Channel state information estimation method and system based on kung method

Abstract

The invention discloses a channel state information estimation method and system based on a kung method. The method comprises the following steps: S1, executing preliminary channel estimation according to a received signal; s2, implementing a kung method according to a result obtained by the preliminary channel estimation to obtain estimation of an azimuth angle; s3, dividing the azimuth angle obtained by estimation into a reliable part and an unreliable part; for the unreliable part, executing an iterative algorithm to obtain a final azimuth angle estimated value; and S4, recovering channel state information according to the estimated value of the final azimuth angle of arrival obtained in the step S3. Compared with the prior art, the technical scheme of the invention adopts the kung method to carry out index and decomposition iteration, improves the accuracy of index and decomposition problems, and has the characteristics of high precision and low complexity.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a channel state information estimation method and system based on the Kung method. Background Art

[0002] With the rapid expansion of the user base in the field of wireless communications and the continuous improvement of the requirements for data transmission rate and stability, wireless communication systems are facing unprecedented challenges, namely, the need to be able to efficiently cope with large-scale concurrent user connections and ensure the provision of high-speed and reliable data transmission services. MIMO (Multiple Input Multiple Output) systems can significantly improve the capacity and reliability of wireless communication systems without increasing spectrum bandwidth and transmission power by using multiple transmit and receive antennas for wireless communication. Obtaining accurate channel state information (CSI) is crucial to the performance of MIMO systems. The accuracy of CSI will directly affect the beamforming of the transmitter and the decoding accuracy of the receiver.

[0003] In massive MIMO (multiple-input, multiple-output) wireless communication systems, the structural characteristics of millimeter-wave channels and their underlying mathematical logic are highly consistent with exponents and sequences, and information acquisition can be viewed as the decomposition of exponents and sequences. However, existing decomposition and channel estimation methods have defects such as instability, high complexity, low precision, and weak anti-pollution capabilities.

[0004] Therefore, in view of the technical defects of the prior art, it is necessary to propose a technical solution to solve the technical problems of the prior art. Summary of the invention

[0005] In view of the above-mentioned technical problems existing in the prior art, the present invention provides a channel state information estimation method and system based on the Kung method, which adopts the Kung method to perform exponential and decomposition iterations, improves the accuracy of the exponential and decomposition problems, and has the characteristics of high precision and low complexity.

[0006] In order to solve the technical problems existing in the prior art, the present invention adopts the following technical solutions:

[0007] A channel state information estimation method based on the Kung method comprises the following steps:

[0008] Step S1, performing preliminary channel estimation according to the received signal;

[0009] Step S2: According to the result obtained from the preliminary channel estimation, the Kung method is implemented to obtain an estimate of the azimuth angle;

[0010] Step S3, dividing the azimuth angle obtained by the above estimation into a reliable part and an unreliable part; for the unreliable part, executing an iterative algorithm to obtain a final azimuth angle estimation value;

[0011] Step S4: restore the channel state information according to the final azimuth arrival angle estimation value obtained in step S3.

[0012] As a preferred solution, in step S1, the calculation formula for preliminarily estimating the channel information Y is:

[0013]

[0014] in, is the channel information received by the receiver, μ is the received signal-to-noise ratio, p represents the number of propagation paths, α(θ l ) represents the channel steering vector of the lth path, θ l represents the azimuth arrival angle of the lth path, c l represents the channel gain of the lth path, w is the noise vector with a mean of 0 and a variance of σ 2 The complex Gaussian distribution of .

[0015] Assume that the channel steering vector of the lth path is:

[0016]

[0017] Wherein, the superscript T represents transpose, N is the number of receiving antennas, and j is the imaginary unit.

[0018] Let A = [α(θ 1 ), α(θ 2 ),…,α(θ p )], we can see that A is a Vandermonde matrix, c = [c 1 , c 2 , …, c p ] T is the channel gain vector, and the matrix expression of the preliminary estimated channel information Y is:

[0019]

[0020] Then the vector Y=[y 1 ,y 2 ,…,yN] T , which can be specifically expressed as:

[0021]

[0022] As a preferred solution, step S2 specifically includes:

[0023] Step S2.1, using the initially estimated information vector Y, obtain a preliminary angle estimate through the Kung method, specifically:

[0024] Construct the Hankel matrix: Take positive integers Q and L, satisfying the conditions Q+L-1=N, Q>L; in general, when N is an even number, take When N is an odd number, take And satisfy L = N-Q + 1. The information vector Y = [y 1 ,y 2 , …, y N ] T Construct the following Q×L dimensional matrix;

[0025]

[0026] Among them, H Q×L is a Hankel type matrix, the matrix A Q and A L are Vandermonde matrices of size Q×r and L×r respectively, and the superscript H denotes the conjugate transpose.

[0027] Step S2.2, perform singular value decomposition (SVD) on the Hankel matrix. The specific decomposition formula is:

[0028]

[0029] Among them, U Q×Q is a unitary matrix of size Q×Q; D Q×L is a diagonal matrix of size Q×L, specifically, D=diag(σ 1 , σ 2 , …, σ L ), σ 1 , σ 2 , …, σ L are diagonal elements and satisfy σ 1 ≥σ 2 ≥…≥σ L ≥0; Finally, is another unitary matrix of size L×L.

[0030] Step S2.3, the U obtained in step S2.2 Q×Q Matrix processing, take U Q×Q The 1st to Q-1th rows and 1st to pth columns of the matrix are denoted as U 1 ; Take U Q×Q The 2nd to Qth rows and 1st to pth columns of the matrix are denoted as U 2 , specifically:

[0031] U 1 =U(1:Q-1,1:p)

[0032] U 2 =U(2:Q,1:p)

[0033] Step S2.4, let The size is p×p, calculate U 3 The eigenvalues ​​and eigenvectors are as follows:

[0034] [(λ 1 ,λ 2 , …, λ p )(v 1 , v 2 , …, v p )]=eig(U 3 )

[0035] Among them, eig() is the eigenvalue calculation instruction, and λ 1 ,λ 2 , …, λ p is the eigenvalue, v 1 , v 2 , …, v p is the corresponding eigenvector, satisfying:

[0036] U 3 v j =λ j v j , j = 1, 2, ..., p

[0037] Step S2.5, the eigenvalue λ obtained in step S2.4 is 1 ,λ 2 , …, λ p Converted into phase angle, specifically:

[0038]

[0039] Among them, angle() is the phase angle conversion instruction, and This is the preliminary azimuth arrival angle estimate obtained using the Kung method.

[0040] As a preferred solution, step S3 specifically includes:

[0041] Step S3.1, set the threshold ε and the number of paths p;

[0042] Step S3.2, based on the preliminary azimuth arrival angle estimation value and the corresponding eigenvector obtained in step S2, use the following formula:

[0043]

[0044] Determine the reliability of all preliminary estimates and record the order, where ε is a small positive number that is set in advance, and |||| is the Frobenius norm. θ jis called a reliable angle, otherwise it is called an unreliable angle. All unreliable sets are denoted by Similarly, all reliable sets are

[0045] Step S3.3, based on the sets α and β obtained in step S3.2, merge them into γ = [αβ], then γ = is considered as an iterative codebook.

[0046] Step S3.4: According to step S3.3, the first p of the codebook 1 Perform the following operations for each angle:

[0047] Let j be the ranking of the angle currently being searched, taking the first angle as an example, that is, j = 1. Fix the remaining p-1 angles and search for the optimal value of the current j-th angle. The specific formula is as follows:

[0048]

[0049] Argmin can use the split point solution method, the search range is (-π, π), and the number of quantization points is τ, then the search step is The superscript indicates the number of updates, i.e., θ(1) is the angle after the search update, θ(0) is the angle in the initial codebook γ, and A N×p is the Vandermonde matrix obtained using the codebook γ, also taking j = 1 as an example,

[0050] Step S3.5, according to step S3.4, when j = 2, 3, ..., p 1 When j=1, the same operation is used to solve all the first p1 angles to obtain a set of final azimuth arrival angle estimates.

[0051] In the above technical solution, the Kung method is used for reliable judgment and iterative optimization, which can achieve lower estimation error under the premise of relatively low complexity.

[0052] As a preferred solution, step S4 includes:

[0053] Step S4.1, the final azimuth arrival angle estimate obtained in step S3 Get the reconstructed channel steering vector

[0054] Step S4.2, using the reconstructed channel steering vector and the sorted reconstruction matrix recorded in step S3.2 Specifically The expression is as follows:

[0055]

[0056] Step S4.3, using the reconstructed matrix Get the channel gain coefficient vector Use the following formula:

[0057]

[0058] Step S4.4, according to the reconstructed matrix and the channel gain coefficient vector Reconstruct channel information. The specific formula is as follows:

[0059]

[0060] in, This is the channel restored by the algorithm, and the real channel without noise is

[0061] Accordingly, the present invention also provides a channel state information estimation system based on the Kung method, based on the above estimation method, comprising a preliminary estimation module, a preliminary angle estimation module, an angle reliability judgment module, an angle search module, and a channel information recovery module connected in sequence, wherein:

[0062] A preliminary estimation module, used for performing preliminary channel estimation based on the received signal;

[0063] A preliminary angle estimation module, used to perform preliminary angle estimation by using the Kung method according to the result obtained from the preliminary channel estimation;

[0064] An angle reliability judgment module is used to judge the reliability through an algorithm based on the results obtained from the preliminary angle estimation;

[0065] An angle search module, used to obtain a better angle estimation value through an algorithm based on the unreliable angle estimation value obtained in the angle reliability judgment;

[0066] The channel information recovery module is used to recover the channel state information according to the optimal azimuth arrival angle estimation value.

[0067] As a preferred solution, the system includes 1 transmitting antenna and N receiving antennas. r The antennas are arranged in a uniform linear array (ULA).

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention utilizes the Kung method to execute the index and decomposition iterative algorithm, and achieves lower estimation error under the premise of relatively low complexity, so that the performance of the communication system is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 It is a flow chart of a channel state information estimation method based on the Kung method according to a preferred embodiment of the present invention.

[0072] Figure 2 It is a simulation diagram of the average channel estimation error of the method of the present invention under different signal-to-noise ratios.

[0073] Figure 3 It is a simulation diagram of the average channel estimation error of the method of the present invention under different numbers of receiving antennas.

[0074] Figure 4 It is a schematic diagram of the system structure of an exponential and decomposition iterative algorithm based on the Kung method in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following describes the embodiments of the present invention through specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0076] See also Figure 1 , shown is a flow chart of a channel state information estimation method based on the Kung method according to a preferred embodiment of the present invention, comprising the following steps:

[0077] Step S1, performing preliminary channel estimation according to the received signal;

[0078] Step S2: According to the result obtained from the preliminary channel estimation, the Kung method is implemented to obtain an estimate of the azimuth angle;

[0079] Step S3, dividing the azimuth angle obtained by the above estimation into a reliable part and an unreliable part; for the unreliable part, executing an iterative algorithm to obtain a final azimuth angle estimation value;

[0080] Step S4: restore the channel state information according to the final azimuth arrival angle estimation value obtained in step S3.

[0081] Embodiment 1:

[0082] In this embodiment, a specific example is used to illustrate the method of the present invention. The present invention improves the index and decomposition method in channel estimation and proposes a low-complexity, high-precision iterative algorithm.

[0083] The specific application cases are as follows:

[0084] In this embodiment, it is assumed that the number of transmitting antennas is N t =1, number of receiving antennas N r =128. The general parameter settings are shown in the following table:

[0085] Table 1 Parameter settings

[0086] parameter Value <![CDATA[Number of transmitting antennas N t > 1 <![CDATA[Number of receiving antennas N r > 128 Number of paths p 14 Signal-to-noise ratio snr (unit: dB) 0，2，4，6，8，10 Threshold ε 0.025 The number of rows of the Hankel matrix Q 65 The number of columns of the Hankel matrix L 64

[0087] The present invention utilizes an iterative algorithm to perform channel estimation and solve exponential and decomposition problems. Compared with traditional methods, under the condition of limited channel information, the invention utilizes an iterative algorithm based on the Kung method to achieve the goals of high precision and low complexity, and the performance of the communication system is further improved.

[0088] Specifically, in this embodiment:

[0089] Assume that in the massive MIMO system, the transmitter is equipped with 1 transmitting antenna and the receiver is equipped with 128 receiving antennas.

[0090] In step S1, the channel information obtained by preliminary estimation is assumed to be Y, and the specific calculation formula is:

[0091]

[0092] in, is the channel information received by the receiver, μ is the received signal-to-noise ratio, α(θ l ) represents the channel steering vector of the lth path, θ l represents the azimuth arrival angle of the lth path, which obeys the uniform distribution in the interval (-π, π), c l represents the channel gain of the lth path, w is the noise vector, and it obeys the complex Gaussian distribution with mean 0 and variance 1.

[0093] In the above description, the channel steering vector of the lth path is:

[0094]

[0095] Wherein, T represents transposition, j is an imaginary unit, and in this embodiment j 2 =-1.

[0096] In step S1 of this embodiment, the number of receiving antennas N r =128, obtain preliminary estimated channel information Y based on the received signal, Specifically expressed as:

[0097]

[0098] Step S2 specifically includes:

[0099] Step S2.1, based on the result of the preliminary estimation, obtain a preliminary angle estimation by the Kung method, specifically:

[0100] Construct the Hankel matrix: Take positive integers Q and L, satisfying the conditions Q+L-1=N, Q>L; in general, when N is an even number, take When N is an odd number, take And L = N-Q + 1. In this embodiment, Q = 65, L = 64, the information vector Constructed into the following 65×64 dimensional matrix;

[0101]

[0102] Among them, H 65×64 ∈65×64 is the constructed Hankel matrix.

[0103] Step S2.2, H constructed in step S2.1 65×64 Perform singular value decomposition, the specific formula is:

[0104] H 65×64 =UDV H

[0105] Where U is a unitary matrix of size 65×65; D is a diagonal matrix of size 65×64, specifically, D=diag(σ 1 , σ 2 , …, σ 64 ), σ 1 , σ 2 , …, σ 64 are diagonal elements and satisfy σ 1 ≥σ 2 ≥…≥σ 64 ≥0; Finally, V His another unitary matrix of size 64×64.

[0106] Step S2.3, preliminarily estimate the azimuth arrival angle.

[0107] For the U obtained in step S2.2 65×65 Matrix processing, take U 65×65 The 1st to 64th rows and 1st to 14th columns of the matrix are denoted as U 1 ; Take U 65×65 The 2nd to 65th rows and 1st to 14th columns of the matrix are denoted as U 2 , specifically:

[0108] U 1 =U(1:64,1:14)

[0109] U 2 =U(2:65,1:14)

[0110] Step S2.4, let The size is 14×14, calculate U 3 The eigenvalues ​​and eigenvectors are as follows:

[0111] [(λ 1 ,λ 2 , …, λ 14 )(v 1 , v 2 , …, v 14 )]=eig(U 3 )

[0112] Among them, eig() is the eigenvalue calculation instruction, and λ 1 ,λ 2 , …, λ p is the eigenvalue, v 1 , v 2 , …, v p is the corresponding eigenvector, satisfying:

[0113] U 3 v j =λ j v j , j = 1, 2, ..., 14

[0114] Step S2.5, the eigenvalue λ obtained in S2.4 1 ,λ 2 , …, λ 14 Converted into phase angle, specifically:

[0115]

[0116] Among them, angle() is the phase angle conversion instruction, and This is the preliminary azimuth arrival angle estimate obtained using the Kung method.

[0117] In this embodiment, step S3 specifically includes:

[0118] Step S3.1, set threshold ε = 0.025, number of paths p = 14

[0119] Step S3.2, based on the preliminary azimuth arrival angle estimation value and the corresponding eigenvector obtained in step S2, use the following formula:

[0120]

[0121] Determine the reliability of all preliminary estimates and record the order. |||| is the instruction for solving the Frobenius norm. The angle that satisfies the above formula is called a reliable angle, otherwise it is called an unreliable angle. All unreliable sets are denoted by Similarly, all reliable sets are The purpose of sorting in this step is to ensure that the column sorting of the restored Vandermonde matrix in step S4 is consistent with the real channel matrix. The specific method is: record the sorting number of the current judgment angle, and use positive sorting numbers to indicate reliability and negative sorting numbers to indicate unreliability, and make a sorting vector.

[0122] Step S3.3, based on the sets α and β obtained in step S3.2, merge them into γ = [αβ], then is considered as an iterative codebook.

[0123] Step S3.4: According to step S3.3, the first p of the codebook 1 Perform the following operations for each angle:

[0124] Let j be the ranking of the angle currently being searched, taking the first angle as an example, that is, j=1.

[0125] make Fix the remaining p-1 angles and search for the optimal value of the current j-th angle. The specific formula is as follows:

[0126]

[0127] Argmin can use the split point solution method, the search range is (-π, π), and the number of quantization points is τ, then the search step is The superscript indicates the number of updates, i.e., θ (1) To search for the updated angle, θ (0) is the angle in the initial codebook γ, A N×p is the Vandermonde matrix obtained using the codebook γ, also taking j = 1 as an example,

[0128] Step S3.5, according to step S3.4, when j = 2, 3, ..., p 1 When j=1, the same operation is used to solve all the first p1 angles to obtain a set of final azimuth arrival angle estimates. It is worth noting that since the initial angle of the iteration is obtained by the Kung method in step S2, the actual number of iterations is only one, and the complexity is greatly reduced.

[0129] In this embodiment, step S4 specifically includes:

[0130] Step S4.1, the final azimuth arrival angle estimate obtained in step S3 Get the reconstructed channel steering vector

[0131] Step S4.2, using the reconstructed channel steering vector and the sorted reconstructed Vandermonde matrix recorded in step S3.2 Specifically The expression is as follows:

[0132]

[0133] Step S4.3, using the reconstructed matrix Get the channel gain coefficient vector Use the following formula:

[0134]

[0135] Step S4.4, according to the reconstructed matrix and the channel gain coefficient vector Reconstruct channel information. The specific formula is as follows:

[0136]

[0137] in, This is the channel restored by the algorithm, and the real channel without noise is According to the following formula:

[0138]

[0139] Calculate the estimated error of the recovered channel information compared to the true channel.

[0140] Refer to the attached Figure 2As shown in the figure, when the number of receiving antennas is 128 and the signal-to-noise ratio is 0 dB, the average error is taken after 1000 simulations. The simulation of this algorithm shows that the average channel estimation error is 0.02, which is significantly better than the Kung method. Specifically, the gain of the method of the present invention is greater than 2 dB over the Kung method. As the system signal-to-noise ratio increases, the estimation error gradually decreases.

[0141] See attached Figure 3 As shown, in the simulation of the present invention, when the signal-to-noise ratio is 0 dB, as the number of system antennas increases, the estimation error gradually decreases.

[0142] Example 2

[0143] like Figure 4 As shown, this embodiment is based on the method of Embodiment 1 and discloses a system of exponential and decomposition iterative algorithm based on Kung method, which includes the following modules:

[0144] A preliminary estimation module, used for performing preliminary channel estimation based on the received signal;

[0145] A preliminary angle estimation module, used to perform preliminary angle estimation by using the Kung method according to the result obtained from the preliminary channel estimation;

[0146] An angle reliability judgment module is used to judge the reliability through an algorithm based on the results obtained from the preliminary angle estimation;

[0147] Angle search module: used to obtain a better angle estimation value through an algorithm based on the unreliable angle estimation value obtained in the angle reliability judgment;

[0148] The channel information recovery module is used to recover the channel state information according to the optimal azimuth arrival angle estimation value.

[0149] For other contents of this embodiment, please refer to Embodiment 1.

[0150] The present invention may also be implemented or applied through other different specific implementation methods, and the details in this specification may also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0151] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0152] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A channel state information estimation method based on the Kung method, characterized in that: The steps include: Step S1, performing preliminary channel estimation according to the received signal; Step S2: According to the result obtained from the preliminary channel estimation, the Kung method is implemented to obtain an estimate of the azimuth angle; Step S3, dividing the azimuth angle obtained by the above estimation into a reliable part and an unreliable part; for the unreliable part, executing an iterative algorithm to obtain a final azimuth angle estimation value; Step S4: restore the channel state information according to the final azimuth arrival angle estimation value obtained in step S3.

2. The channel state information estimation method based on the Kung method according to claim 1, characterized in that: In step S1, the calculation formula for the preliminary estimated channel information Y is: in, is the channel information received by the receiver, ρ is the received signal-to-noise ratio, p represents the number of propagation paths, α(θ l ) represents the channel steering vector of the lth path, θ l represents the azimuth arrival angle of the lth path, c l represents the channel gain of the lth path, w is the noise vector with a mean of 0 and a variance of σ 2 The complex Gaussian distribution of Assume that the channel steering vector of the lth path is: Where, the superscript T represents the transpose, N is the number of receiving antennas, and j is the imaginary unit; Let A=[α(θ1),α(θ2),…,α(θ p )], that is, A is a Vandermonde matrix, c = [c1, c2, ..., c p ] T is the channel gain vector, and the matrix expression of the preliminary estimated channel information Y is: Then the initial estimated vector Y = [y1, y2, ..., yN] T , specifically expressed as:

3. The channel state information estimation method based on the Kung method according to claim 1, characterized in that: Step S2 specifically includes: Step S2.1, using the initially estimated information vector Y, obtain a preliminary angle estimate through the Kung method, specifically: Construct the Hankel matrix: Take positive integers Q and L, satisfying the conditions Q+L-1=N, Q>L; when N is an even number, take When N is an odd number, take And satisfy L = N-Q + 1; the information vector Y = [y1, y2, ..., y N ] T Construct the following Q×L dimensional matrix; Among them, H Q×L is a Hankel type matrix, the matrix A Q and A L are Vandermonde matrices of size Q×r and L×r, respectively, and the superscript H indicates the conjugate transpose; Step S2.2, perform singular value decomposition (SVD) on the Hankel matrix. The specific decomposition formula is: Among them, U Q×Q is a unitary matrix of size Q×Q; D Q×L is a diagonal matrix of size Q×L, D=diag(σ1,σ2,…,σ L ),σ1,σ2,…,σ L are diagonal elements and satisfy σ1≥σ2≥…≥σ L ≥0; Finally, is another unitary matrix of size L×L; Step S2.3, the U obtained in step S2.2 Q×Q Matrix processing, take U Q×Q The 1st to Q-1th rows and 1st to pth columns of the matrix are denoted as U1; take U Q×Q The 2nd to Qth rows and the 1st to pth columns of the matrix are denoted as U2, specifically: U1=U(1:Q-1,1:p) U2=U(2:Q,1:p) Step S2.4, let The size is p×p, and the eigenvalues ​​and eigenvectors of U3 are calculated as follows: [(λ1,λ2,…,λ p )(v1, v2,…, v p )]=eig(U3) Among them, eig() is the eigenvalue calculation instruction, and λ1, λ2, …, λ p are eigenvalues, v1, v2, …, v p is the corresponding eigenvector, satisfying: U3v j =λ j v j ,j=1,2,...,p Step S2.5, the eigenvalues ​​λ1, λ2, ..., λ obtained in step S2.4 are p Converted into phase angle, specifically: Among them, angle() is the phase angle conversion instruction, and This is the preliminary azimuth arrival angle estimate obtained using the Kung method.

4. The channel state information estimation method based on the Kung method according to claim 1, characterized in that: Step S3 specifically includes: Step S3.1, set the threshold ε and the number of paths p; Step S3.2, based on the preliminary azimuth arrival angle estimation value and the corresponding eigenvector obtained in step S2, use the following formula: Determine the reliability of all preliminary estimates and record the order, where ε is a small positive number that is set in advance, || || is the Frobenius norm; the angle that satisfies the above formula θ j is called a reliable angle, otherwise it is called an unreliable angle; all unreliable sets are denoted by Similarly, all reliable sets are Step S3.3, based on the sets α and β obtained in step S3.2, merge them into γ = [αβ], then Considered as an iterative codebook; Step S3.4: According to step S3.3, perform the following operations on the first p1 angles of the codebook: Let j be the ranking of the angle currently being searched, taking the first angle as an example, that is, j = 1; then let Fix the remaining p-1 angles and search for the optimal value of the current j-th angle; specifically use the following formula: Argmin can use the split point solution method, the search range is (-π, π), and the number of quantization points is τ, then the search step is The superscript indicates the number of updates, i.e., θ (1) To search for the updated angle, θ (0) is the angle in the initial codebook γ, A N×p is the Vandermonde matrix obtained using the codebook γ, taking j = 1 as an example, Step S3.5, according to step S3.4, when j = 2, 3, ..., p1, the same operation as when j = 1 is used to solve all the first p1 angles to obtain a set of final azimuth arrival angle estimates.

5. The channel state information estimation method based on the Kung method according to claim 1, characterized in that: Step S4 includes: Step S4.1, the final azimuth arrival angle estimate obtained in step S3 Get the reconstructed channel steering vector Step S4.2, using the reconstructed channel steering vector and the sorted reconstruction matrix recorded in step S3.2 Specifically The expression is as follows: Step S4.3, using the reconstructed matrix Get the channel gain coefficient vector Use the following formula: Step S4.4, according to the reconstructed matrix and the channel gain coefficient vector Reconstruct the channel information. The specific formula is as follows: in, This is the channel restored by the algorithm, and the real channel without noise is 6. The channel state information estimation system based on the Kung method according to any one of claims 1 to 5, characterized in that: It includes a preliminary estimation module, a preliminary angle estimation module, an angle reliability judgment module, an angle search module, and a channel information recovery module connected in sequence, wherein: A preliminary estimation module, used for performing preliminary channel estimation based on the received signal; A preliminary angle estimation module, used to perform preliminary angle estimation by using the Kung method according to the result obtained from the preliminary channel estimation; An angle reliability judgment module is used to judge the reliability through an algorithm based on the results obtained from the preliminary angle estimation; An angle search module, used to obtain a better angle estimation value through an algorithm based on the unreliable angle estimation value obtained in the angle reliability judgment; The channel information recovery module is used to recover the channel state information according to the optimal azimuth arrival angle estimation value.

7. The channel state information estimation system based on the Kung method according to claim 6, characterized in that: The system includes 1 transmitting antenna and N receiving antennas. r The antennas are arranged in a uniform linear array.

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