WO2025092998A1 - Information transmission method, apparatus and device - Google Patents

Abstract

The present application relates to the field of communications, and discloses an information transmission method, apparatus and device. The information transmission method of embodiments of the present application comprises: a first device sends at least one set of first information of a first feature to a second device, wherein the first feature is related to position information, and the first information is used for representing uncertainty or probability distribution of the output of a first AI model. In the embodiments of the present application, accurate first information (such as timing quality) can be obtained by means of processing of the first AI model, the first information is used for representing the uncertainty or probability distribution of the output of the first AI model, and more accurate position information can be obtained on the basis of at least one set of first information of the first feature, so that the positioning precision can be significantly improved, and the problem of inaccurate timing quality estimation can be solved.

Description

Information transmission method, device and equipment

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese patent application filed with the China Patent Office on November 3, 2023, with application number 202311467238.0 and invention name “Information Transmission Method, Device and Equipment”, all contents of which are incorporated by reference in this application.

Technical Field

The present application relates to the field of communications, and more specifically, to an information transmission method, apparatus and device.

Background Art

In the New Radio (NR) system, during the positioning process, the positioning accuracy can be improved by reporting the timing quality. However, at present, the timing quality estimation is inaccurate, and how to accurately estimate the timing quality is a problem that needs to be solved.

Summary of the invention

The embodiments of the present application provide an information transmission method, apparatus and device. Accurate first information (such as timing quality) can be obtained after being processed by a first AI model. The first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. At least one set of first information based on the first feature can obtain more accurate location information, thereby significantly improving positioning accuracy and further solving the problem of inaccurate timing quality estimation.

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

The first device sends at least one set of first information of a first feature to the second device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first AI model.

In a second aspect, an information transmission method is provided, comprising:

The second device receives at least one set of first information of the first characteristic from the first device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first AI model.

In a third aspect, an information transmission device is provided, comprising:

a transceiver unit, configured to send at least one set of first information of a first characteristic to a second device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

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

a transceiver unit, configured to receive at least one set of first information of a first feature from a first device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

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

In a sixth aspect, an information transmission device is provided, comprising a processor and a communication interface, wherein the communication interface is used to send at least one set of first information of a first feature to a second device; wherein the first feature is related to location information, and the first information is used to characterize the uncertainty or probability distribution of the output of a first artificial intelligence AI model.

In a seventh aspect, an information transmission device is provided, the information transmission device comprising a transceiver, a processor and a storage The memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

In an eighth aspect, an information transmission device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive at least one set of first information of a first feature from a first device; wherein the first feature is related to location information, and the first information is used to characterize the uncertainty or probability distribution of the output of a first artificial intelligence 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 transmission method as described in the first aspect or the second aspect.

In an embodiment of the present application, accurate first information (such as timing quality) can be obtained after processing by the first AI model. The first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. At least one set of first information based on the first feature can obtain more accurate location information, thereby significantly improving positioning accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

FIG1 is a schematic diagram of a communication system architecture provided in an embodiment of the present application.

FIG2 is a schematic diagram of a neural network provided by the present application.

FIG. 3 is a schematic diagram of a neuron provided in the present application.

FIG4 is a schematic flowchart of an information transmission method provided according to an embodiment of the present application.

FIG5 is a schematic diagram of improving positioning accuracy based on soft information according to an embodiment of the present application.

FIG6 is a schematic block diagram of an information transmission device provided according to an embodiment of the present application.

FIG. 7 is a schematic block diagram of another information transmission device provided according to an embodiment of the present application.

FIG8 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

FIG9 is a schematic diagram of the hardware structure of a terminal provided according to an embodiment of the present application.

FIG10 is a schematic block diagram of a network-side device provided according to an embodiment of the present application.

FIG. 11 is a schematic block diagram of another network-side device provided 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. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the terms "first" and "second" are used to distinguish similar objects, and are not used to describe a specific order or sequence. The word "or" in this application means 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 before and after are in an "or" relationship.

The term "indication" in this application can be 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 Ambient Internet of Things (IoT) system, but can also be used in other wireless communication systems, such as Long Term Evolution (LTE)/LTE-Advanced (LTE-A) system, 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), Wireless Local Area Networks (WLAN), Wireless Fidelity (WiFi), Bluetooth system, 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 terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as ^6th Generation (6G) communication systems.

Fig. 1 shows a block diagram of a wireless communication system applicable to the embodiments of the present application. The wireless communication system includes a terminal 11 and a network side device 12, wherein the terminal 11 can communicate with the network side device 12 directly or through other network elements.

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.

Among them, the access network equipment can also be called Radio Access Network (RAN) equipment, Radio Access Network function or Radio Access Network unit. The access network equipment can include base stations, Wireless Local Area Network (WLAN) access points (AS) or Wireless Fidelity (WiFi) nodes, etc. Among them, the base station may be referred to as a Node B (NB), an evolved Node B (eNB), the next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a Relay Base Station (RBS), a Serving Base Station (SBS), a Base Transceiver Station (BTS), a radio base station, a radio transceiver, a Basic Service Set (BSS), an Extended Service Set (ESS), a home Node B (HNB), a home evolved Node B (home evolved Node B), a Transmission Reception Point (TRP), or some other appropriate term in the art. As long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiments of the present application, only the base station in the NR system is introduced as an example, and the specific type of the base station is not limited.

Among them, the core network equipment may include but is not limited to at least one of the following: core network nodes, core network functions, 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 (U nified Data Management, UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (L-NEF), Binding Support Function (BSF), Application Function (AF), Location Management Function (LMF), etc. It should be noted that, in the embodiments of the present application, only the core network device in the NR system is taken as an example for introduction, and the specific type of the core network device is not limited.

In order to facilitate a better understanding of the embodiments of the present application, the technologies related to the present application are explained.

Artificial intelligence (AI) has been widely used in various fields. Integrating artificial intelligence into wireless communication networks and significantly improving technical indicators such as throughput, latency, and user capacity are important tasks for future wireless communication networks. There are many ways to implement AI modules, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application takes neural networks as an example for illustration, but does not limit the specific type of AI modules.

An exemplary neural network can be shown in FIG2 , where the neural network is composed of neurons, and the neurons can be shown in FIG3 , where α ₁ , α ₂ , … α _K are inputs, w is a weight (multiplicative coefficient), b is a bias (additive coefficient), and σ(.) is an activation function. Common activation functions include Sigmoid, tanh, Rectified Linear Unit (ReLU), and the like.

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 (sometimes 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, we build a neural network model f(.). With the model, we can get the predicted output f(x) based on the input x, and we can calculate the difference between the predicted value and the true value (f(x)-Y), which is the loss function. The purpose of model training is to find the appropriate w, b to minimize the value of the above loss function. The smaller the loss value, the closer the constructed neural network model is to the actual situation.

For example, in the training process of the neural network model, the common optimization algorithms are basically based on the error back propagation (BP) algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of 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 error back propagation stage. 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 preset number of learning times is reached.

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

When these optimization algorithms are backpropagating errors, they can calculate the derivative/partial derivative of neurons based on the error/loss obtained from the loss function, add the influence of the learning rate, the previous gradient/derivative/partial derivative, etc., get the gradient, and pass the gradient to the previous layer.

In order to facilitate a better understanding of the embodiments of the present application, the measurement quality (MeasQuality) or timing quality (TimingQuality) related to the present application is explained.

The fields of OTDOA-MeasQuality (LTE) may be as follows, and the specific description of the OTDOA-MeasQuality field may be as shown in Table 1 below. The error resolution field (error-Resolution) is used to indicate the parameter R used in the error value field (error-Value); the error value field (error-Value) is used to indicate the best estimate of the uncertainty of the observed time difference of arrival (OTDOA) (or time of arrival (TOA)) measurement by the target device; if the error value field (error-Value) provides the sample uncertainty of the OTDOA (or TOA) measurement, the error sample number field (error-NumSamples) is used to indicate how many measurements the target device used to determine the uncertainty (such as the sample size).

Table 1

The information element (IE) of NR-TimingQuality (NR) may be as follows, where the NR-TimingQuality IE defines the quality of the timing value (such as TOA timing quality), and the specific description of the NR-TimingQuality field may be as shown in Table 2 below. Among them, the timing quality value (timingQualityValue) provides an uncertainty estimate of the timing value in meters, and the timing quality resolution (timingQualityResolution) provides the resolution used in the timingQualityValue field.

Table 2

In order to facilitate a better understanding of the embodiments of the present application, the problems solved by the present application are explained.

The current protocol can support reporting the quality or uncertainty of the measurement or estimation of timing measurements (such as Reference Signal Time Difference (RSTD), Time of Arrival (TOA), Round Trip Time (RTT), TOA is used as an example below). In terms of purpose, the original intention of the design of these measurements is similar to the soft information described in this application. They all measure the uncertainty of the estimate. Therefore, the timing measurement combined with timing quality can be regarded as a type of soft information. However, the current timing quality has the following problems:

1) Timing quality only limits the error range of the timing measurement value, but does not know how the timing measurement value is obtained, the probability distribution it obeys, etc., so the timing quality estimate is not accurate enough. Inaccurate timing quality estimates may also Adversely affects positioning performance and algorithm complexity;

2) Timing quality currently only supports reporting of one set of soft information, for example, only supports reporting of a single TOA and its timing quality. However, in fact, for the same TOA estimation, the UE can report multiple sets of TOA soft information.

3) It does not support reporting of soft information of other types of measurement quantities, such as angle information.

In view of the above problems, the embodiment of the present application enhances the soft information reporting as follows:

1) It is clarified that the acquisition method of soft information is based on the output of the AI model, and accurate soft information (such as timing quality) can be obtained after processing by the AI model;

2) Increase the statistical meaning of soft information, change from reporting error range to reporting statistical confidence interval, confidence level or probability density distribution parameters; clarify the meaning of soft information, which helps the network side to better use soft information to improve positioning accuracy;

3) For the same measurement quantity, at least two sets of soft information can be reported, which is conducive to improving positioning accuracy.

4) Supports reporting of soft information of more types of measurement quantities, such as angle information and angle information, enriching the measurement quantities to support multi-feature hybrid positioning (time, angle, background map information, etc.) and further improve positioning accuracy.

To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The above related technologies can be combined arbitrarily with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

FIG. 4 is a schematic flow chart of an information transmission method 200 according to an embodiment of the present application. As shown in FIG. 4 , the information transmission method 200 may include at least part of the following contents:

S210, the first device sends at least one set of first information of a first feature to the second device; wherein the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first AI model;

S220, the second device receives the at least one set of first information of the first feature from the first device.

It should be understood that FIG4 shows the steps or operations of the information transmission method 200, but these steps or operations are merely examples, and the embodiments of the present application may also perform other operations or variations of the operations in FIG4.

In an embodiment of the present application, accurate first information can be obtained after processing by the first AI model, and the first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. The second device can obtain more accurate location information based on at least one set of first information of the first feature, thereby significantly improving positioning accuracy. Specifically, the uncertainty or probability distribution of the output of the first AI model can improve the robustness of the reasoning results of the first AI model. Since the reasoning results of the first AI model cover multiple possible results and their probability distribution, it is conducive to further processing and utilization of the reasoning results, such as combining the reasoning results of the first AI model (including relevant information on uncertainty or probability distribution) with other types of information to obtain a more accurate target result. The first device reports at least one set of first information of the first feature, so that the second device can obtain more accurate location information based on at least one set of first information of the first feature, thereby significantly improving positioning accuracy.

In an embodiment of the present application, the first feature is related to location information, such as the first AI model is used to implement a positioning function.

It should be noted that in the embodiment of the present application, the higher the uncertainty of the output of the first AI model, the lower the accuracy of the output of the first AI model.

In some embodiments, the first device may be a terminal, a network side device, or a third-party server, wherein the network side device may be an access network device or a core network device.

In some embodiments, the second device may be a terminal, a network side device, or a third-party server, wherein the network side device may be an access network device or a core network device.

In some embodiments, the first information is output information of the first AI model, or the first information is determined based on the output information of the first AI model.

In some embodiments, the first AI model can be deployed on the first device side. For example, the first device can directly obtain the first information through the first AI model, or the first device can determine the first information through the output information of the first AI model.

In some embodiments, the first AI model is deployed on the second device side. Specifically, for example, the first device receives the output information of the first AI model from the second device, and the first device can use the output information of the first AI model. For example, the first device may determine the first information by combining the output information of the first AI model with other information, wherein the other information may be as follows: such as motion state information (such as speed, acceleration, etc.), signal quality measurement information (such as reference signal received power (RSRP), signal to interference plus noise ratio (SINR), reference signal received quality (RSRQ), etc.).

In an embodiment of the present application, the first information may also be referred to as soft information (such as probability distribution, confidence level, confidence interval, etc.), wherein the soft information gives the probability distribution or confidence level of the possible results. Optionally, the first AI model may be a soft information AI model, or may not be a soft information AI model (in this case, the soft information can be determined based on the output of the first AI model). Among them, the soft information AI model refers to a type of AI model whose output is soft information (such as probability distribution, confidence level, confidence interval, etc.), including both classic probability models and AI models based on neural networks; the soft information AI model measures the possibility of different prediction results and gives the probability distribution or confidence level of each possible result.

The soft information described in the embodiments of the present application is different from timing quality. The statistical meaning of the soft information is increased. The error range of TOA, RSTD, RTT, etc. is reported instead of reporting the soft information of the first feature in a statistical sense (such as confidence interval, confidence level or parameters of probability density distribution, etc.). The method of obtaining the soft information (obtained through the first AI model) and its meaning are clarified, which helps to better use the soft information to improve positioning accuracy.

It should be noted that compared with the hard value (such as Time of Arrival (TOA), Reference Signal Time Difference (RSTD) etc.) obtained by AI model reasoning, the soft information obtained by AI model reasoning can significantly improve the robustness of the reasoning results. Specifically, soft information can better describe the uncertainty of the world, improve the robustness of model reasoning, and provide better security for some businesses that require relatively high reliability of reasoning. For example, under the same positioning parameters, accurate soft information can improve positioning accuracy by 30% to 60%.

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

In some embodiments, the identifier of the AI model described in this application may be an AI unit identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI model described in this application, or an identifier of a specific scenario, environment, channel feature, or device related to the AI model described in this application, or an identifier of a function, feature, capability, or module related to the AI model described in this application. This application does not make any specific limitations on this.

Exemplarily, at least one set of first information of the first feature is associated with W TRPs, such as a first device (such as a terminal) sends at least one set of first information of the first feature associated with W TRPs to a second device (such as a core network device).

Exemplarily, at least one set of first information of the first feature is associated with the target terminal, such as a first device (such as a TRP) sending at least one set of first information of the first feature associated with the target terminal to a second device (such as a core network device). Further, W TRPs report at least one set of first information of the first feature associated with the target terminal to the core network device respectively.

In some embodiments, when the first device sends at least two sets of first information of the first feature to the second device, for example, three sets of first information of the first feature are reported, wherein one set is a TOA confidence of 90% to 95%, one set is a TOA confidence of 85% to 90%, and one set is a TOA confidence of 95% to 99%; for another example, three sets of first information of the first feature are reported, wherein one set is a TOA confidence interval [a ₁ , b ₁ ], one set is a TOA confidence interval [a ₂ , b ₂ ], and one set is a TOA confidence interval [a ₃ , b ₃ ].

This embodiment supports reporting of at least two sets of soft information of the same measurement quantity (such as the first feature), which is conducive to improving positioning accuracy.

In some embodiments, the first information includes but is not limited to at least one of the following:

M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature;

Wherein, M, N, or T are all positive integers.

In some embodiments, a confidence interval [a, b] in the M confidence intervals is characterized by at least one of the following:

The upper and lower bounds of the confidence interval are b and a, the width of the confidence interval is b-a, and the median value is (b-a)/2.

For example, the first feature is TOA, M=2, and the confidence intervals of the two TOAs are [2,3]m and [10,12]m respectively.

For example, the first feature is TOA, M=2, and the confidence levels of the two TOAs are 90% and 95% respectively.

For example, the first feature is TOA, M=2, and the weights or importance of the confidence intervals of the two TOAs are 0.7 and 0.3 respectively (the confidence level can also be used as the weight).

For example, the first feature is TOA, N=5, and the values of N TOAs and their probabilities may be as follows: 1m: 1/10, 2m: 2/10, 2.5m: 3/10, 3m: 2/10, 4m: 2/10.

In some embodiments, the parameters of the probability density distribution include, but are not limited to, at least one of the following: a mean or expectation of the probability density distribution, a variance or standard deviation of the probability density distribution, and an indication of the type of the probability density distribution.

For example, the first feature is TOA, T=2, and the parameters of the T probability density distributions of the first feature can be as follows: the mean or expectation of the first TOA is 2.5m, the standard deviation is 1m, and the probability density distribution is Gaussian distribution; the mean or expectation of the second TOA is 11m, the standard deviation is 2m, and the probability density distribution is Gaussian distribution.

For example, the first feature is TOA, T=2, and the weights of the T probability density distributions of the first feature may be as follows: for example, the weights or importances of the Gaussian distributions of the two TOAs are 0.7 and 0.3 respectively.

In this embodiment, the statistical meaning of the first information (also referred to as soft information) is defined (such as confidence interval, confidence level of confidence interval, weight of confidence interval, confidence level or probability, parameters of probability density distribution, weight of probability density distribution, etc.); the method of obtaining the first information (obtained through the first AI model) and its meaning are clarified, which helps to better use the first information to improve positioning accuracy.

For example, the type of probability density distribution may include but is not limited to at least one of the following: Gaussian distribution, Poisson distribution.

For example, for a Gaussian distribution, there is a conversion relationship between the confidence interval, the confidence level, the mean, and the standard deviation, such as a typical Gaussian distribution with a mean of μ and a standard deviation of σ.

For example, the 90% confidence interval is: [μ-1.645σ, μ+1.645σ], which means that there is a 90% probability that the predicted target value is within the interval [μ-1.645σ, μ+1.645σ].

For example, the 95% confidence interval is: [μ-1.96σ, μ+1.96σ], which means that there is a 95% probability that the predicted target value is within the interval [μ-1.96σ, μ+1.96σ].

Exemplarily, there may be a conversion relationship between the confidence interval, the confidence level, the mean, and the standard deviation, where the mean is μ, the standard deviation is σ, the coefficient z may be obtained by the probability density distribution function or the type of probability density distribution, and the confidence interval with a confidence level of p% may be as follows:
[μ-z _p% σ, μ+z _p% σ].

Exemplarily, when the agreed probability density distribution type is not displayed, the confidence interval and confidence level are still available. The confidence interval [a, b] at least indicates that the true value is within the confidence interval [a, b]; the confidence level s indicates the possibility that the true value is within the confidence interval [a, b]. In addition, the accuracy of the estimate can also be indicated and judged by the width b-a of the confidence interval. For example, the wider the confidence interval, the higher the uncertainty in the target value estimate. The specific method of estimating the position using the confidence interval and confidence level depends on the implementation of the first device or the second device and is not limited here.

In some embodiments, the first information may include: a type of the first information, wherein the type of the first information may include at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level, or a probability rate, parameters of probability density distribution, and weights of probability density distribution.

In this embodiment, after receiving the first information, the second device may obtain the type of the first information, and then may quickly decode other content included in the first information based on the type of the first information.

Optionally, the first device may also send the type of the first information to the second device before sending the at least one set of first information of the first feature to the second device. Thus, the second device may know the type of the first information in advance, which is beneficial to the subsequent reception of the first information.

For example, the first information includes: the type of the first information and at least one of the following corresponding to the type of the first information:

M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of the T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature.

In some embodiments, the first information may also include: identification information associated with the first AI model or identification information associated with the first feature. Thus, the second device can obtain the identification information associated with the first AI model or the identification information associated with the first feature, which is beneficial for positioning based on at least one set of first information of the first feature.

Exemplarily, the identification information associated with the first information is the same as the identification information associated with the first feature, or the identification information associated with the first information is the same as the identification information associated with the first AI model.

Optionally, the identification information associated with the first AI model includes but is not limited to at least one of the following: at least one transmission reception point (TRP) identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. Optionally, the TRP identifier can be uniquely determined by the cell identifier and the reference signal resource identifier.

Optionally, the identification information associated with the first feature includes but is not limited to at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. Optionally, the TRP identifier can be uniquely determined by the cell identifier and the reference signal resource identifier.

In some embodiments, the first feature includes but is not limited to at least one of the following: line-of-sight TOA, RSTD, angle of arrival (Angle of Arrival, AoA), angle of departure (Angle of Departure, AoD), line-of-sight (Line Of Sight, LOS)/non-line-of-sight (Non Line Of Sight, NLOS) indication, reference signal received power (Reference Signal Received Power, RSRP), reference signal received power path (Reference Signal Received Power Path, RSRPP), location coordinates, RTT, and scene type indication.

In this embodiment, at least two measurement quantities such as line of sight TOA, RSTD, AoA, AoD, LOS/NLOS indication, RSRP), RSRPP, position coordinates, RTT, scene type, etc. can be supported, and multi-feature hybrid positioning (time, angle, background map information, etc.) can be supported, which further improves the positioning accuracy.

Exemplarily, the scene types may be as follows:

Indoors and outdoors;

moving, stationary;

On the ground, in the air;

Layer 1, layer 2, layer 3.

For example, in the case where the first feature includes a scene type indication, the first information is: 90% probability of being indoors, or 85% probability of being in motion, or 80% probability of being on the 1st floor or the first floor.

Exemplarily, when the first feature includes line-of-sight TOA, the first device may report at least two sets of first information of line-of-sight TOA, wherein each set of first information of line-of-sight TOA is associated with a TRP; wherein each set of first information of line-of-sight TOA may include at least one of the following: M confidence intervals of line-of-sight TOA, confidence levels of the M confidence intervals of line-of-sight TOA, weights of the M confidence intervals of line-of-sight TOA, N values of line-of-sight TOA and their probabilities or confidence levels, parameters of T probability density distributions of line-of-sight TOA, and weights of T probability density distributions of line-of-sight TOA.

Exemplarily, when the first feature includes RSTD, the first device may report at least two sets of first information of RSTD, wherein each set of first information of RSTD is associated with two TRPs; wherein each set of first information of RSTD may include at least one of the following: M confidence intervals of RSTD, confidence levels of the M confidence intervals of RSTD, RSTD The weights of the M confidence intervals, the N values of RSTD and their probabilities or confidence levels, the parameters of the T probability density distributions of RSTD, and the weights of the T probability density distributions of RSTD.

Exemplarily, when the first feature includes location coordinates, the first device may report at least two sets of first information of the location coordinates, wherein each set of first information of the location coordinates is associated with at least two TRPs; wherein each set of first information of the location coordinates may include at least one of the following: M confidence intervals of the location coordinates, confidence levels of the M confidence intervals of the location coordinates, weights of the M confidence intervals of the location coordinates, N values of the location coordinates and their probabilities or confidence levels, parameters of T probability density distributions of the location coordinates, and weights of the T probability density distributions of the location coordinates.

In some embodiments, the input information of the first AI model includes but is not limited to at least one of the following:

Time domain channel impulse response, RSRP, frequency domain channel impulse response, time domain waveform of received signal, TRP identifiers of S TRPs, local cell identifiers of S TRPs, global cell identifiers of S TRPs;

Wherein, S is a positive integer.

It should be noted that the input information of the first AI model includes the TRP identifiers of S TRPs, which can indicate that the input information of the first AI model is associated with the S TRPs. Similarly, the input information of the first AI model includes the local cell identifiers of S TRPs, which can indicate that the input information of the first AI model is associated with the S TRPs. Similarly, the input information of the first AI model includes the global cell identifiers of S TRPs, which can indicate that the input information of the first AI model is associated with the S TRPs.

Exemplarily, S TRPs may correspond to S time domain channel impulse responses; or, among the S TRPs, each reference signal resource of each TRP corresponds to a time domain channel impulse response.

Exemplarily, S TRPs may correspond to S RSRPs; or, among the S TRPs, each reference signal resource of each TRP corresponds to one RSRP.

Exemplarily, S TRPs may correspond to S frequency-domain channel impulse responses; or, in the S TRPs, each reference signal resource of each TRP corresponds to a frequency-domain channel impulse response.

Exemplarily, S TRPs may correspond to S time domain waveforms of received signals; or, among the S TRPs, each reference signal resource of each TRP corresponds to a time domain waveform of a received signal.

In this embodiment, the input information of the first AI model includes TRP identifiers of S TRPs, local cell identifiers of S TRPs, or global cell identifiers of S TRPs, which can improve the accuracy of reasoning of the first AI model.

In some embodiments, the time domain channel impulse response includes but is not limited to at least one of the following: time information, power information, and phase information.

In some embodiments, the frequency domain channel impulse response includes but is not limited to at least one of the following: frequency information (such as subcarrier sequence number or frequency domain interval), power information, and phase information.

In some embodiments, the input information of the first AI model is associated with S TRPs, where S is a positive integer. For example, S=1, 2, 3, 4, 5, ...

This embodiment clarifies the TRP associated with the input information of the first AI model, which can improve the accuracy of reasoning of the first AI model.

Exemplarily, the S TRPs may include, but are not limited to: part or all of the at least one TRP identifier included in the identification information associated with the first feature, or part or all of the at least one TRP identifier included in the identification information associated with the first AI model.

In some embodiments, before a first device sends at least one set of first information of a first feature to a second device, the first device receives indication information from the second device; wherein the indication information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information (values of M, N, T, etc.), the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature.

Therefore, in this embodiment, the first device receives the indication information from the second device, and based on the content indicated by the indication information, the first device and the second device can have a consistent understanding of the first information. For example, when the type of the first information is a probability density distribution, the first device and the second device need to have a consistent understanding of the probability density distribution, including at least the type of the probability density distribution and the meaning of the parameters. For example, if the first device reports two parameters, the second device needs to understand the two parameters, and at least needs to know: whether the two parameters are a probability density distribution, what the two parameters belong to, and whether the two parameters belong to the same probability density distribution. What is the probability density distribution, which of the two parameters is the mean and which is the variance or standard deviation, etc.

In some embodiments, before a first device sends at least one set of first information of a first feature to a second device, the first device sends capability information to the second device; wherein the capability information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information (values of M, N, T, etc.), the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature.

Optionally, the capability information may also be replaced by an indication information, which is not limited in this application.

Therefore, in this embodiment, the first device sends capability information to the second device, and based on the content indicated by the capability information, the first device and the second device can have a consistent understanding of the first information. For example, when the type of the first information is a probability density distribution, the first device and the second device need to have a consistent understanding of the probability density distribution, at least including the type of the probability density distribution and the meaning of the parameters. For example, if the first device reports two parameters, the second device needs to understand the two parameters, and at least needs to know: whether the two parameters are a probability density distribution, what probability density distribution the two parameters belong to, which of the two parameters is the mean and which is the variance or standard deviation, etc.

In some embodiments, when the first information type is a probability density distribution, the second device may indicate to the first device (such as through the above-mentioned indication information) the type of the probability density distribution or the value of T, or the protocol may stipulate that the type of the probability density distribution is Gaussian distribution or Poisson distribution by default.

In some embodiments, when the first information type is a confidence interval, the second device may indicate to the first device (such as through the above-mentioned indication information) the confidence level of the confidence interval or the value of M, and it may also be agreed upon by the protocol, such as the default confidence level of the confidence interval is 90%; or, when the first information type is a confidence interval, the second device may indicate to the first device (such as through the above-mentioned indication information) the type of probability density distribution associated with the confidence interval or the value of M, and it may also be agreed upon by the protocol, such as the default type of probability density distribution associated with the confidence interval is Gaussian distribution or Poisson distribution.

In some embodiments, when the first information type is a probability density distribution, the first device may report to the second device (such as through the above-mentioned capability information reporting) the type of the probability density distribution or the value of T, or it may be agreed upon by the protocol such as setting the type of the probability density distribution to be Gaussian distribution or Poisson distribution by default.

In some embodiments, when the first information type is a confidence interval, the first device reports to the second device (such as through the above-mentioned capability information reporting) the confidence level of the confidence interval or the value of M, and it may also be agreed upon by the protocol, such as the default confidence level of the confidence interval is 90%; or, when the first information type is a confidence interval, the first device reports to the second device (such as through the above-mentioned capability information reporting) the type of probability density distribution associated with the confidence interval or the value of M, and it may also be agreed upon by the protocol, such as the default type of probability density distribution associated with the confidence interval is Gaussian distribution or Poisson distribution.

In some embodiments, when the first information type is probability or confidence, the second device may indicate (such as through the above indication information) the value of N to the first device.

In some embodiments, when the first information type is probability or confidence, the first device may report the value of N to the second device (eg, through the capability information reporting described above).

In some embodiments, the second device may indicate to the first device (eg, through the above-mentioned indication information) the correlation between the confidence interval, the confidence level, the mean, and the variance.

In some embodiments, the first device may report to the second device (eg, through the capability information reporting described above) the confidence interval, the correlation between the confidence level and the mean and variance.

In some embodiments, the relationship between the confidence interval, confidence level, mean, and variance may also be agreed upon by protocol.

Exemplarily, when the first information type is a confidence interval or a confidence level, when a probability density distribution type is given, the two types of information, confidence interval, confidence level, mean, and variance, are equivalent and can be converted to each other by table lookup or the like.

In an embodiment of the present application, accurate first information (such as timing quality) can be obtained after processing by the first AI model. The first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. At least one set of first information based on the first feature can obtain more accurate location information, thereby significantly improving positioning accuracy.

The following describes a solution for AI model positioning based on soft information through a specific example. Soft information can improve the robustness of the reasoning results of the AI model because the reasoning results cover multiple possible results and their probability distribution. It is conducive to the further processing and utilization of the reasoning results of the AI model, such as soft information and other types of information (such as other information used for implementation). The information output by the AI model of the current positioning function can be combined to obtain more accurate target results, and it can also provide better security for some businesses that require higher reasoning reliability.

Example 1

The input of the ith AI model is the time-domain channel impulse response (CIR) of the ith transmission reception point (TRP), including the time, power, and phase information of the multipath.

The output of the ith AI model is the mean μ _i and standard deviation σ _i of the line-of-sight TOA between the ith TRP and the terminal;

The line-of-sight TOA estimate _xi between the ith TRP and the terminal is modeled as a Gaussian distribution:

For N TRPs, the likelihood function is modeled as:

Where x = [x ₁ , ..., x _N ] ^T ;

The maximum likelihood estimation problem can be transformed into a weighted least squares problem:

Where μ = [μ ₁ , ..., μ _N ] ^T , Σ is an N*N matrix, and its i-th diagonal element is Its goal is to find a location The N TOA estimates obtained by combining this position with the coordinates of the N TRPs are The weighted distance to the N TOA mean μ estimated by the AI model is the smallest.

because and The relationship between is nonlinear, so it can be solved by linear approximation and greedy algorithm. Taking the particle swarm optimization algorithm as an example, the specific implementation scheme and simulation results are given as shown in Figure 5.

Among them, different optimization algorithms, TRP numbers, and corresponding positioning accuracy can be shown in Table 1 below.

Table 1

In addition, the framework can support mixed positioning of multiple types of soft information. In the above example, only the soft information μ, σ of TOA of N TRPs is given. In addition, soft information of angles can also be included. Its likelihood function can be written as follows:

x, α, and z refer to different types of information, such as TOA, AOD, and AOA, respectively.

Secondly, the number of TRPs for different types of information can also be different. For example, the number of TRPs for x is N ₁ , the number of TRPs for α is N ₂ , and the number of TRPs for z is N ₃ . Their likelihood functions can be written as follows:

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

FIG6 shows a schematic block diagram of an information transmission device 300 according to an embodiment of the present application. As shown in FIG6 , the information transmission device 300 includes:

The transceiver unit 310 is configured to send at least one set of first information of a first feature to a second device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

In some embodiments, the first information includes at least one of the following:

M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature;

Wherein, M, N, or T are all positive integers.

In some embodiments, the confidence interval [a, b] in the M confidence intervals is characterized by at least one of the following:

The upper and lower bounds of the confidence interval are b and a, the width of the confidence interval is b-a, and the median value is (b-a)/2.

In some embodiments, the parameters of the probability density distribution include at least one of the following: a mean or expectation of the probability density distribution, a variance or standard deviation of the probability density distribution, and an indication of the type of the probability density distribution.

In some embodiments, the first information further includes: identification information associated with the first AI model or identification information associated with the first feature;

The identification information associated with the first AI model includes at least one of the following: at least one transmitting/receiving point TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier;

Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier.

In some embodiments, the first information is output information of the first AI model, or the first information is determined based on the output information of the first AI model.

In some embodiments, the first feature includes at least one of the following: line-of-sight arrival time TOA, reference signal time difference RSTD, arrival angle AoA, departure angle AoD, line-of-sight LOS/non-line-of-sight NLOS indication, reference signal received power RSRP, path reference signal received power RSRPP, location coordinates, round-trip transmission time RTT, and scene type indication.

In some embodiments, the input information of the first AI model includes at least one of the following:

Time domain channel impulse response, RSRP, frequency domain channel impulse response, time domain waveform of received signal, TRP identifiers of S TRPs, local cell identifiers of S TRPs, global cell identifiers of S TRPs;

The time domain channel impulse response includes at least one of the following: time information, power information, and phase information; the frequency domain channel impulse response includes at least one of the following: frequency information, power information, and phase information;

Wherein, S is a positive integer.

In some embodiments, the input information of the first AI model is associated with the S TRPs.

In some embodiments, before the information transmission device 300 sends the at least one set of first information of the first feature to the second device, the transceiver unit 310 is further used to receive indication information from the second device;

The indication information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature;

The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution;

The identification information associated with the first AI model includes at least one of the following: at least one TRP identification, at least one cell identification, at least one reference signal identification, at least one reference signal resource ... Reference signal resource set identifier;

Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier.

In some embodiments, before the information transmission device 300 sends the at least one set of first information of the first feature to the second device, the transceiver unit 310 is further used to send capability information to the second device;

The capability information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature;

The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution;

The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier;

Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier.

In some embodiments, the transceiver unit may be a communication interface or a transceiver, or an input/output interface of a communication chip or a system on chip.

It should be understood that the information transmission device 300 according to the embodiment of the present application may correspond to the first device in the method embodiment of the present application, and the above and other operations or functions of each unit in the information transmission device 300 are respectively for implementing the corresponding process of the first device in the method 200 shown in Figure 4. For the sake of brevity, they will not be repeated here.

Therefore, in an embodiment of the present application, accurate first information (such as timing quality) can be obtained after processing by the first AI model. The first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. At least one set of first information based on the first feature can obtain more accurate location information, thereby significantly improving positioning accuracy.

FIG7 shows a schematic block diagram of an information transmission device 400 according to an embodiment of the present application. As shown in FIG7 , the information transmission device 400 includes:

The transceiver unit 410 is configured to receive at least one set of first information of a first feature from a first device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

In some embodiments, the first information includes at least one of the following:

M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature;

Wherein, M, N, or T are all positive integers.

In some embodiments, the confidence interval [a, b] in the M confidence intervals is characterized by at least one of the following:

The upper and lower bounds of the confidence interval are b and a, the width of the confidence interval is b-a, and the median value is (b-a)/2.

In some embodiments, the parameters of the probability density distribution include at least one of the following: a mean or expectation of the probability density distribution, a variance or standard deviation of the probability density distribution, and an indication of the type of the probability density distribution.

In some embodiments, the first information further includes: identification information associated with the first AI model or identification information associated with the first feature;

The identification information associated with the first AI model includes at least one of the following: at least one transmitting/receiving point TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier;

The identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, at least one reference Signal resource set identifier.

In some embodiments, the first information is output information of the first AI model, or the first information is determined based on the output information of the first AI model.

In some embodiments, the first feature includes at least one of the following: line-of-sight arrival time TOA, reference signal time difference RSTD, arrival angle AoA, departure angle AoD, line-of-sight LOS/non-line-of-sight NLOS indication, reference signal received power RSRP, path reference signal received power RSRPP, location coordinates, round-trip transmission time RTT, and scene type indication.

In some embodiments, the input information of the first AI model includes at least one of the following:

Time domain channel impulse response, RSRP, frequency domain channel impulse response, time domain waveform of received signal, TRP identifiers of S TRPs, local cell identifiers of S TRPs, global cell identifiers of S TRPs;

The time domain channel impulse response includes at least one of the following: time information, power information, and phase information; the frequency domain channel impulse response includes at least one of the following: frequency information, power information, and phase information;

Wherein, S is a positive integer.

In some embodiments, the input information of the first AI model is associated with the S TRPs.

In some embodiments, before the information transmission device 400 receives the at least one set of first information of the first feature from the first device, the transceiver unit 410 is further used to send indication information to the first device;

The indication information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature;

The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution;

The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier;

Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier.

In some embodiments, before the information transmission device 400 receives the at least one set of first information of the first feature from the first device, the transceiver unit 410 is further configured to receive capability information from the first device;

The capability information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature;

The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution;

The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier;

Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier.

In some embodiments, the transceiver unit may be a communication interface or a transceiver, or an input/output interface of a communication chip or a system on chip.

It should be understood that the information transmission device 400 according to the embodiment of the present application may correspond to the second device in the method embodiment of the present application, and the above-mentioned and other operations or functions of each unit in the information transmission device 400 are respectively for realizing the corresponding process of the second device in the method 200 shown in Figure 4. For the sake of brevity, they will not be repeated here.

Therefore, in the embodiment of the present application, accurate first information (such as The first information is used to characterize the uncertainty or probability distribution of the output of the first AI model. At least one set of first information based on the first feature can obtain more accurate location information, thereby significantly improving the positioning accuracy.

The information transmission device in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal or a network-side device, or may be a device other than a terminal or a network-side device. Exemplarily, the terminal may include but is not limited to the types of the terminal 11 listed above, the network-side device may include but is not limited to the types of the network-side device 12 listed above, and other devices may be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiment of the present application.

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

As shown in Figure 8, an embodiment of the present application also provides a communication device 500, including a processor 501 and a memory 502, and the memory 502 stores programs or instructions that can be run on the processor 501. For example, when the communication device 500 is a first device, the program or instruction is executed by the processor 501 to implement the various steps executed by the first device in the above-mentioned information transmission method 200 embodiment, and can achieve the same technical effect. To avoid repetition, it is not repeated here; for example, when the communication device 500 is a second device, the program or instruction is executed by the processor 501 to implement the various steps executed by the second device in the above-mentioned information transmission method 200 embodiment, and can achieve the same technical effect. To avoid repetition, it is not repeated 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 performed by the first device or the second device in the method embodiment shown in Figure 4. This terminal embodiment corresponds to the above-mentioned first device or second device 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 9 is a schematic diagram of the hardware structure of a terminal that implements the embodiment of the present application.

The terminal 600 includes but is not limited to: a radio frequency unit 601, a network module 602, an audio output unit 603, an input unit 604, a sensor 605, a display unit 606, a user input unit 607, an interface unit 608, a memory 609 and at least some of the components of a processor 610.

Those skilled in the art will appreciate that the terminal 600 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 610 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 FIG9 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 604 may include a graphics processing unit (GPU) 6041 and a microphone 6042, and the graphics processor 6041 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 606 may include a display panel 6061, and the display panel 6061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 607 includes a touch panel 6071 and at least one of other input devices 6072. The touch panel 6071 is also called a touch screen. The touch panel 6071 may include two parts: a touch detection device and a touch controller. Other input devices 6072 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 RF unit 601 can transmit the data to the processor 610 for processing; in addition, the RF unit 601 can send uplink data to the network side device. Generally, the RF unit 601 includes but is not limited to an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

The memory 609 can be used to store software programs or instructions and various data. The memory 609 can mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area can 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 609 can include a volatile memory or a non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), or Flash Memory. Volatile memory may be Random Access Memory (RAM), Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 609 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

The processor 610 may include at least one processing unit; optionally, the processor 610 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 610.

Exemplarily, the radio frequency unit 601 is configured to send at least one set of first information of a first feature to a second device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

Exemplarily, the radio frequency unit 601 is configured to receive at least one set of first information of a first feature from a first device;

Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model.

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 and achieve the same or corresponding technical effect. 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 4. The network side device embodiment corresponds to the first device or second device method embodiment described above, and each implementation process and implementation method of the method embodiment described above 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 FIG10 , the network side device 700 includes: an antenna 71, a radio frequency device 72, a baseband device 73, a processor 74, and a memory 75. The antenna 71 is connected to the radio frequency device 72. In the uplink direction, the radio frequency device 72 receives information through the antenna 71 and sends the received information to the baseband device 73 for processing. In the downlink direction, the baseband device 73 processes the information to be sent and sends it to the radio frequency device 72. The radio frequency device 72 processes the received information and sends it out through the antenna 71.

The method executed by the network-side device in the above embodiment may be implemented in the baseband device 73, which includes a baseband processor.

The baseband device 73 may include, for example, at least one baseband board, on which at least two chips are arranged, as shown in Figure 10, one of which is, for example, a baseband processor, which is connected to the memory 75 through a bus interface to call the program in the memory 75 to execute the network device operations shown in the above method embodiment.

The network side device may also include a network interface 76, which is, for example, a Common Public Radio Interface (CPRI).

Specifically, the network side device 700 of the embodiment of the present application also includes: instructions or programs stored in the memory 75 and executable on the processor 74. The processor 74 calls the instructions or programs in the memory 75 to execute the method executed by each unit shown in Figure 6 or Figure 7, and achieves 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 FIG11 , the network side device 800 includes: a processor 801, a network interface 802, and a memory 803. The network interface 802 is, for example, a common public radio interface (CPRI).

Specifically, the network side device 800 of the embodiment of the present application also includes: instructions or programs stored in the memory 803 and executable on the processor 801. The processor 801 calls the instructions or programs in the memory 803 to execute the method executed by each unit shown in Figure 6 or Figure 7, 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, the various processes of the above-mentioned information transmission method embodiment are 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 transmission 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 embodiments of the present application further provide 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 transmission 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 performed by the first device in the information transmission method described above, and the second device can be used to execute the steps performed by the second device in the information transmission 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 methods can be implemented by means of a computer software product plus a necessary general hardware platform, and of course, can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, disk, CD, etc.), including several instructions to enable a terminal or a network-side device to execute the methods 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 (27)

An information transmission method, comprising: The first device sends at least one set of first information of a first feature to the second device; Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model. The method according to claim 1, wherein The first information includes at least one of the following: M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature; Wherein, M, N, or T are all positive integers. The method according to claim 2, wherein The confidence interval [a, b] in the M confidence intervals is characterized by at least one of the following: The upper and lower bounds of the confidence interval are b and a, the width of the confidence interval is b-a, and the median value is (b-a)/2. The method according to claim 2, wherein The parameters of the probability density distribution include at least one of the following: a mean or expectation of the probability density distribution, a variance or standard deviation of the probability density distribution, and an indication of the type of the probability density distribution. The method according to any one of claims 2 to 4, wherein The first information further includes: identification information associated with the first AI model or identification information associated with the first feature; The identification information associated with the first AI model includes at least one of the following: at least one transmitting/receiving point TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. The method according to any one of claims 1 to 5, wherein The first information is output information of the first AI model, or the first information is determined based on the output information of the first AI model. The method according to any one of claims 1 to 6, wherein The first feature includes at least one of the following: line-of-sight arrival time TOA, reference signal time difference RSTD, arrival angle AoA, departure angle AoD, line-of-sight LOS/non-line-of-sight NLOS indication, reference signal received power RSRP, path reference signal received power RSRPP, location coordinates, round-trip transmission time RTT, and scene type indication. The method according to any one of claims 1 to 7, wherein The input information of the first AI model includes at least one of the following: Time domain channel impulse response, RSRP, frequency domain channel impulse response, time domain waveform of received signal, TRP identifiers of S TRPs, local cell identifiers of S TRPs, global cell identifiers of S TRPs; The time domain channel impulse response includes at least one of the following: time information, power information, and phase information; the frequency domain channel impulse response includes at least one of the following: frequency information, power information, and phase information; Wherein, S is a positive integer. The method according to claim 8, wherein The input information of the first AI model is associated with the S TRPs. The method according to any one of claims 1 to 9, wherein Before the first device sends the at least one set of first information of the first feature to the second device, the method further includes: The first device receives indication information from the second device; The indication information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature; The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution; The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. The method according to any one of claims 1 to 9, wherein Before the first device sends the at least one set of first information of the first feature to the second device, the method further includes: The first device sends capability information to the second device; The capability information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature; The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution; The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. An information transmission method, comprising: The second device receives at least one set of first information of the first characteristic from the first device; Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model. The method according to claim 12, wherein The first information includes at least one of the following: M confidence intervals of the first feature, confidence levels of the M confidence intervals of the first feature, weights of the M confidence intervals of the first feature, N values of the first feature and their probabilities or confidence levels, parameters of T probability density distributions of the first feature, and weights of the T probability density distributions of the first feature; Wherein, M, N, or T are all positive integers. The method according to claim 13, wherein The confidence interval [a, b] in the M confidence intervals is characterized by at least one of the following: The upper and lower bounds of the confidence interval are b and a, the width of the confidence interval is b-a, and the median value is (b-a)/2. The method according to claim 13, wherein The parameters of the probability density distribution include at least one of the following: a mean or expectation of the probability density distribution, a variance or standard deviation of the probability density distribution, and an indication of the type of the probability density distribution. The method according to any one of claims 13 to 15, wherein The first information also includes: identification information associated with the first AI model or identification information associated with the first feature; The identification information associated with the first AI model includes at least one of the following: at least one sending and receiving point TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. The method according to any one of claims 12 to 16, wherein The first information is output information of the first AI model, or the first information is determined based on the output information of the first AI model. The method according to any one of claims 12 to 17, wherein The first feature includes at least one of the following: line-of-sight arrival time TOA, reference signal time difference RSTD, arrival angle AoA, departure angle AoD, line-of-sight LOS/non-line-of-sight NLOS indication, reference signal received power RSRP, path reference signal received power RSRPP, location coordinates, round-trip transmission time RTT, and scenario type. The method according to any one of claims 12 to 18, wherein The input information of the first AI model includes at least one of the following: Time domain channel impulse response, RSRP, frequency domain channel impulse response, time domain waveform of received signal, TRP identifiers of S TRPs, local cell identifiers of S TRPs, global cell identifiers of S TRPs; The time domain channel impulse response includes at least one of the following: time information, power information, and phase information; the frequency domain channel impulse response includes at least one of the following: frequency information, power information, and phase information; Wherein, S is a positive integer. The method according to claim 19, wherein The input information of the first AI model is associated with the S TRPs. The method according to any one of claims 12 to 20, wherein Before the second device receives the at least one set of first information of the first feature from the first device, the method further includes: The second device sends indication information to the first device; The indication information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature; The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution; The identification information associated with the first AI model includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. The method according to any one of claims 12 to 20, wherein Before the second device receives the at least one set of first information of the first feature from the first device, the method further includes: The second device receives capability information from the first device; The capability information is used to indicate at least one of the following: the type of the first feature, the type and parameters of the first information, the type of input information of the first AI model, identification information associated with the first AI model, and identification information associated with the first feature; The type of the first information includes at least one of the following: a confidence interval, a confidence level of a confidence interval, a weight of a confidence interval, a confidence level or probability, a parameter of a probability density distribution, a weight of a probability density distribution; The identification information associated with the first AI model includes at least one of the following: at least one TRP identification, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier; Among them, the identification information associated with the first feature includes at least one of the following: at least one TRP identifier, at least one cell identifier, at least one reference signal identifier, at least one reference signal resource identifier, and at least one reference signal resource set identifier. An information transmission device, comprising: a transceiver unit, configured to send at least one set of first information of a first characteristic to a second device; Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model. An information transmission device, comprising: a transceiver unit, configured to receive at least one set of first information of a first feature from a first device; Among them, the first feature is related to the location information, and the first information is used to characterize the uncertainty or probability distribution of the output of the first artificial intelligence AI model. An information transmission device, the information transmission device is a first device, the information transmission device includes a transceiver, a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information transmission method as described in any one of claims 1 to 11 are implemented. An information transmission device, the information transmission device is a second device, the information transmission device includes a transceiver, a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information transmission method as described in any one of claims 12 to 22 are implemented. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the method of the information transmission method as described in any one of claims 1 to 11, or implements the steps of the information transmission method as described in any one of claims 12 to 22.