An efficient beam training method, device, medium and product under near field communication

Abstract

The application discloses a high-efficiency beam training method, device, medium and product under near field communication, relates to the technical field of communication, and comprises the following steps: selecting code words from the first layer of a stage one codebook according to the index of optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to a base station; determining whether the first cycle parameter is greater than the first maximum cycle parameter, executing the above steps when the first cycle parameter is greater than the first maximum cycle parameter, and setting the second cycle parameter otherwise; selecting code words from the first layer of a stage two codebook according to the index of the current optimal code words; finding out the optimal code words from the selected code words and updating the index; feeding back the updated index to the base station; determining whether the second cycle parameter is greater than the second maximum cycle parameter, outputting the code word at the optimal code word index after updating when the second cycle parameter is greater than the second maximum cycle parameter, and returning to execute the above steps otherwise. The application can effectively improve the reachable rate performance in near field communication and significantly reduce the training and feedback overhead.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an efficient beam training method, device, medium and product for near-field communication. Background Technology

[0002] With the rapid development of wireless communication technology, Extremely Large-Scale Multi-Input Multi-Output (XL-MIMO) antenna technology, due to its high spatial multiplexing rate and spectral efficiency, has been considered suitable for future Sixth Generation (6G) systems. However, since base stations (BS) are equipped with hundreds or even thousands of antennas, the Rayleigh distance can extend to tens of meters. This means that user equipment (UE) may be located in the near-field or far-field region. In this case, the traditional far-field channel model based on the plane wave assumption is no longer applicable. Therefore, a near-field channel model must be considered.

[0003] Unlike far-field channels, near-field channels are determined by both angle and distance. Therefore, using far-field beamforming in a near-field environment leads to a significant decrease in beam gain. This is because the energy of a far-field beam is dispersed only within the angular range, a phenomenon known as the "energy diffusion effect." Developing effective near-field beamforming techniques is crucial to addressing this issue. Effective beamforming relies on accurate Channel State Information (CSI), which can be obtained through accurate channel estimation. However, due to the high-dimensional characteristics of the channel after employing XL-MIMO technology, the pilot overhead required for channel estimation is extremely high.

[0004] Inspired by far-field communication technology, near-field communication also employs implicit beam training techniques to avoid estimating the channel matrix. This implicit process is achieved by sending several predefined directional beams (codewords) to the user equipment and determining their positions by finding the optimal codeword. To implement beam training for near-field communication, the work in the paper "Cui M, Dai L. Channel estimation for extremely large-scale MIMO: far-field or near-field?. IEEE Trans Commun, 2022, 70: 2663-2677." proposes a polarization domain codebook to simultaneously acquire accurate angle and distance information. Unlike the Discrete Fourier Transform (DFT) codebook used in far-field communication, this codebook divides the entire space into different angle-distance grids. This results in the size of the polarization domain codebook being the product of the number of angle and distance samples, making it much larger than the DFT codebook. Therefore, exhaustive search (ES) of all codes in the polarization domain codebook leads to excessively high training overhead, which limits the practical application of XL-MIMO technology.

[0005] To reduce beam training overhead in near-field communication, researchers have introduced several beam training schemes based on polarization domain codebooks. Specifically, the paper "Zhang Y, Wu X, You C. Fast near-field beam training for extremely large-scale array. IEEE Wireless Commun Lett, 2022, 11: 2625-2629" designs a two-phase (TP) near-field beam training scheme. This scheme uses a DFT codebook for angle domain search and then utilizes a polarization domain codebook for range domain search. However, this scheme still relies on an exhaustive search of the entire angle domain, thus there is still significant room for further reduction in training overhead. To further reduce training overhead, the literature “Chen J, Gao F, Jian M, et al. Hierarchical codebook design for near-field mmWave MIMO communications systems. IEEE Wireless Commun Lett, 2023, 12:1926–1930”, “Lu Y, Zhang Z, Dai L. Hierarchical beam training for extremely large-scale MIMO: from far-field to near-field. IEEE Trans Commun, 2024, 72:2247–2259”, and “Wu C, You C, Liu Y, et al. Two-stage hierarchical beam training for near-field communications. IEEE Trans Veh Technol, 2024, 73: 2032-2044” references the literature “Xiao Z, He T, Xia P, et al. Hierarchical codebook design for beamforming training in millimeter-wave communication. IEEE Trans Wireless Commun, 2016, 15: The hierarchical framework in “3380–3392” is extended to near-field beam training.Specifically, the paper "Chen J, Gao F, Jian M, et al. Hierarchical codebook design for near-field mmWave MIMO communications systems. IEEE Wireless Commun Lett, 2023, 12: 1926–1930" designs a hierarchical polarization domain codebook by utilizing the characteristics of the near-field channel. The paper "Lu Y, Zhang Z, Dai L. Hierarchical beam training for extremely large-scale MIMO: from far-field tonear-field. IEEE Trans Commun, 2024, 72: 2247–2259" develops a two-dimensional (2D) hierarchical beam training scheme, in which the hierarchical codebook design is formulated as a phase retrieval problem and solved using the Gerchberg-Saxton (GS) algorithm. The paper “Wu C, You C, Liu Y, et al. Two-stage hierarchical beam training for near-field communications. IEEE Trans Veh Technol, 2024, 73: 2032-2044” proposes a two-stage hierarchical (TSH) beam training scheme. In this scheme, a rough UE orientation is first found in the first stage, and then the angle and distance are estimated more accurately in the second stage.

[0006] Although existing hierarchical beam training schemes have shown advantages over exhaustive search schemes in reducing training overhead, there are still several limiting factors that prevent them from approaching optimal performance: (1) the asymptotic nature of multi-stage search leads to error propagation, which significantly reduces the accuracy of beam alignment; (2) existing hierarchical beam training schemes require frequent feedback between the BS and UE, which leads to greater feedback overhead. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this application provides an efficient beam training method, device, medium, and product for near-field communication.

[0008] To achieve the above objectives, this application provides the following solution: Firstly, this application provides an efficient beam training method for near-field communication, including: S1: Design phase one codebook, and design phase two codebook by combining near-field channel angle-range joint characteristics; S2: Set the index of the optimal codeword and the parameters of the first loop; S3: The base station uses the index of the optimal codeword from the first stage codebook... Select from layer A code word is generated and sent to the user device; S4: User equipment selected from Find the optimal codeword among the codewords and update the index of the optimal codeword; S5: The user equipment feeds back the updated index of the optimal codeword to the base station; S6: Determine whether the first loop parameter is greater than the first maximum loop parameter; if the first loop parameter is less than or equal to the first maximum loop parameter, return to step S3; if the first loop parameter is less than or equal to the first maximum loop parameter, execute step S7 below. S7: Set the parameters for the second loop; S8: The base station starts from the index of the current optimal codeword in the stage 2 codebook. Select from layer A code word is generated and sent to the user device; S9: User equipment selected from Find the optimal codeword among the codewords and update the index of the optimal codeword; S10: The user equipment feeds back the updated index of the optimal codeword to the base station; S11: Determine whether the second loop parameter is greater than the second maximum loop parameter; if the second loop parameter is greater than the second maximum loop parameter, output the codeword at the updated optimal codeword index; if the second loop parameter is less than or equal to the second maximum loop parameter, return to step S8.

[0009] Secondly, this application provides a computer device, including: 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 efficient beam training method for near-field communication provided above.

[0010] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the efficient beam training method for near-field communication provided above.

[0011] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the efficient beam training method for near-field communication provided above.

[0012] According to the specific embodiments provided in this application, this application has the following technical effects: To address the issues of high error propagation and feedback overhead in existing near-field hierarchical beam training schemes, this application provides an efficient beam training method, device, medium, and product for near-field communication. Step S1 enables this application to maintain lower beam training and feedback overhead across various beam training schemes. Steps S3 and S8 reduce feedback overhead while ensuring training accuracy. Furthermore, in step S1, by combining the near-field channel angle-range joint characteristics with the design phase two codebook, the accuracy of beam alignment and the system's achievable rate performance can be effectively improved, thereby significantly enhancing the achievable rate (AR) performance in near-field communication and significantly reducing training and feedback overhead. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating an efficient beam training method for near-field communication provided in an embodiment of this application; Figure 2 This is a schematic diagram of a processing framework provided in an embodiment of this application; Figure 3 A schematic diagram showing the AR performance comparison results of different beam training schemes provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] In one exemplary embodiment, this application provides an efficient beam training method for near-field communication. This method is executed by a computer device, specifically by a terminal or server alone, or by both. In this embodiment, the method is described using a server as an example. Figure 1 As shown, the method includes: S1: Design phase one codebook, and design phase two codebook by combining near-field channel angle-range joint characteristics.

[0018] S1.1: Design phase codebook, including: S1.11: Initialize the third loop parameters.

[0019] S1.12: The first step in constructing the far-field binary tree hierarchical beam training codebook Layer, represented as: (1) In the formula, The first character represents the far-field binary tree hierarchical beam training codebook. The first layer Each code character. The first character represents the far-field binary tree hierarchical beam training codebook. layer, .

[0020] S1.12: When or At that time, the first codebook in the construction phase Layer, represented as: (2) In the formula, The first stage of the codebook layer, The first character represents the far-field binary tree hierarchical beam training codebook. layer. The number of antennas. This represents the verification parameter.

[0021] S1.13: Determine if the third loop parameter is greater than If the third loop parameter is greater than If the codebook construction ends, the first stage codebook is obtained. If so, return to step S1.12.

[0022] S1.2: Design the stage two codebook by combining the angle-range joint characteristics of the near-field channel, including: S1.21: Initialize the fourth loop parameters.

[0023] S1.22: When When setting temporary parameters .when When setting temporary parameters .in, Indicates the number of sampling distances. j This represents the fourth loop parameter. These are temporary parameters.

[0024] S1.23: Combining the angle-range joint characteristics of the near-field channel, construct the second stage codebook. Layer, represented as: (3) (4) In the formula, The first stage of the binary codebook layer, The first stage of the binary codebook The first angle domain on the layer The first and distance domain Each code character. Indicates the first The distance between the antenna and the user equipment , As auxiliary parameters, , ; Indicates the antenna spacing. . For wavelength, for The specific values, For angle parameters, For distance parameters, The number of antennas. For codeword index, These are temporary parameters.

[0025] S1.24: If If the codebook design is successful, the second stage codebook is obtained; otherwise, the process returns to step S1.22.

[0026] S2: Set the index of the optimal codeword and the parameters of the first loop.

[0027] S3: The base station uses the index of the optimal codeword from the first stage codebook... Select from layer The base station retrieves the first codeword from the first stage codebook using formula (5) based on the index of the optimal codeword. Select from layer The codeword is then sent to the user's device.

[0028] (5) In the formula, Indicates the selected A set of code words, This represents the transmission channel between the base station and the user equipment. w Represents code words.

[0029] S4: User equipment selected from Find the optimal codeword among the codewords and its index.

[0030] S5: The user equipment feeds back the updated index of the optimal codeword to the base station.

[0031] S6: Determine whether the first loop parameter is greater than the first maximum loop parameter. If the first loop parameter is less than or equal to the first maximum loop parameter, return to step S3. If the first loop parameter is less than or equal to the first maximum loop parameter, proceed to step S7 below.

[0032] S7: Set the parameters for the second loop.

[0033] S8: The base station starts from the index of the current optimal codeword in the stage 2 codebook. Select from layer The codeword is then sent to the user's device.

[0034] S9: User equipment selected from Find the optimal codeword from the selected codewords and update its index. Specifically, the user equipment uses formula (6) to select the optimal codeword from the selected codewords. Find the optimal codeword among the codewords and update the index of the optimal codeword.

[0035] (6) In the formula, Indicates the selected A set of code words.

[0036] S10: The user equipment feeds back the updated index of the optimal codeword to the base station.

[0037] S11: Determine if the second loop parameter is greater than the second maximum loop parameter. If the second loop parameter is greater than the second maximum loop parameter, output the codeword at the updated optimal codeword index. If the second loop parameter is less than or equal to the second maximum loop parameter, return to step S8.

[0038] In one exemplary embodiment of this application, as shown in... Figure 2 Taking the framework shown above as an example, the specific implementation process of the efficient beam training method for near-field communication provided in this application will be explained. Figure 2 Assume the number of antennas at the BS end is . Number of sampling distances Based on this, in this embodiment, the method provided by this application can be divided into two parts: codebook design and beam training, wherein the beam training part needs to be completed in two stages.

[0039] (a) Codebook design for the BS side.

[0040] 11. Design for Phase 1 codebook: 11.1. Initialize loop parameters . .

[0041] 11.2. Constructing the first part of the far-field binary-tree based hierarchical (BTH) beam training codebook The layers are as shown in formula (1) above.

[0042] 11.3. If or Then the first codebook construction phase The layers are as shown in formula (2).

[0043] after . For the updated loop parameters i .

[0044] 11.4. , For the updated loop parameters m .

[0045] 11.5. If Then the construction of the codebook in Phase 1 ends. Otherwise, proceed with step 11.2 above.

[0046] 12. Codebook design for Phase Two: 12.1. Initialize loop parameters .

[0047] 12.2. If So set temporary parameters ,otherwise .in This indicates the number of sampling distances.

[0048] 12.3. The second stage of the construction phase of the binary codebook For the layer, see the description of formulas (3) and (4) above.

[0049] 12.4. . For the updated loop parameters 12.5. If Then the codebook construction and update in Phase 2 will end. Otherwise, proceed with step 12.2 above.

[0050] (ii) Beam training phase.

[0051] 21. Initialize threshold parameters and .

[0052] 21.1. Initialize the maximum number of loops .

[0053] 21.2. Initialize the maximum number of loops .

[0054] 21.3. Setting Loop Parameters .

[0055] 21.4. If and ,So .otherwise .

[0056] 21.5. .

[0057] 21.6. If If so, proceed to step 21.7; otherwise, proceed to step 21.4.

[0058] 21.7. Setting Loop Parameters .

[0059] 21.8. If and ,So .otherwise 21.9. .

[0060] 21.10. If If so, proceed to step 22 below; otherwise, proceed to step 21.8.

[0061] Phase 22, Part 1.

[0062] 22.1. Set the index of the optimal codeword as... .

[0063] 22.2. Setting Loop Parameters .

[0064] 22.3.BS gives the first stage of the codebook. layer .

[0065] 22.4.BS is based on the current optimal codeword index. From the first stage codebook layer The first one selected One to the first 1 code word, totaling The code word is sent to the UE.

[0066] 22.5. The UE is selected from step 22.4 by formula (5). Find the optimal codeword among the codewords and update the index of the optimal codeword. .

[0067] 22.6. The UE will index the optimal codeword. Feedback to BS.

[0068] 22.7. .

[0069] 22.8. If If so, proceed with the training operation in step 23 below; otherwise, proceed with step 22.3.

[0070] 23. Phase Two.

[0071] 23.1. Setting Loop Parameters .

[0072] 23.2.BS gives the first stage of the binary codebook. layer .

[0073] 23.3.BS is based on the current optimal codeword index. From the Phase 2 Codebook, the first Select the first layer One to the first 1 code word, totaling The code word is sent to the UE.

[0074] 23.4. The UE is selected from step 23.3 by formula (6). Find the optimal codeword among the codewords and update the index of the optimal codeword. .

[0075] 23.5. The UE will index the optimal codeword. Feedback to BS.

[0076] 23.6. .

[0077] 23.7. If If so, then execute the output of the optimal codeword index. If the code is not found, proceed to step 23.2.

[0078] In one exemplary embodiment of this application, the advantages of the efficient beam training method for near-field communication provided in this application are illustrated by means of simulation experiments.

[0079] In the simulation experiment, the number of base station antennas is assumed to be... Number of sampling distances Wavelength set to Meters, corresponding to a 100GHz frequency. Considering the antenna spacing is half a wavelength, the array aperture of the base station can be calculated as... Meters. According to the definition of Rayleigh distance, the boundaries between the near-field and far-field regions in the simulation setup are... Meters. The distance between the BS and the UE is generated by the range (10, 80), which is less than... Therefore, the corresponding channel is in the near-field region. Furthermore, the test data is calculated by averaging the results of 5000 random channel simulations.

[0080] Experiment 1 demonstrates the advantages of this application in beam training and feedback overhead. As shown in Table 1, due to the codebook design stage at the BS end (i.e., step S1), this application maintains lower beam training and feedback overhead among various beam training schemes. Compared with the near-field ES beam training scheme (hereinafter referred to as "Cui M, Dai L. Channel estimation for extremely large-scale MIMO: far-field or near-field?. IEEE Trans Commun, 2022, 70:2663-2677") and the TP beam training scheme (hereinafter referred to as "Zhang Y, Wu X, You C. Fast near-field beam training for extremely large-scale array. IEEE Wireless Commun Lett, 2022, 11: 2625-2629"), this application reduces training overhead by 99.68% and 95.08%, respectively. Furthermore, compared to the near-field TSH beam training scheme (abbreviated as “Wu C, You C, Liu Y, et al. Two-stage hierarchical beam training for near-field communications. IEEE Trans VehTechnol, 2024, 73: 2032-2044”), this application has the same training overhead while reducing feedback overhead by 22.22% due to the design of the beam training stage.

[0081] Table 1. Beam training and feedback overhead for different beam training schemes

[0082] Experiment 2 is used to demonstrate the anti-interference performance of this application under different signal-to-noise ratios. For example... Figure 3As shown, the signal-to-noise ratio (SNR) ranges from 0 dB to 20 dB. It can be seen that the anti-interference performance of this application is superior to existing near-field hierarchical beamforming training schemes. Specifically, this application provides a more comprehensive search for the optimal codeword by extending the hierarchical training process, thus surpassing the near-field TP beamforming training scheme and the near-field TSH beamforming training scheme. Furthermore, compared with perfect channel state information and the near-field ES beamforming training scheme, the performance loss of this application is limited, but its training overhead is lower. This is because the far-field BTH beamforming training scheme (short for "Xiao Z, He T, Xia P, et al. Hierarchical codebook design for beamforming training in millimeter-wave communication. IEEE Trans Wireless Commun, 2016, 15: 3380–3392") cannot obtain the distance information of the user equipment, therefore its adaptive reception performance is far inferior to the proposed EH beamforming training scheme. Figure 3 In this context, CSI stands for Channel State Information, BTH stands for Binary-Tree based Hierarchical, ES stands for Exhaustive Search, TP stands for Two-Phase, and TSH stands for Two-Stage Hierarchical.

[0083] In summary, this application proposes a novel low-overhead beam training scheme based on near-field channel characteristics. First, it designs a near-field efficient hierarchical beam training scheme. Then, simulations verify the effectiveness of the scheme provided in this application. This demonstrates that, addressing the problems of high error propagation and feedback overhead in existing near-field hierarchical beam training schemes, this application designs corresponding low training and low feedback overhead mechanisms. By combining the angle-range joint characteristics of the near-field channel, it can significantly reduce training and feedback overhead, effectively improving beam alignment accuracy and achievable rate performance.

[0084] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores efficient beam training data for near-field communication. The I / O interfaces allow the processor to exchange information with external devices. The communication interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements an efficient beam training method for near-field communication.

[0085] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 4 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0086] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0087] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0088] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0089] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0090] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this 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 (RRAM), 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0091] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0093] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An efficient beam training method for near-field communication, characterized in that, include: S1: Design phase one codebook, and design phase two codebook by combining near-field channel angle-range joint characteristics; S2: Set the index of the optimal codeword and the parameters of the first loop; S3: The base station uses the index of the optimal codeword from the first stage codebook... Select from layer A code word is generated and sent to the user device; S4: User equipment selected from Find the optimal codeword among the codewords and update the index of the optimal codeword; S5: The user equipment feeds back the updated index of the optimal codeword to the base station; S6: Determine whether the first loop parameter is greater than the first maximum loop parameter; if the first loop parameter is less than or equal to the first maximum loop parameter, return to step S3; if the first loop parameter is less than or equal to the first maximum loop parameter, execute step S7 below. S7: Set the parameters for the second loop; S8: The base station starts from the index of the current optimal codeword in the stage 2 codebook. Select from layer A code word is generated and sent to the user device; S9: User equipment selected from Find the optimal codeword among the codewords and update the index of the optimal codeword; S10: The user equipment feeds back the updated index of the optimal codeword to the base station; S11: Determine whether the second loop parameter is greater than the second maximum loop parameter; if the second loop parameter is greater than the second maximum loop parameter, output the codeword at the updated optimal codeword index; If the second loop parameter is less than or equal to the second maximum loop parameter, then return to step S8.

2. The efficient beam training method for near-field communication according to claim 1, characterized in that, Step S1, the process of designing the first-stage codebook and designing the second-stage codebook in conjunction with the near-field channel angle-range joint characteristics, includes: S1.1: Design phase codebook, including: S1.11: Initialize the parameters of the third loop; S1.12: The first step in constructing the far-field binary tree hierarchical beam training codebook layer; S1.12: When or At that time, the first codebook in the construction phase layer; For the third loop parameter, The number of antennas. Indicates the verification parameters; S1.13: Determine if the third loop parameter is greater than If the third loop parameter is greater than If the codebook construction ends, the first stage codebook is obtained; if the third loop parameter is less than or equal to If so, return to step S1.12; S1.2: Design the stage two codebook by combining the angle-range joint characteristics of the near-field channel, including: S1.21: Initialize the fourth loop parameters; S1.22: When When setting temporary parameters ;when When setting temporary parameters ;in, Indicates the number of sampling distances. j This represents the fourth loop parameter. These are temporary parameters; S1.23: Combining the angle-range joint characteristics of the near-field channel, construct the second stage codebook. layer; S1.24: If If the codebook design is successful, the second stage codebook is obtained; otherwise, the process returns to step S1.

22.

3. The efficient beam training method for near-field communication according to claim 2, characterized in that, The far-field binary tree hierarchical beam training codebook's first Layers are represented as: ； In the formula, The first character represents the far-field binary tree hierarchical beam training codebook. The first layer Each code character; The first character represents the far-field binary tree hierarchical beam training codebook. layer, .

4. The efficient beam training method for near-field communication according to claim 2, characterized in that, The first stage of the codebook Layers are represented as: ； In the formula, The first stage of the codebook layer, The first character represents the far-field binary tree hierarchical beam training codebook. layer.

5. The efficient beam training method for near-field communication according to claim 2, characterized in that, The first stage of the binary codebook Layers are represented as: ； ； In the formula, The first stage of the binary codebook layer, The first stage of the binary codebook The first angle domain on the layer The first and distance domain Each code character Indicates the first The distance between the antenna and the user equipment ; Indicates the antenna spacing. For wavelength, for The specific values, For angle parameters, For distance parameters, The number of antennas. For codeword index, These are temporary parameters.

6. The efficient beam training method for near-field communication according to claim 1, characterized in that, In step S3, the base station uses the formula Based on the index of the optimal codeword, from the first stage of the codebook... Select from layer A code word is generated and sent to the user device; In the formula, Indicates the selected A set of code words, This represents the transmission channel between the base station and the user equipment. w Represents code words.

7. The efficient beam training method for near-field communication according to claim 1, characterized in that, In step S9, the user equipment uses the formula From the selected Find the optimal codeword among the codewords and update the index of the optimal codeword; In the formula, Indicates the selected A set of code words, This represents the transmission channel between the base station and the user equipment. w Represents code words.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the efficient beam training method for near-field communication according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the efficient beam training method for near-field communication as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the efficient beam training method for near-field communication as described in any one of claims 1-7.

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