Hybrid beam forming anti-interference method for millimeter wave large-scale array

Abstract

The invention relates to a hybrid beam forming anti-interference method for a millimeter wave large-scale array, and the method comprises the steps: S1, enabling a far-field signal to enter a linear antenna array which is uniformly distributed, and enabling the linear antenna array to form an analog receiving matrix through the received snapshot data; s2, the linear antenna array is divided into sub-arrays, each sub-array reconstructs a covariance matrix of interference and noise by using the data received in the step S1, and then an analog beam forming weight coefficient is obtained; s3, the analog beam forming weight coefficient in the S2 is quantized, the analog beam forming weight coefficient is quantized into a discrete value according to the precision of a phase shifter, and analog beam forming output is obtained according to the discrete value; and S4, acquiring an analog beam forming output signal at a digital port through an analog-to-digital converter, solving a digital beam forming weight through a sampling covariance matrix inversion algorithm, and obtaining a final output. Compared with the prior art, the method has the advantages of effective and remarkable anti-interference performance and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of array signal processing, and in particular to a hybrid beamforming anti-interference method for a large-scale millimeter wave array. Background Art

[0002] In communication systems, anti-interference technology is the key to ensure signal clarity and mitigate the impact of interference. Especially in modern communication systems with complex propagation environments, effective anti-interference schemes are essential to improve system performance and reliability. Therefore, the development and implementation of advanced anti-interference strategies and algorithms is a key research direction in the field of communication technology. Adaptive digital beamforming algorithms are widely regarded as key technologies for effectively suppressing interference. Capon introduced the minimum variance distortionless response (MVDR) algorithm. Based on this premise, a large number of studies have been conducted to enhance the anti-interference performance of algorithms using signal covariance matrix reconstruction. In the case of a large number of low-frequency bands being occupied, the use of high-frequency millimeter wave technology as a mainstream solution is becoming more and more inevitable. Compared with traditional frequency bands, the loss of millimeter wave bands has increased significantly. In order to offset this attenuation, large-scale arrays are considered to provide sufficient gain. In 2010, TL Marzetta first proposed the concept of massive multiple-input multiple-output (MIMO) and proved through simulation that traditional digital beamforming can achieve optimal performance. However, traditional adaptive beamforming methods are not suitable for massive MIMO systems due to their high overhead and large array size.

[0003] Hybrid beamforming integrates analog and digital beamforming techniques to optimize the transmission and reception efficiency of signals. This approach helps to strategically distribute beamforming responsibilities in the analog and digital domains, providing a balance between superior performance and economic feasibility. The core challenge lies in optimally balancing analog and digital beamforming within hardware limitations. Hybrid beamforming architectures have received great attention in contemporary massive antenna systems. T.Lin proposed two methods to address the constant modulus constraint, significantly improving the performance of the beamformer. Instead of targeting optimal digital beamforming, these two methods directly minimize the mean square error, marking a significant reduction in bit error rate. The Orthogonality Constraint and Phase Extraction (OCPE) method is proposed, which involves orthogonal constraints for digital beamforming and phase extraction methods for analog beamforming, allowing the algorithm to approach the performance of a fully digital beamformer while using fewer RF links, thereby improving energy efficiency in massive MIMO systems. Currently, these hybrid beamforming algorithms do not consider the maximum signal-to-interference-plus-noise ratio (SINR) as an anti-interference metric, nor do they consider the significant performance degradation due to hardware limitations.

[0004] After searching, Chinese patent publication number CN118249869A discloses an adaptive beamforming anti-interference method based on a large-scale array system. Specifically, it discloses that first, a full-digital beamforming optimization model is constructed, and the zero-point broadening is considered to solve the optimal full-digital beamforming weight vector; then, the OMP algorithm is used to obtain the full-digital beamforming weight vector as the optimal quantity to construct an analog beamforming matrix and a digital beamforming matrix; finally, the anti-interference effects of the full-digital and hybrid beamforming are analyzed and compared. However, the existing patent adopts a fully connected structure, that is, each RF link is connected to all antennas through a phase shifter. This method requires a large number of phase shifters and has high resource requirements. At the same time, the existing patent adopts a joint optimization method, first finding the optimal solution based on the MVDR criterion, and then finding the corresponding analog and digital weights through the OMP algorithm to approximate the optimal solution. The core idea of ​​this method is to select an analog weight, and then finally use digital beamforming to compensate for the shortcomings of analog beamforming and approximate the optimal value. This method cannot take into account the output results of analog beamforming, which may result in analog beamforming not having the effect of suppressing interference. When the interference is large, considering the limited accuracy of the ADC, the signal received by the subsequent digital beamforming may exceed the dynamic range of the ADC, resulting in a sharp decline in the anti-interference effect.

[0005] Therefore, how to study an effective anti-interference method under the hardware limitations of millimeter-wave large-scale arrays has become a technical problem that needs to be solved. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a hybrid beamforming anti-interference method for millimeter-wave large-scale arrays. When faced with non-ideal phase shifters and analog-to-digital converters (ADCs), an analog-to-digital grading algorithm is used to amplify the desired signal and suppress interference. A novel covariance matrix reconstruction algorithm reduces the interference intensity in the analog beamforming stage, ensuring that the signal in the digital stage does not exceed the dynamic range of the ADC. At the same time, the sampling matrix inversion algorithm enhances the anti-interference capability in the digital beamforming stage. The method has effective and significant anti-interference performance.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] According to one aspect of the present invention, a hybrid beamforming anti-interference method for a millimeter wave large-scale array is provided, the method comprising the following steps:

[0009] Step S1, the far-field signal is incident on a uniformly arranged linear antenna array, and the linear antenna array forms a simulated receiving matrix with the received snapshot data;

[0010] Step S2, dividing the linear antenna array into sub-arrays, each sub-array reconstructing the covariance matrix of interference plus noise using the data received in step S1, and then obtaining the simulated beamforming weight coefficients;

[0011] Step S3, quantizing the analog beamforming weight coefficients of step S2, quantizing the analog beamforming weight coefficients into discrete values ​​according to the phase shifter accuracy, and obtaining analog beamforming outputs according to the discrete values;

[0012] Step S4, collecting the analog beamforming output signal through the analog-to-digital converter at the digital port, solving the digital beamforming weights through the sampling covariance matrix inversion algorithm, and obtaining the final output.

[0013] As a preferred technical solution, the linear antenna array in step S1 is a linear array composed of antenna array elements with uniform intervals d, where d is half the wavelength of the frequency.

[0014] As a preferred technical solution, the simulated receiving matrix in step S1 provides a data source for subsequent beamforming.

[0015] As a preferred technical solution, the sub-arrays in S2 are evenly arranged sub-arrays.

[0016] As a preferred technical solution, in step S2, the received signal of the linear antenna array is expressed as:

[0017] x N =As+n,

[0018] Where A represents the popularity matrix of the receiving array, s represents the desired signal and interference signal propagated in space, and n is Gaussian white noise;

[0019] In order to obtain the steering vector of the array receiving signal, the power spectrum of the entire spatial domain is required. The Capon power spectrum is described in the entire space as:

[0020]

[0021] in is the sample covariance matrix with T snapshots, T, x N (t) a N (θ);

[0022] The estimated value of the array manifold matrix is ​​determined by analyzing the Capon spectra of the desired signal and the interference within a predetermined angle range.

[0023] The eigenvalue decomposition of the covariance matrix of the receiving array is

[0024]

[0025] in is the noise power, λ m is the m eigenvalues ​​of R decreasing, u m is the eigenvector corresponding to the eigenvalue, is its conjugate transpose, Λs and Λn are diagonal matrices composed of corresponding eigenvalues, U s and U n They represent the matrices consisting of eigenvalues ​​and eigenvectors respectively. For U s The conjugate transpose of , U is the matrix composed of all eigenvectors obtained by signal and noise decomposition, U H Its conjugate transpose, I N is the identity matrix of dimension N;

[0026] The estimated value Substitute the above formula and traverse the directions of L spatial domains, is the steering vector in L directions, and the total noise power estimation value is:

[0027]

[0028] By averaging all L noise power estimates, we get

[0029] Multiply R by the array A on the left and the transpose of A on the right, and then convert the estimated value and estimated values Substituting this into the estimated value of the signal power is

[0030]

[0031] The covariance matrix of the reconstructed interference plus noise is

[0032]

[0033] in is the power distribution of the desired signal, and the weight vector of the first subarray is expressed as

[0034]

[0035] Since the sub-arrays are divided evenly, the weight coefficients of each sub-array are the same.

[0036] As a preferred technical solution, the A=[a N (θ0),a N (θ1),…,a N (θ Q)], Q represents the number of interference signals, when Q is 0 it represents the desired signal, and the steering vector in the array flow is expressed as Where θ represents the angle of the incident signal, d is the spacing between antenna elements, and λ represents the wavelength of the incident signal.

[0037] As a preferred technical solution, the x N Obtained by adding an additional auxiliary subarray equipped with a complete RF chain.

[0038] As a preferred technical solution, the specific process of obtaining the simulated beamforming weight coefficients is as follows:

[0039] The weight coefficients of each sub-array are quantized according to the phase shifter accuracy B1, and the components inside the phase shifter are expressed as

[0040]

[0041] in B1 represents the accuracy of the phase shifter at the analog end. The signal output by the analog beamformer is

[0042]

[0043] Where W RF is the analog beamforming weight matrix of all sub-arrays in the entire system, and x is the signal received by the analog end.

[0044] As a preferred technical solution, the simulated beamforming weight matrix W of all sub-arrays in the entire system is RF Specifically:

[0045]

[0046] Where W RF is the simulated beamforming weight matrix of the entire system.

[0047] As a preferred technical solution, step S4 is specifically as follows:

[0048] The output signal y of the analog beamforming RF The normalized signal is then quantized by a finite precision analog-to-digital converter to produce a discrete digital representation, denoted by x DB ;

[0049] Given an ADC with a resolution of B2 bits, x DB The quantitative value of

[0050]

[0051] Digital beamforming is performed at the digital end, and the virtual steering vector with a spacing of Nd is constructed as follows:

[0052]

[0053] Where N RF Indicates the number of RF chains in the system;

[0054] The weight vector of digital beamforming is obtained as

[0055]

[0056] in Represents the sampling covariance matrix of the signal received at the digital end;

[0057] Finally, the anti-interference signal after analog and digital beamforming can be obtained, which is expressed as

[0058]

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] 1) The present invention considers the impact of hardware limitations on the anti-interference performance of large-scale millimeter-wave arrays. The hardware limitations mainly include limited-precision phase shifters and ADCs, and proposes a hierarchical hybrid beamforming method with maximizing the signal-to-interference-noise ratio as the optimization goal to reduce the suppression of hardware limitations on anti-interference performance;

[0061] 2) The present invention innovatively uses a new interference plus noise covariance matrix reconstruction algorithm in the analog beamforming anti-interference part, which effectively suppresses strong interference signals at the analog end and saves costs;

[0062] 3) The present invention has excellent performance under relatively strong interference conditions. This feature enables it to perform well in strong interference scenarios, further expanding its applicability;

[0063] 4) The present invention adopts a partial connection structure, that is, the antenna array is divided into sub-arrays, and each radio frequency link is only connected to one antenna sub-array, which can significantly reduce the number of phase shifters and save resources;

[0064] 5) The present invention adopts hierarchical beamforming, that is, analog beamforming is first performed to suppress part of the interference, and then digital beamforming is performed, which effectively suppresses the interference at the analog end, thereby avoiding the phenomenon of "causing a sharp decline in anti-interference effect" in the existing patent. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a model diagram of a hybrid beamforming system;

[0066] Figure 2A flow chart of the practical hybrid beamforming anti-interference method in large-scale millimeter-wave arrays;

[0067] Figure 3 The performance results of the algorithm compared with the existing algorithms under different interference-to-signal ratios are shown in the figure;

[0068] Figure 4 It is a performance result diagram under different phase shifter accuracies;

[0069] Figure 5 The figure shows the performance results under different numbers of snapshots. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0071] The present invention provides a practical hybrid beamforming anti-interference method based on millimeter wave large-scale array, including a hybrid beamforming system model, analog beamforming based on a new interference signal plus noise covariance matrix reconstruction algorithm, and digital beamforming based on a sampling signal covariance matrix inversion algorithm.

[0072] The system model of hybrid beamforming is as follows: Figure 1 As shown in the figure, the target is Q far-field interference signals and one desired signal. The receiving array is a uniform linear array composed of M antenna elements. The spacing between adjacent elements is denoted as d and d = λ / 2, where λ is the wavelength corresponding to the center frequency of the broadband signal. RF RF chains are divided into N RF N-dimensional sub-matrices.

[0073] The anti-interference part of the present invention is divided into two parts: analog beamforming and digital beamforming. In order to achieve the best anti-interference effect, both anti-interference parts consider maximizing the signal-to-interference-plus-noise ratio (SINR) as the optimization goal. SINR can be expressed as

[0074]

[0075] where x s and x i represent the desired signal and interference signal respectively, n represents Gaussian white noise, represents the power of the desired signal, w is the antenna channel weighting coefficient, which represents the analog and digital beamforming weights in the present invention, and R i+nrepresents the covariance matrix of interference plus noise, a(θ0) represents the desired signal steering vector;

[0076] It is generally believed that the standard MVDR beamformer is mathematically equivalent to optimizing SINR. This equivalence can be succinctly stated as

[0077]

[0078] This criterion makes the subsequent derivation of MVDR weights simple, and the optimal weight can be expressed as

[0079]

[0080] The key point of solving is the covariance matrix of interference plus noise. In the present invention, the covariance matrix of interference plus noise is reconstructed at the analog end, and the covariance matrix of interference plus noise is replaced by the received signal sampling covariance matrix at the digital end;

[0081] The received signal of the antenna array is expressed as

[0082] x N =As+n,

[0083] where A=[a N (θ0),a N (θ1),…,a N (θ Q )] represents the popularity matrix of the receiving array, Q represents the number of interference signals, when Q is 0, it represents the desired signal, by default there is only one desired signal, and the steering vector in the array popularity can be expressed as s represents the desired signal and interference signal propagating in space, and n is Gaussian white noise;

[0084] where x N This can be obtained by adding an additional auxiliary subarray equipped with a complete RF chain;

[0085] In order to obtain the steering vector of the array receiving signal, the power spectrum of the entire spatial domain is required. The Capon power spectrum in the entire space can be described as

[0086]

[0087] is the sample covariance matrix with snapshot number T. First, the estimated value of the estimated array manifold matrix is ​​determined by analyzing the Capon spectra of the desired signal and the interference within a predetermined angle range. The array popularity matrix contains estimates of the desired signal and interfering signal steering vectors;

[0088] The eigenvalue decomposition of the covariance matrix of the receiving array is

[0089]

[0090] in is the noise power, λ m is the m eigenvalues ​​of R decreasing, u m is the eigenvector corresponding to the eigenvalue, is its conjugate transpose, Λs and Λn are diagonal matrices composed of corresponding eigenvalues, U s and U n They represent the matrices consisting of eigenvalues ​​and eigenvectors respectively. For U s The conjugate transpose of , U is the matrix composed of all eigenvectors obtained by signal and noise decomposition, U H Its conjugate transpose, I N is the identity matrix of dimension N;

[0091] The estimated value Substitute into the above formula and traverse the directions of L spatial domains, expressed as

[0092]

[0093] The total noise power estimate can be obtained as

[0094]

[0095] By averaging all L noise power estimates, we get

[0096] Multiplying R by the array A on the left and the transpose of A on the right gives

[0097]

[0098] The estimated value and estimated values Substituting this into the estimated value of the signal power is

[0099]

[0100] The covariance matrix of interference plus noise can be reconstructed as

[0101]

[0102] in is the power distribution of the interference signal and the desired signal, is the power distribution of the desired signal, and the weight vector of the first subarray can be expressed as

[0103]

[0104] Since the sub-arrays are divided evenly, the weight coefficients of each sub-array are the same, which can be expressed as

[0105]

[0106] Where k represents the kth sub-matrix, k = 1, 2, ..., N RF , where N RF Indicates the number of radio frequency links, which is equivalent to the number of sub-arrays;

[0107] The weight coefficients of each subarray are quantized according to the phase shifter accuracy B1, and the components inside the phase shifter can be expressed as

[0108]

[0109] in The quantized analog beamforming weight of each subarray is w k ,The simulated beamformer weights of the entire system are expressed as

[0110]

[0111] The output of analog beamforming can be expressed as

[0112]

[0113] Where x is the signal received by the analog end.

[0114] The analog beamforming output signal is collected at the digital port through a finite precision analog-to-digital converter, and the digital beamforming weights are solved by the sampling covariance matrix inversion algorithm to obtain the final output;

[0115] The output signal y of the analog beamforming RF The normalized signal is then quantized by an analog-to-digital converter (ADC) with finite precision to produce a discrete digital representation, denoted by x DB ;

[0116] Given an ADC with a resolution of B2 bits, x DB The quantitative value of can be expressed as

[0117]

[0118] Digital beamforming is performed at the digital end, and the virtual steering vector with a spacing of Nd is constructed as follows:

[0119]

[0120] The weight vector of digital beamforming can be obtained as

[0121]

[0122] in Represents the sampling covariance matrix of the signal received at the digital end;

[0123] Finally, the anti-interference signal after analog and digital beamforming can be obtained, which can be expressed as

[0124]

[0125] Based on the simulation parameters shown in Table 1, the present invention is simulated on an Intel Core i7@3.6GHz processor and 32GB LPDDR4@2133MHz memory. The simulation parameters are shown in Table 1:

[0126] Table 1

[0127]

[0128]

[0129] Considering multi-interference anti-interference, the interference angle is randomly generated. In order to compare the existing algorithms, the signal-to-interference-to-noise (SINR) ratio of the output signal of the final hybrid beamforming is introduced as an indicator, such as Figure 3 As shown in the figure, considering the hardware limitation and different signal-to-interference ratios (ISR), the proposed algorithm is significantly better than the traditional digital adaptive beamforming algorithm and other hybrid beamforming algorithms: pure digital algorithm, robust adaptive algorithm, beam pointing algorithm and hybrid beamforming algorithm of traditional orthogonal matching pursuit.

[0130] In order to study the performance of the algorithm under different phase shifter accuracies, SINR is also used as an indicator, such as Figure 4 As shown in the figure, a comparison of the output SINR of the algorithm proposed in the present invention and the full digital beamforming under different phase shifter accuracies is given, and the ISR range is 70dB to 150dB. With the improvement of the phase shifter accuracy, the interference resistance capability of the algorithm proposed by us is enhanced, and it significantly exceeds the full digital beamforming under the phase shifter accuracy of 4bit. This feature enables it to perform well under the conditions of hardware limitations and further expands its applicability.

[0131] Figure 5 The output SINR of the present invention under different snapshots is compared. The results show that when the number of snapshots is less than 200, the performance of the beamformer improves with the increase of the number of snapshots; when the number of snapshots is greater than 200, the performance of the beamformer remains basically unchanged.

[0132] Unlike existing methods that only consider the improvement of the maximum signal-to-interference-noise ratio of reception without considering hardware limitations, this specification proposes a practical anti-interference algorithm that integrates analog and digital beamforming technologies. Under the premise of ensuring that the digital signal remains within the dynamic range of the analog-to-digital converter, this method first uses a finite-precision phase shifter to reduce analog interference, and then maximizes the anti-interference potential of digital beamforming. The present invention uses the minimum variance distortion-free response (MVDR) criterion, introduces a new covariance matrix reconstruction algorithm in the analog stage, and introduces a sampling matrix inversion algorithm in the digital stage. The present invention verifies the effectiveness of the proposed algorithm in anti-interference through simulation results. This method improves the practical applicability of hybrid beamforming in actual scenarios.

[0133] In summary, the hybrid beamforming method proposed in the present invention integrates analog and digital beamforming, and significantly enhances the suppression of strong interference under non-ideal conditions of ADC and phase shifter.

[0134] In summary, the present invention is very effective in suppressing interference signals under the condition of hardware limitations.

[0135] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A hybrid beamforming anti-interference method for millimeter wave large-scale arrays, characterized in that: The method comprises the following steps: Step S1, the far-field signal is incident on a uniformly arranged linear antenna array, and the linear antenna array forms a simulated receiving matrix with the received snapshot data; Step S2, dividing the linear antenna array into sub-arrays, each sub-array reconstructing the covariance matrix of interference plus noise using the data received in step S1, and then obtaining the simulated beamforming weight coefficients; Step S3, quantizing the analog beamforming weight coefficients of step S2, quantizing the analog beamforming weight coefficients into discrete values ​​according to the phase shifter accuracy, and obtaining analog beamforming outputs according to the discrete values; Step S4, collecting the analog beamforming output signal through the analog-to-digital converter at the digital port, solving the digital beamforming weights through the sampling covariance matrix inversion algorithm, and obtaining the final output.

2. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 1, characterized in that: The linear antenna array in step S1 is a linear array composed of antenna array elements with uniform intervals d, where d is half the wavelength of the frequency.

3. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 1, characterized in that: The simulated receiving matrix in step S1 provides a data source for subsequent beamforming.

4. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 1, characterized in that: The sub-arrays in S2 are evenly arranged sub-arrays.

5. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 1, characterized in that: In step S2, the received signal of the linear antenna array is expressed as: x N =As+n, Where A represents the popularity matrix of the receiving array, s represents the desired signal and interference signal propagated in space, and n is Gaussian white noise; In order to obtain the steering vector of the array receiving signal, the power spectrum of the entire spatial domain is required. The Capon power spectrum is described in the entire space as: in is the sample covariance matrix with T snapshots, T, x N (t) a N (θ); The estimated value of the array manifold matrix is ​​determined by analyzing the Capon spectra of the desired signal and the interference within a predetermined angle range. The eigenvalue decomposition of the covariance matrix of the receiving array is in is the noise power, λ m is the m eigenvalues ​​of R decreasing, u m is the eigenvector corresponding to the eigenvalue, is its conjugate transpose, Λ s and Λ n are diagonal matrices of corresponding eigenvalues, U s and U n They represent the matrices consisting of eigenvalues ​​and eigenvectors respectively. For U s The conjugate transpose of , U is the matrix composed of all eigenvectors obtained by signal and noise decomposition, U H Its conjugate transpose, I N is the identity matrix of dimension N; The estimated value Substitute the above formula and traverse the directions of L spatial domains, is the steering vector in L directions, and the total noise power estimation value is: By averaging all L noise power estimates, we get Multiply R by the array A on the left and the transpose of A on the right, and then convert the estimated value and estimated values Substituting this into the estimated value of the signal power is The covariance matrix of the reconstructed interference plus noise is in is the power distribution of the desired signal, and the weight vector of the first subarray is expressed as Since the sub-arrays are divided evenly, the weight coefficients of each sub-array are the same.

6. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 5, characterized in that: A=[a N (θ0),a N (θ1),…,a N (θ Q )], Q represents the number of interference signals, when Q is 0 it represents the desired signal, and the steering vector in the array flow is expressed as Where θ represents the angle of the incident signal, d is the spacing between antenna elements, and λ represents the wavelength of the incident signal.

7. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 5, characterized in that: The x N Obtained by adding an additional auxiliary subarray equipped with a complete RF chain.

8. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 5, characterized in that: The specific process of obtaining the analog beamforming weight coefficients is as follows: The weight coefficients of each sub-array are quantized according to the phase shifter accuracy B1, and the components inside the phase shifter are expressed as in B1 is the accuracy of the phase shifter, and the signal output by the analog beamformer is Where W RF is the analog beamforming weight matrix of all sub-arrays in the entire system, and x is the signal received by the analog end.

9. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 8, characterized in that: The simulated beamforming weight matrix W of all sub-arrays in the whole system is RF Specifically: Where W RF is the simulated beamforming weight matrix of the entire system.

10. The hybrid beamforming anti-interference method for millimeter wave large-scale array according to claim 8, characterized in that: The step S4 is specifically as follows: The output signal y of the analog beamforming RF The normalized signal is then quantized by a finite precision analog-to-digital converter to produce a discrete digital representation, denoted by x DB ; Given an ADC with a resolution of B2 bits, x DB The quantitative value of Digital beamforming is performed at the digital end, and the virtual steering vector with a spacing of Nd is constructed as follows: Where N RF Indicates the number of RF chains in the anti-interference system; The weight vector of digital beamforming is obtained as in Represents the sampling covariance matrix of the signal received at the digital end; Finally, the anti-interference signal after analog and digital beamforming can be obtained, which is expressed as

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