Interference signal elimination method and system based on index and decomposition and resampling

Abstract

The invention discloses an interference signal elimination method and system based on indexes, decomposition and resampling, and the method comprises the following steps: S1, calculating the center frequency and sampling frequency of intermediate frequency sampling according to the frequency band of communication operation; s2, sampling on the sampling frequency band and in the same symbol time interval to obtain a sampling sample; s3, estimating an incident angle of each path of signal by using a Prong-Kung algorithm according to the sampling sample obtained in the step S2; and S4, according to the incident angle estimated in the step S3, interference is removed through a least square method, and the path gain of the required signal is obtained. According to the technical scheme, based on indexes and decomposition, the Prony-Kung algorithm and the resampling technology are used for channel estimation, useful signals and interference signals are separated, and the purpose of interference removal is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an interference signal elimination method and system based on exponential sum decomposition and resampling. Background Art

[0002] With the widespread deployment of 5G networks and satellite communication systems, spectrum resource sharing and electromagnetic compatibility issues have become increasingly prominent. Since 5G base stations and satellite earth stations operate in adjacent frequency bands, when they operate simultaneously in the same area, the strength and spectrum characteristics of 5G signals may interfere with satellite communications, which can lead to reduced signal quality, reduced data transmission rates, and even communication interruptions. Therefore, especially in high-density urban areas and where spectrum resources are scarce, solving the interference problem of signals with similar frequencies is crucial to ensuring the stability and reliability of satellite communication systems.

[0003] In wireless communication environments, there are many challenges in processing the separation of signals in adjacent frequency bands. First, when the Prony-Kung method is used to process exponentials and sequences with similar parameter values, it may cause a large deviation between the estimated parameters and the actual values, thus affecting the accuracy and stability of the estimation. Secondly, channel estimation is a key step in recovering the distortion and fading of the signal during channel transmission. However, the highly dynamic and time-varying characteristics of wireless communication channels significantly increase the complexity of channel estimation, which in turn places higher requirements on the accuracy and robustness of channel state information.

[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 problems existing in the prior art, the present invention proposes an interference signal elimination method and system based on exponential sum decomposition and resampling, which is based on exponential sum decomposition and utilizes the Prony-Kung algorithm and resampling technology to perform channel estimation and separate useful signals from interference signals to achieve the purpose of interference removal.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The interference signal elimination method based on exponential sum decomposition and resampling includes the following steps:

[0008] Step S1, calculating the center frequency and sampling frequency of the intermediate frequency sampling according to the frequency band of the communication operation.

[0009] Step S2: sampling within the sampling frequency band and the same symbol time interval to obtain sampling samples.

[0010] Step S3: Estimate the incident angle of each signal using the Prong-Kung algorithm based on the sampled samples.

[0011] Step S4: According to the estimated incident angle, the interference is removed by the least square method, and the path gain of the desired signal is obtained.

[0012] As a preferred solution, the specific steps of step S1 are as follows:

[0013] Intermediate frequency sampling means that the sampling frequency can be set at a certain frequency point. Here, the sampling is performed at the frequency point after the received signal is downconverted. There are P signals, of which there are P-1 interference signals. Assume that the frequency of the signal sampling is f c , sampling time interval Δt, symbol duration T0, total number of samples N, then

[0014]

[0015] Assuming the center frequency of the target signal is f0, first reduce the center frequency f0 of the signal to f c , and then sample at time intervals.

[0016] As a preferred solution, the specific steps of step S2 are as follows:

[0017] Assume that the bandwidth to be detected is B, and there are P users sending signals in this bandwidth. The center frequency of the pth user is f p , the signal bandwidth is B p After the signal passes through the filter, it is resampled in a frequency band with a bandwidth of B, and the sampling is N times. These data come from the same symbol time interval, and the mathematical expression of the nth sampling signal is:

[0018]

[0019]

[0020] Where ρ represents the ratio of the effective power of the signal to the effective power of the noise, c p represents the path gain and is assumed to be constant during the sampling time, s p represents the pth random transmission signal (in the training phase, assuming the training sequence is 1), f p represents the frequency point position of the pth signal in the frequency band, Δt is the time interval for collecting signal data (equally spaced sampling), and w n is complex white Gaussian noise.

[0021] Sampling N times in N·Δt time obtains {y1,y2,…y N}, the vector form is:

[0022]

[0023] The sampled signal is expressed mathematically in the following matrix form:

[0024]

[0025] Here we first make a preliminary channel estimation:

[0026]

[0027] As a preferred solution, the specific steps of step S3 are as follows:

[0028] Step S3.1: Construct a Hankel matrix based on the received signal and the set reference information:

[0029] Let θ p =2f p ′Δt, so the mathematical expression of the nth sampling signal can be written as:

[0030]

[0031] Here, it is assumed that the transmitted signal is 1 and is at the frequency point f1 after the useful signal is downconverted. ′ Upsampling, then f2′, f3′…f p ′ refers to the frequency point of the interference signal after frequency reduction. At this time, the matrix of the sampling signal can be written as:

[0032]

[0033] The Hankel matrix is ​​constructed from the sampled signal and related parameters as follows:

[0034]

[0035] Where Q and L are positive integers, satisfying the conditions Q+L-1=N and Q≥L≥P. When N is an even number, then An integer of, when N is an odd number, then take An integer.

[0036] Step S3.2: Perform singular value decomposition on Hankel to estimate the incident angle of each signal:

[0037] Perform singular value decomposition (SVD) on the Hankel matrix, that is:

[0038] H Q×L =UDV H

[0039] Among them, U and V are unitary matrices of size Q×L and L×L respectively, and D=diag(d1,d2,…d L) is an L×L diagonal matrix with d1≥d2≥…d P ≥…≥d L ≥0.

[0040] Then the column vectors of U are a set of standard orthogonal bases of the space generated by the column vectors of H. The first row to the Q-1 row and the first column to the P column of the matrix U are recorded as U1; the second row to the Q row and the first column to the P column of the matrix U are recorded as U2, as shown below:

[0041] U1=U(1:Q-1,1:P),U2=U(2:Q,1:P)

[0042] Estimate eigenvalues:

[0043] eig{(U1 H U1) -1 U1 H U2}

[0044] available Where eig{·} means taking the eigenvalue.

[0045] According to the formula: The estimated arrival angle is obtained, which is the incident angle of each signal, recorded as where angle{·} represents the angle at which the eigenvalue is taken.

[0046] As a preferred solution, the specific steps of step S4 are as follows:

[0047] Using the angle estimated in step S3, construct:

[0048]

[0049] According to the least squares method, the path gain is estimated:

[0050]

[0051] Note θ c =2πf c Δt, where f c is the center frequency of the intermediate frequency sampling. This patent samples at the frequency point f1′ after the useful signal is downconverted. Take i=1,2…,P in turn, and find Based on the characteristics of the Prony-Kung algorithm, the vector The i-th component in This is the path gain of concern, which separates the useful signal from the interference signal and achieves the purpose of removing the interference signal.

[0052] The present invention also discloses an interference elimination system based on exponential sum decomposition and resampling, comprising the following modules:

[0053] IF sampling module: Calculates the center frequency and sampling frequency of IF sampling according to the frequency band of communication operation.

[0054] Sampling sample module: samples within the sampling frequency band and the same symbol time interval to obtain sampling samples.

[0055] Matrix construction and singular value decomposition module: construct the Hankel matrix according to the sampling signal and the set parameter information, and perform singular value decomposition on it.

[0056] Interference removal signal module: According to the estimated incident angle, the least square method is used to remove interference and obtain the path gain of the required signal.

[0057] The existing technical methods have the following technical problems: when processing exponentials and sequences with similar parameter values, it may cause a large deviation between the estimated parameters and the actual values. Secondly, channel estimation is a key step in recovering the distortion and fading of the signal during the channel transmission process.

[0058] Compared with the prior art, the present invention utilizes an interference signal elimination method and system based on exponential sum decomposition and resampling, which has the following beneficial effects:

[0059] Based on exponential sum decomposition, Prony-Kung algorithm and resampling technology are used to perform channel estimation and separate useful signals from interference signals to achieve the purpose of interference removal. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] 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.

[0061] Figure 1 This is a flow chart of the interference signal elimination method based on exponential sum decomposition and resampling in Example 1.

[0062] Figure 2 This is the overall structural framework diagram of the interference elimination system of index sum decomposition and resampling in Example 2.

[0063] Figure 3 This is a simulation diagram of the bit error rate of the method of the present invention when the number of sampling samples is 100 and the signal-to-noise ratio changes.

[0064] Figure 4 This is a simulation diagram of the bit error rate of the method of the present invention when the signal-to-noise ratio is 10dB and the number of sampling samples changes.

[0065] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0066] The technical solution provided by the present invention will be further described below in conjunction with the accompanying drawings.

[0067] The existing technical methods have the following technical problems: when processing exponentials and sequences with similar parameter values, it may cause a large deviation between the estimated parameters and the actual values. Secondly, channel estimation is a key step in recovering the distortion and fading of the signal during the channel transmission process.

[0068] like Figure 1 As shown, in this embodiment, a specific example is used to illustrate the method of the present invention. The present invention proposes an interference signal elimination method based on exponential sum decomposition and resampling, comprising the following steps:

[0069] Step S1, calculating the center frequency and sampling frequency of the intermediate frequency sampling according to the frequency band of the communication operation;

[0070] Step S2, sampling on the sampling frequency band and in the same symbol time interval to obtain sampling samples;

[0071] Step S3, using the Prong-Kung algorithm to estimate the incident angle of each signal according to the sampling samples obtained in step S2;

[0072] Step S4: According to the incident angle estimated in step S3, the interference is removed by the least square method, and the path gain of the desired signal is obtained.

[0073] Example 1

[0074] The specific application cases are as follows:

[0075] Assume that there are P users sending signals, one of which is a useful signal, and the other 5G signals are interference signals. The received signal is resampled on the frequency band. The following Table 1 gives the general parameter settings, and interference cancellation is performed according to the parameters in Table 1.

[0076] Table 1 Parameter values

[0077] name parameter set up Number of users pp 1 Number of interference signals D 2 Simulation times Num 10000 Number of samples N 100 <![CDATA[Matrix H Q×L Number of rows]]> Q 51 <![CDATA[Matrix H Q×L Number of columns]]> L 50 Signal-to-Noise Ratio SNR [-4:2:10]dB Path Gain C Obeys a complex Gaussian distribution of N(0,1)

[0078] The present invention utilizes an interference signal elimination method based on exponential sum decomposition and resampling, estimates the incident angle of each signal through the Prony-Kung algorithm and resampling technology, and uses the least squares method to remove the interference signal and obtain the path gain of the required signal, which significantly improves the accuracy of signal decoding and transmission and greatly reduces the bit error rate.

[0079] In this embodiment, specifically:

[0080] The number of sampling samples is 100, there are 3 signals, 2 of which are interference signals, and the specific steps in step S1 are as follows:

[0081] Intermediate frequency sampling means that the sampling frequency can be set at a certain frequency point. Here, the sampling is performed at the frequency point after the received signal is downconverted. There are 3 signals, of which 2 are interference signals. Assume that the frequency of the signal sampling is f c , sampling time interval Δt, symbol duration T0, total number of samples is 100, then

[0082]

[0083] Assuming the center frequency of the target signal is f0, first reduce the center frequency f0 of the signal to f c , and then sample at time intervals.

[0084] The specific process of step S2 is as follows:

[0085] After the signal passes through the filter, it is resampled on the frequency band and sampled 100 times. These data come from the same symbol time interval. The mathematical expression of the nth sampling signal is:

[0086]

[0087] Where ρ represents the ratio of the effective power of the signal to the effective power of the noise, c p represents the path gain and is assumed to be constant during the sampling time, s p represents the pth random transmission signal (in the training phase, assuming the training sequence is 1), f p represents the frequency point position of the pth signal in the frequency band, Δt is the time interval for collecting signal data (equally spaced sampling), and w n is complex Gaussian white noise. Sampling 100 times in N·Δt time yields {y1,y2,…y 100}, the vector form is:

[0088]

[0089] Its matrix expression is:

[0090]

[0091] Among them, A 100×3 is the channel steering vector matrix, C 3×3 is the complex gain coefficient matrix of the channel path, S 3×1 is the transmit signal matrix, W 100×1 is the noise matrix.

[0092] First make a preliminary channel estimate:

[0093]

[0094] The specific process of step S3 is as follows:

[0095] Step S3.1: Construct a Hankel matrix based on the received signal and the set reference information:

[0096] Let θ p =2πf p ′Δt, sampling at the frequency point f1′ after the useful signal is downconverted, so the mathematical expression of the nth sampling signal can be written as:

[0097]

[0098] Here, it is assumed that the transmitted signals are all 1, and are sampled at the frequency point f1′ after the useful signal is downconverted. Then f2′, f3′…f p ′ refers to the frequency point of the interference signal after frequency reduction. At this time, the matrix of the sampling signal can be written as:

[0099]

[0100] The Hankel matrix is ​​constructed from the received signal and related parameters as follows:

[0101]

[0102] Where Q and L are positive integers, satisfying the conditions Q+L-1=n and Q≥L≥P. When N is an even number, then When N is an odd number, then An integer, where Q is 51 and L is 50.

[0103] Step S3.2: Perform singular value decomposition on Hankel to estimate the incident angle of each signal:

[0104] Perform singular value decomposition (SVD) on the Hankel matrix, that is:

[0105] H 51×50 =UDV H

[0106] Among them, U and V are unitary matrices of size 51×50 and 50×50 respectively, and D=diag(d1,d2,…d 50 ) is a 50×50 diagonal matrix with d1≥d2≥d3≥…≥d 50 ≥0.

[0107] Then the column vectors of U are a set of standard orthogonal bases of the space generated by the column vectors of H. The first to the 50th row and the first to the third column of the matrix U are recorded as U1, and the second to the 51st row and the first to the third column of the matrix U are recorded as U2, as shown below:

[0108] U1=U(1:50,1:3),U2=U(2:51,1:3)

[0109] Estimate eigenvalues:

[0110] eig{(U1 H U1) -1 U1 H U2}

[0111] available Where eig{·} means taking the eigenvalue.

[0112] According to the formula: The estimated arrival angle is obtained, which is the incident angle of each signal, recorded as where angle{·} represents the angle at which the eigenvalue is taken.

[0113] As a preferred solution, the specific steps of step S4 are as follows:

[0114] Using the angle estimated in step S3, construct:

[0115]

[0116] According to the least squares method, the path gain is estimated:

[0117]

[0118] Note θ c =2πf c Δt, where f c is the center frequency of the intermediate frequency sampling. This patent samples at the frequency point f1′ after the useful signal is downconverted. Take i=1,2,3 in turn, and find Based on the characteristics of the Prong-Kung algorithm, the vector The i-th component in This is the path gain of concern, which separates the useful signal from the interference signal and achieves the purpose of removing the interference signal.

[0119] Example 2

[0120] like Figure 2 As shown, this embodiment, based on the method of Embodiment 1, discloses an interference signal elimination system based on exponential sum decomposition and resampling, including the following modules:

[0121] IF sampling module: Calculates the center frequency and sampling frequency of IF sampling according to the frequency band of communication operation.

[0122] Sampling sample module: samples within the sampling frequency band and the same symbol time interval to obtain sampling samples.

[0123] Matrix construction and singular value decomposition module: construct the Hankel matrix according to the sampling signal and the set parameter information, and perform singular value decomposition on it.

[0124] Interference removal signal module: According to the estimated incident angle, the least square method is used to remove interference and obtain the path gain of the required signal.

[0125] The system implements the above-mentioned interference signal elimination method based on exponential sum decomposition and resampling, and the specific steps are not repeated here.

[0126] See also Figure 3 , which is a simulation diagram of the bit error rate when the number of sampling samples is 100 and the signal-to-noise ratio changes using the method of the present invention; wherein, when the simulation is performed 10,000 times and the signal-to-noise ratio is 10dB, the bit error rate can reach 0.015.

[0127] See also Figure 4 , which is a simulation diagram of the bit error rate when the signal-to-noise ratio is 10dB and the number of sampling samples changes using the method of the present invention; when the simulation is performed 10,000 times and the number of sampling samples is 100, the bit error rate is 0.0175.

[0128] Depend on Figure 3 and Figure 4 It can be seen from the simulation results that the bit error rate is significantly reduced compared with the prior art, which verifies that the technical solution of the present invention can effectively separate the useful signal from the interference signal to achieve the purpose of interference removal.

[0129] For other contents of this embodiment, please refer to Embodiment 1.

[0130] 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.

[0131] 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 method for eliminating interference signals based on exponential sum decomposition and resampling, characterized in that: The steps include: Step S1, calculating the center frequency and sampling frequency of the intermediate frequency sampling according to the frequency band of the communication operation; Step S2, sampling on the sampling frequency band and in the same symbol time interval to obtain sampling samples; Step S3, using the Prong-Kung algorithm to estimate the incident angle of each signal according to the sampling samples obtained in step S2; Step S4: According to the incident angle estimated in step S3, the interference is removed by the least square method, and the path gain of the desired signal is obtained.

2. The interference signal elimination method based on exponential sum decomposition and resampling according to claim 1, characterized in that: In step S1, the intermediate frequency sampling is to set the sampling frequency at a certain frequency point, and sample at the frequency point after the received signal is downconverted. There are P signals, of which there are P-1 interference signals; assuming that the frequency of the signal sampling is f c , sampling time interval Δt, symbol duration T0, total number of samples is N, then: That is, assuming that the center frequency of the target signal is f0, first reduce the center frequency f0 of the signal to f c , and then sample at time intervals.

3. The interference signal elimination method based on exponential sum decomposition and resampling according to claim 2 is characterized in that: Step S2 is specifically as follows: Assume that the bandwidth to be detected is B, and there are P users sending signals in this bandwidth. The center frequency of the pth user is f p , the signal bandwidth is B p ; After the signal passes through the filter, it is resampled in a frequency band with a bandwidth of B, and sampled N times; these data come from the same symbol time interval, and the mathematical expression of the nth sampling signal is: Where ρ represents the ratio of the effective power of the signal to the effective power of the noise, c p represents the path gain and is assumed to be constant during the sampling time, s p represents the pth random transmission signal, f p represents the frequency point position of the pth signal in the frequency band, Δt is the time interval for collecting signal data, and w n is complex Gaussian white noise; Then the preliminary channel estimate is:

4. The interference signal elimination method based on exponential sum decomposition and resampling according to claim 3 is characterized in that: Step S3 is as follows: Step S3.1, construct the Hankel matrix according to the sampled signal and the set reference information: Let θ p =2πf p ′Δt, so the mathematical expression of the nth sampling signal is written as: Assume that the transmitted signals are all 1, and are sampled at the frequency f1′ after the useful signal is downconverted, then f2′, f3′…f p ′ refers to the frequency point of the interference signal after frequency reduction; at this time, the matrix of the sampling signal can be written as: The Hankel matrix is ​​constructed from the sampled signal and related parameters as follows: Where Q and L are positive integers, satisfying the conditions Q+L-1=n and Q≥L≥P; when N is an even number, then When N is an odd number, then An integer of ; Step S3.2: Perform singular value decomposition on Hankel to estimate the incident angle of each signal: Perform singular value decomposition (SVD) on the Hankel matrix, that is: H Q×L =UDV H Among them, U and V are unitary matrices of size Q×L and L×L respectively, and D=diag(d1,d2,…d L ) is an L×L diagonal matrix with d1≥d2≥…d P ≥…≥d L ≥0; Then the column vectors of U are a set of standard orthogonal bases of the space generated by the column vectors of H. The first row to the Q-1 row and the first column to the P column of the matrix U are recorded as U1; the second row to the Q row and the first column to the P column of the matrix U are recorded as U2, as shown below: U1=U(1:Q-1,1:P),U2=U(2:Q,1:P) Estimate eigenvalues: own{(U1 H U1) -1 U1 H U2} get Where eig{·} means taking the eigenvalue; According to the formula: The estimated arrival angle is obtained, which is the incident angle of each signal, recorded as where angle{·} represents the angle at which the eigenvalue is taken.

5. The interference signal elimination method based on exponential sum decomposition and resampling according to claim 4 is characterized in that: Step S4 is specifically as follows: Using the angle estimated in step S3, construct: According to the least squares method, the path gain is estimated: Note θ c =2πf c Δt, where f c is the center frequency of the intermediate frequency sampling, and samples at the frequency point f1′ after the useful signal is downconverted; take i=1,2…,P in turn, and find value, find the i corresponding to the minimum value; based on the characteristics of the Prony-Kung algorithm, the vector The i-th component in This is the path gain of concern, which separates the useful signal from the interference signal and achieves the purpose of removing the interference signal.

6. Interference cancellation system based on exponential sum decomposition and resampling, characterized in that, Includes the following modules: IF sampling module: calculates the center frequency and sampling frequency of IF sampling according to the frequency band of communication operation; Sampling sample module: sampling within the sampling frequency band and the same symbol time interval to obtain sampling samples; Matrix construction and singular value decomposition module: construct the Hankel matrix according to the sampled signal and the set parameter information, and perform singular value decomposition on it; Interference removal signal module: According to the estimated incident angle, the least square method is used to remove interference and obtain the path gain of the required signal.

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