Path loss determination method and apparatus, storage medium, and electronic device

Abstract

Embodiments of the present disclosure provide a path loss determination method and apparatus, a storage medium, and an electronic device. The method comprises: determining a superimposed field image of a target grid scene on the basis of a first image, a second image, and an incident field image of the target grid scene, wherein the first image is used for indicating the position of a first object in the target grid scene, and the second image is used for indicating the position of a second object in the target grid scene; determining a first loss function on the basis of the incident field image and the superimposed field image, and determining a second loss function on the basis of the superimposed field image and drive test data of the target grid scene; determining a target loss function on the basis of the first loss function and the second loss function; and training a neural network model on the basis of the target loss function, so as to determine the path loss of the target grid scene by means of the trained neural network model. The method can solve the problem in the prior art that path loss estimation methods cannot take both accuracy and calculation efficiency into account.

Description

Method and device for determining path loss, storage medium, and electronic device

[0001] This disclosure claims priority to a Chinese patent application filed with the Patent Office of China on February 22, 2024, with application number 202410199012.5 and invention name “Method and device for determining path loss, storage medium and electronic device”, the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0002] The embodiments of the present disclosure relate to the field of communications, and in particular, to a method and device for determining path loss, a storage medium, and an electronic device. Background Art

[0003] Path loss estimation is the process of estimating the path loss of wireless signal transmission given base station location information, engineering parameters, building distribution information in urban scenarios, and the location where users receive signals.

[0004] Regarding path loss, according to the driving modes of existing solutions, there are two driving modes: knowledge-driven and data-driven.

[0005] Among these approaches, data-driven approaches are used to estimate path loss for wireless signal transmission. These methods employ deep learning models such as artificial neural networks, convolutional neural networks, graph neural networks, and generative adversarial networks to estimate path loss. Relying on trained models, these methods can quickly generate results in batches. Furthermore, due to the powerful ability of neural networks to fit complex nonlinear relationships, they achieve both accuracy and efficiency. However, data-driven approaches rely heavily on the data used during training, resulting in a lack of generalization.

[0006] The path loss of wireless signal transmission is estimated through a knowledge-driven approach. For example, various channel models under the 3GPP standard (3rd Generation Partnership Project) start from the nature of electromagnetic wave transmission, solve Maxwell's equations at different scales, calculate the electromagnetic field distribution, and obtain the path loss by statistically analyzing the energy attenuation of the signal. This method fully considers the building information and electromagnetic propagation laws in urban scenes, thereby improving the accuracy and generalization of path loss estimation. However, in the solution process, it is often necessary to divide the scene into fine-grained grids and perform complex calculations in each grid, which increases the amount of calculation and reduces the computational efficiency.

[0007] That is to say, the path loss estimation method in the prior art cannot take into account both accuracy and computational efficiency.

[0008] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology.

[0009] Summary of the Invention

[0010] The embodiments of the present disclosure provide a method and device for determining path loss, a storage medium, and an electronic device to at least solve the problem that the path loss estimation method in the prior art cannot achieve both accuracy and computational efficiency.

[0011] According to one embodiment of the present disclosure, a method for determining path loss is provided, including: determining an overlay field image of a target grid scene based on a first image, a second image and an incident field image of the target grid scene, wherein the first image is used to indicate the position of a first object in the target grid scene, and the second image is used to indicate the position of a second object in the target grid scene; determining a first loss function based on the incident field image and the overlay field image; and determining a second loss function based on the overlay field image and road test data of the target grid scene; determining a target loss function based on the first loss function and the second loss function; and training a neural network model based on the target loss function to determine the path loss of the target grid scene through the trained neural network model.

[0012] In an exemplary embodiment, before determining the superimposed field image of the target grid scene based on the first image, the second image and the incident field image of the target grid scene, the method further includes: determining the first position of the first object in the target grid scene based on the first image, and determining the second position of the second object in the target grid scene based on the second image; determining the incident field of each grid in the target grid scene based on the first position and the second position; and determining the incident field image of the target grid scene based on the incident field of each grid.

[0013] In an exemplary embodiment, determining a superimposed field image of a target grid scene based on a first image, a second image and an incident field image of the target grid scene includes: determining a first real part in complex form corresponding to the incident field image and a first imaginary part in complex form corresponding to the incident field image; determining a first incident field image based on the first real part, and determining a second incident field image based on the first imaginary part; determining the superimposed field image based on the first image, the second image, the first incident field image and the second incident field image.

[0014] In an exemplary embodiment, the superimposed field image is determined based on the first image, the second image, the first incident field image and the second incident field image, including: extracting the first image feature of the first image, the second image feature of the second image, the third image feature of the first incident field image and the fourth image feature of the second incident field image, respectively; determining the associated image features of the first image, the second image, the first incident field image and the second incident field image based on the first image feature, the second image feature, the third image feature and the fourth image feature; and determining the superimposed field image based on the associated image features and the volume integral equation.

[0015] In an exemplary embodiment, determining a second loss function based on the overlay field image and the road test data of the target grid scene includes: inputting the overlay field image into a road loss map converter to instruct the road loss map converter to convert the overlay field image into a path loss map corresponding to the target grid scene; correcting the path loss map based on the road test data and the overlay field image to determine a corrected path loss map; and determining the second loss function based on the corrected target path loss map and the road test data.

[0016] In an exemplary embodiment, the path loss map is corrected according to the road test data and the overlay field image to determine a corrected target path loss map, including: inputting the overlay field image into a deep learning network to determine a second real part in complex form corresponding to the overlay field image and a second imaginary part in complex form corresponding to the overlay field image; calculating the path loss data of the path loss map according to the second real part and the second imaginary part; and correcting the path loss map according to the deviation between the path loss data and the road test data to determine the target path loss map.

[0017] In an exemplary embodiment, training a neural network model according to the target loss function includes: a calculation step of calculating the gradient of the target loss function; an adjustment step of adjusting the gradient according to a preset optimization algorithm to update the neural network model according to the adjusted gradient; a determination step of determining the calculation result of the target loss function according to the updated neural network model; and looping the adjustment step and the determination step until the final calculation result is less than or equal to a preset threshold.

[0018] In an exemplary embodiment, determining a target loss function based on the first loss function and the second loss function includes: determining a first weight of the first loss function in the neural network model, or determining a second weight of the second loss function in the neural network model; when the first weight has been determined, determining a first product of the first weight and the first loss function, and determining the sum of the first product and the second loss function as the target loss function; when the second weight has been determined, determining a second product of the second weight and the second loss function; and determining the sum of the second product and the first loss function as the target loss function.

[0019] According to another embodiment of the present disclosure, a device for determining path loss is provided, including: a first determination module, configured to determine an overlay field image of a target grid scene based on a first image, a second image and an incident field image of the target grid scene, wherein the first image is used to indicate the position of a first object in the target grid scene, and the second image is used to indicate the position of a second object in the target grid scene; a second determination module, configured to determine a first loss function based on the incident field image and the overlay field image; and determine a second loss function based on the overlay field image and road test data of the target grid scene; a third determination module, configured to determine a target loss function based on the first loss function and the second loss function; and a training module, configured to train a neural network model based on the target loss function, so as to determine the path loss of the target grid scene through the trained neural network model.

[0020] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0021] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments.

[0022] According to yet another embodiment of the present disclosure, a computer program product is provided, including computer instructions, which implement the steps of the method described in various embodiments of the present disclosure when executed by a processor.

[0023] Through the present disclosure, the superimposed field image of the target grid scene is determined based on the first image for indicating the position of the first object in the target grid scene, the second image for indicating the position of the second object in the target grid scene, and the incident field image of the target grid image. The first loss function is determined based on the incident field image and the superimposed field image, and the second loss function is determined based on the superimposed field image and the road test data. The target loss function for training the neural network model is determined based on the first loss function and the second loss function, and then the path loss of the target grid scene is determined by the trained neural network model. That is, the embodiment of the present disclosure defines a technical solution for determining the first loss function by the superimposed field image and the incident field image, and determining the second loss function by the road test data and the superimposed field image, and then determining the target loss function for training the neural network model based on the first loss function and the second loss function, so as to calculate the path loss by the trained neural network model. In the present disclosure, the physical formula describing the incident field and the superimposed field is introduced into the target loss function, and the road test data is introduced into the target loss function. This solves the problem in the existing technology that the path loss estimation method cannot take into account both accuracy and computational efficiency. After the neural network model is trained through the target loss function, it not only improves the accuracy of path loss estimation but also improves the computational efficiency of path loss estimation in the process of determining path loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG1 is a hardware structure block diagram of a computer terminal for a method for determining path loss according to an embodiment of the present disclosure;

[0025] FIG2 is a flow chart of a method for determining path loss according to an embodiment of the present disclosure;

[0026] FIG3 is a schematic diagram of a path loss estimation model according to an optional embodiment of the present disclosure;

[0027] FIG4 is a schematic diagram of a two-dimensional wireless communication scenario according to an optional embodiment of the present disclosure;

[0028] FIG5 is a network structure diagram of a knowledge-driven module UNet according to an optional embodiment of the present disclosure;

[0029] FIG6 is a network structure diagram of a data-driven module UNet according to an optional embodiment of the present disclosure;

[0030] FIG7 is a structural block diagram of a device for determining path loss according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0033] The method embodiments provided in the embodiments of the present disclosure can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 is a hardware structure block diagram of a computer terminal for a method for determining path loss in an embodiment of the present disclosure. As shown in Figure 1, the computer terminal may include one or more (only one is shown in Figure 1) processors 102 (the processor 102 may include but is not limited to a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that the structure shown in Figure 1 is only for illustration and does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than those shown in Figure 1, or have a configuration different from that shown in Figure 1.

[0034] Memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the path loss determination method in the embodiments of the present disclosure. Processor 102 executes the computer programs stored in memory 104 to execute various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory located remotely from processor 102, which can be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0035] The transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a computer terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] In this embodiment, a method for determining path loss is provided, which is run on the computer terminal in FIG1 . FIG2 is a flow chart of the method for determining path loss according to an embodiment of the present disclosure. As shown in FIG2 , the flow chart includes the following steps:

[0037] Step S202: determining a superimposed field image of the target grid scene based on the first image, the second image, and the incident field image of the target grid scene, wherein the first image is used to indicate the position of the first object in the target grid scene, and the second image is used to indicate the position of the second object in the target grid scene;

[0038] It is understood that the first object may be a base station, and thus the first image may be an image indicating the location of the base station in the target grid scene. The second object may be a building, and thus the second image may be an image indicating the location of the building in the target grid scene. The target grid scene includes: base stations, buildings, roads, etc.

[0039] For example, when the first object is a base station and the second object is a building, a two-dimensional space containing at least the base station and the building can be gridded using the moment method to form a target grid scene. The base station position in the target grid scene is set to a first value (optionally, the first value is 1), and the other positions in the target grid scene except the base station position are set to a second value (optionally, the second value is 0), thereby generating a first image. Similarly, the building position in the target grid scene is set to a first value (optionally, the first value is 1), and the other positions in the target grid scene except the building position are set to a second value (optionally, the second value is 0), thereby generating a second image.

[0040] Step S204, determining a first loss function based on the incident field image and the superimposed field image; and determining a second loss function based on the superimposed field image and the drive test data of the target grid scene;

[0041] Step S206, determining a target loss function according to the first loss function and the second loss function;

[0042] It can be understood that the target loss function is determined by the first and second loss functions. That is, the neural network model trained using the target loss function satisfies both the physical formulas in the first loss function and the drive test data in the second loss function. This allows the neural network model to improve the accuracy and generalization of path loss estimation while also increasing the computational efficiency of path loss estimation.

[0043] Step S208: training a neural network model according to the target loss function to determine the path loss of the target grid scene through the trained neural network model.

[0044] Through the above steps, the superposition field image of the target grid scene is determined based on the first image for indicating the position of the first object in the target grid scene, the second image for indicating the position of the second object in the target grid scene, and the incident field image of the target grid image. A first loss function is determined based on the incident field image and the superposition field image, and a second loss function is determined based on the superposition field image and the road test data. A target loss function for training the neural network model is determined based on the first loss function and the second loss function, and then the path loss of the target grid scene is determined by the trained neural network model. That is, the embodiment of the present disclosure defines a technical solution for determining the first loss function by the superposition field image and the incident field image, and determining the second loss function by the road test data and the superposition field image, and then determining the target loss function for training the neural network model based on the first loss function and the second loss function, so as to calculate the path loss by the trained neural network model. In the present disclosure, both the physical formula describing the incident field and the superposition field are introduced into the target loss function, and the road test data are introduced into the target loss function. The problem that the path loss estimation method in the existing technology cannot take into account both accuracy and computational efficiency is solved. After the neural network model is trained through the target loss function, in the process of determining the path loss, both the accuracy of the path loss estimation and the computational efficiency of the path loss estimation are improved.

[0045] Optionally, before determining the superimposed field image of the target grid scene based on the first image, the second image and the incident field image of the target grid scene in step S202, the method further includes: determining the first position of the first object in the target grid scene based on the first image, and determining the second position of the second object in the target grid scene based on the second image; determining the incident field of each grid in the target grid scene based on the first position and the second position; and determining the incident field image of the target grid scene based on the incident field of each grid.

[0046] It can be seen from the above technical solution that before determining the superimposed field image, it is necessary to determine the incident field image of the target grid scene. There are many methods for determining the incident field image. In the present disclosure, the incident field image can be determined based on the first image and the second image. Specifically: by formula: Determine the incident field result of the second position (each building) relative to the first position (base station) in the target grid scene, and then determine the incident field image based on the incident field result. Represents the incident field result, p n Represents the second position, ptx represents the first position, and G(·) represents the Green's function in two-dimensional space.

[0047] in, Represents the second form of the 0th order Hankel function, which is a type of Bessel function, and k is the wave number ε0 is the dielectric constant in free space, and μ0 represents the magnetic permeability in a vacuum, often referred to as vacuum permeability, which is a physical constant.

[0048] Optionally, the above-mentioned step S202 of determining the superimposed field image of the target grid scene based on the first image, the second image and the incident field image of the target grid scene includes: determining the first real part in complex form corresponding to the incident field image and the first imaginary part in complex form corresponding to the incident field image; determining the first incident field image based on the first real part, and determining the second incident field image based on the first imaginary part; determining the superimposed field image based on the first image, the second image, the first incident field image and the second incident field image.

[0049] Wherein, determining the superimposed field image according to the first image, the second image, the first incident field image and the second incident field image includes: respectively extracting the first image feature of the first image, the second image feature of the second image, the third image feature of the first incident field image and the fourth image feature of the second incident field image; determining the associated image features of the first image, the second image, the first incident field image and the second incident field image according to the first image feature, the second image feature, the third image feature and the fourth image feature; and determining the superimposed field image according to the associated image features and the volume integral equation.

[0050] The above process defines the technical solution for determining the superimposed field image, specifically:

[0051] 1) determining a first incident field image and a second incident field image;

[0052] The incident field image has a complex form and can be divided into a real image determined by the first real part (i.e., the first incident field image of the present disclosure) and an imaginary image determined by the first imaginary part (i.e., the second incident field image of the present disclosure). The real image can be used to indicate the intensity distribution of light, i.e., the brightness or darkness distribution of light. The imaginary image can be used to indicate phase information, i.e., the phase distribution of the light wave.

[0053] 2) determining a first image feature of the first image, a second image feature of the second image, a third image feature of the first incident field image, and a fourth image feature of the second incident field image;

[0054] The first image, the second image, the first incident field image, and the second incident field image are input into a unified network model (i.e., a convolutional neural network with UNet as its core, also referred to as a UNet network model). The encoder extracts the first image features, the second image features, the third image features, and the fourth image features. The image features include, but are not limited to, image color, texture, shape, and edge features.

[0055] 3) determining associated image features;

[0056] The decoder determines the associated image feature through the first image feature, the second image feature, the third image feature, and the fourth image feature. Specifically:

[0057] The decoder can complete the determination of associated image features through a series of steps, such as feature matching: determining the association relationship between the first image feature, the second image feature, the third image feature and the fourth image feature; alignment and registration: registering the first image feature, the second image feature, the third image feature and the fourth image feature through an image registration algorithm; and feature fusion: fusing features through weighted averaging, feature cascading, etc.

[0058] 4) Determine a superposition field image according to the associated image features and the volume integral equation.

[0059] Through the volume integral equation (I+Wχ)E tot =E inc And the associated image features determine the superimposed field image. Among them, E tot represents the superimposed field image, E inc Represents the incident field image, W represents the N×N coefficient matrix obtained by integrating the Green's function in the unit grid, and χ represents a diagonal matrix whose diagonal elements are the contrast of each grid.

[0060] After determining the superposition field image, the first loss function can be determined based on the incident field image and the superposition field image, and the volume integral equation (I+Wχ)E in matrix form is converted into tot =E inc Introducing the first loss function, taking the incident field image as the learning label, continuously training the superposition field image, and optimizing the network parameters, the output superposition field is close to the theoretical numerical calculation result. E' tot Represents the output superposition field.

[0061] After determining the overlay field image, a second loss function can also be determined based on the overlay field image and the road test data of the target grid scene, including: inputting the overlay field image into a road loss map converter to instruct the road loss map converter to convert the overlay field image into a path loss map corresponding to the target grid scene; correcting the path loss map based on the road test data and the overlay field image to determine a corrected path loss map; and determining the second loss function based on the corrected target path loss map and the road test data.

[0062] Among them, the path loss map is corrected according to the road test data and the overlay field image to determine the corrected target path loss map, including: inputting the overlay field image into a deep learning network to determine the second real part in complex form corresponding to the overlay field image and the second imaginary part in complex form corresponding to the overlay field image; calculating the path loss data of the path loss map according to the second real part and the second imaginary part; and correcting the path loss map according to the deviation between the path loss data and the road test data to determine the target path loss map.

[0063] It is understood that the overlay field image is converted into a path loss map of the target grid scene by the path loss map converter. Further, the second real part and the second imaginary part of the overlay field image are determined. By formula Determine the magnitude of the superposition field. represents the amplitude of the superposition field, represents the second real part, Represents the second imaginary part. By superimposing the field amplitudes, we can determine the energy of the electric field, and thus obtain a preliminary result of path loss, namely path loss data. The aforementioned drive test data is actual drive test data, and there will be a certain deviation between the path loss data and the drive test data. This deviation can be learned through the UNet network, and the path loss map can be corrected to obtain a corrected path loss map.

[0064] Furthermore, the second loss function is determined by the corrected path loss map and the drive test data. Where pl' represents the corrected path loss map, pl dat Represents drive test data.

[0065] Optionally, training a neural network model according to the target loss function includes: a calculation step of calculating the gradient of the target loss function; an adjustment step of adjusting the gradient according to a preset optimization algorithm to update the neural network model according to the adjusted gradient; a determination step of determining the calculation result of the target loss function according to the updated neural network model; and looping the adjustment step and the determination step until the final calculation result is less than or equal to a preset threshold.

[0066] The role of the objective loss function is to train the neural network model and improve the accuracy, generalization and computational efficiency of the neural network model in estimating path loss.

[0067] By determining the gradient of the target loss function and updating the neural network model by continuously adjusting the gradient of the target loss function, when the calculation result of the target loss function determined by the neural network model is less than or equal to the preset threshold, it can be determined that the neural network model training is completed.

[0068] Optionally, determining the target loss function based on the first loss function and the second loss function includes: determining the first weight of the first loss function in the neural network model, or determining the second weight of the second loss function in the neural network model; when the first weight has been determined, determining the first product of the first weight and the first loss function, and determining the sum of the first product and the second loss function as the target loss function; when the second weight has been determined, determining the second product of the second weight and the second loss function; and determining the sum of the second product and the first loss function as the target loss function.

[0069] When λ is the first weight, the objective loss function When λ is the second weight, the objective loss function in, represents the first loss function, represents the second loss function,

[0070] In order to better understand the process of the above-mentioned method for determining the path loss, the implementation process of the above-mentioned method for determining the path loss is described below in combination with an optional embodiment, but it is not intended to limit the technical solution of the embodiment of the present disclosure.

[0071] In wireless communications, channel modeling is generally based on two aspects: large-scale fading and small-scale fading. Large-scale fading primarily considers path loss, which consists of propagation loss and shadow fading. Propagation loss is related to the distance between the transmitter and receiver, while shadow fading is caused by large obstacles in the scene (such as mountains and buildings). Small-scale fading is primarily caused by multipath effects and is related to the dynamic changes in the wireless transmission environment.

[0072] Existing methods for estimating path loss, whether knowledge-driven or data-driven, present certain challenges. For example, methods based on traditional empirical formulas and protocol standards lack accuracy and generalizability. Methods based on electromagnetic calculations require numerical solutions to Maxwell's equations, which consumes computing and storage resources and impacts computational efficiency. Data-driven neural network methods, however, lack generalizability because their model effectiveness depends on training data. In other words, existing methods for estimating path loss fail to balance accuracy, generalizability, and computational efficiency.

[0073] An optional embodiment of the present disclosure proposes a knowledge-data collaborative path loss estimation model. This optional embodiment of the present disclosure uses volume integral equations as the physical knowledge of electromagnetic calculations and replaces traditional numerical solution methods by designing and constructing neural networks. Specifically:

[0074] Optional embodiment (1)

[0075] FIG3 is a schematic diagram of a path loss estimation model according to an optional embodiment of the present disclosure. As shown in FIG3 , the path loss estimation model includes: an input part, a knowledge-driven module, and a data-driven module.

[0076] 1) Input: This section is used to construct digital representations of base stations and scatterers by obtaining the spatial distribution and size information of typical buildings and roads in urban scenes from public map data. This involves gridding the two-dimensional scene using the method of moments (i.e., the target grid scene of the present disclosure). Figure 4 is a schematic diagram of a two-dimensional wireless communication scenario according to an optional embodiment of the present disclosure. As shown in Figure 4, the wireless communication scenario in an urban area can be considered an electromagnetic propagation scenario, with base stations as the excitation source and buildings, vegetation, etc. in the city as scatterers.

[0077] The location information in a two-dimensional scene can be represented by a tuple p = (x, y), where x represents the horizontal coordinate and y represents the vertical coordinate. For the base station location map (i.e., the first image of the present disclosure), the grid where the base station location (i.e., the first object of the present disclosure) is located is set to 1, and the remaining grids are set to 0. For the building distribution map (i.e., the second image of the present disclosure), the grid covered by the building (i.e., the second object of the present disclosure) is set to 1, and the uncovered grid is set to 0. At the same time, according to the incident field calculation formula: Determine the incident field results for each grid cell.

[0078] in, Represents the incident field result of each grid, p tx represents the base station location, p n represents building coverage, and G(·) represents the Green's function in two-dimensional space: represents the second form of the 0th order Hankel function, which is a type of Bessel function. k is the wave number, The electric field incident field image of the target grid scene is thus determined. Since the complex form corresponding to the electric field incident field image has two parts, the real part and the imaginary part, the electric field incident field image can be split into the incident field real part image and the incident field imaginary part image.

[0079] Therefore, the input images of the input part are: base station location map, building distribution map, incident field real part image and incident field imaginary part image.

[0080] The above four images are input into the convolutional neural network with UNet as the core.

[0081] 2) Knowledge-driven module: used to capture the complex nonlinear relationship between base station location, building distribution, incident electric field and target electric field superposition field through UNet.

[0082] The volume integral equation (I+Wχ)E in matrix form tot =E inc Introduced into the loss function, where E inc Represents the incident electric field, where I is the N×N identity matrix, W is the N×N coefficient matrix obtained by integrating the Green's function within the unit grid, and χ is a diagonal matrix whose diagonal elements are the contrast of each grid. tot Represents the target electric field superposition field.

[0083] FIG5 is a network structure diagram of a knowledge-driven module UNet according to an optional embodiment of the present disclosure. As shown in FIG5 , in the knowledge-driven module, the incident field E inc As a learning label, the network parameters are optimized through continuous training to make the output superposition field E' tot It is close to the theoretical numerical calculation results.

[0084] Furthermore, the first loss function is determined based on the incident field and the superposition field after training.

[0085] 3) Data driven module: The electric field strength calculated by electromagnetics is converted into a path loss map through the path loss map converter. In the knowledge driven module, the UNet network outputs the results of two channels, namely the real part of the estimated superposition field and the real part of the superposition field. and the imaginary part Calculate the amplitude of the superposition field by formula: The energy of the electric field is then calculated and compared with the transmission power of the transmitting source to determine the preliminary distribution of the path loss.

[0086] The preliminary distribution results of the path loss calculated by electromagnetics have certain deviations from the actual road test data. Figure 6 is a diagram of the network structure of the data driving module UNet according to an optional embodiment of the present disclosure. In the data driving module, the present disclosure adopts the UNet network structure shown in Figure 6 to learn the deviation between the preliminary distribution results of the path loss and the actual road test data, and corrects the intermediate output to obtain the corrected path loss map pl' (i.e., the corrected path loss map of the present disclosure). During the training process, the path loss map data pl dat The road test data disclosed in this disclosure is used as the true value label and compared with the corrected road loss map pl′. The gap between them is narrowed by continuously optimizing the network parameters, making the estimation result closer to the distribution of the real data.

[0087] Furthermore, the second loss function is determined:

[0088] After determining the first loss function and the second loss function, the two loss functions can be combined to form the loss function of the entire network. Specifically, the relative weight corresponding to the first loss function or the relative weight of the second loss function can be determined to determine the target loss function.

[0089] After determining the target loss function, the neural network model needs to be trained. By calculating the gradient of the target loss function and performing gradient backpropagation, while continuously reducing the result of the target loss function, the purpose of optimizing the network parameters is achieved, so that the output result of the neural network model gradually approaches the true value of the road test data.

[0090] Finally, the trained neural network model estimates path loss. Specifically, the target scene (i.e., the target grid scene disclosed herein) is preprocessed according to the input dimensions to produce a base station location map, a building distribution map, and an electric field incident distribution map. These features are then fed into the trained neural network model, and the resulting corrected path loss map serves as the model's path loss estimation result.

[0091] Optional embodiment (two)

[0092] The RadioMapSeer dataset is used as a dataset, which includes 700 areas in six different cities around the world, each with 80 different base station locations. For each scenario, the dataset includes a base station location map (the first image in this disclosure), a building distribution map (the second image in this disclosure), and a road damage map.

[0093] The relative dielectric constant of the building and the relative dielectric constant of the air are set, and the real image (i.e., the first incident field image disclosed in the present invention) and the imaginary image (i.e., the second incident field image disclosed in the present invention) of the incident field are calculated based on the location information of the base station. 500 of the 700 different areas are randomly selected as training sets, 100 as validation sets, and the remaining 100 as test sets. This division allows the model to face different building distribution characteristics during training and testing, and while accurately evaluating the model performance, it can also ensure the verification of the model's generalization ability. By setting the relevant training parameters (epoch number, batch size, learning rate, optimizer, etc.), continuously sampling from the training set and inputting it into the model, the parameters of the model are updated, and finally a trained model (i.e., the trained neural network model disclosed in the present invention) is obtained.

[0094] Furthermore, for the test scenario, the training data is gridded according to its pixels and dimensions, and converted into corresponding base station location maps, building distribution maps, and incident field real and imaginary maps. These four images are input into the model, and the corrected path loss map is used as the path loss estimation result of the model output.

[0095] An optional embodiment of the present disclosure integrates the loss function of the physical formula with the loss function based on the road test data, so that the output result satisfies both the volume integral equation of physical knowledge and the distribution law of actual road test data, thereby improving the accuracy, efficiency and generalization of the road loss estimation.

[0096] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory (ROM / RAM), a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present disclosure.

[0097] This embodiment also provides a path loss determination device, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described are omitted for clarity. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0098] FIG7 is a structural block diagram of a device for determining path loss according to an embodiment of the present disclosure. As shown in FIG7 , the device includes:

[0099] a first determining module 72 configured to determine a superimposed field image of the target grid scene based on a first image, a second image, and an incident field image of the target grid scene, wherein the first image is used to indicate a position of a first object in the target grid scene, and the second image is used to indicate a position of a second object in the target grid scene;

[0100] A second determination module 74 is configured to determine a first loss function based on the incident field image and the superimposed field image; and determine a second loss function based on the superimposed field image and the drive test data of the target grid scene;

[0101] A third determining module 76 is configured to determine a target loss function based on the first loss function and the second loss function;

[0102] The training module 78 is configured to train a neural network model according to the target loss function, so as to determine the path loss of the target grid scene through the trained neural network model.

[0103] In an exemplary embodiment, the first determination module 72 is further configured to determine the first position of the first object in the target grid scene based on the first image, and determine the second position of the second object in the target grid scene based on the second image; determine the incident field of each grid in the target grid scene based on the first position and the second position; and determine the incident field image of the target grid scene based on the incident field of each grid.

[0104] In an exemplary embodiment, the first determination module 72 is further configured to determine a first real part in complex form corresponding to the incident field image and a first imaginary part in complex form corresponding to the incident field image; determine a first incident field image based on the first real part, and determine a second incident field image based on the first imaginary part; determine the superimposed field image based on the first image, the second image, the first incident field image and the second incident field image.

[0105] In an exemplary embodiment, the first determination module 72 is further configured to respectively extract the first image feature of the first image, the second image feature of the second image, the third image feature of the first incident field image, and the fourth image feature of the second incident field image; determine the associated image features of the first image, the second image, the first incident field image, and the second incident field image based on the first image feature, the second image feature, the third image feature, and the fourth image feature; and determine the superimposed field image based on the associated image features and the volume integral equation.

[0106] In an exemplary embodiment, the second determination module 74 is further configured to input the overlay field image into a road loss map converter to instruct the road loss map converter to convert the overlay field image into a path loss map corresponding to the target grid scenario; correct the path loss map according to the road test data and the overlay field image to determine a corrected path loss map; and determine the second loss function according to the corrected target path loss map and the road test data.

[0107] In an exemplary embodiment, the second determination module 74 is further configured to input the overlay field image into a deep learning network to determine a second real part in complex form corresponding to the overlay field image and a second imaginary part in complex form corresponding to the overlay field image; calculate the path loss data of the path loss map based on the second real part and the second imaginary part; and correct the path loss map based on the deviation between the path loss data and the road test data to determine the target path loss map.

[0108] In an exemplary embodiment, the training module 78 is further configured as a calculation step: calculating the gradient of the target loss function; an adjustment step: adjusting the gradient according to a preset optimization algorithm to update the neural network model according to the adjusted gradient; a determination step: determining the calculation result of the target loss function according to the updated neural network model; and looping the adjustment step and the determination step until the final calculation result is less than or equal to a preset threshold.

[0109] In an exemplary embodiment, the third determination module 76 is further configured to determine a first weight of the first loss function in the neural network model, or to determine a second weight of the second loss function in the neural network model; when the first weight has been determined, determine a first product of the first weight and the first loss function, and determine the sum of the first product and the second loss function as the target loss function; when the second weight has been determined, determine a second product of the second weight and the second loss function; and determine the sum of the second product and the first loss function as the target loss function.

[0110] Through the above-mentioned device, the superimposed field image of the target grid scene is determined based on the first image for indicating the position of the first object in the target grid scene, the second image for indicating the position of the second object in the target grid scene, and the incident field image of the target grid image. The first loss function is determined based on the incident field image and the superimposed field image, and the second loss function is determined based on the superimposed field image and the road test data. The target loss function for training the neural network model is determined based on the first loss function and the second loss function, and then the path loss of the target grid scene is determined by the trained neural network model. That is, the embodiment of the present disclosure defines a technical solution for determining the first loss function by the superimposed field image and the incident field image, and determining the second loss function by the road test data and the superimposed field image, and then determining the target loss function for training the neural network model based on the first loss function and the second loss function, so as to calculate the path loss by the trained neural network model. In the present disclosure, the physical formula describing the incident field and the superimposed field is introduced into the target loss function, and the road test data is introduced into the target loss function. This solves the problem in the existing technology that the path loss estimation method cannot take into account both accuracy and computational efficiency. After the neural network model is trained through the target loss function, it not only improves the accuracy of path loss estimation but also improves the computational efficiency of path loss estimation in the process of determining path loss.

[0111] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0112] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0113] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0114] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0115] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0116] In an exemplary embodiment, the computer program product may further include computer instructions, which, when executed by a processor, implement the steps of the method described in various embodiments of the present disclosure.

[0117] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0118] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0119] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A method for determining path loss, comprising: determining a superimposed field image of the target grid scene according to a first image, a second image, and an incident field image of the target grid scene, wherein the first image is used to indicate a position of a first object in the target grid scene, and the second image is used to indicate a position of a second object in the target grid scene; Determining a first loss function based on the incident field image and the superimposed field image; and determining a second loss function based on the superimposed field image and the drive test data of the target grid scene; Determine a target loss function according to the first loss function and the second loss function; A neural network model is trained according to the target loss function to determine the path loss of the target grid scene through the trained neural network model.

2. The method according to claim 1, wherein Before determining the superimposed field image of the target grid scene according to the first image, the second image, and the incident field image of the target grid scene, the method further includes: determining a first position of the first object in the target grid scene according to the first image, and determining a second position of the second object in the target grid scene according to the second image; Determine an incident field for each grid in the target grid scene according to the first position and the second position; An incident field image of the target grid scene is determined according to the incident field of each grid.

3. The method according to claim 1, wherein Determining a superimposed field image of the target grid scene according to the first image, the second image, and the incident field image of the target grid scene includes: determining a first real part of a complex number form corresponding to the incident field image and a first imaginary part of a complex number form corresponding to the incident field image; Determine a first incident field image according to the first real part, and determine a second incident field image according to the first imaginary part: The superimposed field image is determined according to the first image, the second image, the first incident field image, and the second incident field image.

4. The method according to claim 3, wherein: Determining the superimposed field image according to the first image, the second image, the first incident field image, and the second incident field image includes: respectively extracting a first image feature of the first image, a second image feature of the second image, a third image feature of the first incident field image, and a fourth image feature of the second incident field image; Determine associated image features of the first image, the second image, the first incident field image, and the second incident field image according to the first image feature, the second image feature, the third image feature, and the fourth image feature: The superimposed field image is determined according to the associated image features and the volume integral equation.

5. The method according to claim 1, wherein Determining a second loss function according to the superimposed field image and the drive test data of the target grid scene includes: Inputting the superimposed field image into a path loss map converter to instruct the path loss map converter to convert the superimposed field image into a path loss map corresponding to the target grid scene; Correcting the path loss map according to the drive test data and the superimposed field image to determine a corrected path loss map: The second loss function is determined according to the corrected target path loss map and the drive test data.

6. The method according to claim 5, wherein: Correcting the path loss map according to the drive test data and the superimposed field image to determine a corrected target path loss map includes: Inputting the superimposed field image into a deep learning network to determine a second real part of the complex form corresponding to the superimposed field image and a second imaginary part of the complex form corresponding to the superimposed field image; calculating path loss data of the path loss map based on the second real part and the second imaginary part; The path loss map is corrected according to a deviation between the path loss data and the drive test data to determine the target path loss map.

7. The method according to claim 1, wherein Training a neural network model according to the target loss function includes: Calculation step: Calculate the gradient of the target loss function; Adjustment step: adjusting the gradient according to a preset optimization algorithm to update the neural network model according to the adjusted gradient: Determining step: determining a calculation result of the target loss function according to the updated neural network model; The adjusting step and the determining step are executed cyclically until the final calculation result is less than or equal to a preset threshold.

8. The method according to claim 1, wherein Determining a target loss function according to the first loss function and the second loss function includes: Determining a first weight of the first loss function in the neural network model, or determining a second weight of the second loss function in the neural network model; When the first weight has been determined, determining a first product of the first weight and the first loss function, and determining a sum of the first product and the second loss function as the target loss function; When the second weight has been determined, a second product of the second weight and the second loss function is determined; and a sum of the second product and the first loss function is determined as the target loss function.

9. A device for determining path loss, comprising: a first determining module, configured to determine a superimposed field image of the target grid scene based on a first image, a second image, and an incident field image of the target grid scene, wherein the first image is used to indicate a position of a first object in the target grid scene, and the second image is used to indicate a position of a second object in the target grid scene; a second determining module configured to determine a first loss function based on the incident field image and the superimposed field image; and to determine a second loss function based on the superimposed field image and the drive test data of the target grid scene; a third determining module, configured to determine a target loss function according to the first loss function and the second loss function; A training module is configured to train a neural network model according to the target loss function, so as to determine the path loss of the target grid scene through the trained neural network model.

10. A computer-readable storage medium having a computer program stored therein, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

12. A computer program product comprising computer instructions, wherein: When the computer instructions are executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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