A hybrid interference mitigation method based on forward successive mean cut-off spectrum sensing

Abstract

The hybrid interference suppression method based on FCME spectrum sensing comprises the following steps: FCME-based spectrum sensing, hybrid interference suppression step, and equalization demodulation decoding step. The application constructs a hybrid interference suppression module group on the basis of spectrum sensing, selects different interference suppression algorithms for different modules, and combines them. The method has strong spectrum sensing ability under complex hybrid interference, and is more accurate in interference suppression than other methods. The decoding module further enhances the anti-interference robustness.

Description

Technical Field

[0001] This invention relates to hybrid interference suppression based on wireless broadband communication, belonging to the field of communication signal processing technology, and specifically relates to a hybrid interference suppression method based on forward continuous mean cut spectrum sensing. Background Technology

[0002] With the rapid proliferation of wireless broadband devices, wireless services have permeated all aspects of people's lives and are becoming increasingly important as a crucial component of society's telecommunications infrastructure. However, security threats have become a major issue concerning the confidentiality, integrity, and availability of wireless communications. Compared to other security threats such as eavesdropping and data forgery, wireless networks are particularly vulnerable to radio interference attacks.

[0003] With advancements in software-defined radio, a small, inexpensive USB device can easily be programmed into a jammer, covering a 20MHz bandwidth below 6GHz and delivering up to 100mW of transmission power. The ease of launching jamming attacks makes protecting wireless networks from both intentional and unintentional interference threats urgent. This is especially true in military communications scenarios, where the complex battlefield environment implies a more complex electromagnetic environment, inevitably subjecting military communication systems to sophisticated electromagnetic interference. Furthermore, intentional electromagnetic interference from the enemy can further degrade military communication performance. Therefore, the ability to resist complex electromagnetic interference is an essential enabling technology for both civilian and military wireless communications.

[0004] For wireless broadband communication systems, interference can be categorized based on the signal's frequency domain characteristics into single-tone interference and partial-band interference. Common narrowband and broadband interference can be classified as partial-band interference, while multi-tone interference can be considered as the superposition of several single-tone interferences. Therefore, mixed interference is the superposition of several types of interference.

[0005] Currently, there is limited literature on hybrid interference suppression techniques. Current research is primarily based on GNSS systems, and its main limitation is...

[0006] (1) Mixed interference is relatively simple, including a mixture of single-tone interference and pulse interference, and a mixture of pulse interference and narrowband interference.

[0007] (2) Lacking awareness of the spectrum, it can only detect and identify specific interferences. Summary of the Invention

[0008] To address the shortcomings of existing hybrid interference suppression methods, this invention proposes a hybrid interference suppression method based on forward consecutive mean excision (FCME) spectrum sensing. This hybrid interference suppression method based on forward consecutive mean excision (FCME) spectrum sensing specifically includes the following steps:

[0009] Step 1: Spectrum sensing based on forward continuous mean cut-off FCME;

[0010] Suppose the receiver receives a baseband signal r; composed of the desired signal d, Gaussian white noise w, and interference j, i.e.

[0011] r=d+w+j (1)

[0012] The received signal is represented as r = [r(1)...r(N)] T Where N represents the length of a frame of the received signal, and r(1)...r(N) represent the first to Nth symbols in a frame of the signal, respectively; the received signal is transformed to the frequency domain by FFT, and is represented as

[0013]

[0014] Each value of R(k) is called a frequency bin, and k represents the index of the frequency bin;

[0015] Calculate the amplitude spectrum of the received signal.

[0016]

[0017] Where Re and Im represent the real and imaginary part operations, respectively; the amplitude spectrum Ψ = {Ψ(k)|k∈I1} is sent as the output block to the FCME spectrum sensing; assuming I m and J m Let Im represent the sets of indices of frequency bins for which interference was not detected and the sets of indices of frequency bins for which interference was detected, respectively. Then, the index set I1 is the set of indices of frequency bins for which interference was not detected in the first iteration, i.e., I1 contains all indices 1,...,N. In FCME spectrum sensing, the indices of frequency bins for which interference was detected are derived from the first index set Im. m Move to the second index set J m This operation is performed once in each iteration; the initial assumption is that no frequency bins are disturbed, i.e., J1 is an empty set; the iteration stops when the maximum number of iterations is reached or no new disturbed frequency bins are found.

[0018] Step 2: Hybrid interference suppression step;

[0019] Based on the spectrum sensing results, a hybrid interference suppression module group is determined. The interference suppression algorithm includes a time-domain notch filter and a frequency-domain threshold cutoff filter. The time-domain notch filter is hereinafter referred to as a "notch filter". For single-tone interference, a notch filter is suitable for interference suppression. When using a second-order notch filter, the received signal {r(n)|n=1,...,N} is fed into the notch filter as input. Assuming the output signal is x(n), the system function H(z) of the notch filter is expressed as:

[0020]

[0021] Where X(z) and R(z) are the Z-transforms of x(n) and r(n), respectively; δ and μ are the two gain coefficients of the second-order notch filter. If the absolute values ​​of δ and μ are both less than 1, the notch filter is stable; these two coefficients determine the -3dB bandwidth B and the notch frequency ω, respectively. N , represented as

[0022]

[0023] μ=cos(ω N ),ω N ∈[0,π] (6)

[0024] Among them, bandwidth B and notch frequency ω N Determined based on the results of spectrum sensing;

[0025] For interference in certain frequency bands, a frequency domain threshold cutoff algorithm is used to suppress interference, as detailed below;

[0026] Assume the sum of the desired signal and Gaussian white noise is v = d + w = ​​{v(n) | n = 1, ..., N}, and the interference is j = {j(n) | n = 1, ..., N}, where v(n) and j(n) represent their nth symbols, and n takes values ​​from 1 to N. When interference is present, the received signal frequency domain amplitude value...

[0027]

[0028] Where J(k) and V(k) represent the Fourier transforms (FFTs) of j(n) and v(n), respectively; when there is no interference, the received signal frequency domain amplitude value

[0029]

[0030] Define two hypotheses, H0 and H1, representing the absence of interference and the presence of interference, respectively, with corresponding observations as follows:

[0031]

[0032] Both observations follow a Rayleigh distribution; the observation space is partitioned according to the maximum posterior probability: the posterior probabilities of the two hypotheses are defined as P(H1|M(k)) and P(H0|M(k)), where P(·) represents the probability of the event, and P(H1|M(k)) and P(H0|M(k)) represent the probabilities of hypothesis H1 or H0 occurring given observation M(k); the two posterior probabilities are compared, and the hypothesis with the larger posterior probability is considered valid.

[0033]

[0034] The above expression means that if the greater than sign is true, then H1 is true, and vice versa; according to Bayes' theorem,

[0035] P(H1|M(k))=P(M(k)|H1)P(H1) / P(M(k)) (12)

[0036] P(H0|M(k))=P(M(k)|H0)P(H0) / P(M(k)) (13)

[0037] Substituting equations (18) and (19) into equation (17) yields

[0038]

[0039] Since both J(k)+V(k) and V(k) follow a complex Gaussian distribution, therefore

[0040]

[0041] In the formula, and Let J(k) and V(k) be the variances, respectively; substituting equation (21) into (20) yields...

[0042]

[0043] When H1 is true, it indicates that interference exists, and the frequency block R(k) is assigned a value of 0; when H0 is true, it indicates that interference does not exist, and no processing is done on this frequency block.

[0044] Step 3: Equalization demodulation and decoding steps;

[0045] Assuming ideal synchronization is achieved, the frequency domain R0(k) of the received signal after interference suppression satisfies

[0046] R0(k)=S(k)H(k)+W(k)+I0(k) (17)

[0047] Where S(k), H(k), and W(k) represent the FFT of the transmitted signal s(n), the channel impulse response, and the noise, respectively, and I0(k) represents the residual interference; then the output result of the frequency domain equalization is... satisfy

[0048]

[0049] right Perform an iFFT transform to obtain the equalized time domain. for,

[0050]

[0051] in, It was used for demodulation;

[0052] Error correction coding can be used to further reduce the bit error rate.

[0053] In one embodiment of the present invention, the iterative processing flow in step 1 is as follows:

[0054] Step 1: Initialization, let m = 1, and set it to J. m I m S m and N m Assign initial values, where J m For an empty set, I m Includes 1 to N, S m It is the summation of the amplitude spectrum, N m It is set I m The number of elements;

[0055] Step 2: Iterative processing, consisting of two nested loops. The first loop first calculates S under the current iteration. m and N m The value of I; then, enter the second loop, the second loop iterates through the current I. m The frequency bin corresponding to the included index, if the amplitude spectrum Ψ(k) is greater than the threshold T*S m / N m If T represents the threshold parameter, then the index k corresponding to this frequency bin is changed from I. m Move to J m ;

[0056] The threshold parameter T used in the iterative processing is theoretically determined based on the statistics of the non-interference received signal; the complex Gaussian noise and the desired signal still follow a complex Gaussian distribution after the FFT transformation, therefore the amplitude spectrum of the received signal is approximately a Rayleigh distribution with two degrees of freedom; using these assumptions, the threshold parameter T can be theoretically determined as follows: the first moment of the two-degree-of-freedom Rayleigh distributed random variable Ψ is...

[0057]

[0058] In the formula, σ 2 To calculate the variance of an independent Gaussian random variable with zero mean; the Γ function is defined as...

[0059]

[0060] In the formula, e is the natural constant; the cumulative density function of the Rayleigh distributed random variable is:

[0061]

[0062] Where ψ is the independent variable, and solving equation (6) for ψ yields...

[0063]

[0064] In the formula, ln is the natural logarithm; in FCME spectrum sensing, all values ​​ψ in the current amplitude spectrum are determined according to the threshold T*S. m / N m =T*E(Ψ) is divided into two groups, one with interference and one without interference; let the target threshold ψ target =T*E(Ψ), then

[0065]

[0066] It can be seen that the threshold parameter T is independent of the noise variance; the parameter F(ψ) target ) is the target cumulative density function value, representing the relative number of observations contained in the expected set;

[0067] Set an interference indicator variable a(k), represented in binary; assign a value to a(k) based on whether interference exists in the frequency band.

[0068]

[0069] After iterative processing, a(k) is the result of spectrum sensing.

[0070] In another embodiment of the invention, the threshold parameter T used in the iterative process is based on the mean or variance.

[0071] In one specific embodiment of the present invention, the error correction coding adopts RS coding or low-density parity-check (LDPC) coding.

[0072] This method constructs a hybrid interference suppression module group based on spectrum sensing, selecting different interference suppression algorithms for different modules and combining them. The method of this invention has strong spectrum sensing capabilities under complex hybrid interference, and is also more accurate in interference suppression than other methods. The decoding module further enhances the robustness against interference.

[0073] The method of this invention can provide support for the suppression of mixed interference in complex electromagnetic environments, and effectively solves the problems of poor performance in suppressing mixed interference in civilian and military scenarios. Attached Figure Description

[0074] Figure 1 The flowchart of the hybrid interference suppression method based on FCME spectrum sensing of the present invention is shown;

[0075] Figure 2 Example of a mixed interference spectrum diagram is shown;

[0076] Figure 3 This illustrates the structure of the hybrid interference suppression module group designed in this invention;

[0077] Figure 4 This invention demonstrates the output results of its hybrid interference suppression for actual wireless broadband signals.

[0078] Figure 5 This illustrates the equalization demodulation and decoding process designed according to the present invention. Detailed Implementation

[0079] The flowchart of the hybrid interference suppression method based on FCME spectrum sensing of this invention is as follows: Figure 1 As shown, it includes four steps: FCME-based spectrum sensing, hybrid interference suppression, equalization demodulation, and decoding, as detailed below.

[0080] Step 1: FCME-based spectrum sensing;

[0081] The spectrum sensing processing flow based on FCME is as follows: Figure 1 As shown. Assume the receiver receives a baseband signal r, which consists of the desired signal d, Gaussian white noise w, and interference j, i.e.

[0082] r = d + w + j (26)

[0083] The received signal can be represented as r = [r(1)...r(N)] T Where N represents the length of a frame of the received signal, and r(1)...r(N) represent the first to Nth symbols in a frame of the signal, respectively. First, the received signal is transformed into the frequency domain by FFT, and can be represented as...

[0084]

[0085] Here, each value of R(k) is called a frequency bin, and k represents the index of the frequency bin. Then, the amplitude spectrum of the received signal is calculated.

[0086]

[0087] Where Re and Im represent the real and imaginary part operations, respectively. The amplitude spectrum Ψ = {Ψ(k)|k∈I1} is fed into the FCME spectrum sensing as an output block. Assume I m and J m Let Im represent the indices of frequency bins where interference was not detected and the sets of indices of frequency bins where interference was detected, respectively. Then, the index set I1 is the set of indices of frequency bins where interference was not detected in the first iteration; that is, I1 contains all indices 1,...,N. In FCME spectrum sensing, the indices of frequency bins where interference was detected are derived from the first index set Im. m Move to the second index set J mThis operation is performed once per iteration. The initial assumption is that no frequency bins are disturbed, i.e., J1 is an empty set. Iteration stops when the maximum number of iterations is reached or no new disturbed frequency bins are found. The iteration process is as follows:

[0088] Step 1: Initialization, let m = 1, and set it to J. m I m S m and N m Assign initial values, where J m For an empty set, I m Includes 1 to N, S m It is the summation of the amplitude spectrum, N m It is set I m The number of elements.

[0089] Step 2: Iterative processing, consisting of two nested loops. The first loop first calculates S under the current iteration. m and N m The value of I. Then, enter the second loop, which iterates through the current I. m The frequency bin corresponding to the included index, if the amplitude spectrum Ψ(k) is greater than the threshold T*S m / N m T represents the threshold parameter, so the index k corresponding to this frequency bin is changed from I. m Move to J m .

[0090] The threshold parameter T used in the iterative process can be theoretically determined based on statistics of the non-interference received signal, such as the mean and variance. Complex Gaussian noise and the desired signal still follow a complex Gaussian distribution after the FFT transform; therefore, the amplitude spectrum of the received signal is approximately a Rayleigh distribution with two degrees of freedom. Using these assumptions, the threshold parameter T can be theoretically determined as follows: The first moment of the two-degree-of-freedom Rayleigh distributed random variable Ψ is...

[0091]

[0092] In the formula, σ 2 To calculate the variance of an independent Gaussian random variable with zero mean, the Γ function is defined as follows:

[0093]

[0094] In the formula, e is the natural constant. The cumulative density function of the Rayleigh distributed random variable is:

[0095]

[0096] Where ψ is the independent variable, the solution to equation (6) is obtained by solving for ψ.

[0097]

[0098] In the formula, ln is the natural logarithm. In FCME spectrum sensing, all values ​​ψ in the current amplitude spectrum are determined according to the threshold T*S. m / N m =T*E(Ψ) is divided into two groups: one group contains interfering frequency cells, and the other group contains non-interfering frequency cells. Let the target threshold ψ target =T*E(Ψ), then

[0099]

[0100] As can be seen from formula (7), the threshold parameter T is independent of the noise variance. Parameter F(ψ) target F(ψ) is the target cumulative density function value, which represents the relative number of observations included in the expected set. target The value was set to 0.99, which means that on average 1% of the frequency bins were incorrectly detected as being interfered with.

[0101] Set an interference indicator variable a(k), represented in binary. Assign a value to a(k) based on whether interference exists in the frequency band.

[0102]

[0103] After iterative processing, a(k) is the result of spectrum sensing. Figure 2 An example of spectrum sensing results is shown. Figure 2 (a) is an example of the amplitude spectrum of the received signal when mixed interference is present. Figure 2 (b) shows the corresponding spectrum sensing results.

[0104] Step 2: Hybrid interference suppression step;

[0105] Mixed interference suppression steps are as follows Figure 3 As shown. In this step, based on the spectrum sensing results, the hybrid interference suppression module group is determined. The interference suppression algorithm includes a time-domain notch filter (hereinafter referred to as "notch filter"), a frequency-domain threshold cutoff filter, etc. Notch filters are suitable for single-tone interference. The second-order notch filter used in the invention is described below. The received signal {r(n)|n=1,...,N} is fed into the notch filter as input. Assuming the output signal is x(n), the system function H(z) of the notch filter is expressed as:

[0106]

[0107] Where X(z) and R(z) are the Z-transforms of x(n) and r(n), respectively. δ and μ are two gain coefficients of the second-order notch filter; if the absolute values ​​of both δ and μ are less than 1, the notch filter is stable. These two coefficients determine the -3dB bandwidth B and the notch frequency ω, respectively. N , can be represented as

[0108]

[0109] μ=cos(ω N ),ω N ∈[0,π] (37)

[0110] Among them, bandwidth B and notch frequency ω N The determination is based on the results of spectrum sensing.

[0111] For partial frequency band interference, a frequency domain thresholding algorithm is used for interference suppression. The key to frequency domain thresholding is to detect interference by setting a threshold, and then remove the frequency bands where interference exists to complete interference suppression. During interference detection, this invention employs a signal detection technique based on hypothesis testing for accurate interference detection. Based on the spectrum sensing results, detection is only required within the interference frequency bands. The specific algorithm is described below.

[0112] Assume the sum of the desired signal and Gaussian white noise is v = d + w = ​​{v(n) | n = 1, ..., N}, and the interference is j = {j(n) | n = 1, ..., N}, where v(n) and j(n) represent their nth symbols, and n takes values ​​from 1 to N. When interference is present, the received signal frequency domain amplitude value...

[0113]

[0114] Where J(k) and V(k) represent the Fourier transforms (FFTs) of j(n) and v(n), respectively. The received signal frequency domain amplitude value is [value missing] when there is no interference.

[0115]

[0116] Define two hypotheses, H0 and H1, representing the absence of interference and the presence of interference, respectively, with corresponding observations as follows:

[0117]

[0118] Both observations follow a Rayleigh distribution. The observation space is partitioned according to the maximum posterior probability: First, the posterior probabilities of the two hypotheses are defined as P(H1|M(k)) and P(H0|M(k)), where P(·) represents the probability of the event, and P(H1|M(k)) and P(H0|M(k)) represent the probabilities of hypothesis H1 or H0 occurring given observation M(k). The two posterior probabilities are compared, and the hypothesis with the larger posterior probability is considered valid.

[0119]

[0120] The above expression means that if the greater than sign is true, then H1 is true; otherwise, H0 is true. According to Bayes' theorem,

[0121] P(H1|M(k))=P(M(k)|H1)P(H1) / P(M(k)) (43)

[0122] P(H0|M(k))=P(M(k)|H0)P(H0) / P(M(k)) (44)

[0123] Substituting equations (18) and (19) into equation (17) yields

[0124]

[0125] Since both J(k)+V(k) and V(k) follow a complex Gaussian distribution, therefore

[0126]

[0127] In the formula, and Let J(k) and V(k) be the variances, respectively. Substituting equation (21) into equation (20) yields...

[0128]

[0129] When H1 is true, it indicates that interference exists, and the frequency block R(k) is assigned a value of 0; when H0 is true, it indicates that interference does not exist, and no processing is done on this frequency block. Figure 4 This is the output result of mixed interference suppression for actual wireless broadband signals, where Figure 4 (a) represents the amplitude spectrum before interference suppression. Figure 4 (b) represents the amplitude spectrum after interference suppression.

[0130] Step 3: Equalization demodulation and decoding steps;

[0131] The flowchart of the equalization demodulation and decoding steps is as follows: Figure 5As shown. The equalization, demodulation, and decoding steps include FFT transform, frequency domain equalization, demodulation, iFFT transform, and error correction decoding. Assuming ideal synchronization is achieved, the frequency domain R0(k) of the interference-suppressed received signal satisfies

[0132] R0(k)=S(k)H(k)+W(k)+I0(k) (48)

[0133] Where S(k), H(k), and W(k) represent the FFTs of the transmitted signal s(n), the channel impulse response, and the noise, respectively, and I0(k) represents residual interference. This term exists because interference suppression cannot eliminate all interference, and residual interference is unavoidable. The output result of frequency domain equalization is... satisfy

[0134]

[0135] right Perform an iFFT transform to obtain a balanced time-domain result. for

[0136]

[0137] in It was used for demodulation.

[0138] Although interference suppression measures exist, satisfactory performance is still not achieved after interference suppression in many complex scenarios. In such cases, error correction coding (“error correction coding” is well known to those skilled in the art) is needed to further reduce the bit error rate and improve system performance. Effective design of error correction coding has proven crucial for achieving acceptable bit error rates in wireless broadband systems. RS coding and low-density parity-check (LDPC) coding exhibit good error correction performance. Therefore, this invention employs these two error correction codes. LDPC coding approaches Shannon capacity on additive white Gaussian noise (AWGN) and many other channel types. Depending on the specific circumstances, a single code or a concatenation of two error correction codes can be used.

Claims

1. A hybrid interference suppression method based on forward continuous mean cut spectrum sensing, characterized in that, Specifically, it includes the following steps; Step 1: Spectrum sensing based on forward continuous mean cut-off FCME; Suppose the receiver receives a baseband signal r; composed of the desired signal d, Gaussian white noise w, and interference j, i.e. r=d+w+j (1) The received signal is represented as r = [r(1)...r(N)] T Where N represents the length of a frame of the received signal, and r(1)...r(N) represent the first to Nth symbols in a frame of the signal, respectively; the received signal is transformed to the frequency domain by FFT, and is represented as Each value of R(k) is called a frequency bin, and k represents the index of the frequency bin; Calculate the amplitude spectrum of the received signal. Where Re and Im represent the real and imaginary part operations, respectively; the amplitude spectrum Ψ = {Ψ(k)|k∈I1} is sent as the output block to the FCME spectrum sensing; assuming I m and J m Let Im represent the sets of indices of frequency bins for which interference was not detected and the sets of indices of frequency bins for which interference was detected, respectively. Then, the index set I1 is the set of indices of frequency bins for which interference was not detected in the first iteration, i.e., I1 contains all indices 1,...,N. In FCME spectrum sensing, the indices of frequency bins for which interference was detected are derived from the first index set Im. m Move to the second index set J m This operation is performed once in each iteration; the initial assumption is that no frequency bins are disturbed, i.e., J1 is an empty set; the iteration stops when the maximum number of iterations is reached or no new disturbed frequency bins are found. Step 2: Hybrid interference suppression step; Based on the spectrum sensing results, a hybrid interference suppression module group is determined. The interference suppression algorithm includes a time-domain notch filter and a frequency-domain threshold cutoff filter. The time-domain notch filter is hereinafter referred to as a "notch filter". For single-tone interference, a notch filter is suitable for interference suppression. When using a second-order notch filter, the received signal {r(n)|n=1,...,N} is fed into the notch filter as input. Assuming the output signal is x(n), the system function H(z) of the notch filter is expressed as: Where X(z) and R(z) are the Z-transforms of x(n) and r(n), respectively; δ and μ are the two gain coefficients of the second-order notch filter. If the absolute values ​​of δ and μ are both less than 1, the notch filter is stable; these two coefficients determine the -3dB bandwidth B and the notch frequency ω, respectively. N , represented as μ=cos(ω) N ),oh N ∈[0,π] (6) Among them, bandwidth B and notch frequency ω N Determined based on the results of spectrum sensing; For interference in certain frequency bands, a frequency domain threshold cutoff algorithm is used to suppress interference, as detailed below; Assume the sum of the desired signal and Gaussian white noise is v = d + w = ​​{v(n) | n = 1, ..., N}, and the interference is j = {j(n) | n = 1, ..., N}, where v(n) and j(n) represent their nth symbols, and n takes values ​​from 1 to N. When interference is present, the received signal frequency domain amplitude value... Where J(k) and V(k) represent the Fourier transforms (FFTs) of j(n) and v(n), respectively; when there is no interference, the received signal frequency domain amplitude value Define two hypotheses, H0 and H1, representing the absence of interference and the presence of interference, respectively, with corresponding observations as follows: Both observations follow a Rayleigh distribution; the observation space is partitioned according to the maximum posterior probability: the posterior probabilities of the two hypotheses are defined as P(H1|M(k)) and P(H0|M(k)), where P(·) represents the probability of the event, and P(H1|M(k)) and P(H0|M(k)) represent the probabilities of hypothesis H1 or H0 occurring given observation M(k); the two posterior probabilities are compared, and the hypothesis with the larger posterior probability is considered valid. The above expression means that if the greater than sign is true, then H1 is true, and vice versa; according to Bayes' theorem, P(H1|M(k))=P(M(k)|H1)P(H1) / P(M(k)) (12) P(H0|M(k))=P(M(k)|H0)P(H0) / P(M(k)) (13) Substituting equations (18) and (19) into equation (17) yields Since both J(k)+V(k) and V(k) follow a complex Gaussian distribution, therefore In the formula, and Let J(k) and V(k) be the variances, respectively; substituting equation (21) into (20) yields... When H1 is true, it indicates that interference exists, and the frequency cell R(k) is assigned a value of 0; when H0 is true, it indicates that interference does not exist, and no processing is performed on the frequency cell. Step 3: Equalization demodulation and decoding steps; Assuming ideal synchronization is achieved, the frequency domain R0(k) of the received signal after interference suppression satisfies R0(k)=S(k)H(k)+W(k)+I0(k) (17) Where S(k), H(k), and W(k) represent the FFT of the transmitted signal s(n), the channel impulse response, and the noise, respectively, and I0(k) represents the residual interference; then the output result of the frequency domain equalization is... satisfy right Perform an iFFT transform to obtain a balanced time-domain result. for, in, It was used for demodulation; Error correction coding can be used to further reduce the bit error rate.

2. The hybrid interference suppression method based on FCME spectrum sensing as described in claim 1, characterized in that, In step 1, the iterative processing flow is as follows: Step 1: Initialization, let m = 1, and set it to J. m I m S m and N m Assign initial values, where J m For an empty set, I m Includes 1 to N, S m It is the summation of the amplitude spectrum, N m It is set I m The number of elements; Step 2: Iterative processing, consisting of two nested loops. The first loop first calculates S under the current iteration. m and N m The value of I; then, enter the second loop, the second loop iterates through the current I. m The frequency bin corresponding to the included index, if the amplitude spectrum Ψ(k) is greater than the threshold T*S m / N m If T represents the threshold parameter, then the index k corresponding to this frequency bin is changed from I. m Move to J m ; The threshold parameter T used in the iterative processing is theoretically determined based on the statistics of the non-interference received signal; the complex Gaussian noise and the desired signal still follow a complex Gaussian distribution after the FFT transformation, therefore the amplitude spectrum of the received signal is approximately a Rayleigh distribution with two degrees of freedom; using these assumptions, the threshold parameter T can be theoretically determined as follows: the first moment of the two-degree-of-freedom Rayleigh distributed random variable Ψ is... In the formula, σ 2 To calculate the variance of an independent Gaussian random variable with zero mean; the Γ function is defined as... In the formula, e is the natural constant; the cumulative density function of the Rayleigh distributed random variable is: Where ψ is the independent variable, and solving equation (6) for ψ yields... In the formula, ln is the natural logarithm; in FCME spectrum sensing, all values ​​ψ in the current amplitude spectrum are determined according to the threshold T*S. m / N m =T*E(Ψ) is divided into two groups, one with interference and one without interference; let the target threshold ψ target =T*E(Ψ), then It can be seen that the threshold parameter T is independent of the noise variance; the parameter F(ψ) target ) is the target cumulative density function value, representing the relative number of observations contained in the expected set; Set an interference indicator variable a(k), represented in binary; assign a value to a(k) based on whether interference exists in the frequency band. After iterative processing, a(k) is the result of spectrum sensing.

3. The hybrid interference suppression method based on FCME spectrum sensing as described in claim 2, characterized in that, The threshold parameter T used in the iterative process is based on the mean or variance.

4. The hybrid interference suppression method based on FCME spectrum sensing as described in claim 1, characterized in that, Error correction coding uses RS coding or low-density parity check (LDPC) coding.

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