Method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication

Abstract

The invention discloses a full-duplex communication multistage self-interference suppression and channel equalization integration method. The method comprises the following steps: S1, giving representations of a receiving signal and a coupling self-interference signal of a receiving end passing through an RRC filter with the sampling rate being K times of the baseband sampling rate; s2, discrete sampling is carried out on the received signal and the feedback signal at a receiving end; s3, carrying out minimum mean square error estimation on an expected symbol; s4, estimating a symbol by using a multi-stage iteration mode, and estimating according to a minimum mean square error to obtain an optimal coefficient equation of the adaptive filter; and S5, updating the coefficient of the adaptive filter in an iteration mode by using an RLS algorithm, and obtaining an estimated value of the expected signal by using the updated coefficient. The method has lower system complexity and lower inter-stage delay, and total quantization noise is reduced based on channel estimation and equalization.

Description

Technical Field

[0001] The invention relates to the field of full-duplex broadband wireless communication systems, and in particular to a method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication. Background Art

[0002] Full-duplex technology has emerged as a potential solution to the problem of tight bandwidth resources in next-generation (6G) wireless communication systems. By transmitting and receiving simultaneously on the same frequency, full-duplex technology almost doubles the network capacity and reduces data transmission latency. At the physical layer, the main challenge facing full-duplex communication is self-interference in the received signal, which reduces the signal-to-interference-and noise ratio (SINR) of the signal, making the system capacity much lower than expected, that is, twice that of a half-duplex communication system.

[0003] In full-duplex communication, self-interference suppression technology can be divided into passive self-interference suppression and active self-interference suppression. Passive self-interference suppression relies on directional antennas, cross-polarization and other methods to increase the loss of self-interference at the receiving end when it propagates. Active self-interference suppression estimates the self-interference channel, reconstructs the self-interference signal in the analog and digital domains, and subtracts it from the received signal.

[0004] In a typical full-duplex system, after the passive and analog domain active self-interference suppression, the received signal still has 20-45dB of residual self-interference components, which need to be offset to the bottom noise by digital domain self-interference suppression. The existing multi-stage self-interference suppression technology regards the desired signal as structured noise to further improve the self-interference suppression capability of the digital domain. By reconstructing the desired signal, the oblique projection estimation operator of the self-interference signal can be obtained, and the system bit error rate is close to the theoretical curve after multiple iterations. It is worth noting that this type of iterative self-interference suppression algorithm does not consider the equalization of the desired signal. This means that in each round of iteration, the reconstruction of the desired signal requires a frequency domain or time domain equalizer, and the hardware resources and delay of the system increase linearly with the number of iterations. In addition, the above algorithms all use LS estimation, and the estimation accuracy of each level of iteration still has room for improvement. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an integrated method for multi-stage self-interference suppression and channel equalization in full-duplex communication, which has lower system complexity and inter-stage delay, and reduces the overall quantization noise based on channel estimation and equalization.

[0006] The object of the present invention is achieved through the following technical scheme: a method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication, in which, in each stage, the input signal is subtracted from the hard demodulation of the expected symbol of the previous stage as the reference signal of the adaptive filter, the self-interference signal coupled by the receiver and the soft demodulation of the expected symbol are used as the input signal, the error of the adaptive filter is used as the input of the nonlinear function tanh, and the output of the tanh function is used as the MMSE estimation value of the expected symbol of this stage. At the 0th level iteration, the expected symbol is set to all zeros as the initial value.

[0007] Among them, the initial size of the diagonal loading matrix in the second stage can be set according to the signal-to-noise ratio of the self-interference signal and the expected signal in different environments to achieve the best initial diagonal loading parameters;

[0008] Specifically, the method of the present application comprises the following steps:

[0009] S1. For a communication system consisting of two full-duplex communication nodes with exactly the same communication system architecture, the two nodes send signals and receive signals from another node at the same frequency and time; for any node, give the representation of the received signal and the coupled self-interference signal at the receiving end after the RRC filter with a sampling rate of K times the baseband sampling rate;

[0010] S2. discretely sampling the received signal and the feedback signal at the receiving end;

[0011] S3. Perform minimum mean square error estimation of the desired symbol, and express the minimum mean square error estimation of the desired signal as a combination of a linear filter and a nonlinear function;

[0012] S4. Estimate the symbol using a multi-stage iteration method, and obtain the optimal coefficient equation of the adaptive filter according to the minimum mean square error estimation;

[0013] S5. Using the RLS algorithm, the adaptive filter coefficients are updated in an iterative manner, and the updated coefficients are used to obtain an estimated value of the desired signal.

[0014] The beneficial effects of the present invention are as follows: the present invention integrates the equalization module during the reconstruction of the desired signal during self-interference suppression, has lower system complexity and lower inter-stage delay, eliminates the decrease in the self-interference suppression capability of each stage of the equalizer caused by quantization error, and reduces the overall quantization noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a full-duplex transceiver with integrated multi-stage self-interference suppression and channel equalization assisted by a desired signal;

[0016] Figure 2A schematic diagram of the structure of a multi-stage self-interference suppression and channel equalization integrated module assisted by a desired signal;

[0017] Figure 3 It is a schematic diagram of the cascade structure of the integrated module of multi-stage self-interference suppression and channel equalization;

[0018] Figure 4 Schematic diagram of the relationship between the self-interference suppression capability of each stage and the SNR of the desired signal when INR=40dB is fixed;

[0019] Figure 5 Schematic diagram of the relationship between the self-interference suppression capability of each stage and the SNR of the desired signal when INR=40dB is fixed and the desired channel is an ISI channel;

[0020] Figure 6 When INR is fixed at 40dB, the bit error rate of each level is proportional to the expected signal E b Schematic diagram of the relationship between / N0;

[0021] Figure 7 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0023] like Figure 7 As shown, a method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication includes the following steps:

[0024] S1. For a communication system consisting of two full-duplex communication nodes with exactly the same communication system architecture, the two nodes send signals and receive signals from another node at the same frequency and time; for any node, give the representation of the received signal and the coupled self-interference signal at the receiving end after the RRC filter with a sampling rate of K times the baseband sampling rate;

[0025] Any full-duplex node described in step S1 includes a transmitting end, a receiving end, a coupler and a transmitting and receiving antenna;

[0026] The transmitting end includes a QPSK modulation module, a shaping filter, a DAC module, an up-conversion mixer, and a power amplifier; the output end of the QPSK modulation module is connected to a coupler through the shaping filter, the DAC module, the up-conversion mixer, and the power amplifier in sequence, and the coupler is connected to a transceiver antenna;

[0027] The receiving end includes a low noise amplifier, a first ADC module, a second ADC module, a first down-conversion mixer, a second down-conversion mixer, a self-interference suppression filter, an adaptive filtering module and a symbol estimation module;

[0028] The input end of the first down-conversion mixer is connected to the output end of the power amplifier, the input end of the low noise amplifier is connected to the coupler, and the output end of the low noise amplifier is connected to the second down-conversion mixer; the output end of the first down-conversion mixer is connected to the first ADC module, the output end of the second down-conversion mixer is connected to the second ADC module, the first ADC module and the second ADC module are both connected to the adaptive filtering module, the output end of the second ADC module is also connected to the self-interference suppression filter, the output ends of the adaptive filtering module and the self-interference suppression filter are both connected to the symbol estimation module, and the symbol estimation module outputs the received signal.

[0029] In step S1, for any node, the received signal and the coupled self-interference signal of the receiving end after the RRC filter with a sampling rate of K times the baseband sampling rate are given.

[0030] At the receiving end of a full-duplex communication node, there are two input signals, namely the received signal at the receiving antenna and the coupled self-interference signal;

[0031] Assume that the kth transmission symbol sent by the full-duplex communication node is denoted as s I,k , the subscript I represents the interference signal, and the qth transmitted symbol from another node is denoted as s D,q , the subscript D indicates that the symbol is the expected signal;

[0032] The received signal includes the desired signal including ISI, the self-interference signal including ISI and Gaussian white noise, where ISI represents inter-symbol interference and the received signal is expressed as

[0033]

[0034] Where x(t) is the transmission signal of the self-interference signal output by the power amplifier, g(t) is the signal pulse waveform, c0(t) represents the channel impulse response from ADC to PA;

[0035] is the expected signal waveform of another node, z(t) represents zero-mean Gaussian white noise with variance N0;

[0036] The discrete sampling of the received signal after the RRC filter is expressed as

[0037]

[0038] Where M is the received pulse dispersion length of the self-interference symbol, L is the received pulse dispersion length of the desired signal symbol, and K is the up-sampling multiple of the RRC filter;

[0039] Similarly, the discrete sampling of the coupled self-interference signal after the RRC filter is expressed as

[0040]

[0041] where z ref [n] is a zero-mean Gaussian white noise independent of z[n] with variance N1.

[0042] S2. discretely sampling the received signal and the feedback signal at the receiving end;

[0043] The step S2 comprises:

[0044] Let the receiving vector

[0045] Self-interference wireless channel vector Desired signal wireless channel vector Gaussian white noise vector The relationship between the received symbol, the self-interference symbol and the expected signal symbol is

[0046] r=Xh I +Yh D +z,

[0047] The self-interference signal matrix X = [x[0], x[-1], ..., x[-KM+1]], The expected signal matrix Y = [y[0], y[-1], ..., y[-KL+1]], the self-interference symbol vector

[0048] The discrete-time model of the feedback signal is expressed as

[0049] x ref =Xh0+z ref ,

[0050] in are h0(t) and z ref (t); similar to the noise vector of the received signal, z ref It is also a Gaussian random variable with zero mean and covariance matrix N1I.

[0051] S3. Perform a minimum mean square error (MMSE) estimate of the desired symbol, and express the minimum mean square error estimate of the desired signal as a combination of a linear filter and a nonlinear function;

[0052] MMSE estimation of the expected symbol. Assume that the received pulse dispersion length of the self-interference symbol is M, and the received pulse dispersion length of the expected signal symbol is L. Then D,0 The relevant transmission symbol set is SI = {s I,0 , …, s I,M-1}, S D = {s D,1 , …, s D,L-1}. According to the MMSE (Minimum Mean Square Error) criterion, the estimated desired signal at time t = 0 is

[0053]

[0054] Denote the content of the summation in the latter part of the expression as Since each symbol transmitted by Node 1 and Node 2 is independent of each other, so And its expression can be obtained as

[0055]

[0056] where tanh is the hyperbolic tangent function tanh(z) = (e z - e -z ) / (e z + e -z ).

[0057] Assume that self-interference cancellation is performed at K times the baseband rate, and the input of the tanh function needs to be sampled at the symbol rate 1 / KT = 1 / T'. At the same time, considering that the system starts transmitting symbols after time 0, the estimated symbols are delayed by max(M, L) symbol periods.

[0058] From the orthogonality of the basis functions of the K - L expansion, it can be obtained that when N → ∞, the input of the non - linear function of

[0059]

[0060] Substitute and replace the matched filter of the desired channel with an RRC filter The signal after symbol synchronization at the output of the filter is expressed as:

[0061] Since the received pulse dispersion length of the self - interference symbol is generally small, without loss of generality, assume M < L, that is, max(M, L) = L. Let r[n] be the output of the received signal passing through the RRC filter, u I [n] be the output of the feedback signal passing through the RRC filter, u D [n] be the output of the desired symbol after decision passing through the RRC filter large, w I,n and w D,nare the matched filter coefficients of the self-interference signal and the desired signal respectively. The MMSE estimation of the desired signal can be expressed as a combination of a linear filter and a nonlinear function:

[0062]

[0063] S4. Estimate the symbol using a multi-stage iteration method, and obtain the optimal coefficient equation of the adaptive filter according to the minimum mean square error estimation;

[0064] Since the estimation formula requires knowledge of the expected symbol before, a multi-level iteration method is used to estimate the symbol. The MMSE estimation result of the expected signal at the i-th iteration is y (i) [n], i = 0, 1, ..., the corresponding input is the result of the previous stage hard demodulation reconstruction Instead, where the estimate of the desired signal is zero when i=0:

[0065] To describe the algorithm in a more concise form, we write the MMSE estimate of the received symbols in a more compact form. Let

[0066]

[0067] The superscript (i) represents the i-th iteration. Then the optimal solution of the i-th coefficient satisfies

[0068] k=0,1,…,KM+KL-K

[0069] The above equations are a Wiener-Hopf equation, from which the optimal coefficients of the i-th order filter can be obtained: In practice, the coefficients are generally converged to the optimal value through an adaptive filter algorithm.

[0070] The corresponding symbol estimate after equalization is

[0071]

[0072] S5. Using the RLS algorithm, the adaptive filter coefficients are updated in an iterative manner, and the updated coefficients are used to obtain an estimated value of the desired signal, thereby obtaining an estimated value of the desired signal with a smaller error.

[0073] 1. Initialize the initial value P of each level of iteration (i) [0] = δ -1 I, w (i) [0]=0.

[0074] 2. For each level of iteration i = 0, 1, 2, ..., calculate

[0075]

[0076] w (i) [n] = w (i) [n-1]+k (i) [n]ξ * [n]

[0077] P (i) [n] = λ -1 P (i) [n-1]-λ -1 k (i) [n]u (i)H P[n-1]

[0078] 3. Each level It is the MMSE estimate of the desired signal, which can be demodulated after downsampling by a factor of K.

[0079] 4. Since the expected symbol of the previous stage needs to be hard demodulated and passed through the RRC filter, the input of each stage y (i-1) Both r[n] and r[n] need to be delayed by D samples to align the output of the RRC filter, where D is the group delay of the RRC filter.

[0080] Iterate continuously at i=0,1,2,… until the bit error rate of the expected signal meets the system requirements; for example:

[0081] The expected signal estimate y of the i-th level (i) [n], received signal r[n], coupled signal u of self-interference signal I [n], continue to calculate the coefficients according to the RLS algorithm to obtain the expected signal estimate y of the i+1th level (i+1) [n] until the bit error rate of the expected signal meets the system requirements.

[0082] In the embodiments of the present application, Figures 1 to 3 The figures are respectively a schematic diagram of a desired signal assisted multi-stage self-interference suppression and channel equalization integrated full-duplex transceiver, a schematic diagram of a desired signal assisted multi-stage self-interference suppression and channel equalization integrated module structure, and a schematic diagram of a multi-stage self-interference suppression and channel equalization integrated module cascade structure. On this basis,

[0083] The proposed multi-stage self-interference suppression and channel equalization integration method is simulated, analyzed and evaluated below. The specific parameter settings are shown in the following table.

[0084] Table 1. Simulation parameter settings for the integrated multi-stage self-interference suppression and channel equalization method

[0085]

[0086] In the integrated method of multi-stage self-interference suppression and channel equalization, the input self-interference power is

[0087]

[0088] Where X(f),C I (f), G(f) are the self-interference signal x(t) output by PA and the self-interference wireless channel c I (t) and the power spectral density function of the shaping filter impulse response g(t), and U I (f) = X ref (f) G(f) is the output power spectrum density of the self-interference feedback signal after passing through the shaping filter at the receiving end.

[0089] The self-interference signal in the received signal is denoted as X r (f) = U I (f)-N1G(f), define each level w I (i) The power spectral density is W I (i) (f), then the residual self-interference power output by each filter stage is

[0090]

[0091] We will use the self-interference suppression ratio to measure the performance of the method. The interference suppression ratio is defined as the ratio of the sum of the self-interference and the additive noise power before and after self-interference suppression. The interference suppression ratio of the i-th iteration is:

[0092]

[0093] Attached Figure 4 With attached Figure 5 The self-interference suppression capability of each level of the integrated method of multi-level self-interference suppression and channel equalization under ISI-free channel and ISI channel is given. The interference-noise ratio (INR) of the self-interference in the figure is fixed at 40dB. The increase in the signal-to-noise ratio of the desired signal means the decrease in the interference-to-signal ratio (Interference-to-SignalRatio) of the self-interference signal, and the increase in the variance of the estimated value of the self-interference channel, which leads to a decrease in the self-interference suppression capability. Multi-level self-interference suppression can alleviate this phenomenon to a certain extent. The self-interference capability of this algorithm increases with the increase in the number of iterations.

[0094] It can also be seen from the figure that the algorithm is not greatly affected by ISI after the third iteration, and the self-interference suppression capabilities of the ISI-free channel and the ISI channel at the third iteration are consistent.

[0095] Attached Figure 6When INR is fixed at 40dB, the bit error rate of each level is proportional to the expected signal E b / N0. Without the assistance of the reconstructed desired signal, as the desired signal E b As / N0 increases, the bit error rate decreases slowly and there is a bit error rate platform. As the number of iterations increases, the bit error rate also decreases significantly.

[0096] The bit error rate of different iterations of phase one and phase two shows that when only the first phase is used for self-interference suppression, the bit error rate decreases slowly as the expected signal noise ratio increases, and there is a bit error rate platform when it reaches the vicinity. After the expected signal reconstructed in the second phase participates in self-interference suppression, the bit error rate drops significantly compared to the first phase.

[0097] The above is a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication, characterized in that: The following steps are involved: S1. For a communication system consisting of two full-duplex communication nodes with exactly the same communication system architecture, the two nodes send signals and receive signals from another node at the same frequency and time; for any node, give the representation of the received signal and the coupled self-interference signal at the receiving end after the RRC filter with a sampling rate of K times the baseband sampling rate; S2. discretely sampling the received signal and the feedback signal at the receiving end; S3. Perform minimum mean square error estimation of the desired symbol, and express the minimum mean square error estimation of the desired signal as a combination of a linear filter and a nonlinear function; S4. Estimate the symbol using a multi-stage iteration method, and obtain the optimal coefficient equation of the adaptive filter according to the minimum mean square error estimation; S5. Using the RLS algorithm, the adaptive filter coefficients are updated in an iterative manner, and the updated coefficients are used to obtain an estimated value of the desired signal.

2. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: Any full-duplex node described in step S1 includes a transmitting end, a receiving end, a coupler and a transmitting and receiving antenna; The transmitting end includes a QPSK modulation module, a shaping filter, a DAC module, an up-conversion mixer, and a power amplifier; the output end of the QPSK modulation module is connected to a coupler through the shaping filter, the DAC module, the up-conversion mixer, and the power amplifier in sequence, and the coupler is connected to a transceiver antenna; The receiving end includes a low noise amplifier, a first ADC module, a second ADC module, a first down-conversion mixer, a second down-conversion mixer, a self-interference suppression filter, an adaptive filtering module and a symbol estimation module; The input end of the first down-conversion mixer is connected to the output end of the power amplifier, the input end of the low noise amplifier is connected to the coupler, and the output end of the low noise amplifier is connected to the second down-conversion mixer; the output end of the first down-conversion mixer is connected to the first ADC module, the output end of the second down-conversion mixer is connected to the second ADC module, the first ADC module and the second ADC module are both connected to the adaptive filtering module, the output end of the second ADC module is also connected to the self-interference suppression filter, the output ends of the adaptive filtering module and the self-interference suppression filter are both connected to the symbol estimation module, and the symbol estimation module outputs the received signal.

3. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 2, characterized in that: The full-duplex node further includes a local oscillator module, which is used to provide a local oscillator signal for the up-conversion mixer, the first down-conversion mixer and the second down-conversion mixer.

4. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: In step S1, for any node, the received signal and the coupled self-interference signal of the receiving end after the RRC filter with a sampling rate of K times the baseband sampling rate are given. At the receiving end of a full-duplex communication node, there are two input signals, namely the received signal at the receiving antenna and the coupled self-interference signal; Assume that the kth transmission symbol sent by the full-duplex communication node is denoted as s I,k , the subscript I represents the interference signal, and the qth transmitted symbol from another node is denoted as s D,q , the subscript D indicates that the symbol is the expected signal; The received signal includes the desired signal including ISI, the self-interference signal including ISI and Gaussian white noise, where ISI represents inter-symbol interference and the received signal is expressed as Where x(t) is the transmission signal of the self-interference signal output by the power amplifier, g(t) is the signal pulse waveform, c0(t) represents the channel impulse response from ADC to PA; is the expected signal waveform of another node, z(t) represents zero-mean Gaussian white noise with variance N0; The discrete sampling of the received signal after the RRC filter is expressed as Where M is the received pulse dispersion length of the self-interference symbol, L is the received pulse dispersion length of the desired signal symbol, and K is the up-sampling multiple of the RRC filter; Similarly, the discrete sampling of the coupled self-interference signal after the RRC filter is expressed as where z ref [n] is a zero-mean Gaussian white noise independent of z[n] with variance N1.

5. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: The step S2 comprises: Let the receiving vector Self-interference wireless channel vector Desired signal wireless channel vector Gaussian white noise vector The relationship between the received symbol, the self-interference symbol and the expected signal symbol is r=Xh I +Yh D +z, The self-interference signal matrix X = [x[0], x[-1], ..., x[-KM+1]], The expected signal matrix Y = [y[0], y[-1], ..., y[-KL+1]], the self-interference symbol vector The discrete-time model of the feedback signal is expressed as x ref =Xh0+z ref , in are h0(t) and z ref (t); similar to the noise vector of the received signal, z ref It is also a Gaussian random variable with zero mean and covariance matrix N1I.

6. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: The step S3 comprises: Assuming that the received pulse dispersion length of the self-interference symbol is M, and the received pulse dispersion length of the expected signal symbol is L, then the expected signal s at time t = 0 is D,0 The relevant transmission symbol set is S I ={s I,0 ,…,s I,M-1 },S D ={s D,1 ,…,s D,L-1 }; According to the MMSE criterion, the expected signal at time t = 0 is estimated as: The sum of the second half of the expression is recorded as Find its expression as in Tanh is the hyperbolic tangent function: tanh(z)=(e z -e -z ) / (e z +e -z ); Assuming that self-interference suppression is performed at K times the baseband rate, the input of the tanh function needs to be sampled at the symbol rate 1 / KT = 1 / T′; considering that the system starts sending symbols after time 0, the estimated symbol is delayed by max(M,L) symbol periods; Assume M < L, that is, max(M, L) = L. Let r[n] be the output of the received signal passing through the RRC filter, and u I [n] be the output of the feedback signal passing through the RRC filter, and u D [n] be the output of the expected symbol after decision passing through the RRC filter, and w I,n and w D,n be the matched filter coefficients of the self-interference signal and the desired signal respectively; Express the MMSE estimate of the desired signal in the form of the combination of a linear filter and a non-linear function: MMSE refers to the minimum mean square error estimation.

7. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: The step S4 comprises: The expected signal MMSE estimation result of the i-th iteration is y (i) [n], i = 0, 1, ..., the corresponding input is the result of the previous stage hard demodulation reconstruction Instead, the hard-demodulated symbols are passed through the RRC filter and upsampled K times, where when i = 0, the estimated value of the desired signal is zero: y (0) =0; MMSE refers to minimum mean square error estimation; To write the MMSE estimate of the received symbols in a more compact form, define The superscript i represents the i-th level iteration, then the optimal solution of the i-th level coefficient satisfies The above equations are a Wiener-Hopf equation, that is, the optimal coefficient equation of the adaptive filter; from this, the optimal coefficient of the i-th filter is obtained 8. The method for integrating multi-stage self-interference suppression and channel equalization in full-duplex communication according to claim 1, characterized in that: The step S5 comprises: S501. Initialize the initial value P of each level of iteration (i) [0] = δ -1 O,w (i) [0] = 0; S502. For each level of iteration i=0,1,2,…, calculate S503. Each level It is the MMSE estimate of the desired signal, which can be demodulated after K times downsampling; S504. Since the expected symbol of the previous stage needs to be hard demodulated and passed through the RRC filter, the input u of each stage I [n],y (i-1) Both r[n] and r[n] are delayed by D samples to align the output of the RRC filter, where D is the group delay of the RRC filter.

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