CN117639837A - Self-interference suppression method for full-duplex transceiver - Google Patents
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Abstract
The invention discloses a self-interference suppression method of a full duplex transceiver, which comprises the following steps: s1, constructing a nonlinear model structure, and carrying out reverse cancellation on signals of a transmission domain receiving and transmitting antenna separation and an auxiliary channel based on the nonlinear model structure; s3, adopting an analog domain iteration method, taking the signals subjected to reverse cancellation in the step S1 as performance reference standard, taking the feature matrix in the step S2 as the basis, and pre-coding the auxiliary channel digital domain signals through the iteration method; s4, adopting a digital domain iteration method and taking the feature matrix in the step S2 as a characteristic, and carrying out self-interference cancellation on a self-interference reconstruction signal formed by the feature vector and the feature matrix of the digital domain basis function according to the step S3. The invention realizes good self-interference suppression effect through self-interference channel estimation and self-interference signal reconstruction.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a self-interference suppression method for a full duplex transceiver.
Background
A full duplex transceiver is a wireless communication transceiver system that is simultaneously transmitting and receiving and operating on the same frequency channel. Due to the strong self-interference signal at the near end, leakage into the receiver through the wireless will affect the reception of the information of interest at the same channel by the receiver. The existing methods mainly comprise active self-interference cancellation and passive self-interference suppression. Among these, in the propagation domain, the main techniques include antenna separation, antenna beam separation, antenna polarization orthogonality, etc. In multi-antenna MIMO systems as well as phased array systems, self-interference suppression can also be performed by beamforming. The analog domain self-interference suppression is mainly a multi-tap method and a digital auxiliary self-interference suppression method. In the digital domain, the self-interference suppression method mainly comprises the steps of traditional residual self-interference signal reconstruction, neural network and blind source separation and the like. Among other things, the digital-assisted self-interference suppression method has the advantage that the self-interference channel can be modeled and nonlinear distortion due to IQ imbalance and PA nonlinearity can be suppressed. However, for random signals such as phase noise and white gaussian noise, the suppression capacity is limited due to the inability to model. The traditional least square method estimates the self-interference channel, and the inverse of a large matrix is needed to be solved, so that the calculation complexity is high, and engineering realization is not easy. For this reason, it is necessary to develop a self-interference suppression method for a full duplex transceiver.
Disclosure of Invention
The present invention is directed to a self-interference suppression method for a full duplex transceiver, which overcomes the drawbacks of the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method of self-interference suppression for a full duplex transceiver, comprising the steps of:
s1, constructing a nonlinear model structure, and carrying out reverse cancellation on signals of a transmission domain receiving and transmitting antenna separation and an auxiliary channel based on the nonlinear model structure;
s2, estimating model parameters based on a nonlinear model structure and a least square method;
s3, adopting an analog domain iteration method, taking the signals subjected to reverse cancellation in the step S1 as performance reference standard, taking the feature matrix in the step S2 as the basis, and pre-coding the auxiliary channel digital domain signals through the iteration method;
s4, adopting a digital domain iteration method and taking the feature matrix in the step S2 as a characteristic, and carrying out self-interference cancellation on a self-interference reconstruction signal formed by the feature vector and the feature matrix of the digital domain basis function according to the step S3. In addition, the step can run synchronously with the step S3, and the characteristic parameters are uniformly formed to perform nonlinear processing.
Further, the step S1 includes:
s10, constructing a nonlinear model structure of an in-band duplex system based on a transmitting path, a digital auxiliary cancellation channel and a receiving path;
s11, transmitting a linear frequency modulation signal from a digital processor of a transceiver;
s12, enabling the linear frequency modulation signal to enter a receiving channel through a combiner of a cancellation channel at a receiving antenna to form an auxiliary channel signal;
s13, the obtained auxiliary channel digital signal is the inverse phase of the transmitting channel digital signal.
Further, the step S2 specifically includes:
s21, constructing a feature matrix X based on the nonlinear model structure constructed in the step S1, overlapping different nonlinear features of the transmitted digital domain signals to form one-dimensional information of the feature matrix, replacing estimation coefficients in the one-dimensional information with coefficients of a finite impulse response FIR filter, and performing digital domain precoding;
s22, when M samples of LS are observed, the received signal vector y is expressed as: y=xh+z, where y represents M samples of data from the receiver at any time n in the evaluation stage, X is a nonlinear feature matrix of the channel, h is a model parameter estimated by using a least square method under a specific sample, and z is receiver noise;
wherein X is represented in the form of a vector matrix,
wherein, gamma p，q [n，M]＝[ψ p，q [n]，ψ p，q [n+1]，…，ψ p，q [n+M-1]]The eigenvectors of the auxiliary transmit channels are expressed as: x is x aux [n，M]＝[x aux [n]，x aux [n+1]，…，x aux [n+M-1]]，ψ p，q [n]＝x q [n](x * [n]) p- q
S23 LS minimizes the sum of squares of the residuals y-Xh| 2 The unknown coefficients are estimated as:
wherein X is H Representing the conjugate transpose of the vector matrix.
Further, in the step S3, a basis function of the auxiliary channel digital domain signal is formed based on the constructed feature matrix X:
the auxiliary channel digital domain signal is expressed as:
x aux [n]＝ω aux U
wherein U is a basis function feature matrix omega aux The digital domain feature vector of the auxiliary channel is iterated;
the analog domain iteration method in the step S3 includes the following steps: a step of
S30, setting the characteristic vector of the analog domain to zero;
s31, determining iteration times, self-interference suppression performance and iteration step length;
s32, calculating to obtain a characteristic vector value according to an iteration formula.
Further, in the step S4, the self-interference reconstruction signal formed by the feature vector and the feature matrix of the digital domain basis function is expressed as:
the digital domain iteration method in the step S4 includes:
s40, setting the digital domain feature vector to zero;
s41, determining iteration times, self-interference suppression performance and iteration step length;
s43, calculating to obtain a characteristic vector value according to an iteration formula.
Further, the transceiver antenna in the step S1 includes TX and RX, the nonlinear model structure includes a transmitter, a digital auxiliary transmitting channel and a receiver, and the transmitter transmits SI signals to the transmitter antenna through a digital-to-analog converter DAC, a low pass filter LPF, an IQ mixer and a power amplifier PA; the auxiliary transmitter is connected to the self-interference cancellation point before the low noise amplifier of the receiving channel through the DAC, the LPF, the IQ mixer and the power divider; the receiver transmits the received signal through a low noise amplifier LNA, an IQ mixer, a low pass filter LPF and an analog-to-digital converter ADC.
Further, in the step S1, the wideband same amplitude inversion of the transmission signal is controlled, and the wideband inversion formula is: x is x aux [n]＝x tx [n]e jπ Where xtx is represented as the main transmit channel digital domain signal.
Further, the digital domain received signal of the receiver in the step S2 is expressed as:
in the method, in the process of the invention,which is the theoretical value of the characteristic parameter of the main emission channel, and is the same as d' p，q [l]Representing channel parameters, z, of the auxiliary transmission channel all [n]Represented as receiver noise;
the digital domain received signal of the receiver during the estimation phase is expressed as:
y＝[y′[n]，...，y′[n+M-1]] T ，z＝[z all [n]，...，z all [n+M-1]] T ，
h＝[d 1，0 [0]，...，d 1，0 [L-1]，d 1，1 [0]，...，d′ 1，0 [0]，...，b 1 ，b 2 ] T wherein L is the wireless channel impulse response h tx [l]Is a length of (2);
further, the calculation formula of the iterative algorithm in step S3 is as follows:
ω aux [m+1]＝ω aux [m]-μ[y H [m]U[m]]
where m represents the sequence number of the iteration, y H [m]The method is characterized in that the method comprises the steps of representing the conjugate transpose of a self-interference signal vector received by a self-interference suppression and cancellation h pair receiver, mu represents the iterative step length, and the iterative step length satisfies the following conditions:
wherein lambda is max (R)＝E[U[m]U H [m]]。
Further, the calculation formula of the iterative algorithm in step S4 is as follows:
ω digital [l+1]＝ω digital [l]+μ[y H [m]U[m]-ω digital [l]U[m]U H [m]]
the iterative step size satisfies the following mathematical expression:
R＝U H [m]U[m]an autocorrelation matrix, sigma, representing a feature matrix max (R) represents the maximum eigenvalue of the autocorrelation matrix.
Compared with the prior art, the invention has the advantages that: the self-interference cancellation method provided by the invention is based on a radio frequency nonlinear modeling and self-adaptive iteration method, adopts a domain self-interference suppression method, and can flexibly select the cancellation method according to requirements; the method can be integrated into one step in the analog domain and the digital domain, and the nonlinear processing is performed, so that the method is suitable for a more flexible nonlinear environment on the basis of meeting the requirement that the self-interference cancellation performance is basically close to the theoretical limit, simultaneously reduces the calculation time and the calculation complexity, and meets the design requirements of narrow-band and various radio frequency non-ideal in-band full duplex technologies. The method of the invention considers the radio frequency non-ideal factors including IQ imbalance, LO phase noise, PA nonlinearity, multipath reflection and Gaussian white noise, and is mainly processed in the digital domain, so that the method can adapt to more complex radio frequency non-ideal factors while having good performance under a narrow-band background, and is also suitable for the MIMO and phased array systems.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an in-band duplex system architecture constructed in the method of the present invention;
FIG. 2 is a power spectral density function of S2 only;
FIG. 3 is a power spectral density function of the S2 step+analog domain iterative method;
FIG. 4 is a power spectral density function of the S2 step + analog domain iteration + digital domain LS method;
FIG. 5 is a power spectral density function of the S2 step+analog domain orthogonal iteration method;
FIG. 6 is a power spectral density function of the S2 step + analog domain iteration + digital domain LS method;
fig. 7 shows self-interference cancellation performance for each of the methods in Plan1 at different back-off powers;
fig. 8 is a graph of self-interference cancellation performance for each of the methods in Plan2 at different back-off powers;
fig. 9 is a graph of self-interference cancellation performance for each of the methods in Plan3 at different back-off powers;
fig. 10 shows self-interference cancellation performance for each of the methods in Plan4 at different back-off powers;
FIG. 11 is an iteration process self-interference suppression performance of two analog domain iteration methods in Plan 1;
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
Referring to fig. 1, the embodiment discloses a self-interference suppression method of a full duplex transceiver, which includes the following steps:
s1, constructing a nonlinear model structure, and carrying out reverse cancellation on signals of a transmission domain receiving and transmitting antenna separation and an auxiliary channel based on the nonlinear model structure;
s2, estimating model parameters based on a nonlinear model structure and a least square method;
step S3, adopting an analog domain iteration method, taking the signals of the reverse cancellation in the step S1 as performance reference standard, taking the characteristic matrix in the step S2 as a basis, and pre-coding the auxiliary channel digital domain signals by the iteration method;
and S4, adopting a digital domain iteration method and taking the characteristic matrix in the step S2 as a characteristic, and carrying out self-interference cancellation on a self-interference reconstruction signal formed by the digital domain basis function characteristic vector and the characteristic matrix according to the step S3. In addition, the step can run synchronously with the step S3, and the characteristic parameters are uniformly formed to perform nonlinear processing.
The step S1 includes:
s10, constructing a nonlinear model structure of an in-band duplex system based on a transmitting path, a digital auxiliary cancellation channel and a receiving path;
step S11, transmitting a linear frequency modulation signal from a digital processor of a transceiver;
step S12, enabling the linear frequency modulation signals to enter a receiving channel through a combiner of a cancellation channel at a receiving antenna to form auxiliary channel signals;
and S13, inverting the obtained auxiliary channel digital signal into the transmitting channel digital signal.
The step S2 specifically includes:
s21, constructing a feature matrix X based on the nonlinear model structure constructed in the step S1, overlapping different nonlinear features of the transmitted digital domain signals to form one-dimensional information of the feature matrix, replacing estimation coefficients in the one-dimensional information with coefficients of a finite impulse response FIR filter, and performing digital domain precoding;
step S22, when M samples of LS are observed, the received signal vector y is expressed as: y=xh+z, where y represents M samples of data from the receiver at any time n in the evaluation stage, X is a nonlinear feature matrix of the channel, h is a model parameter estimated by using a least square method under a specific sample, and z is receiver noise;
wherein X is represented in the form of a vector matrix,
wherein, gamma p，q [n，M]＝[ψ p，q [n]，ψ p，q [n+1]，…，ψ p，q [n+M-1]]The eigenvectors of the auxiliary transmit channels are expressed as: x is x aux [n，M]＝[x aux [n]，x aux [n+1]，…，x aux [n+M-1]]，ψ p，q [n]＝x q [n](x * [n]) p-q
Step S23, LS minimizes the sum of squares of residuals y-Xh|| 2 The unknown coefficients are estimated as:
wherein X is H Representing the conjugate transpose of the vector matrix.
In the step S3, a basis function of the auxiliary channel digital domain signal is formed based on the constructed feature matrix X:
the auxiliary channel digital domain signal is expressed as:
x aux [n]＝ω aux U
wherein U is a basis function feature matrix omega aux The digital domain feature vector of the auxiliary channel is iterated;
the analog domain iteration method in the step S3 includes the following steps:
step S30, setting the characteristic vector of the analog domain to zero;
step S31, determining iteration times, self-interference suppression performance and iteration step length;
and S32, calculating to obtain a characteristic vector value according to an iteration formula.
The self-interference reconstruction signal formed by the digital domain basis function feature vector and the feature matrix in the step S4 is expressed as follows:
the digital domain iteration method in the step S4 includes:
step S40, setting the digital domain special vector to be zero;
step S41, determining iteration times, self-interference suppression performance and iteration step length;
and S43, calculating to obtain a characteristic vector value according to an iteration formula.
As shown in fig. 1, the transceiver antenna in the step S1 includes TX and RX, the nonlinear model structure includes a transmitter, a digital auxiliary transmitting channel and a receiver, and the transmitter transmits SI signals to the transmitter antenna through a digital-to-analog converter DAC, a low pass filter LPF, an IQ mixer and a power amplifier PA; the auxiliary transmitter is connected to the self-interference cancellation point before the low noise amplifier of the receiving channel through the DAC, the LPF, the IQ mixer and the power divider; the receiver transmits a received signal to a digital signal through a low noise amplifier LNA, an IQ mixer, a low pass filter LPF and an analog-to-digital converter ADC; and shares a Local Oscillator (LO) in the IQ mixer.
The system architecture is mainly used for simulating and verifying the effectiveness of the method, but the method is still effective in a multiple-input multiple-output phased array system. The radio frequency non-idealities introduced in an in-band full duplex system mainly include: phase noise, IQ imbalance, power amplifier nonlinearity, multipath and gaussian white noise. The following simulation graphs demonstrate the performance of the method in these non-ideal factors, and the theoretical limit is to verify the evaluation accuracy of the method, and the accuracy of the method is high close to the theoretical limit.
In the step S1, the wideband same amplitude inversion of the transmitting signal is controlled, and the wideband inversion formula is as follows: x is x aux [n]＝x tx [n]e j π Where xtx is represented as the main transmit channel digital domain signal.
The digital domain received signal of the receiver in said step S2 is expressed as:
in the method, in the process of the invention,which is the theoretical value of the characteristic parameter of the main emission channel, and is the same as d' p，q [l]Representing channel parameters, z, of the auxiliary transmission channel all [n]Represented as receiver noise;
the digital domain received signal of the receiver during the estimation phase is expressed as:
y＝[y′[n]，...，y′[n+M-1]] T ，z＝[z all [n]，...，z all [n+M-1]] T ，
h＝[d 1，0 [0]，...，d 1，0 [L-1]，d 1，1 [0]，...，d′ 1，0 [0]，...，b 1 ，b 2 ] T wherein L is the wireless channel impulse response h tx [l]Is a length of (2);
the calculation formula of the iterative algorithm in the step S3 is as follows:
ω aux [m+1]＝ω aux [m]-μ[y H [m]U[m]]
where m represents the sequence number of the iteration, y H [m]The method is characterized in that the method comprises the steps of representing the conjugate transpose of a self-interference signal vector received by a self-interference suppression and cancellation h pair receiver, mu represents the iterative step length, and the iterative step length satisfies the following conditions:
wherein lambda is max (R)＝E[U[m]U H [m]]。
The calculation formula of the iterative algorithm in the step S4 is as follows:
ω digital [l+1]＝ω digital [l]+μ[y H [m]U[m]-ω digital [l]U[m]U H [m]]
the iterative step size satisfies the following mathematical expression:
R＝U H [m]U[m]an autocorrelation matrix representing a characteristic matrix, σmax (R) representing a maximum characteristic value of the autocorrelation matrix.
As shown in fig. 2, fig. 2 is a power spectral density function of a signal. It can be found from the power spectral densities of the PA1 signal and the TX1 signal that the main reason for the broadening of its frequency band is due to the power amplifier nonlinearity. The receiver of the proposed method receives a self-interference signal, RX representing the power spectral density function of the remaining self-interference signal of the receiver. The residual self-interference is at the fundamental frequency, and the power amplifier output signal is at the X band. Due to the limited self-interference suppression performance of step S2, the remaining self-interference significantly exceeds the receiver noise floor.
As shown in fig. 3, fig. 3 is a power spectral density function of the S1 step + analog domain iterative method. RX is a function of the power spectral density of the remaining self-interfering signal of the receiver. Comparing fig. 2, the self-interference suppression performance is greatly improved.
As shown in fig. 4, fig. 4 is a power spectral density function of the S1 step + analog domain iteration + digital domain LS method. RX is a function of the power spectral density of the remaining self-interfering signal of the receiver. Comparing fig. 3, the self-interference suppression performance is improved. And substantially reduced to the receiver noise floor level.
As shown in fig. 5, fig. 5 is a power spectral density function of the s1 step+analog domain orthogonal iteration method. RX is a function of the power spectral density of the remaining self-interfering signal of the receiver. The method of fig. 5 and 6 is an optimization of the method of fig. 3 and 4. Its self-interference suppression performance is between fig. 3 and fig. 4.
As shown in fig. 6, fig. 6 is a power spectral density function of the S1 step + analog domain quadrature iteration + digital domain LS method. Its self-interference suppression performance is similar to that of fig. 4.
As shown in fig. 7, fig. 7 is the self-interference suppression performance of each method in Plan 1. The method comprises the steps of (1) in Plan1, wherein (1) is the step S1, (2) is the step S1+the analog domain iteration method, (3) is the step S1+the analog domain iteration+the digital domain LS method, (4) is the step S1+the analog domain orthogonal iteration method, and (5) is the step S1+the analog domain orthogonal iteration+the digital domain LS method. Wherein the cancelation 4 and the cancelation 5 are optimizations of the cancelation 2 and the cancelation 3 methods. Simulation results show that the main aspect of optimization is calculation time, and the calculation time after optimization is reduced by 15ms.
As shown in fig. 8, the self-interference suppression performance of each of the methods in fig. 8Plan 2. The method comprises the steps of (1) in Plan1, wherein (1) is the step S1, (2) is the step S1+the analog domain iteration method, (3) is the step S1+the analog domain iteration+the digital domain LS method, (4) is the step S1+the analog domain orthogonal iteration method, and (5) is the step S1+the analog domain orthogonal iteration+the digital domain orthogonal LS method. Wherein the cancelation 4 and the cancelation 5 are optimizations of the cancelation 2 and the cancelation 3 methods. Simulation results show that the main aspect of optimization is calculation time, and the calculation time after optimization is reduced by 15ms. The orthogonal method is to simplify the characteristic matrix and remove redundant vectors. This has the advantage of reducing the channel iterative modeling time.
As shown in fig. 9, fig. 9 is the self-interference suppression performance of each method in Plan 3. The method comprises the steps of (1) in Plan1, wherein (1) is the step S1, (2) is the step S1+the analog domain iteration method, (3) is the step S1+the analog domain iteration+the digital domain iteration method, (4) is the step S1+the analog domain orthogonal iteration method, and (5) is the step S1+the analog domain orthogonal iteration+the digital domain orthogonal iteration method. Wherein the cancelation 4 and the cancelation 5 are optimizations of the cancelation 2 and the cancelation 3 methods. Simulation results show that the main aspect of optimization is calculation time, and the calculation time after optimization is reduced by 20ms.
As shown in fig. 10, fig. 10 is the self-interference suppression performance of each method in Plan 4. The cancelation 1 in Plan1 is the S1 step, the cancelation 2 is the S1 step+the analog domain digital domain joint iteration, and the cancelation 3 is the S1 step+the analog domain digital domain orthogonal joint iteration. Wherein the cancelation 3 is an optimization of the cancelation 2 method. Simulation results show that the main aspect of optimization is calculation time, and the calculation time after optimization is reduced by 29ms. Comparing the Plan3 orthogonal iteration cancellation method, the calculation time is reduced from 860ms to 410ms.
As shown in fig. 11, fig. 11 is the iterative process self-interference suppression performance of two analog domain iterative methods in Plan 1. The cancelation 2 and cancelation 4 differ only in whether the feature matrix is orthogonalized or not, resulting in a final rise in self-interference isolation from 70dB to 90dB. At the same time, the calculation time was reduced from 896ms to 888ms.
The method of the present invention is not limited to use with single transmit and single receive systems, but is applicable to multiple transmit and single receive systems and to multiple transmit and multiple receive phased array systems; the method can realize similar performance by only fine tuning.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the patentees may make various modifications or alterations within the scope of the appended claims, and are intended to be within the scope of the invention as described in the claims.
Claims (10)
1. A method for self-interference suppression in a full duplex transceiver, comprising the steps of:
s1, constructing a nonlinear model structure, and carrying out reverse cancellation on signals of a transmission domain receiving and transmitting antenna separation and an auxiliary channel based on the nonlinear model structure;
s2, estimating model parameters based on a nonlinear model structure and a least square method;
s3, adopting an analog domain iteration method, taking the signals subjected to reverse cancellation in the step S1 as performance reference standard, taking the feature matrix in the step S2 as the basis, and pre-coding the auxiliary channel digital domain signals through the iteration method;
s4, adopting a digital domain iteration method and taking the feature matrix in the step S2 as a characteristic, and carrying out self-interference cancellation on a self-interference reconstruction signal formed by the feature vector and the feature matrix of the digital domain basis function according to the step S3.
2. The method for self-interference suppression of a full duplex transceiver according to claim 1, wherein the step S1 includes:
s10, constructing a nonlinear model structure of an in-band duplex system based on a transmitting path, a digital auxiliary cancellation channel and a receiving path;
s11, transmitting a linear frequency modulation signal from a digital processor of a transceiver;
s12, enabling the linear frequency modulation signal to enter a receiving channel through a combiner of a cancellation channel at a receiving antenna to form an auxiliary channel signal;
s13, the obtained auxiliary channel digital signal is the inverse phase of the transmitting channel digital signal.
3. The method for self-interference suppression of a full duplex transceiver according to claim 1, wherein the step S2 specifically comprises:
s21, constructing a feature matrix X based on the nonlinear model structure constructed in the step S1, overlapping different nonlinear features of the transmitted digital domain signals to form one-dimensional information of the feature matrix, replacing estimation coefficients in the one-dimensional information with coefficients of a finite impulse response FIR filter, and performing digital domain precoding;
s22, when M samples of LS are observed, the received signal vector y is expressed as: y=xh+z, where y represents M samples of data from the receiver at any time n in the evaluation stage, X is a nonlinear feature matrix of the channel, h is a model parameter estimated by using a least square method under a specific sample, and z is receiver noise;
wherein X is represented in the form of a vector matrix,
wherein, gamma p,q [n,M]＝[ψ p,q [n],ψ p,q [n+1],…,ψ p,q [n+M-1]]The eigenvectors of the auxiliary transmit channels are expressed as: x is x aux [n,M]＝[x aux [n],x aux [n+1],…,x aux [n+M-1]]，ψ p,q [n]＝x q [n](x * [n]) p-q
S23 LS minimizes the sum of squares of the residuals y-Xh| 2 The unknown coefficients are estimated as:
wherein X is H Representing the conjugate transpose of the vector matrix.
4. The method for self-interference suppression of full-duplex transceiver according to claim 1, wherein the step S3 forms a basis function of the auxiliary channel digital domain signal based on the constructed feature matrix X:
the auxiliary channel digital domain signal is expressed as:
x aux [n]＝ω aux U
wherein U is a basis function feature matrix, ψ aux The digital domain feature vector of the auxiliary channel is iterated;
the analog domain iteration method in the step S3 includes the following steps: a step of
S30, setting the characteristic vector of the analog domain to zero;
s31, determining iteration times, self-interference suppression performance and iteration step length;
s32, calculating to obtain a characteristic vector value according to an iteration formula.
5. The method for self-interference suppression of a full duplex transceiver according to claim 1, wherein the self-interference reconstruction signal formed by the digital domain basis function feature vector and the feature matrix in the step S4 is expressed as:
the digital domain iteration method in the step S4 includes:
s40, setting the digital domain feature vector to zero;
s41, determining iteration times, self-interference suppression performance and iteration step length;
s43, calculating to obtain a characteristic vector value according to an iteration formula.
6. The method of self-interference suppression for full duplex transceiver according to claim 1, wherein the transceiver antenna in step S1 includes TX and RX, the nonlinear model structure includes a transmitter, a digital auxiliary transmission channel and a receiver, and the transmitter transmits SI signals to the transmitter antenna through a digital-to-analog converter DAC, a low pass filter LPF, an IQ mixer and a power amplifier PA; the auxiliary transmitter is connected to the self-interference cancellation point before the low noise amplifier of the receiving channel through the DAC, the LPF, the IQ mixer and the power divider; the receiver transmits the received signal through a low noise amplifier LNA, an IQ mixer, a low pass filter LPF and an analog-to-digital converter ADC.
7. The method for self-interference suppression of full-duplex transceiver according to claim 1, wherein the step S1 is to control wideband same-amplitude inversion of transmission signals, and the formula of wideband inversion is as follows: x is x aux [n]＝x tx [n]e jπ Where xtx is represented as the main transmit channel digital domain signal.
8. The method of self-interference suppression for a full duplex transceiver according to claim 1, wherein the digital domain received signal of the receiver in step S2 is represented as:
in the method, in the process of the invention,which is the theoretical value of the characteristic parameter of the main emission channel, and is the same as d' p,q [l]Representing channel parameters, z, of the auxiliary transmission channel all [n]Represented as receiver noise;
the digital domain received signal of the receiver during the estimation phase is expressed as:
y＝[y′[n],...,y′[n+M-1]] T ，z＝[z all [n],…,z all [n+M-1]] T ，
h＝[d 1,0 [0],…,d 1,0 [L-1],d 1,1 [0],…,d′ 1,0 [0],...,b 1 ,b 2 ] T wherein L is the wireless channel impulse response h tx [l]Is a length of (2);
9. the method for self-interference suppression of a full duplex transceiver according to claim 1, wherein the iterative algorithm of step S3 has a calculation formula as follows:
ω aux [m+1]＝ω aux [m]-μ[y H [m]U[m]]
where m represents the sequence number of the iteration, y H [m]The method is characterized in that the method comprises the steps of representing the conjugate transpose of a self-interference signal vector received by a self-interference suppression and cancellation h pair receiver, mu represents the iterative step length, and the iterative step length satisfies the following conditions:
wherein lambda is max (R)＝E[U[m]U H [m]]。
10. The method for self-interference suppression of a full duplex transceiver according to claim 1, wherein the iterative algorithm of step S4 has a calculation formula as follows:
ω digital [l+1]＝ω digital [l]+μ[y H [m]U[m]-ω digital [l]U[m]U H [m]]
the iterative step size satisfies the following mathematical expression:
R＝U H [m]U[m]an autocorrelation matrix, sigma, representing a feature matrix max (R) represents the maximum eigenvalue of the autocorrelation matrix.
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