Modularized XL-MIMO near-field beam training method

Abstract

The invention discloses a modular XL-MIMO near-field beam training method, which belongs to the technical field of XL-MIMO beam training, and comprises the following steps: activating antennas of a head module and a tail module in an array to carry out far-field beam scanning, determining a code word index with the maximum beam gain of the head module and the tail module, estimating the code word index with the maximum beam gain of the whole array, and taking the code word index with the maximum beam gain as a center, the method comprises the following steps: selecting a plurality of candidate angles, carrying out non-uniform distance sampling by adopting a polar domain codebook, generating candidate sampling points, distributing each candidate sampling point to a collaborative module for training, determining the energy of each candidate sampling point, further determining an optimal polar domain near-field codeword index, and completing modular XL-MIMO near-field beam training. The method not only can effectively reduce the training overhead, but also can maximize the accuracy of beam gain and angle estimation under the condition of limited resources.

Description

Technical Field

[0001] The present invention relates to a modular XL-MIMO near-field beam training method, belonging to the technical field of XL-MIMO beam training. Background Art

[0002] Ultra-large-scale multiple-input multiple-output (XL-MIMO) technology, by adopting an extremely large-scale antenna array (ELAA), significantly improves the spatial resolution and spectral efficiency of wireless communication and has become a key enabling technology for sixth-generation (6G) wireless networks.

[0003] Beam training, through the interaction between the base station and the user, transmits predefined training sequences and uses algorithms to select candidate beam directions from the precoding codebook. The user terminal measures the channel quality corresponding to each beam and feeds back the optimal beam index or the complete channel state information to the base station. Subsequently, the base station designs the beamforming vector according to the feedback information, so as to concentrate the energy on the main propagation path of the user. As a flexible technology, beam training can reduce interference, improve signal quality and system capacity by dynamically optimizing the beam direction, thus significantly enhancing the overall performance of the wireless communication system. In the XL-MIMO system, the increase in the number of antennas leads to a significant increase in the number of symbols required for beam training, thus greatly increasing the training overhead. At the same time, when traditional far-field beam training methods are applied to near-field communication systems, significant performance degradation often occurs.

[0004] To address these issues, a polarization domain codebook specifically for the near field is proposed, where the angular domain is uniformly sampled and the distance domain is non-uniformly sampled to reduce the size of the codebook and the high training cost brought by complex near-field codebooks. To reduce the training overhead of beam training, an efficient two-stage beam training method is proposed. Specifically, in the first stage, the candidate azimuth of the user is determined by far-field beam scanning, and in the second stage, by using a customized polar domain codebook, the optimal effective distance of the user is found given the candidate angles. To further reduce the training overhead, a hierarchical beam training method is proposed. In the first stage, the central subarray of the ELAA is used for far-field hierarchical codebook search to obtain a rough estimate of the user's direction. In the second stage, based on the rough user direction, a designed polar coordinate domain hierarchical codebook is used to further refine the user direction and distance information. In addition, a deep learning-based near-field beam training method is also proposed. Specifically, a deep neural network (DNN) is trained using a near-field codebook containing angle and distance information to reduce the training overhead, and a near-field beam training method with supplementary codewords is proposed to improve the beamforming performance. However, existing XL-MIMO beam training schemes still face challenges in terms of training overhead and computational complexity.

[0005] Although the non-modular beam training scheme has significant advantages in terms of signal processing accuracy and system coordination, its limitations in scalability and computational complexity have prompted researchers to turn to the exploration of modular beam training. Modular beam training can effectively share the computational load and improve the processing speed and efficiency by dispersing the computational tasks to multiple nodes. Specifically, a modular two-stage beam training scheme is proposed. In the first stage, each subarray independently uses multiple far-field channel steering vectors for analog combining. In the second stage, for each codeword in the predefined hybrid field codebook, a dedicated digital combiner is designed to combine the outputs of the analog combiners in the first stage. The codeword corresponding to the dedicated digital combiner is selected from the hybrid field codebook to achieve the maximum combining power. However, in the case of downlink beam training, this scheme still faces a large beam training overhead and high computational complexity. Summary of the Invention

[0006] The object of the present invention is to provide a modular XL-MIMO near-field beam training method, which can not only effectively reduce the training overhead, but also maximize the beam gain and the accuracy of angle estimation under limited resources.

[0007] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a modular XL-MIMO near-field beam training method, which is applied to a modular XL-MIMO system with a base station deploying a uniform linear array. The array is divided into several modules, each module has several antennas, and each module serves users through a radio frequency chain. The method includes: Activate the antennas of the first and last modules in the array for far-field beam scanning to determine the codeword index with the maximum beam gain of the first and last modules; Based on the codeword indexes with the maximum beam gain of the first and last modules, estimate the codeword index with the maximum beam gain of the entire array; Centered on the codeword index with the maximum beam gain of the entire array, select several candidate angles to form a candidate angle index set; For each candidate angle in the candidate angle index set, perform non-uniform distance sampling using a polar codebook to generate candidate sampling points, and form a candidate sampling point index set from all candidate sampling points; Assign each candidate sampling point in the candidate sampling point index set to a collaborative module for training to determine the energy of each candidate sampling point; According to the energy of each candidate sampling point, determine the optimal polar near-field codeword index to complete the modular XL-MIMO near-field beam training.

[0008] In combination with the first aspect, further, the codeword index with the maximum beam gain of the entire array is: ; wherein, represents the codeword index with the maximum beam gain of the entire array, represents rounding down, represents the total number of modules in the array, represents the number of antennas in each module, represents the sine value of the actual angle with the maximum beam gain of the entire array, , wherein, and respectively represent the codeword indexes with the maximum beam gain of the first and last modules.

[0009] In combination with the first aspect, further, the candidate angle index set is: ; wherein, represents the candidate angle index set, represents the codeword index with the maximum beam gain of the entire array, represents rounding down, represents the total number of candidate angles.

[0010] In combination with the first aspect, further, the sampling distances corresponding to each candidate sampling point are: ; Among them, represents the th candidate angle of the th candidate sampling point corresponding sampling distance, represents the total number of candidate sampling points corresponding to each candidate angle, represents the threshold distance for restricting the column coherence between the near-field steering vectors, , among which, represents the correlation coefficient between codewords, represents the wavelength of the spherical wave, represents the size of the array.

[0011] Combined with the first aspect, further, the cooperation module is composed of the th module and the th module in the array, , among which, represents the total number of modules in the array, represents rounding down; When each candidate sampling point in the candidate sampling point index set is assigned to the cooperation module for training, it is assigned to two modules in the cooperation module at the same time.

[0012] Combined with the first aspect, further, determining the energy of each candidate sampling point includes: Calculating the far-field codeword indexes of two modules in the cooperation module corresponding to each candidate sampling point; Based on the far-field codeword indexes of two modules in the cooperation module corresponding to each candidate sampling point, controlling the two modules in the cooperation module corresponding to each candidate sampling point to send pilot signals respectively, and superimposing the received signals of the user based on the cooperation module corresponding to each candidate sampling point to obtain the energy of each candidate sampling point.

[0013] Combined with the first aspect, further, calculating the far-field codeword indexes of two modules in the cooperation module corresponding to each candidate sampling point includes: Based on the trigonometric function relationship, calculating the sine values of two modules in the cooperation module corresponding to each candidate sampling point; Based on the sine values of two modules in the cooperation module corresponding to each candidate sampling point, calculating the far-field codeword indexes of two modules in the cooperation module corresponding to each candidate sampling point; Among them, the calculation formula for the sine values of two modules in the cooperation module corresponding to each candidate sampling point is: ; Among them, , represents the th candidate angle of the The sine values of two modules in the collaborative module corresponding to a candidate sampling point, indicating the th candidate angle of the th candidate sampling point with respect to the central antenna of the entire array, indicating the th candidate angle of the th candidate sampling point corresponding sampling distance, , indicating the th position coordinates of the central antennas of two modules in the collaborative module, indicating the number of antennas of each module, indicating the spacing distance between adjacent antennas; The calculation formula for the far - field codeword indices of two modules in the collaborative module corresponding to each candidate sampling point is: ; where, , indicating the th candidate angle of the th candidate sampling point corresponding far - field codeword indices of two modules in the collaborative module, , indicating for the two modules in the th collaborative module, dividing to evenly into parts, the th sine value of the angle; The received signal of the user based on the collaborative module corresponding to each candidate sampling point is: ; where, indicating the value of when , indicating the value of when , , indicating the received signals of two modules in the collaborative module corresponding to the th candidate angle of the th candidate sampling point for the user, , indicating the th channel between two modules in the collaborative module and the user, indicating conjugate transpose, , respectively represent the steering vectors with respect to and ; represents the pilot signals sent by two modules in each collaborative module, and represent the additive white Gaussian noise of two modules in the th collaborative module; The energy of each candidate sampling point is: ; where represents the th candidate angle and the th candidate sampling point energy of the

[0014] Combined with the first aspect, further, the optimal extreme near-field codeword index is: ; where corresponds to the optimal angle among the candidate angles, corresponds to the optimal sampling point among the candidate sampling points, represents the th candidate angle and the th candidate sampling point energy of the

[0015] In a second aspect, the present invention provides a computer device, including: a storage medium for storing a computer program; a processor for executing the computer program to implement the modular XL-MIMO near-field beam training method described in the first aspect.

[0016] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the modular XL-MIMO near-field beam training method described in the first aspect.

[0017] In a fourth aspect, the present invention provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the modular XL-MIMO near-field beam training method described in the first aspect.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The modular XL-MIMO near-field beam training method provided by the present invention adopts a partial antenna activation strategy, only activating the antennas of the first and last modules of the array for far-field beam scanning. In this way, the candidate angles of the first and last modules can be determined, and at the same time, the number of symbols required for beam training is greatly reduced. Since the antenna array is divided into multiple modules, the number of antennas in each module is reduced compared to the entire array, which results in a corresponding reduction in the number of codewords and resolution. To improve the estimation accuracy of the actual angle with the maximum energy, two modules with the farthest distance apart are selected for calculation. This strategy can not only effectively reduce the training overhead, but also maximize the beam gain and the accuracy of angle estimation under limited resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 FIG. is a schematic diagram of the performance comparison of the modular XL-MIMO near-field beam training method provided by the embodiment of the present invention with the existing hybrid-field beam training method and fast beam training method under different signal-to-noise ratios; Figure 2 FIG. is a schematic diagram of the performance comparison of the modular XL-MIMO near-field beam training method provided by the embodiment of the present invention with the existing hybrid-field beam training method and fast beam training method under different overhead limitations; Figure 3 FIG. is a schematic diagram of the performance comparison of the modular XL-MIMO near-field beam training method provided by the embodiment of the present invention with the existing hybrid-field beam training method and fast beam training method when the number of antennas in each module is fixed and the number of modules is changed. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solution of the present invention will be further described in detail below in conjunction with the specific embodiments.

[0021] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention. Without conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0022] The embodiment of the present invention provides a modular XL-MIMO near-field beam training method, which is applied to a modular XL-MIMO system with a base station deploying a uniform linear array. The array is divided into several modules, each module has several antennas, and each module serves users through a radio frequency chain.

[0023] In this embodiment, the modular XL-MIMO near-field beam training method includes: Activate the antennas of the first and last modules in the array for far-field beam scanning, and determine the codeword index with the maximum beam gain for the first and last modules; Based on the codeword indices with the maximum beam gain for the first and last modules, estimate the codeword index with the maximum beam gain for the entire array; Centered on the codeword index with the maximum beam gain for the entire array, select several candidate angles to form a candidate angle index set; For each candidate angle in the candidate angle index set, perform non-uniform distance sampling using a polar domain codebook to generate candidate sampling points, and form a candidate sampling point index set from all candidate sampling points; Assign each candidate sampling point in the candidate sampling point index set to a collaborative module for training to determine the energy of each candidate sampling point; Based on the energy of each candidate sampling point, determine the optimal polar domain near-field codeword index to complete the modular XL-MIMO near-field beam training.

[0024] The embodiment of the present invention provides a modular XL-MIMO near-field beam training method, aiming to solve the problems of large training overhead and high computational complexity in traditional near-field beam training, while meeting the system's demand for the average achievable rate. Utilizing the geometric relationship between modules and a partially connected hybrid architecture, by distributing the computational tasks to multiple collaborative modules, efficient and flexible beamforming is achieved. In the first stage, candidate angles are determined through partial far-field beam scanning, significantly reducing the search range and thus the training overhead; in the second stage, combined with a polar domain codebook and a modular beam training method, the near-field codewords under the candidate angles are jointly searched to accurately determine the optimal distance of the user, improving the accuracy and efficiency of distance estimation.

[0025] The embodiment of the present invention provides a modular XL-MIMO near-field beam training method, which is applied to a modular XL-MIMO system with a base station deploying a uniform linear array. The array is divided into modules, each module has antennas, the base station has a total of antennas, each module serves a single-antenna user through a radio frequency chain, and the base station communicates with the single-antenna user through a total of radio frequency chains to transmit signals to the single-antenna user.

[0026] In this embodiment, the modular XL-MIMO near-field beam training method specifically includes the following steps: Step 1: Construct a channel model; In this embodiment, the channel model is: ; Among them, represents the user's received signal, , ,..., respectively represent the received signals of the user based on the 1st, …, module, represents the symbol matrix transmitted by the base station to the user, represents the analog beamforming matrix of the entire array, The value of is determined by the channel state information and is used to operate on to optimize the system performance. , , …, respectively represent the channels between the 1st, …, antenna roots and the user, represents the transpose conjugate, represents additive white Gaussian noise.

[0027] Considering the single-scattering ring and Rayleigh fading model, the channel between the module and the user can be expressed as: ; where represents the channel between the th module and the user, represents being subject to a Gaussian distribution, represents the channel covariance matrix, .

[0028] Assume that the position of the scattering ring relative to the th module is , where represents the radius of the scattering ring, represents the angle along the inside of the scattering ring, represents the distance between the center of the scattering ring and the central antenna of the th module, represents the angle between the center of the scattering ring and the central antenna of the th module, represents the transpose, then the specific form of the channel covariance can be expressed as: ; where represents the th row and th column element of represents the average received energy of the central antenna of the module, represents the wavelength of the spherical wave, represents the distance between the scattering ring and the central antenna of the th module, denotes the distance between the scattering ring and the central antenna of the entire array, denotes the position coordinates of the central antenna of the th module, denotes the angle of the scattering ring relative to the reference antenna, denotes with respect to the probability density function, obeys the von Mises distribution, where, denotes the zero-order Bessel function, denotes the concentration degree of the probability distribution, , denotes the angle when the probability density function appears at its peak.

[0029] Define the steering vector where, denotes the angle of the user relative to the central antenna of the module, denotes the distance between the user and the central antenna of the module, denotes the distance between the user and the th antenna of the module. For each module, the user is usually in the far-field region, i.e., , then the steering vector can also be expressed as: .

[0030] Assume where, , …, respectively denote the analog beamforming matrices of the 1st, …, th module, which are designed by the far-field discrete Fourier transform codebook of the module, where, , …, , …, respectively denote the 1st, …, , …, th codewords of where, denotes the steering vector with respect to , denotes the sine value of the to evenly divided into parts and the th angle, .

[0031] The base station selects a codeword in and sends it to the user, then the user is based on the th received signal of the is: ; wherein, represents the th codeword selected from the th codeword, represents the pilot signal sent by the th module, represents the th element of

[0032] Step 2: Convert the beam training problem into a problem of designing a precoding matrix; In this embodiment, the beam training problem is converted into a problem of designing a precoding matrix, and the spectral efficiency is maximized by designing the . Therefore, the objective function of the beam training problem can be expressed as: .

[0033] To solve the objective function, each codeword in needs to be tested, and this method is called far-field beam scanning.

[0034] Define the th combiner , then the received signal based on which the user is is: .

[0035] Step 3: Activate the antennas of the first and last modules in the array for far-field beam scanning, and determine the codeword indexes with the maximum beam gains of the first and last modules; In this embodiment, the huge overhead required for far-field beam scanning is addressed by selecting candidate angles. The partial antenna activation strategy is adopted, and only the antennas of the first and last modules in the array are activated for far-field beam scanning. In this way, the candidate angles of the first and last modules in the array can be determined, and at the same time, the number of symbols required for beam training is greatly reduced. Since the array is divided into multiple modules, the number of antennas in each module is reduced compared with the entire array , which results in a corresponding reduction in the number and resolution of codewords. To improve the estimation accuracy of the actual angle with the maximum beam gain, two modules with the farthest distance apart can be selected for calculation. This strategy can not only effectively reduce the training overhead, but also maximize the beam gain and the accuracy of angle estimation under limited resources.

[0036] Specifically, define the precoding matrix , wherein, represents For a zero matrix, it can be expressed as: .

[0037] After far-field beam scanning, by using the , …, , …, -constituted received signal matrix 's first, th column vectors , , the codeword indices , with the maximum beam gains of the head and tail modules can be obtained, where , …, , …, respectively represent the received signals of the user based on the first, …, , …, th combiners , …, , …, . represents 's zero vector, represents 's matrix.

[0038] Step 4: Estimate the codeword index with the maximum beam gain of the entire array based on the codeword indices with the maximum beam gains of the head and tail modules; Considering the scattering ring power position spectrum and the geometric relationship between the modules, the codeword index with the maximum beam gain of the entire array can be roughly estimated from the codeword indices with the maximum beam gains of the head and tail modules.

[0039] The sine value of the actual angle with the maximum beam gain and the codeword index with the maximum beam gain are related as given by , . From the codeword indices , with the maximum beam gains of the head and tail modules, the sine values

[0040] of the actual angles with the maximum beam gains of the head and tail modules can be deduced. For the entire array, the sine value of the actual angle with the maximum beam gain can be expressed as:

[0041] Given that the total number of antennas in the entire array is , for the entire array, the codeword index It can be expressed as: ; wherein, represents taking the integer part.

[0042] Step Five: Centering on the codeword index with the maximum beam gain of the entire array, select several candidate angles to form a candidate angle index set; Due to the existence of power fluctuations, received noise, and small-scale fading, the estimation in Step Four for may not be accurate enough. To solve this problem, a new angle selection scheme is adopted in this embodiment.

[0043] Centering on the estimated in Step Four, select candidate angles to form a candidate angle index set , wherein, represents rounding down, represents the total number of candidate angles.

[0044] Step Six: For each candidate angle in the candidate angle index set, perform non-uniform distance sampling using the polar codebook to generate candidate sampling points, and form a candidate sampling point index set from all candidate sampling points; This embodiment provides a custom polar domain beam training method, namely effective distance estimation, for estimating the effective distance of the scattering ring.

[0045] Specifically, a polar codebook is used, and each codeword in the polar codebook corresponds to a specific angle-distance pair.

[0046] For each candidate angle in the candidate angle index set, perform non-uniform distance sampling using the polar codebook to generate candidate sampling points, and form a candidate sampling point index set from all candidate sampling points.

[0047] The sampling distances corresponding to each candidate sampling point are: ; wherein, represents the th candidate angle of the th candidate sampling point corresponding sampling distance, represents the total number of candidate sampling points corresponding to each candidate angle, represents the threshold distance restricting the column coherence between near-field steering vectors, , wherein, represents the correlation coefficient between codewords, represents the wavelength of the spherical wave, represents the size of the array.

[0048] For each candidate angle, candidate sampling points are generated, and the candidate sampling points are arranged in ascending order of sampling distance to form a candidate sampling point index subset.

[0049] For candidate angles, a total of candidate sampling points are generated, and a total of candidate sampling point index subsets are formed.

[0050] In the order of ascending candidate angles, the candidate sampling points with the smallest sampling distance are sequentially selected from the candidate sampling point index subsets corresponding to the candidate angles for arrangement, and non-repetitive cyclic selection is performed on all candidate sampling points to form a candidate sampling point index set , where represents the th candidate sampling point in .

[0051] Step Seven: Assign each candidate sampling point in the candidate sampling point index set to the collaborative module for training to determine the energy of each candidate sampling point; For the problem of polarized sampling, this embodiment adopts a method of modular effective distance estimation to solve it. Due to the weakening of the near-field effect, the user is located in the far-field region of the module. Therefore, the traditional near-field beam scanning method is no longer applicable. In this embodiment, two modules transmit angular domain codewords to the candidate sampling points, and the received signals are used to determine the energy of each candidate sampling point.

[0052] In this embodiment, the combination of the two modules used to determine the energy of the candidate sampling points is defined as the collaborative module.

[0053] Specifically, the th module and the th module in the array form the th collaborative module, , where represents the total number of collaborative modules, , where represents the total number of modules in the array, represents rounding down.

[0054] In this embodiment, each candidate sampling point in is sequentially and cyclically assigned to the 1st,... th collaborative modules for training to determine the energy of each candidate sampling point. Among them, when assigning to the collaborative module, it is simultaneously assigned to the two modules in the collaborative module for effective distance scanning.

[0055] The index set of candidate sampling points assigned to each collaborative module is as follows: ; Among them, represents the index set of candidate sampling points assigned to the th collaborative module, represents the th candidate sampling point in , that is, the th candidate sampling point assigned to the th collaborative module. The index of the th candidate sampling point assigned to the th collaborative module in is .

[0056] In this embodiment, determining the energy of each candidate sampling point specifically includes the following steps: Step 1: Calculate the far-field codeword indices of two modules in the collaborative module corresponding to each candidate sampling point; In this embodiment, calculating the far-field codeword indices of two modules in the collaborative module corresponding to each candidate sampling point specifically includes the following steps: Step ①: Based on the trigonometric function relationship, calculate the sine values of two modules in the collaborative module corresponding to each candidate sampling point; In this embodiment, based on the index set of candidate sampling points assigned to each collaborative module, use the trigonometric function relationship to calculate the sine values of two modules in the collaborative module corresponding to each candidate sampling point respectively.

[0057] The calculation formula for the sine values of two modules in the collaborative module corresponding to each candidate sampling point is: ; Among them, , represent the sine values of two modules in the collaborative module (the th collaborative module) corresponding to the th candidate sampling point of the th candidate angle, represent the th candidate angle The angle of the th candidate sampling point relative to the central antenna of the entire array, , represent the position coordinates of the central antennas of two modules in the th collaborative module, represents the interval distance between adjacent antennas.

[0058] Step ②: Based on the sine values of the two modules in the collaborative module corresponding to each candidate sampling point, calculate the far-field codeword indexes of the two modules in the collaborative module corresponding to each candidate sampling point.

[0059] In this embodiment, the calculation formula for the far-field codeword indexes of the two modules in the collaborative module corresponding to each candidate sampling point is: ; where , represent the far-field codeword indexes of the two modules in the collaborative module (the th collaborative module) corresponding to the th candidate sampling point of the th candidate angle, , represent the sine values of the th angle after evenly dividing to into parts for the two modules in the th collaborative module.

[0060] Step 2: Based on the far-field codeword indexes of the two modules in the collaborative module corresponding to each candidate sampling point, control the two modules in the collaborative module corresponding to each candidate sampling point to send pilot signals respectively, and superimpose the received signals of the user based on the collaborative module corresponding to each candidate sampling point to obtain the energy of each candidate sampling point.

[0061] In this embodiment, when controlling the two modules in the collaborative module corresponding to each candidate sampling point to send pilot signals respectively , the received signal of the user based on the collaborative module corresponding to each candidate sampling point is: ; where represents the value of when , represents the value of when , , represent the received signals of the two modules in the collaborative module (the th collaborative module) corresponding to the th candidate sampling point of the th candidate angle of the user, , represent the channels between the two modules in the th collaborative module and the user, , respectively represent with respect to​​ , 's steering vector, , denotes the additive white Gaussian noise of two modules in the th collaborative module.

[0062] The energy of each candidate sampling point is: ; where denotes the th candidate angle 's th candidate sampling point's energy.

[0063] Step Eight: Determine the optimal extreme-field near-field codeword index according to the energy of each candidate sampling point, and complete the modular XL-MIMO near-field beam training.

[0064] In this embodiment, the optimal extreme-field near-field codeword index is: ; where corresponds to the best angle among the candidate angles, and corresponds to the best sampling point among the candidate sampling points.

[0065] To verify the modular XL-MIMO near-field beam training method provided by the embodiments of the present invention, the modular XL-MIMO near-field beam training method provided by the embodiments of the present invention is applied to a modular XL-MIMO narrowband downlink communication system for simulation, and the performance of beam training is evaluated through the average achievable rate ESE.

[0066] Specifically, the array is divided into 64 modules, each module has 6 antennas, the base station has a total of 384 antennas, each module serves a single-antenna user through a radio frequency chain (RF chain), and the base station communicates with the single-antenna user through 64 radio frequency chains in total to transmit signals to the single-antenna user.

[0067] The modular XL-MIMO near-field beam training method provided by the embodiments of the present invention, compared with the existing hybrid-field beam training method and fast beam training method, the performance comparison at different signal-to-noise ratios is as Figure 1 shown, the performance comparison under different overhead limitations is as Figure 2 shown, and the performance comparison when changing the number of modules while fixing the number of antennas in each module is as Figure 3 shown.

[0068] From Figures 1 to 3It can be seen that the modular XL-MIMO near-field beam training method provided by the embodiments of the present invention reduces the training overhead of traditional near-field beam training while maintaining comparable beamforming performance compared with the existing hybrid-field beam training method and fast beam training method.

[0069] An embodiment of the present invention provides a computer device, including: A storage medium for storing a computer program; A processor for executing the computer program to implement the modular XL-MIMO near-field beam training method provided by any embodiment of the present invention.

[0070] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the modular XL-MIMO near-field beam training method provided by any embodiment of the present invention.

[0071] An embodiment of the present invention provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the modular XL-MIMO near-field beam training method provided by any embodiment of the present invention.

[0072] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0076] The foregoing are only preferred embodiments of the present invention, and it should be noted that for those of ordinary skill in the art, without departing from the technical principles of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A modular XL-MIMO near-field beam training method, applied to a modular XL-MIMO system with a base station deploying a uniform linear array, the array is divided into several modules, each module has several antennas, and each module serves users through radio frequency chains, characterized in that, Including: Activating the antennas of the first and last modules in the array to perform far-field beam scanning, and determining the codeword indices with the maximum beam gains of the first and last modules; Estimating the codeword index with the maximum beam gain of the entire array based on the codeword indices with the maximum beam gains of the first and last modules; Centering on the codeword index with the maximum beam gain of the entire array, selecting a number of candidate angles to form a candidate angle index set; For each candidate angle in the candidate angle index set, performing non-uniform distance sampling using a polar domain codebook to generate candidate sampling points, and forming a candidate sampling point index set from all the candidate sampling points; Assigning each candidate sampling point in the candidate sampling point index set to a cooperation module for training to determine the energy of each candidate sampling point; Determining the optimal polar domain near-field codeword index according to the energy of each candidate sampling point to complete the modular XL-MIMO near-field beam training.

2. The modular XL-MIMO near-field beam training method according to claim 1, wherein The codeword index with the maximum beam gain of the entire array is: ； Among them, denotes the codeword index with the maximum beam gain of the entire array, denotes rounding, denotes the total number of modules in the array, denotes the number of antennas per module, denotes the sine value of the actual angle with the maximum beam gain of the entire array, , where, and respectively denote the codeword indices with the maximum beam gain of the head and tail modules.

3. The modular XL-MIMO near-field beam training method according to claim 1, characterized in that The candidate angle index set is: ； Among them, represents the candidate angle index set, represents the codeword index with the maximum beam gain of the entire array, represents rounding down, represents the total number of candidate angles.

4. The modular XL-MIMO near-field beam training method according to claim 1, characterized in that The sampling distances corresponding to the candidate sampling points are: ； Among them, represents the th candidate angle of the th candidate sampling point corresponding sampling distance, represents the total number of candidate sampling points corresponding to each candidate angle, represents the threshold distance for restricting the column coherence between the near-field steering vectors, , among which, represents the correlation coefficient between codewords, represents the wavelength of the spherical wave, represents the size of the array.

5. The modular XL-MIMO near-field beam training method according to claim 1, characterized in that The collaboration module consists of the -th module and the -th module in the array, , where represents the total number of modules in the array, and represents rounding down; When assigning each candidate sampling point in the candidate sampling point index set to a cooperation module for training, it is assigned to two modules in the cooperation module at the same time.

6. The modular XL-MIMO near-field beam training method according to claim 5, wherein Determining the energy of each candidate sampling point includes: Calculating the far-field codeword indices of the two modules in the cooperation module corresponding to each candidate sampling point; Based on the far-field codeword indices of the two modules in the cooperation module corresponding to each candidate sampling point, controlling the two modules in the cooperation module corresponding to each candidate sampling point to respectively send pilot signals, and superimposing the received signals of the user based on the cooperation module corresponding to each candidate sampling point to obtain the energy of each candidate sampling point.

7. The modular XL-MIMO near-field beam training method according to claim 6, wherein Calculating the far-field codeword indices of the two modules in the cooperation module corresponding to each candidate sampling point includes: Calculating the sine values of the two modules in the cooperation module corresponding to each candidate sampling point based on trigonometric function relationships; Calculating the far-field codeword indices of the two modules in the cooperation module corresponding to each candidate sampling point based on the sine values of the two modules in the cooperation module corresponding to each candidate sampling point; Among them, the calculation formula for the sine values of the two modules in the cooperation module corresponding to each candidate sampling point is: ； Among them, , represent the sine values of two modules in the cooperation module corresponding to the th candidate sampling point of the th candidate angle, The represents the angle of the th candidate sampling point of the th candidate angle relative to the central antenna of the entire array, The represents the sampling distance corresponding to the th candidate sampling point of the th candidate angle, The , represent the position coordinates of the central antennas of two modules in the th cooperation module, represents the number of antennas of each module, represents the spacing distance between adjacent antennas; The calculation formula for the far-field codeword indices of the two modules in the cooperation module corresponding to each candidate sampling point is: ； Among them, , represent the far - field codeword indices of two modules in the collaborative module corresponding to the th candidate sampling point of the th candidate angle; , represent the sine value of the th angle after evenly dividing to into parts for two modules in the th collaborative module.​ The received signal of the user based on the cooperation module corresponding to each candidate sampling point is: ； Among them, represents the value when is . represents the value when is . , represent the received signals of two modules in the cooperation module corresponding to the th candidate angle and the th candidate sampling point of the user. , represent the channels between two modules in the th cooperation module and the user. represents the transpose conjugate. , respectively represent the steering vectors with respect to and . represents the pilot signals sent by two modules in each cooperation module. , represent the additive white Gaussian noise of two modules in the th cooperation module. The energy of each candidate sampling point is: ； Among them, represents the energy of the th candidate sampling point of the th candidate angle.

8. The modular XL-MIMO near-field beam training method according to claim 1, characterized in that The optimal polar domain near-field codeword index is: ； Among them, corresponds to the best angle among the candidate angles, corresponds to the best sampling point among the candidate sampling points, represents the th candidate angle of the th candidate sampling point.

9. A computer device, characterized in that, Including: A storage medium for storing a computer program; A processor for executing the computer program to implement the modular XL-MIMO near-field beam training method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the modular XL-MIMO near-field beam training method according to any one of claims 1 to 8.

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