Multimodal wireless environment prediction method, device, equipment and storage medium

Abstract

The embodiments of the present application provide a multimodal wireless environment prediction method, device, electronic device and storage medium, which belong to the field of wireless communication technology. The method includes: determining the spatial position parameters of multiple sampling points on the signal channel corresponding to the receiving position of at least one receiver in the target area; mapping the corresponding spatial position parameters of each sampling point from the same signal channel to obtain spatial signal characteristics, and rendering the spatial signal characteristics to obtain the corresponding discrete angle power spectrum of each sampling point; determining the predicted reference signal power of the signal channel based on the pre-acquired array coefficients and the corresponding discrete angle power spectrum of each sampling point; performing signal modeling on the target area based on the corresponding predicted reference signal powers of all signal channels to obtain the wireless channel modeling results of the target area. The present application can improve the accuracy of wireless environment prediction results when the required measured data is in short supply.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a multimodal wireless environment prediction method, apparatus, electronic device, and storage medium. Background Technology

[0002] A channel is the medium for information transmission in a communication system, and channels include both wired and wireless channels. Studying the propagation characteristics of wireless channels and establishing corresponding wireless channel models can help researchers better understand the transmission performance of communication systems, thereby enabling predictions of multimodal wireless environments and facilitating subsequent optimization of the communication system.

[0003] In related technologies, deterministic channel modeling is achieved based on channel measurement data acquired by specific measuring instruments and geographical images of the target area where the communication system is located. However, such channel modeling methods require a large amount of accurate measured data as support. When the required measured data is scarce, related technologies suffer from poor accuracy in predicting wireless environments. Summary of the Invention

[0004] The main objective of this application is to provide a multimodal wireless environment prediction method, apparatus, electronic device, and storage medium, which aims to improve the accuracy of wireless environment prediction results when the required measured data is scarce.

[0005] To achieve the above objectives, a first aspect of this application proposes a multimodal wireless environment prediction method, the method comprising:

[0006] Obtain the array coefficients of the target base station located within the target area, and the receiving location of at least one receiver within the target area;

[0007] Determine at least one signal channel formed between the receiving location and the target area, and determine the spatial location parameters of multiple sampling points on the signal channel;

[0008] The spatial location parameters are input into a pre-trained wireless channel model. The corresponding spatial location parameters of each sampling point from the same signal channel are mapped to obtain spatial signal features. The spatial signal features are then rendered to obtain the discrete angular power spectrum of each sampling point.

[0009] Based on the array coefficients and the corresponding discrete angle power spectrum of each sampling point, the predictive reference signal power of the signal channel is determined.

[0010] Signal modeling of the target area is performed based on the predicted reference signal power of all signal channels to obtain the wireless channel modeling results of the target area.

[0011] In some embodiments, the target area includes a plurality of spatial grids;

[0012] Determine at least one signal channel formed between the receiving location and the target area, and determine the spatial location parameters of multiple sampling points on the signal channel, including:

[0013] Acquire the corresponding two-dimensional image of the target area, and determine the camera pose and camera orientation when capturing the two-dimensional image;

[0014] Select one of multiple spatial grids as the target spatial grid, and determine at least one signal channel formed between the target spatial grid and the receiving position, the signal channel including multiple sampling points;

[0015] Based on the camera pose and camera orientation, the three-dimensional coordinate information and observation direction information of each sampling point on each signal channel are determined, and the spatial position parameters are obtained.

[0016] In some embodiments, the spatial location parameters of each sampling point from the same signal channel are mapped to obtain spatial signal features, including:

[0017] Acquire point cloud data for the target area;

[0018] Using point cloud data as signal constraints, the three-dimensional coordinate information and observation direction information are mapped and processed based on a preset encoding function to obtain spatial signal features, which include discrete radiation signal values ​​and volume density values.

[0019] In some embodiments, the spatial signal features are rendered to obtain the discrete angular power spectrum corresponding to each sampling point, including:

[0020] For each sampling point, obtain the interval distance value between the current sampling point and its neighboring sampling points;

[0021] Based on the interval distance and volume density values, the transmittance and opacity values ​​of the sampling points are calculated.

[0022] The signal weights derived from the volume density values ​​are determined based on the product of the transmittance and opacity values.

[0023] The discrete angular power spectrum of the sampling points is obtained by multiplying the discrete radiation signal value and the signal weight.

[0024] In some embodiments, the wireless channel model is trained through the following steps, including:

[0025] Obtain the sample array coefficients of the sample base stations located within the sample area, and the sample receiving location of at least one sample receiver within the sample area;

[0026] Determine at least one sample signal channel formed between the sample receiving location and the sample area, and determine the sample spatial position parameters of multiple sample sampling points on the sample signal channel;

[0027] The sample space location parameters are input into the initial wireless channel model. The corresponding sample space location parameters of each sample sampling point from the same sample signal channel are mapped to obtain the sample space signal features. The sample space signal features are then rendered to obtain the corresponding sample discrete angle power spectrum of each sample sampling point.

[0028] Based on the sample array coefficients and the corresponding discrete angle power spectrum of each sample sampling point, determine the sample reference signal power of the sample signal channel.

[0029] The model loss value is calculated based on the sample reference signal power. The parameters of the initial wireless channel model are then adjusted based on the model loss value to obtain the trained wireless channel model.

[0030] In some embodiments, the model loss values ​​include wireless signal loss values ​​and point cloud depth loss values;

[0031] The model loss value is calculated based on the sample reference signal power, including:

[0032] Obtain the power of the verification reference signal and the depth of the verification point cloud in the sample region;

[0033] The wireless signal loss value is calculated based on the difference between the sample reference signal power and the verification reference signal power.

[0034] The depth value of the sample point cloud is determined based on multiple sample sampling points from the same sample signal channel;

[0035] The point cloud depth loss value is calculated based on the difference between the sample point cloud depth value and the validation point cloud depth value.

[0036] In some embodiments, a model loss value is calculated based on the sample reference signal power, and the parameters of the initial wireless channel model are adjusted based on the model loss value to obtain a trained wireless channel model, including:

[0037] The total loss value is obtained by superimposing the wireless signal loss value and the point cloud depth loss value.

[0038] The parameters of the initial wireless channel model are jointly adjusted based on the total loss value to obtain the trained wireless channel model.

[0039] To achieve the above objectives, a second aspect of this application provides a multimodal wireless environment prediction device, the device comprising:

[0040] The acquisition module is used to acquire the array coefficient of the target base station located within the target area, and the receiving position of at least one receiver within the target area;

[0041] The sampling point determination module is used to determine at least one signal channel formed between the receiving position and the target area, and to determine the spatial position parameters of multiple sampling points on the signal channel;

[0042] The rendering module is used to input spatial location parameters into a pre-trained wireless channel model, map the corresponding spatial location parameters of each sampling point from the same signal channel to obtain spatial signal features, and render the spatial signal features to obtain the corresponding discrete angular power spectrum of each sampling point.

[0043] The prediction module is used to determine the prediction reference signal power of the signal channel based on the array coefficients and the corresponding discrete angular power spectrum of each sampling point.

[0044] The target result is used to model the target area based on the corresponding predicted reference signal power of all signal channels, and obtain the wireless channel modeling result of the target area.

[0045] To achieve the above objectives, a third aspect of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0046] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0047] The multimodal wireless environment prediction method, apparatus, electronic device, and storage medium proposed in this application acquire the array coefficients of a target base station located within a target area, and the receiving position of at least one receiver within the target area; determine at least one signal channel formed between the receiving position and the target area, and determine the spatial position parameters of multiple sampling points on the signal channel; input the spatial position parameters into a pre-trained wireless channel model, map the corresponding spatial position parameters of each sampling point from the same signal channel to obtain spatial signal features, and render the spatial signal features to obtain the corresponding discrete angular power spectrum of each sampling point; determine the predicted reference signal power of the signal channel based on the array coefficients and the corresponding discrete angular power spectrum of each sampling point; and perform signal modeling of the target area based on the corresponding predicted reference signal power of all signal channels to obtain the wireless channel modeling result of the target area. This application can improve the accuracy of channel modeling results when the required measured data is scarce. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of an optional application scenario of the multimodal wireless environment prediction device provided in this application embodiment;

[0049] Figure 2 This is an optional flowchart of the multimodal wireless environment prediction method provided in the embodiments of this application;

[0050] Figure 3 yes Figure 2 An optional implementation flowchart of step 102 in the process;

[0051] Figure 4 This is a schematic diagram of an optional multilayer perceptron for the multimodal wireless environment prediction method provided in this application embodiment;

[0052] Figure 5 yes Figure 2 An optional implementation flowchart of step 103 in the process;

[0053] Figure 6 yes Figure 2 Another optional implementation flowchart for step 103 in the process;

[0054] Figure 7 This is another optional flowchart of the multimodal wireless environment prediction method provided in the embodiments of this application;

[0055] Figure 8 yes Figure 7 An optional implementation flowchart of step 505 in the diagram;

[0056] Figure 9 yes Figure 7 Another optional implementation flowchart for step 505 in the process;

[0057] Figure 10 This is a schematic diagram of a target area of ​​the multimodal wireless environment prediction method provided in the embodiments of this application;

[0058] Figure 11 This is a schematic diagram of target area data acquisition for the multimodal wireless environment prediction method provided in this application embodiment;

[0059] Figure 12 This is an optional flowchart of the multimodal wireless environment prediction device provided in the embodiments of this application;

[0060] Figure 13 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0064] A channel is the medium for information transmission in a communication system, and there are two types of channels: wired channels and wireless channels. Studying the propagation characteristics of wireless channels and establishing corresponding wireless channel models can help researchers better understand the transmission performance of communication systems, thus facilitating subsequent optimization.

[0065] In related technologies, deterministic channel modeling is achieved based on channel measurement data acquired by specific measuring instruments and geographic images of the target area where the communication system is located. However, such channel modeling methods require a large amount of accurate measured data as support. When the required measured data is scarce, the accuracy of the channel modeling results in these technologies is poor.

[0066] Based on this, embodiments of this application provide a multimodal wireless environment prediction method, apparatus, electronic device, and storage medium, aiming to improve the accuracy of wireless environment prediction results when the required measured data is scarce.

[0067] The multimodal wireless environment prediction method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, it should be noted that the multimodal wireless environment prediction method in this application relates to the field of wireless communication technology. It can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the wireless environment prediction method, but is not limited to the above forms.

[0068] Furthermore, the multimodal wireless environment prediction method provided in this application can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0069] It should also be noted that in this application embodiment, when it involves information related to user characteristics such as basic user information or user identity, the user's permission or consent will be obtained first. Furthermore, the collection, use, and processing of this data will comply with relevant laws, regulations, and standards. In addition, when this application embodiment needs to obtain sensitive personal information of a user, the user's individual permission or consent will be obtained first. Only after obtaining the user's individual permission or consent will the necessary data for the normal operation of this application embodiment be obtained. For example, before the multimodal wireless environment prediction device in this application embodiment obtains the wireless channel modeling results of the target area using the multimodal wireless environment prediction method proposed in this application embodiment, the consent of relevant personnel in the target area will be obtained; otherwise, the array coefficients of the target base station located in the target area, as well as other data used to obtain the wireless channel modeling results, cannot be obtained. That is, all relevant data obtained in this application embodiment is authorized data after obtaining consent, and will not be elaborated further here.

[0070] Next, the application scenarios of the multimodal wireless environment prediction device in the embodiments of this application are described, such as... Figure 1 As shown, Figure 1 This is a schematic diagram of an optional application scenario of the multimodal wireless environment prediction device provided in this application embodiment. A target base station (BS) equipped with a downlink multiple-input multiple-output (MIMO) wireless system is set up in the target area. The target base station is equipped with an N... T An array of antennas, and the target base station uses a codebook. Different beams are used to transmit reference signals to scan the entire target area, thereby enabling the propagation of wireless signals within the target area. However, the target area typically contains numerous and cluttered buildings, such as... Figure 1 In the target area, in addition to the target base station, there are also building groups A and B. The buildings in the building groups will block the propagation path of the wireless signal, thus causing signal attenuation.

[0071] In response to this situation, the relevant technologies need to complete the wireless channel modeling work based on a large amount of accurate measured data. The measured data obtained usually includes, but is not limited to, accurate geographic images and channel measurement data obtained by high-precision measuring instruments from the target area. It is understandable that if the relevant technologies cannot obtain the required measured data, then the wireless channel modeling work cannot be carried out.

[0072] In contrast, the multimodal wireless environment prediction device in this embodiment can still process a small number of spatial location parameters based on a pre-trained wireless channel model to obtain the predicted reference signal power, even without a large amount of measured data. This reduces the dependence of wireless channel modeling on actual environmental information while ensuring the accuracy of the corresponding wireless channel modeling results for the output target area.

[0073] After understanding the example application scenarios of the multimodal wireless environment prediction device proposed in this application, the multimodal wireless environment prediction method proposed in the embodiments of this application will be described next.

[0074] In this application embodiment, the description will focus on a multimodal wireless environment prediction device, which can be integrated into a computer device, such as a server. Figure 2 As shown, Figure 2 This is an optional flowchart of the multimodal wireless environment prediction method provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, the following steps 101 to 105. When the multimodal wireless environment prediction device executes the multimodal wireless environment prediction method, the specific process is as follows. It should be noted that this embodiment... Figure 2 The order of steps 101 to 105 is not specifically limited. The order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0075] Step 101: Obtain the array coefficient of the target base station located within the target area, and the receiving location of at least one receiver within the target area.

[0076] Step 101 will be described in detail below.

[0077] In some embodiments, the target area is divided into L spatial grids, and the receiver can be positioned in any one of the spatial grids within the target area. Specifically, the receiver (User Equipment, UE) in the l-th spatial grid is able to measure the channel impulse response at time t and report beam-referenced signal receiving power (RSRP) information to perform tasks such as localized statistical channel modeling for wireless channel modeling. This information includes key parameters reflecting the wireless signal strength in the target area, such as the incident angle from the target base station to the receiver and the channel gain.

[0078] In this context, a receiver refers to an electronic device used to receive analog or digital amplitude modulation, frequency modulation, or digital broadcast signals from radio frequencies and convert them into corresponding audio, video, or digital signals. Exemplarily, the receiver in this application embodiment includes, but is not limited to, mobile phones, computers, tablets, etc.; and, to help improve the accuracy of the final obtained wireless environment prediction results, this application embodiment can also obtain multiple receiving positions of the same receiver. For example, the receiver can be moved along a preset trajectory within the target area; or, this application embodiment can set up receivers at multiple locations in the target area and obtain the receiving positions of the receivers at each location; the specific settings can be configured according to actual conditions, and this application embodiment does not limit this.

[0079] The target base station can be adaptively configured according to the different target areas being applied. This application does not limit this, but some specific examples are as follows:

[0080] Macro Site: Macro sites are typically used in open outdoor target areas. Macro sites have high power and coverage range, and can provide wireless coverage over a large area. They are usually installed on iron towers or high-rise buildings and are suitable for open areas such as cities, suburbs and rural areas.

[0081] Micro Site: Micro sites are small in size and are typically used indoors or in densely populated areas such as business centers and office buildings. They have a relatively small coverage area but can provide high signal quality and capacity.

[0082] Pico Site: Pico sites are smaller than micro sites and are typically used to provide coverage for localized areas, such as shopping malls, hotels, and stadiums.

[0083] Femto Site: Femto sites are mainly used by home users. They are similar in size to home routers and can provide wireless coverage within the home to improve indoor signal quality and data rates for home users.

[0084] Furthermore, to determine the predicted reference signal power within the target area, and thus obtain the corresponding wireless channel modeling results for the target area, the array coefficients of the target base station located within the target area must first be obtained. The steps for obtaining the array coefficients are as follows:

[0085] ① Obtain the antenna steering vector of the target base station;

[0086] ② Determine the array coefficient of the target base station based on the antenna steering vector and the preset beam vector.

[0087] The target base station is equipped with an antenna array, which refers to a large number of antennas integrated within the target base station. These antennas work together to achieve specific wireless communication functions. Especially in 5G (5th Generation Mobile Communication Technology), this type of antenna array is often referred to as a massive MIMO (Massively Multi-Atmosphere Antenna Array). The placement of each antenna in the antenna array, such as their orientation, affects the signal transmitted by the target base station. Based on this, the wireless channel modeling results output in this embodiment of the application help optimize the antenna array (such as adjusting the antenna orientation) by feeding back the signal transmission situation within the target area, thereby enabling the wireless signals transmitted by the target base station to propagate better within the target area.

[0088] Furthermore, the angular space within the target area is used to define the directional range of the target base station antenna. The three-dimensional angular space of the target base station is divided, and the free space is uniformly discretized into NV elevation angles and NH azimuth angles, resulting in N spatial angles. N = NV * NH, where N is the number of spatial angles, NV is the number of elevation angles, and NH is the number of azimuth angles. The elevation incident angle (tilt AoD) of the i-th elevation angle is θ. i The azimuth angle of incidence (AoD) of the j-th azimuth is φ. j It should be noted that the actual number of propagation paths from the target base station to the receiver is less than N. The antenna array steering vector describes the radiation or reception capability of the antenna array in different directions, including steering vectors in multiple different directions. The antenna steering vector is a steering matrix, denoted as S, and the steering vector in the nth direction (spatial angle) is denoted as s(θ). n ,φ n Therefore, the antenna steering vector S is expressed as follows: <1> :

[0089] S=[s(θ1,φ1),…,s(θ N ,φ N )] T <1>

[0090] Where T represents transpose. In the formulas listed in the embodiments of this application, the same parameters have the same meaning, and repeated parts will not be described again.

[0091] Furthermore, the preset beamforming vector includes beamforming vectors for M beams, and the preset beamforming vector is represented as W = [w1, ..., w M], where represents the beamforming vector of the i-th beam. The measurement matrix is ​​calculated based on the antenna array steering vector S and the preset beamforming vector W, thus determining the array coefficients of the target base station. The specific calculation method for the array coefficients is as follows: <2> As shown:

[0092] Φ=(|W H S| 2 ) <2>

[0093] Where Φ is the array coefficient of the target base station; H represents the conjugate transpose operation; |·| 2 It takes the absolute value of the element and squares it.

[0094] Step 102: Determine at least one signal channel formed between the receiving position and the target area, and determine the spatial position parameters of multiple sampling points on the signal channel.

[0095] Step 102 is described in detail below.

[0096] In some embodiments, due to the influence of factors such as reflection, refraction and scattering during the propagation of wireless signals, multiple signal channels are usually formed between the receiving position of the receiver and the target area. At least one signal channel is selected from the multiple signal channels, and each signal channel has multiple sampling points. The spatial position parameters of each sampling point are determined so that the spatial position parameters can be input into the pre-trained wireless channel model to obtain the discrete angular power spectrum of each sampling point, thereby helping to determine the predicted reference signal power of the signal channel where the sampling point is located.

[0097] The following is a detailed explanation of how to determine the spatial location parameters of each sampling point.

[0098] In some embodiments, as Figure 3 As shown, Figure 3 yes Figure 2 An optional implementation flowchart of step 102 in the process involves determining at least one signal channel formed between the receiving location and the target area, and determining the spatial location parameters of multiple sampling points on the signal channel, including the following steps 201 to 203:

[0099] Step 201: Obtain the corresponding two-dimensional image of the target area and determine the camera pose and camera orientation when capturing the two-dimensional image.

[0100] Step 202: Select one of the multiple spatial grids as the target spatial grid, and determine at least one signal channel formed between the target spatial grid and the receiving position, the signal channel including multiple sampling points.

[0101] Step 203: Based on the camera pose and camera orientation, determine the three-dimensional coordinate information and observation direction information of each sampling point on each signal channel to obtain spatial position parameters.

[0102] Steps 201 to 203 are described in detail below.

[0103] In some embodiments, the target area includes multiple spatial grids. The receiver can form a signal channel with any spatial grid, so one of the multiple spatial grids is selected as the target spatial grid, and at least one signal channel is formed between the receiver's receiving position and the target spatial grid, as well as multiple sampling points on the signal channel. The sampling points can be randomly selected, or they can be sampled at equal intervals on the signal channel. The embodiments of this application do not limit the method of selecting the sampling points.

[0104] Furthermore, the spatial location parameters of each sampling point are determined based on Neural Radiance Fields (NeRF). Specifically, the NeRF is used to generate a high-quality 3D reconstruction model. It employs deep learning techniques to extract the geometric shape and texture information of the target object from images at at least one viewpoint. This shape and texture information is then used to generate a continuous 3D radiation field, enabling the presentation of a highly realistic 3D model at any angle and distance. Understandably, the 3D model includes physical obstacles such as buildings, metal objects (shielding materials), and reflective surfaces.

[0105] Furthermore, to generate a corresponding 3D model of the target region based on the sampling points, it is first necessary to determine the spatial position parameters corresponding to each sampling point that conforms to the neural radiation field input. Specifically, at least one 2D image of the target region is acquired, along with the camera parameters used to capture the 2D image. These camera parameters include, but are not limited to, camera pose and camera orientation. Combining the camera parameters, while ensuring that the sampling points in 2D images captured from different perspectives are consistent in 3D space, the spatial position parameters of the sampling points are obtained. These spatial position parameters include 3D coordinate information and observation direction information. The 3D coordinate information is denoted as (x, y, z), and the observation direction information is denoted as... The spatial location parameters are then used as input to the pre-trained wireless channel model of the subsequent network.

[0106] Step 103: Input the spatial location parameters into the pre-trained wireless channel model, map the corresponding spatial location parameters of each sampling point from the same signal channel to obtain spatial signal features, and render the spatial signal features to obtain the corresponding discrete angular power spectrum of each sampling point.

[0107] Step 103 will be described in detail below.

[0108] In some embodiments, the pre-trained wireless channel model maps the input spatial location parameters to obtain the spatial signal features corresponding to the spatial location parameters of each sampling point on each signal channel. Specifically, the neural radiation field set in the wireless channel model is configured with an eight-layer multilayer perceptron (MLP), and the hidden layer dimensions of the MLP are set as follows: Figure 4 As shown, Figure 4 This is a schematic diagram of an optional multilayer perceptron for the multimodal wireless environment prediction method provided in this application embodiment. It should be noted that the number of network layers of the MLP can be set according to the actual situation, and this application embodiment does not limit this.

[0109] The processing in step 103 involves the extraction of multimodal features in signal processing, specifically including but not limited to power spectrum, phase spectrum, power spectral density, etc., which are common multimodal features in signal processing.

[0110] Understandably, without the need for actual measured channel data, the wireless channel model maps the spatial location parameters of easily obtainable sampling points to obtain the spatial signal characteristics reflecting the signal strength of each sampling point on each signal channel. Compared with traditional multimodal wireless environment prediction methods, the multimodal wireless environment prediction method proposed in this application does not require detailed physical modeling and parameter measurement of the channel environment within the target area. This reduces the computational resource cost of determining the wireless channel modeling results for the target area, and also improves the accuracy of wireless environment prediction by improving the accuracy of the wireless channel modeling results based on a highly accurate three-dimensional model reconstruction of the neural radiation field.

[0111] In some embodiments, as Figure 5 As shown, Figure 5 yes Figure 2 An optional implementation flowchart of step 103 in the diagram involves mapping the spatial position parameters of each sampling point from the same signal channel to obtain spatial signal features, including the following steps 301 to 302:

[0112] Step 301: Obtain point cloud data for the target area.

[0113] Step 302: Using point cloud data as signal constraints, the three-dimensional coordinate information and observation direction information are mapped and processed based on a preset encoding function to obtain spatial signal features, which include discrete radiation signal values ​​and volume density values.

[0114] Steps 301 to 302 are described in detail below.

[0115] In some embodiments, point cloud data of the target area is acquired so that the neural radiation field can be mapped to spatial position parameters based on the point cloud data as a signal constraint. Here, point cloud data refers to a set of vectors in a three-dimensional coordinate system, where each point contains three-dimensional coordinates, and some may contain color information (RGB) or reflection intensity information. The mapping process based on point cloud data as a signal constraint can further ensure the accuracy of the input three-dimensional coordinate information and observation direction information, thereby enabling the processing of high-accuracy spatial signal features based on accurate spatial position parameters.

[0116] Point cloud data is often used in combination with other types of sensor data (such as images, depth maps, radar signals, etc.) to provide a more comprehensive understanding of the multimodal environment, thereby providing accurate spatial location information, as well as rich color and texture information.

[0117] Furthermore, the spatial signal characteristics include the volume density value σ of the sampling point and the discrete radiation signal value, which in turn includes the radiation signal amplitude A and the signal phase ψ. The spatial signal characteristics are denoted as (σ, A, ψ).

[0118] Furthermore, based on point cloud data as signal constraints, the wireless channel model uses a multilayer perceptron deployed within it to perform position encoding γ on each input spatial location parameter. L (·) operation, γ L (·) is a sequence from R to R 2M The mapping, in form, uses the following formula <3> The encoding function shown maps the input spatial location parameters:

[0119] Y L (p)=(sin(2 0 πp), cos(2 0 πp), ..., sin(2 M-1 πp), cos(2 M-1 πp)) <3>

[0120] Furthermore, in practical applications, wireless channel models typically output the real and imaginary parts (I, Q) related to the radiated signal amplitude A and signal phase φ, rather than directly outputting the radiated signal amplitude A and signal phase φ. This is because the signal phase, modulo 2π, is not differentiable at certain points. Based on this, the following equation... <4> and <5> Convert (I, Q) into the radiation signal amplitude A and signal phase A:

[0121]

[0122] In some embodiments, as Figure 6 As shown, Figure 6 yes Figure 2 Another optional implementation flowchart of step 103 involves rendering the spatial signal features to obtain the discrete angular power spectrum corresponding to each sampling point, including the following steps 401 to 404:

[0123] Step 401: For each sampling point, obtain the interval distance value between the current sampling point and the neighboring sampling points.

[0124] Step 402: Calculate the transmittance and opacity values ​​of the sampling points based on the interval distance and volume density values.

[0125] Step 403: Determine the signal weights obtained from the volume density values ​​based on the product of the transmittance and opacity values.

[0126] Step 404: Based on the product of the discrete radiation signal value and the signal weight, the discrete angular power spectrum of the sampling point is obtained.

[0127] Steps 401 to 404 are described in detail below.

[0128] In some embodiments, by the following formula <6> and <7> The transmittance value T at the sampling point was calculated. rp and opacity value α rp :

[0129]

[0130] Where rp represents the p-th sampling point on the r-th signal channel; σ p δ is the volume density value of the current sampling point p calculated from the neural radiation field. p exp(·) represents the distance between the current sampling point p and its neighboring sampling point (p+1); exp(·) represents the natural exponential function.

[0131] Furthermore, through the following formula <8> The signal weight w is calculated. rp :

[0132] W rp =T rp α rp <8>

[0133] Furthermore, through the following formula <9> and <10> Based on the product of the discrete radiated signal value and the signal weight, the discrete angular power spectrum S of the sampling points is calculated. r :

[0134]

[0135]

[0136] Where e is the natural constant.

[0137] Step 104: Determine the predicted reference signal power of the corresponding signal channel based on the array coefficients and the discrete angle power spectrum of each sampling point.

[0138] Step 104 is described in detail below.

[0139] In some embodiments, by the following formula <11> and <12> Determine the predictive reference signal power RSRP of the signal channel:

[0140] Δ(r)=|S r | 2 <11>

[0141] RSRP≈φΔ(r) <12>

[0142] Specifically, based on the discrete angular power spectra of multiple sampling points from the same signal channel, the channel angular power spectrum Δ(r) of the corresponding signal channel is determined; then, based on the product of the array coefficients and the channel angular power spectrum, the predictive reference signal power RSRP of the signal channel where multiple sampling points are located is determined.

[0143] Step 105: Perform signal modeling on the target area based on the corresponding predicted reference signal power of all signal channels to obtain the wireless channel modeling results of the target area.

[0144] Step 105 is described in detail below.

[0145] In some embodiments, after obtaining the corresponding predicted reference signal power for each signal channel, signal modeling is performed on the target area based on multiple predicted reference signal powers to obtain the wireless channel modeling result for the target area. It is understood that the predicted reference signal power effectively reflects the signal strength on each signal channel, thereby achieving a higher accuracy in wireless channel modeling to predict multimodal wireless environments. Specifically, this is achieved through the following formula... <13> and <14> Determine the predictive reference signal power (RSRP) of the target area. final :

[0146] X = [Δ(r1), ..., Δ(r)] N )] T <13>

[0147] RSRP final ≈φX <14>

[0148] In some embodiments, as Figure 7 As shown, Figure 7This is another optional flowchart of the multimodal wireless environment prediction method provided in the embodiments of this application. The wireless channel model is trained through the following steps 501 to 505:

[0149] Step 501: Obtain the sample array coefficients of the sample base stations located within the sample area, and the sample receiving location of at least one sample receiver within the sample area.

[0150] Step 502: Determine at least one sample signal channel formed between the sample receiving position and the sample area, and determine the sample spatial position parameters of multiple sample sampling points on the sample signal channel.

[0151] Step 503: Input the sample space location parameters into the initial wireless channel model, map the corresponding sample space location parameters of each sample sampling point from the same sample signal channel to obtain the sample space signal features, and render the sample space signal features to obtain the corresponding sample discrete angle power spectrum of each sample sampling point.

[0152] Step 504: Determine the sample reference signal power of the sample signal channel based on the sample array coefficients and the corresponding sample discrete angle power spectrum of each sample sampling point.

[0153] Step 505: Calculate the model loss value based on the sample reference signal power, and adjust the parameters of the initial wireless channel model based on the model loss value to obtain the trained wireless channel model.

[0154] Steps 501 to 505 are described below.

[0155] In some embodiments, before the wireless channel model is formally put into practical application, it needs to be trained to improve the wireless channel model's ability to process input data and the accuracy of output results. Specifically, the acquisition of relevant sample data is input into the initial wireless channel model. The specific implementation methods for the initial wireless channel model to process the input sample data, as described in steps 501 to 504, are similar to steps 101 to 104 and will not be repeated here.

[0156] Furthermore, the model loss value is calculated based on the sample reference power output by the initial wireless channel model, and the parameters of the initial wireless channel model are adjusted based on the model loss value to obtain the trained wireless channel model. The adjusted parameters include, but are not limited to, path loss parameters, shadow fading parameters, multipath effect parameters, Doppler effect parameters, and noise parameters. The specific parameters to be adjusted can be set according to the actual situation, and this application embodiment does not impose any limitations on this.

[0157] In some embodiments, as Figure 8 As shown, Figure 8 yes Figure 7 An optional implementation flowchart of step 505 in the figure, which calculates the model loss value based on the sample reference signal power, includes the following steps 601 to 604:

[0158] Step 601: Obtain the verification reference signal power and verification point cloud depth values ​​of the sample area.

[0159] Step 602: Calculate the wireless signal loss value based on the difference between the sample reference signal power and the verification reference signal power.

[0160] Step 603: Determine the sample point cloud depth value based on multiple sample sampling points from the same sample signal channel.

[0161] Step 604: Calculate the point cloud depth loss value based on the difference between the sample point cloud depth value and the validation point cloud depth value.

[0162] Steps 601 to 604 are described in detail below.

[0163] In some embodiments, the model loss values ​​include wireless signal loss and point cloud depth loss. The parameters of the initial wireless channel model are adjusted using the calculated wireless signal loss and point cloud depth loss values ​​to obtain the trained wireless channel model.

[0164] Specifically, through the following formula <15> The calculated wireless signal loss value L signal :

[0165] L signal =||RSRP test -φX||2 <15>

[0166] Among them, RSRP test To verify the reference signal power, φX is the sample reference signal power; ||RSRP test -φX||2 is the I2 norm function.

[0167] Furthermore, through the following formula <16> and <17> The point cloud depth loss value L was calculated. depth :

[0168]

[0169] Where z is the depth value of the sample point cloud. To verify the point cloud depth value, p n and p f These represent the nearest and farthest sampling points on a given signal channel, respectively; w rpδ represents the signal weight at the p-th sampling point on the corresponding signal channel r. p-1 The distance between the p-th sampling point and the (p-1)-th sampling point is represented by ; D represents the set of all signal channels; E is used to characterize the expected calculation.

[0170] In some embodiments, as Figure 9 As shown, Figure 9 yes Figure 7 Another optional implementation flowchart of step 505 in the diagram involves calculating the model loss value based on the sample reference signal power, adjusting the parameters of the initial wireless channel model based on the model loss value, and obtaining the trained wireless channel model, including the following steps 701 to 702:

[0171] Step 701: Superimpose the wireless signal loss value and the point cloud depth loss value to obtain the total loss value.

[0172] Step 702: Jointly adjust the parameters of the initial wireless channel model based on the total loss value to obtain the trained wireless channel model.

[0173] Steps 701 to 702 are described in detail below.

[0174] In some embodiments, by the following formula <18> The total loss value L is calculated as follows:

[0175] L = L signal +L depth <18>

[0176] In addition to training the initial wireless channel model using both the wireless signal loss value and the point cloud depth loss value separately, a total loss value can be obtained by superimposing the wireless signal loss value and the point cloud depth loss value. The parameters of the initial wireless channel model can then be jointly adjusted based on the total loss value to obtain the trained wireless channel model. This improves the output accuracy of the trained wireless channel model in this embodiment while reducing energy waste.

[0177] Furthermore, the performance of the trained wireless channel model was validated:

[0178] like Figure 10 and Figure 11 As shown, Figure 10 This is a schematic diagram of a target area for the multimodal wireless environment prediction method provided in this application embodiment. Figure 11This is a schematic diagram of target area data acquisition for the multimodal wireless environment prediction method provided in this application embodiment. For a certain target area, a channel is generated using Ray Tracing, while the horizontal angle and downtilt angle of the base station are adjusted by 20 degrees and 2 degrees respectively, and 3 dB noise is added. The traditional NeRF2, NeWRF, and LSCM methods are selected as control groups, and the test comparison results are as follows:

[0179]

[0180] It should be noted that when the same dataset is processed using four different methods, and considering the Mean Absolute Error (MAE) and Top8-MAE parameters (smaller values ​​indicate better performance), the model proposed in this application's embodiments shows better performance. This demonstrates that the model proposed in this application's embodiments, while implementing a multimodal wireless environment prediction method, also addresses the problems of complex wireless signal propagation and data scarcity, thus achieving better performance.

[0181] like Figure 12 As shown, Figure 12 This is an optional flowchart of the multimodal wireless environment prediction device provided in the embodiments of this application. The multimodal wireless environment prediction device includes the following modules 801 to 805:

[0182] The acquisition module 801 is used to acquire the array coefficient of the target base station located in the target area, and the receiving position of at least one receiver in the target area.

[0183] The sampling point determination module 802 is used to determine at least one signal channel formed between the receiving position and the target area, and to determine the spatial position parameters of multiple sampling points on the signal channel.

[0184] The rendering module 803 is used to input spatial location parameters into a pre-trained wireless channel model, map the corresponding spatial location parameters of each sampling point from the same signal channel to obtain spatial signal features, and render the spatial signal features to obtain the corresponding discrete angular power spectrum of each sampling point.

[0185] The prediction module 804 is used to determine the prediction reference signal power of the signal channel based on the array coefficients and the corresponding discrete angular power spectrum of each sampling point.

[0186] Target result 805 is used to perform signal modeling of the target area based on the corresponding predicted reference signal power of all signal channels, and to obtain the wireless channel modeling result of the target area.

[0187] The multimodal wireless environment prediction method, apparatus, electronic device, and storage medium proposed in this application acquire the array coefficients of a target base station located within a target area, and the receiving position of at least one receiver within the target area; determine at least one signal channel formed between the receiving position and the target area, and determine the spatial position parameters of multiple sampling points on the signal channel; input the spatial position parameters into a pre-trained wireless channel model, map the corresponding spatial position parameters of each sampling point from the same signal channel to obtain spatial signal features, and render the spatial signal features to obtain the corresponding discrete angular power spectrum of each sampling point; determine the predicted reference signal power of the signal channel based on the array coefficients and the corresponding discrete angular power spectrum of each sampling point; and perform signal modeling of the target area based on the corresponding predicted reference signal power of all signal channels to obtain the wireless channel modeling result of the target area. This application can improve the accuracy of channel modeling results when the required measured data is scarce.

[0188] The specific implementation of this multimodal wireless environment prediction device is basically the same as the specific implementation of the multimodal wireless environment prediction method described above, and will not be repeated here.

[0189] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described multimodal wireless environment prediction method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0190] like Figure 13 As shown, Figure 13 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes:

[0191] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0192] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called and executed by the processor 901 using the multimodal wireless environment prediction method of the embodiments of this application.

[0193] The input / output interface 903 is used to implement information input and output;

[0194] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0195] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0196] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0197] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multimodal wireless environment prediction method.

[0198] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0199] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0200] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0201] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0203] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0204] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0205] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0206] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0207] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0208] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0209] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A multimodal wireless environment prediction method, characterized in that, The method includes: Obtain the array coefficients of the target base station located within the target area, and the receiving location of at least one receiver within the target area; Determine at least one signal channel formed between the receiving position and the target area, and determine the spatial position parameters of multiple sampling points on the signal channel; The spatial location parameters are input into a pre-trained wireless channel model. The spatial location parameters of each sampling point from the same signal channel are mapped to obtain spatial signal features. The spatial signal features are then rendered to obtain the discrete angular power spectrum of each sampling point. The predicted reference signal power of the signal channel is determined based on the array coefficients and the discrete angle power spectrum corresponding to each sampling point. Signal modeling of the target area is performed based on the predicted reference signal power corresponding to all the signal channels to obtain the wireless channel modeling result of the target area.

2. The method according to claim 1, characterized in that, The target area includes multiple spatial grids; Determining at least one signal channel formed between the receiving location and the target area, and determining the spatial position parameters of multiple sampling points on the signal channel, includes: Acquire a two-dimensional image of the target area and determine the camera pose and camera orientation when capturing the two-dimensional image; Select one of the plurality of spatial grids as the target spatial grid, and determine at least one signal channel formed between the target spatial grid and the receiving position, wherein the signal channel includes the plurality of the sampling points; Based on the camera pose and the camera orientation, the three-dimensional coordinate information and observation direction information of each sampling point on each signal channel are determined to obtain the spatial position parameters.

3. The method according to claim 2, characterized in that, The spatial position parameters corresponding to each sampling point from the same signal channel are mapped to obtain spatial signal features, including: Obtain point cloud data of the target area; Using the point cloud data as signal constraints, the three-dimensional coordinate information and the observation direction information are mapped based on a preset encoding function to obtain spatial signal features, wherein the spatial signal features include discrete radiation signal values ​​and volume density values.

4. The method according to claim 3, characterized in that, The rendering process of the spatial signal features to obtain the discrete angle power spectrum corresponding to each sampling point includes: For each of the sampling points, obtain the interval distance value between the current sampling point and its neighboring sampling points; Based on the interval distance value and the volume density value, the transmittance value and opacity value of the sampling point are calculated. The signal weights obtained from the volume density values ​​are determined based on the product of the transmittance value and the opacity value. The discrete angular power spectrum of the sampling point is obtained by multiplying the discrete radiation signal value and the signal weight.

5. The method according to claim 1, characterized in that, The wireless channel model is obtained through the following steps: Obtain the sample array coefficients of the sample base stations located within the sample area, and the sample receiving positions of at least one sample receiver within the sample area; Determine at least one sample signal channel formed between the sample receiving position and the sample area, and determine the sample spatial position parameters of multiple sample sampling points on the sample signal channel; The sample space location parameters are input into the initial wireless channel model. The corresponding sample space location parameters of each sample sampling point from the same sample signal channel are mapped to obtain sample space signal features. The sample space signal features are then rendered to obtain the corresponding sample discrete angle power spectrum of each sample sampling point. The sample reference signal power of the sample signal channel is determined based on the sample array coefficients and the sample discrete angle power spectrum corresponding to each sample sampling point. The model loss value is calculated based on the sample reference signal power, and the parameters of the initial wireless channel model are adjusted based on the model loss value to obtain the trained wireless channel model.

6. The method according to claim 5, characterized in that, The model loss values ​​include wireless signal loss values ​​and point cloud depth loss values; The step of calculating the model loss value based on the sample reference signal power includes: Obtain the verification reference signal power and verification point cloud depth values ​​of the sample region; The wireless signal loss value is calculated based on the difference between the sample reference signal power and the verification reference signal power. The depth value of the sample point cloud is determined based on multiple sample sampling points from the same sample signal channel; The point cloud depth loss value is calculated based on the difference between the sample point cloud depth value and the verification point cloud depth value.

7. The method according to claim 6, characterized in that, The step of calculating the model loss value based on the sample reference signal power, and adjusting the parameters of the initial wireless channel model based on the model loss value to obtain the trained wireless channel model includes: The total loss value is obtained by superimposing the wireless signal loss value and the point cloud depth loss value. The parameters of the initial wireless channel model are jointly adjusted based on the total loss value to obtain the trained wireless channel model.

8. A multimodal wireless environment prediction device, characterized in that, The device includes: The acquisition module is used to acquire the array coefficient of the target base station located in the target area, and the receiving position of at least one receiver in the target area; The sampling point determination module is used to determine at least one signal channel formed between the receiving position and the target area, and to determine the spatial position parameters of multiple sampling points on the signal channel; The rendering module is used to input the spatial location parameters into a pre-trained wireless channel model, perform mapping processing on the spatial location parameters corresponding to each sampling point from the same signal channel to obtain spatial signal features, and perform rendering processing on the spatial signal features to obtain the discrete angle power spectrum corresponding to each sampling point. The prediction module is used to determine the prediction reference signal power of the signal channel based on the array coefficients and the discrete angle power spectrum corresponding to each sampling point. The target result is used to perform signal modeling on the target area based on the predicted reference signal power corresponding to all the signal channels, so as to obtain the wireless channel modeling result of the target area.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the multimodal wireless environment prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multimodal wireless environment prediction method according to any one of claims 1 to 7.

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