the first information to the network side device, such as AI function-related information, AI model-related information, and device capability information.

Exemplarily, AI model related information can be divided into two categories:

a) Representation information of the model, which is used to reconstruct the model. The representation information of the model includes the model structure, parameters, computational complexity, and parameter quantity;

b) Underlying information of the model, which is used to represent the relevant information of the dataset used to train the model;

For example, the AI model identifier, the inference delay range of the AI model, the RSRP range applicable to the AI model, the RSRP distribution applicable to the AI model, the SINR range applicable to the AI model, the SINR distribution applicable to the AI model, the implicit feature distribution applicable to the AI model, the implicit feature acquisition method applicable to the AI model, the number of TRPs supported by the AI model, the number of cells supported by the AI model, the inference accuracy of the AI model, the model calculation complexity, the number of model parameters, etc. can be called the characterization information of the model.

For example, the dataset identifier associated with the AI model, the type of model input data supported by the AI model, the type of model output data supported by the AI model, etc. can be called the potential information of the model.

It should be noted that the developer of the model owns all the information of the model. The representation information of the model is just a bunch of parameters and operation logic. The potential information of the model gives the model a soul.

The dataset related information includes:

a) Input and output information (basic information, also called static information): In essence, AI model training learns the relationship between input and output. This information is reflected in the model parameters, including the type of model input data, the type of model output data; size; preprocessing; post-processing, etc.

b) Data set description information (also called dynamic information): used to describe the attributes of the data set, such as area ID, SINR range, error distribution, etc.

It should be noted that the input and output information is the basic information required for the model to run; the descriptive information of the data set is related to the generalization and applicability conditions of the model.

FIG. 4 is a second flow chart of the information reporting method provided in an embodiment of the present application. As shown in FIG. 4 , the method is applied to a second device, including:

Step 201: The second device receives first information sent by the first device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; device capability information related to the first device and at least one of the AI function and the AI model.

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

AI function identifier, data set identifier included in the AI function, type of model input data supported by the AI function, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, configuration information of PRS supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function.

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

AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model.

Optionally, the first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information;

The second information includes at least one of the following:

AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function;

The third information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model.

Optionally, the type of the model input data includes at least one of the following:

Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

Optionally, the type of the model output data includes at least one of the following:

Arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate that the first device and the second device are in line-of-sight LOS or non-line-of-sight NLOS.

Optionally, the PRS configuration information includes at least one of the following:

Signal bandwidth; comb structure, time domain multipath resolution.

Optionally, the reasoning accuracy includes at least one of the following:

The highest inference accuracy corresponding to each of the different types of model output data;

For any type of the model output data, the highest inference accuracy corresponding to each of the different types of model input data.

Optionally, the AI function includes at least one AI model.

Optionally, the first information is reported through the capability of the first device.

Optionally, the first information also includes: a prerequisite feature group that supports the AI function.

Optionally, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information.

Optionally, after the second device receives the first information of the first AI model sent by the first device, the method further includes:

If the first information of the second AI model has at least one parameter that is the same as that of the first AI model, the second device receives the first information of the second AI model sent by the first device, and the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model.

Optionally, when the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes:

The AI model identifier corresponding to the same parameters.

Optionally, for the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group that supports the first parameter.

Optionally, for the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling.

Optionally, the device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following:

Ability to support model deployment;

The ability to measure model input data;

The support capability for model deployment includes at least one of the following:

The maximum supported model computational complexity;

The maximum number of model parameters supported;

The maximum number of models supported;

Model storage capabilities;

Computational capacity;

whether to support receiving the model from the second device;

whether to support deployment of the model from the second device;

Ability to compile AI models;

The ability to measure model input data includes at least one of the following:

The data types supported for measurement;

The maximum number of paths supported for measurement;

The maximum number of additional paths supported for measurement;

Maximum number of channel taps supported for measurement;

The maximum number of TRPs supported for measurement;

A PRS that supports the maximum number of TRPs measured within a preset measurement time window;

Supported preprocessing methods for model input data.

Optionally, the preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization.

The specific implementation process and technical effects of the method of this embodiment are similar to those in the first device side method embodiment. For details, please refer to the detailed introduction in the first device side method embodiment, and no further details will be given here.

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

FIG. 5 is one of the structural diagrams of the information reporting device provided in the embodiment of the present application. As shown in FIG. 5 , the information reporting device is applied to the first device, including:

The sending module 110 is used to send first information to the second device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; device capability information of the first device related to at least one of the AI function and the AI model.

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

AI function identifier, data set identifier included in the AI function, type of model input data supported by the AI function, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, configuration information of PRS supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function.

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

AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model.

Optionally, the first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information;

The second information includes at least one of the following:

AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function;

The third information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model.

Optionally, the type of the model input data includes at least one of the following:

Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

Optionally, the type of the model output data includes at least one of the following:

Arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate that the first device and the second device are in line-of-sight LOS or non-line-of-sight NLOS.

Optionally, the PRS configuration information includes at least one of the following:

Signal bandwidth; comb structure, time domain multipath resolution.

Optionally, the reasoning accuracy includes at least one of the following:

The highest inference accuracy corresponding to each of the different types of model output data;

For any type of the model output data, the highest inference accuracy corresponding to each of the different types of model input data.

Optionally, the AI function includes at least one AI model.

Optionally, the first information is reported through the capability of the first device.

Optionally, the first information also includes: a prerequisite feature group supporting the AI function.

Optionally, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information.

Optionally, the sending module 110 is further configured to:

After the first device sends first information of the first AI model to the second device, if the first information of the second AI model has at least one parameter the same as that of the first AI model, the first information of the second AI model is sent to the second device, and the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model.

Optionally, when the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes:

The AI model identifier corresponding to the same parameters.

Optionally, for the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group that supports the first parameter.

Optionally, for the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling.

Optionally, the device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following:

Ability to support model deployment;

The ability to measure model input data;

The support capability for model deployment includes at least one of the following:

The maximum supported model computational complexity;

The maximum number of model parameters supported;

The maximum number of models supported;

Model storage capabilities;

Computational capacity;

whether to support receiving the model from the second device;

whether to support deployment of the model from the second device;

Ability to compile AI models;

The ability to measure model input data includes at least one of the following:

The data types supported for measurement;

The maximum number of paths supported for measurement;

The maximum number of additional paths supported for measurement;

Maximum number of channel taps supported for measurement;

The maximum number of TRPs supported for measurement;

A PRS that supports the maximum number of TRPs measured within a preset measurement time window;

Supported preprocessing methods for model input data.

Optionally, the preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization.

The device of this embodiment can be used to execute the method of any one of the embodiments in the aforementioned first device side method embodiment. Its specific implementation process and technical effects are similar to those in the first device side method embodiment. For details, please refer to the detailed introduction in the first device side method embodiment, which will not be repeated here.

FIG. 6 is a second structural diagram of an information reporting device provided in an embodiment of the present application. As shown in FIG. 6 , the information reporting device is applied to a second device, including:

The receiving module 210 is used to receive first information sent by a first device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; and device capability information related to the first device and at least one of the AI function and the AI model.

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

AI function identifier, data set identifier included in the AI function, type of model input data supported by the AI function, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, configuration information of PRS supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function.

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

AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model.

Optionally, the first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information;

The second information includes at least one of the following:

AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function;

The third information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model.

Optionally, the type of the model input data includes at least one of the following:

Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

Optionally, the type of the model output data includes at least one of the following:

Arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate that the first device and the second device are in line-of-sight LOS or non-line-of-sight NLOS.

Optionally, the PRS configuration information includes at least one of the following:

Signal bandwidth; comb structure, time domain multipath resolution.

Optionally, the reasoning accuracy includes at least one of the following:

The highest inference accuracy corresponding to each of the different types of model output data;

For any type of the model output data, the highest inference accuracy corresponding to each of the different types of model input data.

Optionally, the AI function includes at least one AI model.

Optionally, the first information is reported through the capability of the first device.

Optionally, the first information also includes: a prerequisite feature group that supports the AI function.

Optionally, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information.

Optionally, the receiving module 210 is further configured to:

After the second device receives the first information of the first AI model sent by the first device, if the first information of the second AI model has at least one parameter that is the same as that of the first AI model, receiving The first information of the second AI model sent by the first device, wherein the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model.

Optionally, when the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes:

The AI model identifier corresponding to the same parameters.

Optionally, for the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group that supports the first parameter.

Optionally, for the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling.

Optionally, the device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following:

Ability to support model deployment;

The ability to measure model input data;

The support capability for model deployment includes at least one of the following:

The maximum supported model computational complexity;

The maximum number of model parameters supported;

The maximum number of models supported;

Model storage capabilities;

Computational capacity;

whether to support receiving the model from the second device;

whether to support deployment of the model from the second device;

Ability to compile AI models;

The ability to measure model input data includes at least one of the following:

The data types supported for measurement;

The maximum number of paths supported for measurement;

The maximum number of additional paths supported for measurement;

Maximum number of channel taps supported for measurement;

The maximum number of TRPs supported for measurement;

A PRS that supports the maximum number of TRPs measured within a preset measurement time window;

Supported preprocessing methods for model input data.

Optionally, the preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization.

The device of this embodiment can be used to execute the method of any one of the embodiments in the aforementioned second device side method embodiment. Its specific implementation process and technical effects are similar to those in the second device side method embodiment. For details, please refer to the detailed introduction in the second device side method embodiment, which will not be repeated here.

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

The information reporting device provided in the embodiment of the present application can implement the various processes implemented by the method embodiments of Figures 2 to 4 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

As shown in FIG7 , the embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702, wherein the memory 702 stores a program or instruction that can be run on the processor 701. For example, when the communication device 700 is a terminal, the program or instruction is executed by the processor 701 to implement the various steps of the above-mentioned information reporting method embodiment, and can achieve the same technical effect. When the communication device 700 is a network side device, the program or instruction is executed by the processor 701 to implement the various steps of the above-mentioned information reporting method embodiment, and can achieve the same technical effect. To avoid repetition, the technical effects will not be described here.

The embodiment of the present application also provides a terminal, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps in the method embodiment shown in Figure 2. This terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the terminal embodiment, and can achieve the same technical effect. Specifically, Figure 8 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application. The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, and at least some of the components of the processor 810.

Those skilled in the art will appreciate that the terminal 800 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 810 through a power management system, so as to implement functions such as managing charging, discharging, and power consumption management through the power management system. The terminal structure shown in FIG8 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently, which will not be described in detail here.

It should be understood that in the embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of the static picture or video obtained by the image capture device (such as a camera) in the video capture mode or the image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

In the embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 801 can transmit the data to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device. Generally, the radio frequency unit 801 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

The memory 809 can be used to store software programs or instructions and various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instruction required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 809 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memories.

The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 810.

Among them, the radio frequency unit 801 is used to send first information to the second device, and the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; device capability information related to the terminal and at least one of the AI function and the AI model.

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

AI function identifier, dataset identifier included in the AI function, and model input data type supported by the AI function type, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, PRS configuration information supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function.

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

AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model.

Optionally, the first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information;

The second information includes at least one of the following:

AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function;

The third information includes at least one of the following:

AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model.

Optionally, the type of the model input data includes at least one of the following:

Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP.

Optionally, the type of the model output data includes at least one of the following:

Arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate that the first device and the second device are in line-of-sight LOS or non-line-of-sight NLOS.

Optionally, the PRS configuration information includes at least one of the following:

Signal bandwidth; comb structure, time domain multipath resolution.

Optionally, the reasoning accuracy includes at least one of the following:

The highest inference accuracy corresponding to each of the different types of model output data;

For any type of the model output data, the highest inference accuracy corresponding to each of the different types of model input data.

Optionally, the AI function includes at least one AI model.

Optionally, the first information is reported through the capability of the first device.

Optionally, the first information also includes: a prerequisite feature group that supports the AI function.

Optionally, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information.

Optionally, the radio frequency unit 801 is further configured to:

After sending the first information of the first AI model to the second device, if the first information of the second AI model has at least one parameter the same as that of the first AI model, sending the first information of the second AI model to the second device, where the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, difference between the first AI model and the second AI model Related information.

Optionally, when the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes:

The AI model identifier corresponding to the same parameters.

Optionally, for the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group that supports the first parameter.

Optionally, for the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device.

Optionally, the second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling.

Optionally, the device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following:

Ability to support model deployment;

The ability to measure model input data;

The support capability for model deployment includes at least one of the following:

The maximum supported model computational complexity;

The maximum number of model parameters supported;

The maximum number of models supported;

Model storage capabilities;

Computational capacity;

whether to support receiving the model from the second device;

whether to support deployment of the model from the second device;

Ability to compile AI models;

The ability to measure model input data includes at least one of the following:

The data types supported for measurement;

The maximum number of paths supported for measurement;

The maximum number of additional paths supported for measurement;

Maximum number of channel taps supported for measurement;

The maximum number of TRPs supported for measurement;

A PRS that supports the maximum number of TRPs measured within a preset measurement time window;

Supported preprocessing methods for model input data.

Optionally, the preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization.

It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment shown in Figure 2, and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

The embodiment of the present application also provides a network side device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the steps of the method embodiment shown in Figure 2 or Figure 4. The network side device embodiment corresponds to the above-mentioned network side device method embodiment, and each implementation process and implementation method of the above-mentioned method embodiment can be applied to the network side device embodiment, and can achieve the same technical effect.

Specifically, the embodiment of the present application also provides a network side device. As shown in Figure 9, the network side device 900 includes: an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94 and a memory 95. The antenna 91 is connected to the radio frequency device 92. In the uplink direction, the radio frequency device 92 receives information through the antenna 91 and sends the received information to the baseband device 93 for processing. In the downlink direction, the baseband device 93 processes the information to be sent and sends it to the radio frequency device 92. The radio frequency device 92 processes the received information and sends it out through the antenna 91.

The method executed by the network-side device in the above embodiment may be implemented in the baseband device 93, which includes a baseband processor.

The baseband device 93 may include, for example, at least one baseband board, on which a plurality of chips are arranged, as shown in FIG9 , one of the chips is, for example, a baseband processor, which is connected to the memory 95 via a bus interface to call The program in the memory 95 executes the network device operations shown in the above method embodiments.

The network side device may also include a network interface 96, which is, for example, a Common Public Radio Interface (CPRI).

Specifically, the network side device 900 of the embodiment of the present application also includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute the methods executed by the modules shown in Figure 5 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

Specifically, the embodiment of the present application further provides a network side device. As shown in FIG10 , the network side device 1000 includes: a processor 1001, a network interface 1002 and a memory 1003. The network interface 1002 is, for example, a common public radio interface (CPRI).

Specifically, the network side device 1000 of the embodiment of the present application also includes: instructions or programs stored in the memory 1003 and executable on the processor 1001. The processor 1001 calls the instructions or programs in the memory 1003 to execute the method executed by each module shown in Figure 6 and achieves the same technical effect. To avoid repetition, it will not be repeated here.

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

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

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

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

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

An embodiment of the present application also provides a communication system, including: a first device and a second device, wherein the first device can be used to execute the steps of the information reporting method described above, and the second device can be used to execute the steps of the information reporting method described above.

It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment 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 reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, it can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including several instructions to enable the terminal or network side device to execute the method described in each embodiment of the present application.

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

Claims (45)

An information reporting method, comprising: The first device sends first information to the second device, where the first information is used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information of the first device related to at least one of the AI function and the AI model. The method according to claim 1, wherein The first information includes at least one of the following: AI function identifier, data set identifier included in the AI function, type of model input data supported by the AI function, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, configuration information of PRS supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function. The method according to claim 1, wherein The first information includes at least one of the following: AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model. The method according to claim 1, wherein The first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information; The second information includes at least one of the following: AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function; The third information includes at least one of the following: AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model. The method according to any one of claims 2 to 4, wherein: The type of the model input data includes at least one of the following: Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP. The method according to any one of claims 2 to 4, wherein: The type of the model output data includes at least one of the following: Arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate that the first device and the second device are in line-of-sight LOS or non-line-of-sight NLOS. The method according to any one of claims 2 to 4, wherein: The PRS configuration information includes at least one of the following: Signal bandwidth; comb structure, time domain multipath resolution. The method according to any one of claims 2 to 4, wherein: The reasoning accuracy includes at least one of the following: The highest inference accuracy corresponding to different types of model output data; For any type of model output data, the highest inference accuracy corresponding to different types of model input data. The method according to any one of claims 1 to 8, wherein: The AI function includes at least one AI model. The method according to any one of claims 2 or 5-8, wherein the first information is reported through the capability of the first device. The method according to claim 10, wherein The first information also includes: a prerequisite feature group supporting the AI function. The method according to claim 10, wherein The first information also includes: a feature group serial number corresponding to the feature group supporting the capability of the first device. The method according to any one of claims 3 or 5-8, wherein: The first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information. The method according to any one of claims 3 or 5-8, wherein, after the first device sends the first information of the first AI model to the second device, the method further comprises: If the first information of the second AI model has at least one parameter that is the same as that of the first AI model, the first device sends the first information of the second AI model to the second device, and the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model. The method according to any one of claims 3 or 5-8, wherein: In a case where the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes: The AI model identifier corresponding to the same parameters. The method according to any one of claims 3, 5-8 or 13-15, wherein: For the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group supporting the first parameter. The method according to any one of claims 3, 5-8 or 13-15, wherein: For the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device. The method according to claim 4, wherein The second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling. The method according to any one of claims 1 to 18, wherein: The device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following: Ability to support model deployment; The ability to measure model input data; The support capability for model deployment includes at least one of the following: The maximum supported model computational complexity; The maximum number of model parameters supported; The maximum number of models supported; Model storage capabilities; Computational capacity; whether to support receiving the model from the second device; whether to support deployment of the model from the second device; Ability to compile AI models; The ability to measure model input data includes at least one of the following: The data types supported for measurement; The maximum number of paths supported for measurement; The maximum number of additional paths supported for measurement; Maximum number of channel taps supported for measurement; The maximum number of TRPs supported for measurement; A PRS that supports the maximum number of TRPs measured within a preset measurement time window; Supported preprocessing methods for model input data. The method according to claim 19, wherein The preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization. A method for receiving information, comprising: The second device receives first information sent by the first device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; and device capability information of the first device related to at least one of the AI function and the AI model. The method according to claim 21, wherein The first information includes at least one of the following: AI function identifier, data set identifier included in the AI function, type of model input data supported by the AI function, type of model output data supported by the AI function, downlink positioning reference signal PRS identifier supported by the AI function, configuration information of PRS supported by the AI function, inference delay range supported by the AI function, reference signal received power RSRP range supported by the AI function, RSRP distribution supported by the AI function, signal to interference and noise ratio SINR range supported by the AI function, SINR distribution supported by the AI function, implicit feature distribution supported by the AI function, implicit feature acquisition method supported by the AI function, area identifier supported by the AI function, transmission receiving point TRP identifier supported by the AI function, cell identifier supported by the AI function, number of TRPs supported by the AI function, number of cells supported by the AI function, inference accuracy supported by the AI function. The method according to claim 21, wherein The first information includes at least one of the following: AI model identifier, dataset identifier associated with the AI model, type of model input data supported by the AI model, type of model output data supported by the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, inference delay range of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, inference accuracy of the AI model. The method according to claim 21, wherein The first information includes at least one of the following: second information and third information, the second information is used to indicate AI function related information, and the third information is used to indicate AI model related information; The second information includes at least one of the following: AI function identification, model input data types supported by the AI function, model output data types supported by the AI function, and inference latency range supported by the AI function; The third information includes at least one of the following: AI model identifier, dataset identifier associated with the AI model, downlink positioning reference signal PRS identifier of the AI model, configuration information of the PRS of the AI model, RSRP range applicable to the AI model, RSRP distribution applicable to the AI model, SINR range applicable to the AI model, SINR distribution applicable to the AI model, implicit feature distribution applicable to the AI model, implicit feature acquisition method applicable to the AI model, area identifier applicable to the AI model, transmission receiving point TRP identifier applicable to the AI model, cell identifier applicable to the AI model, number of TRPs supported by the AI model, number of cells supported by the AI model, and inference accuracy of the AI model. The method according to any one of claims 22 to 24, wherein: The type of the model input data includes at least one of the following: Time domain channel impulse response; delay power spectrum; delay spectrum; RSRP. The method according to any one of claims 22 to 24, wherein: The type of the model output data includes at least one of the following: arrival time TOA; reference signal time difference RSTD; first indication information; arrival angle AOA; departure angle AOD; position coordinates; the first indication information is used to indicate the position between the first device and the second device In line-of-sight (LOS) or non-line-of-sight (NLOS). The method according to any one of claims 22 to 24, wherein: The PRS configuration information includes at least one of the following: Signal bandwidth; comb structure, time domain multipath resolution. The method according to any one of claims 22 to 24, wherein: The reasoning accuracy includes at least one of the following: The highest inference accuracy corresponding to different types of model output data; For any type of model output data, the highest inference accuracy corresponding to different types of model input data. The method according to any one of claims 21 to 28, wherein: The AI function includes at least one AI model. The method according to any one of claims 22 or 25-28, wherein the first information is reported through the capability of the first device. The method according to claim 30, wherein The first information also includes: a prerequisite feature group supporting the AI function. The method according to claim 30, wherein The first information also includes: a feature group serial number corresponding to the feature group supporting the capability of the first device. The method according to any one of claims 23 or 25-28, wherein: The first information is represented by information of a preset length obtained by encoding, and information at different positions in the information of the preset length is used to represent different parameters in the first information. The method according to any one of claims 23 or 25-28, wherein, after the second device receives the first information of the first AI model sent by the first device, the method further comprises: If the first information of the second AI model has at least one parameter that is the same as that of the first AI model, the second device receives the first information of the second AI model sent by the first device, and the first information of the second AI model includes at least one of the following: difference information between the second AI model and the first AI model, and association information between the first AI model and the second AI model. The method according to any one of claims 23 or 25-28, wherein: In a case where the first information includes information of multiple AI models, and the information of the multiple AI models has the same parameters, the first information includes: The AI model identifier corresponding to the same parameters. The method according to any one of claims 23, 25-28 or 3-35, wherein: For the first parameter in the first information reported through the capability of the first device, the first information further includes: a prerequisite feature group supporting the first parameter. The method according to any one of claims 23, 25-28 or 3-35, wherein: For the first information reported through the capability of the first device, the first information further includes: a feature group serial number corresponding to a feature group supporting the capability of the first device. The method according to claim 24, wherein The second information is reported through the capability of the first device, and the third information is carried through the second information through radio resource control RRC signaling or media access control MAC-control element CE or long term evolution positioning protocol LPP signaling. The method according to any one of claims 21 to 38, wherein: The device capability information of the first device related to at least one of the AI function and the AI model includes at least one of the following: Ability to support model deployment; The ability to measure model input data; The support capability for model deployment includes at least one of the following: The maximum supported model computational complexity; The maximum number of model parameters supported; The maximum number of models supported; Model storage capabilities; Computational capacity; whether to support receiving the model from the second device; whether to support deployment of the model from the second device; Ability to compile AI models; The ability to measure model input data includes at least one of the following: The data types supported for measurement; The maximum number of paths supported for measurement; The maximum number of additional paths supported for measurement; Maximum number of channel taps supported for measurement; The maximum number of TRPs supported for measurement; Supported preprocessing methods for model input data. The method according to claim 39, wherein The preprocessing method of the model input data includes at least one of the following: truncation processing, fast Fourier transform FFT and normalization. An information reporting device, comprising: A sending module is used to send first information to a second device, where the first information is used to indicate at least one of the following types of information: information related to the AI function; information related to the AI model; and device capability information related to the first device and at least one of the AI function and the AI model. An information receiving device, comprising: A receiving module is used to receive first information sent by a first device, where the first information is used to indicate at least one of the following types of information: information related to an AI function; information related to an AI model; and device capability information related to the first device and at least one of the AI function and the AI model. A first device comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information reporting method as described in any one of claims 1 to 20 are implemented. A second device comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information receiving method as described in any one of claims 21 to 40 are implemented. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the information reporting method as described in any one of claims 1 to 20, or implements the steps of the information receiving method as described in any one of claims 21 to 40.