Codebook-free near-field beam forming method and device based on deep learning, equipment, medium and product

Abstract

The invention discloses a codebook-free near-field beam forming method and device based on deep learning, equipment, a medium and a product, and relates to the field of near-field communication, and the method comprises the steps: constructing and training a convolutional neural network model; the convolutional neural network model comprises a plurality of feature extraction modules, and each feature extraction module comprises two convolutional layers, two regularization layers and two activation layers; the filling is set to be 1 and the size of a convolution kernel is set to be 2 * 2 in the first layer of convolution, and the filling is set to be 0 and the size of the convolution kernel is set to be 2 * 2 in the second layer of convolution; inputting the current channel state information into the trained convolutional neural network model, and determining an optimal beam forming vector; and adjusting a transmitting signal of an antenna array based on the optimal forming vector to form a directional beam, and completing near-field beam forming. According to the invention, the overhead brought by beam training can be effectively reduced, and higher beam forming precision can be obtained.

Description

Technical Field

[0001] The present application relates to the field of near-field communication, and in particular to a codebook-free near-field beamforming method, device, equipment, medium and product based on deep learning. Background Art

[0002] Near field communication has the potential to improve communication coverage and signal quality, but it also faces a series of technical challenges:

[0003] Complex channel modeling: Near-field channels have more propagation paths and complex signal attenuation characteristics. Traditional far-field channel models are difficult to accurately describe channel characteristics in near-field environments.

[0004] Complex channel state information: Near-field channel state information is more complex, and traditional methods find it difficult to fully learn the diverse channel characteristics, resulting in inaccurate channel estimation.

[0005] High beam training overhead: Since the near-field beam codebook contains angle and distance information, it is more complex than the far-field codebook that is only related to the angle, which greatly increases the overhead of beam training. Summary of the invention

[0006] The purpose of this application is to provide a codebook-free near-field beamforming method, device, equipment, medium and product based on deep learning, which can efficiently extract and estimate features of near-field channels and reduce the overhead of beam training.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a codebook-free near-field beamforming method based on deep learning, comprising:

[0009] Constructing and training a convolutional neural network model; the convolutional neural network model includes a plurality of feature extraction modules, each of which includes two convolutional layers, two regularization layers, and two activation layers; in the first convolution layer, the padding is set to 1, and the convolution kernel size is 2×2; in the second convolution layer, the padding is set to 0, and the convolution kernel size is 2×2;

[0010] Input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector;

[0011] The transmit signal of the antenna array is adjusted based on the optimal shaping vector to form a directional beam, thereby completing near-field beamforming.

[0012] In a second aspect, the present application provides a codebook-free near-field beamforming device based on deep learning, comprising:

[0013] A model building and training module, used to build and train a convolutional neural network model; the convolutional neural network model includes multiple feature extraction modules, each of which includes two convolutional layers, two regularization layers and two activation layers; in the first convolution layer, the padding is set to 1 and the convolution kernel size is 2×2, and in the second convolution layer, the padding is set to 0 and the convolution kernel size is 2×2;

[0014] An optimal beamforming vector determination module is used to input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector;

[0015] The near-field beamforming module is used to adjust the transmission signal of the antenna array based on the optimal beamforming vector to form a directional beam, thereby completing the near-field beamforming.

[0016] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the deep learning-based codeless near-field beamforming methods described above.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned deep learning-based codebook-free near-field beamforming methods.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned deep learning-based codeless near-field beamforming methods.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application provides a codebook-free near-field beamforming method, device, equipment, medium and product based on deep learning. Through the ingenious design of the convolution kernel and padding size in the convolutional neural network model, the convolutional neural network model can effectively extract the characteristics of the channel state information, thereby deriving the optimal beamforming vector. Relying on the powerful data processing capability and efficient parallel computing capability of the convolutional neural network model, the present application can not only effectively reduce the overhead caused by beam training, but also obtain higher beamforming accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flowchart of a codebook-free near-field beamforming method based on deep learning provided in one embodiment of the present application;

[0023] Figure 2 is a schematic diagram of a near field communication model between a base station and a user;

[0024] Figure 3 It is a structural diagram of the convolutional neural network model and feature extraction module;

[0025] Figure 4 This is a training diagram of the convolutional neural network model;

[0026] Figure 5 Schematic diagram of the achievable rate under different signal-to-noise ratios;

[0027] Figure 6 Schematic diagram of the effect of carrier frequency on achievable rate;

[0028] Figure 7 Schematic diagram of the achievable rate at different distances;

[0029] Figure 8 Schematic diagram of the achievable rate at different angles;

[0030] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] With the rapid development of mobile communication technology, 6G communication technology has become a research hotspot for the next generation of wireless communication systems. 6G technology aims to achieve higher spectrum efficiency, lower power consumption, and a higher number and density of antennas to meet the needs of future intelligentization, the Internet of Things and other fields for high speed, low latency and high reliability. In order to achieve these goals, ultra-large-scale antenna array technology is considered to be one of the important technologies of 6G.

[0033] In traditional wireless communication systems, antenna array technology is mainly focused on far-field communication, that is, when the signal propagates over a long distance. In this case, the signal wavefront can be approximated as a plane wave, which simplifies the process of channel modeling and beamforming. However, as the communication frequency band shifts to higher frequency bands (such as millimeter waves and terahertz), the communication distance becomes shorter, the number of antennas increases significantly, and the Rayleigh distance gradually increases, which causes the signal propagation to change from far-field to near-field characteristics. In the near-field environment, the signal wavefront exhibits spherical wave characteristics, and the traditional far-field channel model and beamforming method are no longer applicable. However, near-field communication currently has problems such as complex channel modeling, complex channel state information, and high beam training overhead.

[0034] This application designs a convolutional neural network model, which can efficiently extract and process near-field channel features through offline training. Through offline training and online reasoning of the convolutional neural network model, fast beamforming is achieved, which significantly reduces the overhead of beam training and solves the above-mentioned problems existing in existing near-field communications.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0036] In an exemplary embodiment, Figure 1 As shown, a codebook-free near-field beamforming method based on deep learning is provided, comprising the following steps S1 to S3. Among them:

[0037] S1: Construct and train a convolutional neural network model; the convolutional neural network model includes multiple feature extraction modules, each feature extraction module includes two convolution layers, two regularization layers and two activation layers; in the first convolution layer, the padding is set to 1 and the convolution kernel size is 2×2, and in the second convolution layer, the padding is set to 0 and the convolution kernel size is 2×2.

[0038] S2: Input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector.

[0039] S3: Based on the optimal shaping vector, the transmission signal of the antenna array is adjusted to form a directional beam, thereby completing near-field beamforming.

[0040] Before step S1, the method further includes: constructing a near field communication model between the base station and the user, and collecting channel state information. The constructed near field communication model is as follows: Figure 2 As shown, Figure 2 In, r n is the distance between the user and the nth antenna unit, r is the distance between the center antenna and the user, D nis the distance from the nth antenna unit to the center antenna, θ n is the angle between the nth antenna unit and the user, and θ is the angle between the center antenna and the user.

[0041] Specific:

[0042] Execution entity: computer simulation software.

[0043] Input: number of users K, signal-to-noise ratio SNR, number of antenna units N, and standardized transmit power P.

[0044] Operation: Downlink beam training for a massive multiple-input single-output (MISO) system is studied. The base station (BS) is equipped with a linear array (ULA) with N antenna elements (i.e., antenna elements) with a spacing of d=λ / 2, where λ represents the carrier wavelength. Given that mmWave communications (i.e., near-field communications) primarily rely on line-of-sight (LOS) links, this application considers a single LOS radio frequency (RF) chain.

[0045] When the distance between the antenna array and the receiver is less than the Rayleigh distance (RD), the receiver is in the near field region. The formula for the Rayleigh distance is:

[0046]

[0047] Where D is the maximum size of the antenna array, λ is the wavelength of the carrier, and f is the carrier frequency. Assume that the distance from the center antenna to the user is r and the angle is θ. Considering the characteristics of the near-field spherical wave, the distance between the UE and the nth antenna element is r. n for:

[0048]

[0049] The downlink channel h of the subcarrier between the nth antenna element and the UE n It can be expressed as:

[0050]

[0051] Among them, β n is the path attenuation coefficient of the nth antenna unit.

[0052] By stacking the channels of all antenna elements into a vector, the channel state information h of the kth UE can be obtained k :

[0053] h k =[h1,h2,…,h n …,h N ]

[0054] It can be seen that the near-field antenna response is a function of angle and distance. Based on this, the near-field steering vector can be expressed as:

[0055]

[0056] The downlink received signal y of the kth UE k It can be expressed as:

[0057]

[0058] Where G represents the antenna unit power, w represents the beamforming vector, H represents the transpose, and h k is the channel state information collected for the kth user, s is the downlink transmission signal, and η is the noise and interference that obeys the complex Gaussian distribution. Assume that the downlink transmission signal is |s| 2 =1, the achievable rate of the kth UE can be expressed as:

[0059]

[0060] Among them, h i is the channel state information collected for the i-th user, is the signal-to-noise ratio of the user.

[0061] In the above formula, other non-serving UE interference is regarded as noise. According to the above near field communication model, the channel state information collected from the base station and the user is used as the input of the convolutional neural network model.

[0062] Due to the close correlation between the near-field channel and the angle and distance information, the near-field codebook is usually large in size, and a lot of computational complexity will be introduced when using codebook-based beam design. In practical scenarios, the main focus is on the beam gain toward the target UE position, rather than optimizing the beam gain of each grid in the entire space. This application does not require a predefined codebook, and effectively controls the beamforming shape through deep learning to maximize the achievable data transmission rate. Considering the normal mode constraint |w i | 2 =1, for i=1,…,N, the optimization problem of the beamforming vector w is expressed as:

[0063]

[0064] swt i ∈w,|w i | 2 =1

[0065] The channel state information in the XL-MIMO system is complex, which makes the above problems have significant nonlinearity and non-convexity. This application introduces deep learning as a powerful tool to solve these problems. An unsupervised learning convolutional neural network model is designed to enable the beamforming vector to flexibly adapt and learn under complex channel conditions. The goal of the solution is to find the mapping function Φ of the parameter ξ ξ , which uses the channel state information to predict the optimal beamforming vector w'. The mapping function can be expressed as:

[0066] Φ ξ {h}→{w'}

[0067] Wherein, h represents the channel state information of the user.

[0068] In a specific embodiment, the training process of the convolutional neural network model in step S1 includes: inputting the collected channel state information into the convolutional neural network model, outputting the equivalent phase of the beamforming coefficient; converting the equivalent phase into a beamforming vector using the Euler formula; calculating the user achievable rate based on the beamforming vector and the collected channel state information; constructing a loss function based on the user achievable rate; adjusting the parameters of the convolutional neural network model through the loss function to complete the training of the convolutional neural network model. Specifically:

[0069] () Design a convolutional neural network model

[0070] Execution entity: Computer

[0071] Operation: The input data dimension is 2×N. It is converted to 1×2×N by adding an extra dimension and then input into the convolutional neural network in the figure above to extract complex signal features.

[0072] like Figure 3 As shown in the figure, each feature extraction module includes two convolutional layers, two regularization layers (BatchNorm), and two activation layers (ReLU). In the first convolution layer, the padding is set to 1 and the convolution kernel size is 2×2. In the second convolution layer, the padding is set to 0 and the convolution kernel size is 2×2. By cleverly designing the convolution kernel and padding size, the convolutional neural network can effectively extract the real information, imaginary information, and equivalent phase (real and imaginary parts combined) of the channel state information, while ensuring that the data size remains unchanged before and after the feature extraction module is processed. After the data passes through a feature extraction module, it is downsampled by average pooling. After repeating this process twice, the data size becomes B×C×2×N / 4. Subsequently, it is upsampled twice and processed by the feature extraction module to restore the final data size to B×1×2×N, which is the same as the original input data size. The data is then flattened into a one-dimensional vector, and after passing through the fully connected layer, it is mapped to the equivalent phase as the output using the "Tanh" layer.

[0073] (2) Design loss function

[0074] Execution entity: Computer

[0075] Operation: Apply the Euler formula to convert the equivalent phase α output by the convolutional neural network into the required beamforming vector. The complex output is given by the following formula:

[0076] w'=exp(j·α·π)=cos(πα)+j·sin(πα)

[0077] in, It can be seen that πα has a clear physical meaning, and its elements correspond to the equivalent phases of the beamforming coefficients in w'. Since this application adopts a codebook-free deep learning method, the neural network training is guided by a loss function directly related to the beamforming target. The loss function of the task is defined as the negative value of the achievable rate:

[0078]

[0079] Where Q represents the total number of training samples, that is, the total number of channel state information collected, q is the number of channel state information collected, K represents the total number of users, is the user’s signal-to-noise ratio, and w’ is the beamforming vector obtained based on the convolutional neural network model during the training process.

[0080] (3) Model training

[0081] Execution entity: high-performance computer or GPU server

[0082] Operation: Input the collected channel state information into the convolutional neural network model. First, regularize the real and imaginary parts and stack them as the input data of the convolutional neural network model for training. The architecture of the convolutional neural network model includes multiple convolutional layers and fully connected layers to capture complex channel characteristics. The output results are converted into equivalent phases through the "Tanh" layer. Use the Euler formula to convert the equivalent phase into a beamforming vector, use the formula of the loss function to calculate the loss, and then update the convolutional neural network model parameters through back propagation. As the training progresses, the loss function decreases and the corresponding user achievable rate increases. When the convolutional neural network model converges, the loss function tends to be stable and the user achievable rate is maximized. Save the convolutional neural network model parameters for subsequent deployment.

[0083] In a specific embodiment, the trained convolutional neural network is deployed online. Specifically:

[0084] Execution entity: Base station

[0085] Operation: As Figure 4As shown in the figure, the trained convolutional neural network model is deployed in the base station. During the communication process, the current channel state information is input in real time, and the base station can quickly output the optimal beamforming vector that can achieve the maximum sum rate according to the trained convolutional neural network model. The base station adjusts the transmission signal according to the output to form a directional beam and completes the beamforming.

[0086] This application verifies the technical effect of the above method through simulation experiments, and arranges N=256 equally spaced half-wave linear arrays at the BS. This application is compared with near-field stratification, far-field stratification, and an exhaustive beamforming scheme with a computational overhead limited to 256, using the reachability as an evaluation indicator.

[0087] Figure 5 The variation of reachability with SNR under different beam training schemes is shown, where the SNR increases from -20dB to 20dB. Figure 5 It is clearly shown that the proposed scheme consistently outperforms existing far-field and near-field beam training schemes over a wide range of SNR values. When the SNR is 20dB, the proposed scheme achieves approximately 18%, 30%, and 120% higher reachability than the near-field layered beamforming scheme, the far-field layered beamforming scheme, and the exhaustive search scheme, respectively. It is worth noting that when the SNR is >-5dB, the proposed scheme has a significant performance gain compared to the comparison scheme.

[0088] Carrier frequency is a key factor affecting communication rate. Figure 6 Shown are the achievable rate curves of the scheme proposed in this application and the comparative scheme at different carrier frequencies. For all carrier frequencies, the scheme proposed in this application is always superior to other schemes in terms of achievable rate. For the far-field layered scheme, as the carrier frequency increases, near-field propagation gradually dominates, resulting in the average achievable rate of the far-field scheme basically no longer increasing when the carrier is greater than 40GHz. The best performance of all solutions was observed at a carrier frequency of 50GHz. This is due to the increase in free space propagation losses generated by high-frequency signals during transmission. As the carrier frequency increases, the performance gain obtained by increasing the number of antennas will not be enough to offset the increasing free space propagation losses. When fc>50GHz, the performance of the comparative scheme decreases, while the performance of the scheme proposed in this application remains relatively stable, confirming the robustness of the solution proposed in this application.

[0089] Figure 7The correlation between the achievable rate and the distance from the UE to the BS is shown. When the UE is close to the BS, the scheme proposed in this application has superior rate performance compared with the comparative scheme. For the far-field scheme, as the distance decreases, the advantage of near-field propagation causes the average transmission rate of the far-field scheme to drop rapidly. Due to the limitation of the maximum training cost, the exhaustive scheme suffers from severe degradation in the near-field region, hindering the effective search of the near-field position. In addition, the near-field layered beam training scheme can moderately alleviate the decline in the average achievable rate, but its performance gradually decreases when the distance exceeds 30m. In addition, when the distance is less than 20 meters, its performance lags significantly behind the scheme proposed in this application. In contrast, the present application demonstrates the ability to search for the optimal beamforming vector with minimal pilot overhead. This confirms the robustness of the scheme proposed in this application in near-field and far-field communication scenarios.

[0090] Figure 8 The achievable rate as a function of angle is shown. Here, θ is gradually increased from -60° to 60°. For the far-field layered scheme, the performance degrades significantly as θ approaches 0 degrees. This degradation is attributed to the enhancement of near-field effects near zero angle. In addition, the near-field layered beam training scheme shows significant fluctuations because the near-field hybrid scheme creates a near-field codebook by uniformly sampling angles and distances in a Cartesian coordinate system, which is challenging to achieve stable beamforming performance throughout the near-field environment. In contrast, the scheme proposed in this application consistently achieves the highest average achievable rate over all considered angle ranges. This result highlights the robustness and effectiveness of this application in dealing with the challenges posed by angle changes in near-field communication scenarios.

[0091] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned codebook-free near-field beamforming method based on deep learning. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the codebook-free near-field beamforming device based on deep learning provided below can be referred to the limitations of the codebook-free near-field beamforming method based on deep learning above, and will not be repeated here.

[0092] In an exemplary embodiment, a codebook-free near-field beamforming device based on deep learning is provided, comprising:

[0093] A model building and training module is used to build and train a convolutional neural network model; the convolutional neural network model includes multiple feature extraction modules, each feature extraction module includes two convolution layers, two regularization layers and two activation layers; in the first convolution layer, the padding is set to 1 and the convolution kernel size is 2×2, and in the second convolution layer, the padding is set to 0 and the convolution kernel size is 2×2.

[0094] The optimal beamforming vector determination module is used to input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector.

[0095] The near-field beamforming module is used to adjust the transmission signal of the antenna array based on the optimal beamforming vector to form a directional beam, thereby completing the near-field beamforming.

[0096] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program. The computer device may be a server or a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a code-free near-field beamforming method based on deep learning is implemented.

[0097] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0098] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0099] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0100] It should be noted that the user information (including but not limited to user information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0101] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0102] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0103] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A codebook-free near-field beamforming method based on deep learning, characterized in that: include: Constructing and training a convolutional neural network model; the convolutional neural network model includes a plurality of feature extraction modules, each of which includes two convolutional layers, two regularization layers, and two activation layers; in the first convolution layer, the padding is set to 1, and the convolution kernel size is 2×2; in the second convolution layer, the padding is set to 0, and the convolution kernel size is 2×2; Input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector; The transmit signal of the antenna array is adjusted based on the optimal shaping vector to form a directional beam, thereby completing near-field beamforming.

2. The codebook-free near-field beamforming method based on deep learning according to claim 1, characterized in that: Before building and training the convolutional neural network model, it also includes: Build a near-field communication model between the base station and the user and collect channel status information.

3. The codebook-free near-field beamforming method based on deep learning according to claim 2, characterized in that: The expression of the collected channel state information is: h k =[h1,h2,…,h n …,h N ] Among them, h k is the channel state information collected for the kth user, h n is the downlink channel of the subcarrier between the nth antenna element and the user, N is the number of antenna elements, β n is the path attenuation coefficient of the nth antenna unit, r is the distance between the center antenna and the user, d is the spacing between the antenna units, and θ is the angle between the center antenna and the user.

4. The codebook-free near-field beamforming method based on deep learning according to claim 2, characterized in that: The training process of the convolutional neural network model includes: Inputting the collected channel state information into the convolutional neural network model, and outputting the equivalent phase of the beamforming coefficient; The equivalent phase is converted into a beamforming vector using the Euler formula; Calculating a user achievable rate based on the beamforming vector and the collected channel state information; Constructing a loss function based on the user achievable rate; The parameters of the convolutional neural network model are adjusted by the loss function to complete the training of the convolutional neural network model.

5. The codebook-free near-field beamforming method based on deep learning according to claim 2, characterized in that: The calculation formula of the user achievable rate is: Among them, R k is the achievable rate of the kth user, w is the beamforming vector, H represents the transpose, G is the antenna unit power, and h i is the channel state information collected for the i-th user, is the signal-to-noise ratio of the user.

6. The codebook-free near-field beamforming method based on deep learning according to claim 4, characterized in that: The expression of the loss function Loss is: Where Q is the total amount of channel state information collected, q is the amount of channel state information collected, K is the total number of users, R k is the achievable rate of the kth user, w' is the beamforming vector obtained based on the convolutional neural network model during training, H represents transposition, and h k is the channel state information collected for the kth user, h i is the channel state information of the ith user collected, m is the subcarrier number, is the signal-to-noise ratio of the user.

7. A codebook-free near-field beamforming device based on deep learning, characterized in that: include: A model building and training module, used to build and train a convolutional neural network model; the convolutional neural network model includes multiple feature extraction modules, each of which includes two convolutional layers, two regularization layers and two activation layers; in the first convolution layer, the padding is set to 1 and the convolution kernel size is 2×2, and in the second convolution layer, the padding is set to 0 and the convolution kernel size is 2×2; An optimal beamforming vector determination module is used to input the current channel state information into the trained convolutional neural network model to determine the optimal beamforming vector; The near-field beamforming module is used to adjust the transmission signal of the antenna array based on the optimal beamforming vector to form a directional beam, thereby completing the near-field beamforming.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the codebook-free near-field beamforming method based on deep learning as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the codebook-free near-field beamforming method based on deep learning described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the codebook-free near-field beamforming method based on deep learning described in any one of claims 1 to 6 are implemented.

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