CN110649933A - Adjacent channel interference suppression receiver based on convolution inversion of interference signal out-of-band component - Google Patents
Adjacent channel interference suppression receiver based on convolution inversion of interference signal out-of-band component Download PDFInfo
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Abstract
The invention discloses an adjacent channel interference suppression receiver based on convolution inversion of out-of-band components of interference signals, and relates to the technical field of signal processing devices. The adjacent channel interference suppression receiver comprises: a receiving antenna for receiving a signal having an interference signal frame transmitted by a transmitter; the signal processing module is used for processing the signals received by the receiving antenna and removing the received interference signals; and the receiver body is used for receiving the signals processed by the inversion module and processing the received signals. The receiver does not need a broadband ADC for sampling any more, the circuit structure of the receiver can be effectively simplified, the cost of the receiver can be reduced, the interference suppression capacity loss is less than 6dB when the received signals are processed by the method, and the larger the adjacent channel interference INR is, the smaller the interference suppression capacity loss is.
Description
Technical Field
The invention relates to the technical field of signal processing methods, in particular to an adjacent channel interference suppression receiver based on convolution inversion of out-of-band components of interference signals.
Background
In a dense limited space of wireless communication equipment, such as a vehicle-mounted communication platform or an intelligent home environment, a transmitter and a receiver are close to each other, and the nonlinear component of a high-power transmitting signal interferes with a receiving signal of an adjacent channel, so that the communication quality is reduced, and the receiver working in an adjacent channel frequency band is blocked in serious conditions, so that the communication is interrupted.
Taking a wireless local area network as an example, according to the requirements of an equipment emission spectrum template specified by an 802.11n standard, under the conditions that the emission power is +20dBm and the receiver bottom noise level is-90 dBm, the maximum nonlinear component of an emission signal can reach 0 dBm. According to the free space propagation loss formula, it can be calculated that when the transmitter and the receiver are spaced by 5m, the Interference-to-noise ratio (INR) of the adjacent channel introduced in the receiver is about 40dB, when the spacing is 0.5m, the INR of the adjacent channel introduced can reach 60dB, and when the transmission power is increased or the system operating frequency is reduced, the INR of the adjacent channel is further increased. High levels of adjacent channel interference can severely degrade the reception quality of the desired signal.
In order to solve the problem of adjacent channel interference between wireless communication devices, people develop a related research for actively suppressing the adjacent channel interference by utilizing a cancellation technology, and the principle is that an auxiliary branch is constructed at a receiver, the nonlinear parameter of an interference signal is estimated, the interference cancellation signal is reconstructed, and finally the reconstructed signal is subtracted from a received signal so as to achieve the purpose of suppressing the interference, so that the received signal-to-noise ratio of a desired signal can be effectively improved.
In the auxiliary branch architecture adjacent channel interference suppression receiver provided in the prior art, an antenna receiving signal is divided into two paths after passing through a coupler: obtaining an expected signal containing adjacent channel interference and noise in a receiving branch; and the auxiliary branch circuit obtains an interference signal with nonlinear distortion, so that nonlinear parameters can be estimated, and an interference cancellation signal can be reconstructed. The hardware structure of the method is complex, in order to accurately represent the nonlinear characteristics of the interference signal, the bandwidth of the auxiliary branch should include all nonlinear distortions of the interference signal, which are usually 3-5 times of the channel bandwidth, and the sampling rate of the corresponding (analog-to-digital converter) ADC is at least 6-10 times of the channel bandwidth, and the broadband ADC can greatly increase the cost of the receiver. To alleviate the dependence on the wideband ADC, some prior art techniques use time domain inverse filtering methods to extrapolate the frequency spectrum of the band-limited signal to recover the original signal; some utilize wiener inverse filtering method convolution inversion band-limited signal in order to resume complete bandwidth signal; some proposed narrow-band feedback digital predistortion technology is used for eliminating the influence of feedback path bandwidth limitation on signal recovery; a method of approximating coefficients in steps is also presented to reduce the effect of filter bandwidth limitations on the recovered signal error.
However, in the above studies, the in-band components (including linear components and in-band nonlinear components) after the signal band limitation are used for convolution and inversion to extrapolate the out-of-band nonlinear components. The equivalent band-pass filter forming the out-of-band nonlinear component has zero points in both low-frequency and high-frequency regions, and the zero point in the low-frequency region can bring large distortion to a recovered signal waveform, so that the problem of signal recovery of the application cannot be solved by the conventional research method.
Disclosure of Invention
The technical problem to be solved by the invention is how to provide an adjacent channel interference suppression receiver which does not need a broadband ADC for sampling any more, can effectively simplify the circuit structure of the receiver and reduce the cost of the receiver.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: an adjacent channel interference mitigation receiver based on convolution inversion of out-of-band components of an interfering signal, comprising:
a receiving antenna for receiving a signal having an interference signal frame transmitted by a transmitter;
the signal processing module is used for processing the signals received by the receiving antenna and removing the received interference signals;
and the receiver body is used for receiving the signals processed by the inversion module and processing the received signals.
The further technical scheme is as follows: the signal processing module comprises a band-pass filter, a down converter, an ADC module, a convolution inversion module, a time delay adjustment module, a channel estimation interference reconstruction module and a signal fine adjustment module; the signal received by the receiving antenna is processed by a band-pass filter, a frequency converter and an ADC module in sequence to obtain a digital baseband signal r [ n ], the digital baseband signal is processed by a convolution inversion module to restore a band-pass filtering input signal, then the restored signal is processed by a channel estimation interference reconstruction module to carry out parameter estimation and interference signal reconstruction, meanwhile, a time delay adjusting circuit is added at the output end of the ADC module, the time delay is adjusted by the time delay adjusting circuit to align the waveform of the received interference signal and the reconstructed interference signal on a time domain, the restored original interference signal is utilized to carry out parameter estimation and interference cancellation, and the interference signal in the received signal is removed.
The further technical scheme is that the processing method of the convolution inversion module is as follows: firstly, calculating and eliminating convolution coupling between signal frames, constructing a linear convolution signal frame by partial convolution signal frames, and then correcting a least square solution and inverting by a regularization method to obtain an adjacent channel interference signal.
The further technical scheme is that the construction method of the linear convolution signal frame is as follows:
setting the total length of transmitter interference signal frame x [ N ] as M, where the frame head length is N, the coefficients of N-order FIR band-pass filter are represented by h [ N ], the number is N +1, and M > N +1, and the length of output linear convolution signal frame y [ N ] is N + M according to convolution theorem;
the filtering process is expressed in the form of a matrix product as:
y＝C·x (1)
wherein y is the band-pass filtered signal or observation data, x is the input signal, and the convolution kernel matrix C is defined by the filter coefficient h0,h1,h2,…,hNThe construction is such that the Mbit input signal x is multiplied by C to obtain the first Nbit of the N + Mbit output signal y, yShown as follows:
wherein x'M-N+1,…,x′M-1,x′MIs the last Nbit data, x, of the previous signal frame1,x2,…,xMIs the current frame data; as shown in equation (2), the previous frame data will affect the nth line of the current frame output, that is, the previous Nbit of the output signal y; starting from the N +1 th line, the previous frame data is completely shifted out of the register, and the current frame is not influenced any more; thus, the effect of the previous frame on the current frame overlay can be expressed as x'M-N+1,x′M-1,…,x′MA product vector zb 'of the upper triangular matrix in the C' matrix is shown as a formula (3); m zeros are needed to be supplemented to the rear side of the vector to reach N + Mbit, and an influence value zb of a previous frame on a current frame is obtained;
the convolution kernel matrix and the two triangular matrices can be directly generated by the filter coefficient h [ n ]; by caching continuous three-frame observation signals, intercepting the last N/2bit of the previous frame and the first N/2bit of the next frame, and adding the Mbit of the current frame, an N + Mbit output signal y can be obtained; the front Nbit data of the input signal of the next frame is unknown, but the frame header information is the same, so the frame header of the restored signal x' can be used for replacing the data;
the influence zb of the previous frame on the current frame is subtracted from the output signal y, and then the influence za of the next frame on the current frame is subtracted, so that the influence value of the adjacent frame on the current frame can be eliminated, and a frame of linear convolution signal frame is obtained.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the receiver utilizes the received out-of-band nonlinear signal component, calculates and eliminates coupling between adjacent frames, constructs a linear convolution signal frame by partial convolution signal frames, recovers an original interference signal by convolution inversion through a regularization least square method, and then utilizes the recovered signal to carry out parameter estimation and interference cancellation. Therefore, the receiver does not need a broadband ADC for sampling any more, the circuit structure of the receiver can be effectively simplified, the cost of the receiver can be reduced, the interference rejection capability loss is less than 6dB when the received signals are processed by the method, and the larger the adjacent channel interference INR is, the smaller the interference rejection capability loss is.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a schematic block diagram of a receiver in an embodiment of the invention;
FIG. 2 is a functional block diagram of a convolution inversion circuit in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a spectrum of a convolution inverted signal in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a positional relationship between an observation signal and a superimposed influence vector according to an embodiment of the present invention;
FIG. 5 is a graph comparing waveforms of a recovered signal and an input signal according to an embodiment of the present invention;
fig. 6 is a spectrum diagram of error recovery under the conditions of INR being 40dB and INR being 60dB in the embodiment of the present invention;
FIG. 7 is a diagram illustrating the relationship between the input adjacent channel interference INR and the signal recovery error in the embodiment of the present invention;
FIG. 8 is a graph of the inverted signal and the residual interference spectrum at an INR of 40dB according to an embodiment of the present invention;
FIG. 9 is a graph of the inverted signal and the residual interference spectrum at an INR of 60dB according to an embodiment of the present invention;
fig. 10 is a diagram illustrating the relationship between the interference suppression capability of the received signal INR and the adjacent channel in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
In summary, as shown in fig. 1, an embodiment of the present invention discloses an adjacent channel interference rejection receiver based on convolution inversion of out-of-band components of an interference signal, including:
a receiving antenna for receiving a signal having an interference signal frame transmitted by a transmitter;
the signal processing module is used for processing the signals received by the receiving antenna and removing the received interference signals;
and the receiver body is used for receiving the signals processed by the inversion module and processing the received signals.
Let the transmitter center frequency be w2Center frequency of the receiver being w1Two frequencies located in adjacent channels and having w1>w2(ii) a The influence caused by up-conversion, down-conversion and ADC quantization errors is not considered in the application.
Further, as shown in fig. 1, the signal processing module includes a band-pass filter, a down converter, an ADC module, a convolution inversion module, a delay adjustment module, a channel estimation interference reconstruction module, and a signal fine-tuning module; the convolution inversion circuit in the receiver replaces an auxiliary branch radio frequency front end, the receiver only has a branch radio frequency front end, the branch radio frequency front end comprises a band-pass filter, a down converter and an ADC, and the bandwidth of the ADC is equal to the channel bandwidth of the receiver. After the digital baseband signal r [ n ] output by the ADC is obtained, the band-pass filtering input signal is restored through convolution inversion, and a specific convolution inversion circuit is shown in FIG. 2. And then, parameter estimation and interference signal reconstruction are carried out by utilizing the recovery signal, so that the problem that the receiver of the original auxiliary branch architecture has to rely on a broadband ADC for sampling can be avoided. Because extra time delay is introduced after the digital baseband signal r [ n ] is processed by the convolution inversion, the channel estimation and the interference reconstruction circuit, a time delay adjusting circuit is required to be added at the output end of the ADC, and the waveforms of the received interference signal and the reconstructed interference signal are strictly aligned on the time domain by adjusting the time delay, so that the residual interference after the interference cancellation is minimized.
Receiver principle:
in the co-located receiver, the digital baseband signal r [ n ] after the bandpass filter, down-conversion and ADC can be expressed as:
where z [ n ] is the transmitter interference signal received by the antenna, w [ n ] is the noise introduced during the channel transmission, e [ n ] is the desired signal to be received, and h [ n ] is the impulse response of the band pass filter. Because the amplitudes of w [ n ] and e [ n ] are small, the interference signal z [ n ] is the main component of the signal r [ n ], and the signal k [ n ] after the up-conversion processing can be approximately expressed as:
wherein: w'2Is a second local oscillation frequency of the receiver and has w'2≈w2. Then k [ n ]]Is a carrier frequency of w1-w′2Band-pass signal of (c), k [ n ]]And z [ n ]]The corresponding equivalent baseband spectrum of (a) is shown in fig. 3. It can be seen that in practice k n]Is z [ n ]]Passing through an equivalent center frequency of w1-w′2The band-pass filter of (1) and the convolution inversion circuit aims to exploit the k n known to the receiver]And h [ n ]]To recover the signal z n before band-pass filtering]。
Assuming impulse response of convolution inversion circuit as h' n, after convolution inversion and frequency spectrum second shift, signal y n can be expressed as
The impulse response characteristic of the convolution inversion circuit under an ideal condition meets the following requirements:
h[n]*h′[n]＝δ[n] (4)
the inputs of the channel estimation and interference reconstruction circuit are:
y[n]＝z[n]+w[n]+e[n] (5)
the recovered signal y [ n ] is exactly equal to the original input signal of the band-pass filter, namely the output of the broadband ADC of the receiver with the original auxiliary branch architecture. After the recovery signal y [ n ] is obtained, the subsequent steps of non-linear parameter estimation, interference signal reconstruction and the like are carried out, and the suppression of the adjacent channel interference can be completed.
According to the expression of the residual interference, the residual interference d [ n ] based on convolution inversion adjacent channel interference suppression can be expressed as:
wherein
Receiving adjacent channel interference signals;
for the interference signal reconstructed in the receiver, α represents the bias coefficient of the estimation circuit; d' [ n ]]Is the inherent residual interference consisting of the frequency offset of the receiver and the transmitter and the channel noise.
It can be seen from equation (6) that when the impulse response h' n of the convolution inversion circuit satisfies the ideal characteristics, the residual interference expression is the same as that in the prior art, and the adjacent channel interference suppression performance of the residual interference expression is equivalent to that in the prior art. However, the actual circuit characteristic h 'n can only approach to an ideal characteristic, so that the final estimation error includes an error caused by the original deviation coefficient α, and besides, the h n x h' n convolution characteristic is not ideal, which further increases the deviation of the reconstruction interference and the reception interference, and causes the residual interference after cancellation to be larger, resulting in the deterioration of the adjacent channel interference suppression capability.
Convolution inversion of out-of-band nonlinear signal components:
regularization of least squares pathological solution:
as can be seen from fig. 3, the out-of-band nonlinear signal component as the adjacent channel interference is actually an equivalent band-pass filtered signal of the interference signal. For bandpass filtering under noisy observations, the signal processing procedure can be expressed as
Y＝CX+d (7)
Wherein Y is the filtered output signal or observation data, C is the convolution kernel matrix, X is the input signal, and d is noise. The least squares solution of equation (7) is
When the observation equation is ill-conditioned, the matrix C in equation (8)TThe condition number of C is very large, the inversion is extremely unstable, and the solution is not qualified (ill-posed), so that the regularization treatment is needed.
Tikhonov regularization is a common method for solving the morbidity problem, converting the least squares problem into the following one
min{||Cx-y||2+l||x||2} (9)
Wherein | | Cx-y | ceiling2Representing the data fitting error, | x | | non-calculation2For controlling the smoothness of the solution, l is the regularization parameter.
Making | | Cx-y | | non-woven phosphor2+l||x||2The first derivative to x is equal to zero, and the final solution can be obtained as
xl＝(CTC+l)-1CTy (10)
In the equation (10), since C and y are both known quantities, and the error of the final solution is completely determined by the variable l, the value of the regularization parameter l becomes very critical. In practice, there are many methods for determining the regularization parameters, and the L-curve method and the generalized cross-checking method (GCV method) are the two most commonly used methods.
Since the least square method can only calculate data with a limited length, the input and output of the band-pass filter are required to be in a signal frame form. Setting the length of input signal frame x [ N ] as M, wherein the length of frame head is N; the coefficients of the N-order FIR band-pass filter are expressed by h [ N ], the number is N +1, and M is more than N + 1; according to the convolution theorem, the length of the output linear convolution signal frame y [ N ] is N + M.
The actual FIR filter is in a pipeline working mode, the input signal frame is M bits, the output signal frame is also Mbit, and the output signal frame is part of the linear convolution result N + M and is called as partial convolution[12]It is shown that the current frame and the adjacent previous and next frames are coupled during the convolution process. It is necessary to try to decouple the previous and subsequent frames of the signal in order to correctly recover the current signal frame, i.e. first, an N + Mbit linear convolution signal frame is constructed by observing the obtained Mbit partial convolution signal frame.
Construction of linear convolution signal frame:
the boundary extension method commonly used in signal processing includes: 1) zero value continuation: that is, the signals extending to both sides are represented by zero level; 2) symmetrical continuation: namely, extending outwards by using the edge values at two sides of the current signal; 3) and (3) periodic continuation: that is, the current signal is used as the reference to form the periodic repetitive signal, and then the periodic repetitive signal is truncated to the specified length to achieve the purpose of continuation.
The extension signal constructed by the method obviously has larger error with the real signal. And due to the ill-conditioned problem of the convolution kernel matrix, the introduced errors are amplified and reflected on the fluctuation of the recovery signal. New methods are sought to minimize the errors introduced when constructing linear convolution signals.
Let the coefficient of the FIR band-pass filter of N orders be h [ N ]]＝[h0,h1,h2,...,hN]Then, by h [ n ]]The Toeplitz matrix constructed for the substrate is called the convolution kernel matrix. The actual FIR filter convolution process is expressed by a matrix as shown in formula (11), where C is an (N + M) × M order convolution kernel matrix, x is an input signal, y is an output signal, and the C' matrix is used in the formula only to assist in explaining the coupling relationship between the previous frame and the current frame, and has no practical significance.
Filter coefficient h along main diagonal in convolution kernel matrix C0,h1,h2,...,hNBy the effect of multiplication with the input signalThe effect is equivalent to that the input signal is successively shifted and then multiplied by the filter coefficients. And multiplying the M bit input signal x by the convolution kernel matrix C to obtain an N + M bit column vector output signal y.
The construction method of the linear convolution signal frame is as follows:
setting the total length of transmitter interference signal frame x [ N ] as M, where the frame head length is N, the coefficients of N-order FIR band-pass filter are represented by h [ N ], the number is N +1, and M > N +1, and the length of output linear convolution signal frame y [ N ] is N + M according to convolution theorem;
the filtering process is expressed in the form of a matrix product as:
y＝C·x (12)
wherein y is the band-pass filtered signal or observation data, x is the input signal, and the convolution kernel matrix C is defined by the filter coefficient h0,h1,h2,…,hNThe structure is obtained, the M bit input signal x is multiplied by C to obtain an N + M bit output signal y, and the first N bit of y can be expressed as:
wherein x'M-N+1,…,x′M-1,x′MIs the last Nbit data, x, of the previous signal frame1,x2,…,xMIs the current frame data; as shown in equation (13), the previous frame data will affect the nth row of the current frame output, i.e. the previous Nbit of the output signal y; starting from the N +1 th line, the previous frame data is completely shifted out of the register, and the current frame is not influenced any more; thus, the effect of the previous frame on the current frame overlay can be expressed as x'M-N+1,x′M-1,…,x′MThe vector zb 'of the product with the upper triangular matrix in the C' matrix is shown as formula (14); m zeros are needed to be supplemented to the rear side of the vector to reach N + Mbit, and an influence value zb of a previous frame on a current frame is obtained;
similarly, the main diagonal element of the convolution kernel matrix C continues to extend to the right side, and a lower triangular matrix can be obtained. The influence of the next frame on the current frame is a column vector of the product of the previous N bits of the next frame and the N bits of the lower triangular matrix, and the column vector needs to be supplemented with M zeros to N + M bit lengths on the front side to obtain an influence vector za of the next frame on the current frame. The corresponding position relationship between the observation signal and the two superimposed influence vectors is shown in fig. 4, and each vector in fig. 4 is given in a transposed manner.
In the above signal operation process, the convolution kernel matrix C and the two triangular matrices can be directly constructed and generated by the filter coefficients h [ n ]. By caching continuous three frames of observation data, intercepting the last N/2bit of the previous frame and the first N/2bit of the next frame, and adding the M bit of the current frame, the N + M bit output signal y can be obtained. The recovered signal x' also needs to be buffered for one frame at the receiver in order to calculate the contribution of the previous frame to the current frame. The first N bit data of the input signal of the next frame is unknown, but since the frame header data of each signal frame is the same, the frame header data of the recovered signal x' can be used instead.
The influence value zb of the previous frame is subtracted from the output signal y, and then the influence value za of the next frame is subtracted, so that the influence value of the input signal of the previous frame and the input signal of the next frame on the current output frame can be eliminated, and the linear convolution signal frame is obtained.
The linear convolution signal constructed by the method introduces errors which are obviously smaller than the errors introduced by the three boundary continuation methods, so that the possibility that the errors cause larger distortion to the recovered signal is reduced. However, when the construction method is applied, the recovered output signal has a frame delay compared with the observed signal, and the frame header data length should not be less than the order of the filter, so as to satisfy the condition of constructing the linear convolution signal.
And (3) inversion signal error analysis:
singular value decomposition of convolution kernel matrix C
Wherein U and V are dimension orthogonal unitary matrixes of m × m and n × n, respectively; sigma is diag (sigma)1,…σn)，σ1≥…≥σn> 0 are the singular values of the matrix C. Substituting singular value decomposition expression of matrix C into xλ＝(CTC+λ)-1CTy, can be:
wherein: f (sigma)i) For filtering the factors, from the singular value σiAnd a regularization parameter lambda decision. When sigma isiWhen > lambda, there is f (sigma)i) 1, corresponding to σi(ii) solution retention; when sigma isiWhen < lambda there is f (sigma)i) 0, corresponding to σiThe effect of the filtering factor is to filter out the contribution of small singular values to the solution to reach the effect of a stable solution.
The least squares solution after Tikhonov regularization can be expressed as
The error of the resulting recovered signal from the original input signal is
As can be seen from the above equation, the final recovered signal error includes two parts: partly due to self-signals, when filtering the factor f (σ)i) When 0 is approximately covered, the singular solution sigma of the minimum valueiAnd the corresponding observed data is directly discarded, thereby causing an error between the recovered signal and the original signal; another part is caused by channel noise when filtering factor f (σ)i) 1, singular solution σiThe noise introduced on the corresponding observed data is also amplified by the ill-conditioned matrix, thereby causing errors in the recovered signal.
It can also be seen from the process of constructing the linear convolution signal that, when the influence of the next frame on the current frame is eliminated, since the header data of the next frame is unknown, the header data of the previous frame is used instead of performing calculation, and an error exists between the header data of the previous frame and the actually received header data. The larger the introduced channel noise is, the larger the error of the recovered signal is, so that the signal recovery effect by using the method provided by the application is theoretically proportional to the interference-to-noise ratio of the received interference signal.
The signal recovery effect has a great relationship with the bandpass filter parameter configuration: when the order of the band-pass filter is set to 60 orders and the transition bandwidth is set to 10KHz and is unchanged, the stop-band attenuation value is changed, and the condition number of the generated convolution kernel matrix is shown in Table 1. The larger the stopband attenuation value is, the larger the condition number of generating the convolution kernel matrix is, the more serious the matrix ill-condition problem is, and the more sensitive the equation solution is to the disturbance of the signal, so that the situation that the filter is set with a too harsh stopband attenuation parameter value should be avoided as much as possible during the system configuration.
TABLE 1 different stop-band attenuation values correspond to convolution kernel matrix condition numbers
Computer simulation verification:
and (3) verifying the convolution inversion effect of the out-of-band nonlinear signal component:
in order to verify the signal recovery effect of the convolution inversion method provided by the application, simulation is performed by using Matlab software, and the settings of various parameters in a simulation experiment are shown in Table 2.
Table 2 simulation experiment parameter set-up
To facilitate a measure of the signal recovery effect, Mean Squared Error (MSE) is defined to characterize the difference between the inverted signal and the input signal:
wherein
Representing the signal recovered by convolution inversion, x n]Presentation inputA signal.
Since the side lobe of the interference Signal received by the antenna is the adjacent channel interference, the larger the Signal-to-Noise Ratio (SNR) of the interference Signal is, the higher the side lobe after the corresponding spectrum broadening is, the larger the received adjacent channel interference is, so that the adjacent channel interference INR is in direct proportion to the SNR of the interference Signal received by the antenna, and the relationship between the adjacent channel interference INR and the SNR of the interference Signal received by the antenna is approximately equal to that under the experimental limited condition of the application
INRAdjacent channel interference＝SNRInterference signal-30dB (20)
When the adjacent channel interference INR is 40dB, the recovered signal and the original input signal have waveforms as shown in fig. 5, and the recovered signal MSE is 3.0578E-5. As can be seen from fig. 5, the time domain waveform distortion is mainly reflected at the peaks and valleys of the signal, corresponding to the high frequency components in the signal spectrum. When the adjacent channel interference INR increases to 60dB, the recovery signal MSE is 2.9458E-7, the error of the recovery signal is significantly reduced, and the waveform of the recovery signal almost completely coincides with the input signal.
The relationship between the error of the inverted recovered signal and the original input signal at the same frequency with the adjacent channel interference INR of 40dB and 60dB, respectively, is shown in fig. 6. Since the signal in the passband of the bandpass filter is known at the receiver, the recovery error is small and the inverted signal error is mainly concentrated out of band. However, since the amplitude of the high frequency component itself is smaller, the effect of the same recovery error on the high frequency component is more obvious, as shown by the time domain waveform of the signal in fig. 5, and when the adjacent channel interference INR is increased from 40dB to 60dB, the recovery errors of the low frequency component and the high frequency component are both reduced significantly. The conclusion from the previous analysis, that the signal recovery effect is proportional to the adjacent channel interference INR, is further verified by the analysis of the error signal introduced for different frequencies.
By changing the superimposed white gaussian noise amplitude in the channel, the MSE of the recovered signal under different adjacent channel interference INR is counted, and the relationship between the signal recovery error and the adjacent channel interference INR is shown in fig. 7.
As can be seen from fig. 7, when the adjacent channel interference INR increases from 20dB to 60dB, the recovery signal MSE decreases rapidly; and the INR is above 60dB, the improvement trend of the recovery signal MSE is gradually slowed down. Because the gaussian noise amplitude in the channel decreases as the adjacent channel interference INR increases, the noise introduced in constructing the linear convolution signal also gradually decreases. Due to the ill-conditioned nature of the convolution kernel matrix, small perturbation errors of the input signal can cause large fluctuations of the final solution, and similarly, when the input signal perturbation is improved, the improvement degree of the final solution is more obvious. Therefore, the MSE corresponding to the recovery signal decreases rapidly at the initial stage of improvement of the adjacent channel interference INR, and when the adjacent channel interference INR increases to a certain degree, the channel characteristics gradually approach the non-interference channel, and the improvement degree of the MSE corresponding to the recovery signal also decreases gradually.
In summary, when the received adjacent channel interference INR is greater than 20dB, the equivalent band-pass filtered signal can be effectively recovered by using the convolution inversion method, and the signal recovery effect is in direct proportion to the adjacent channel interference INR.
And (3) verifying the adjacent channel interference suppression effect based on convolution inversion:
in order to verify the feasibility of the proposed method for adjacent channel interference suppression and the influence of recovery signal MSE on the adjacent channel interference suppression performance, simulation verification is performed on the adjacent channel interference suppression performance based on convolution inversion by using Simulink in the section. In the simulation, the channel is set to be a white gaussian noise model, and the rest parameters of the system are set as shown in table 3.
Table 3 simulation system parameter set-up
When the receiver carries out polynomial coefficient estimation, the suppression capability of the system to the adjacent channel interference is investigated by setting the polynomial nonlinear order K to be 5 and the memory depth Q to be 3.
The frequency spectrum of the recovered signal and the residual interference after suppression is shown in fig. 8 when the adjacent channel interference INR is 40 dB. For the convenience of comparative analysis, each signal in the figure is equivalently expressed as a digital baseband signal. In the figure, the transmitter signal curve corresponds to the frequency spectrum of the signal received by the receiver antenna, wherein the center frequency of the transmitter channel is equivalent to zero frequency, and the center frequency of the receiver is equivalent to 50 KHz. The transmitter signal sidelobe forms adjacent channel interference after being filtered by the band-pass filter of the receiver, and corresponds to an adjacent channel interference signal curve in the graph. The recovered signal obtained by convolution inversion corresponds to the inverted signal curve in the plot. And performing parameter estimation and signal reconstruction by using the recovered signal, setting a residual interference curve in the residual interference corresponding graph of the original auxiliary channel architecture receiver by using the same parameters, and performing the residual interference curve in the auxiliary branch architecture method in the residual interference corresponding graph after the residual interference corresponding graph is counteracted.
As can be seen from fig. 8, at the high frequency component above 60KHz and the low frequency component near 20KHz, there is a large error between the inverted signal and the transmitter signal, and the spectrum of the inverted signal in the pass band also fluctuates slightly, and at this time, the corresponding recovery signal MSE is 3.0578E-5. At a frequency point of 30KHz, the method can restrain adjacent channel interference of 36dB, and the auxiliary branch architecture method can restrain adjacent channel interference of 41 dB. At the moment, the error introduced in the convolution inversion process is large, and the average loss of the interference suppression capability of the method provided by the application is about 6dB compared with that of an auxiliary branch architecture method in view of the whole channel bandwidth.
Fig. 9 is a schematic diagram of the frequency spectrum of the recovered signal and the residual interference when the adjacent channel interference INR is 60 dB. Along with the increase of the receiving signal INR, the error of the high-frequency component above 60KHz of the inversion signal is obviously reduced, and the coincidence ratio of the low-frequency component near 20KHz and the inversion signal in the passband with the transmitter signal is higher, at this time, the corresponding recovery signal MSE is 2.9458E-7. At a frequency point of 30KHz, the method can restrain the adjacent channel interference of 57dB approximately, and the auxiliary branch architecture method can restrain the adjacent channel interference of 60dB approximately. At this time, errors introduced in the convolution inversion process are improved to a certain extent compared with those in fig. 8, and the average loss of the interference suppression capability of the method provided by the application is about 4dB compared with that of the auxiliary branch architecture method in view of the whole channel bandwidth.
It can be easily seen from comparing fig. 9 with fig. 8 that, as the received adjacent channel interference INR increases, the convolution inversion error gradually decreases, and the higher the similarity between the recovered signal and the original input signal is, the closer the adjacent channel interference suppression capability of the proposed method is to that of the auxiliary branch architecture method.
The adjacent channel interference suppression capability of the method and the auxiliary branch architecture method provided by the present application under different adjacent channel interference INRs is shown in fig. 10. As can be seen from the figure, the larger the adjacent channel interference INR is, the larger the received adjacent channel interference is, the larger the suppressed adjacent channel interference amplitude after cancellation is, and the general trends of the two methods for suppressing the adjacent channel interference are kept consistent, thereby proving the feasibility and effectiveness of the method provided by the present application.
Meanwhile, due to the existence of channel noise and the ill-conditioned characteristic of a convolution kernel matrix, an error exists between a convolution inversion recovery signal and an original input signal, and the residual interference after the error is counteracted becomes large, so that the adjacent channel interference suppression capability of the method has about 3-6 dB loss compared with that of an auxiliary branch architecture method, and the larger the received adjacent channel interference INR is, the smaller the recovery signal MSE is, and the smaller the loss of the adjacent channel interference suppression capability is.
Claims (4)
1. An adjacent channel interference mitigation receiver based on convolution inversion of out-of-band components of an interfering signal, comprising:
a receiving antenna for receiving a signal having an interference signal frame transmitted by a transmitter;
the signal processing module is used for processing the signals received by the receiving antenna and removing the received interference signals;
and the receiver body is used for receiving the signals processed by the inversion module and processing the received signals.
2. The adjacent channel interference suppression receiver based on convolution inversion of out-of-band components of interference signals according to claim 1, wherein the signal processing module comprises a band-pass filter, a down converter, an ADC module, a convolution inversion module, a delay adjustment module, a channel estimation interference reconstruction module, and a signal fine-tuning module; the signal received by the receiving antenna is processed by a band-pass filter, a frequency converter and an ADC module in sequence to obtain a digital baseband signal r [ n ], the digital baseband signal is processed by a convolution inversion module to restore a band-pass filtering input signal, then the restored signal is processed by a channel estimation interference reconstruction module to carry out parameter estimation and interference signal reconstruction, meanwhile, a time delay adjusting circuit is added at the output end of the ADC module, the time delay is adjusted by the time delay adjusting circuit to align the waveform of the received interference signal and the reconstructed interference signal on a time domain, the restored original interference signal is utilized to carry out parameter estimation and interference cancellation, and the interference signal in the received signal is removed.
3. The adjacent channel interference rejection receiver based on convolution inversion of out-of-band components of interference signals according to claim 2, wherein the convolution inversion module processes the following: firstly, calculating and eliminating convolution coupling between signal frames, constructing a linear convolution signal frame by partial convolution signal frames, and then correcting a least square solution and inverting by a regularization method to obtain an adjacent channel interference signal.
4. The adjacent channel interference rejection receiver based on convolution inversion of out-of-band components of an interference signal as claimed in claim 3, wherein the construction method of the linear convolution signal frame is as follows:
setting the total length of transmitter interference signal frame x [ N ] as M, where the frame head length is N, the coefficients of N-order FIR band-pass filter are represented by h [ N ], the number is N +1, and M > N +1, and the length of output linear convolution signal frame y [ N ] is N + M according to convolution theorem;
the filtering process is expressed in the form of a matrix product as:
y＝C·x (1)
wherein y is the band-pass filtered signal or observation data, x is the input signal, and the convolution kernel matrix C is defined by the filter coefficient h0,h1,h2,…,hNThe construction yields that the Mbit input signal x is multiplied by C to yield an N + Mbit output signal y, the first Nbit of y can be expressed as:
wherein x'M-N+1,…,x′M-1,x′MIs the last Nbit data, x, of the previous signal frame1,x2,…,xMIs the current frame data; as shown in equation (2), the previous frameThe data will affect the nth line of the current frame output, that is, the first Nbit of the output signal y; starting from the N +1 th line, the previous frame data is completely shifted out of the register, and the current frame is not influenced any more; thus, the effect of the previous frame on the current frame overlay can be expressed as x'M-N+1,x′M-1,…,x′MA product vector zb 'of the upper triangular matrix in the C' matrix is shown as a formula (3); m zeros are needed to be supplemented to the rear side of the vector to reach N + Mbit, and an influence value zb of a previous frame on a current frame is obtained;
the convolution kernel matrix and the two triangular matrices can be directly generated by the filter coefficient h [ n ]; by caching continuous three-frame observation signals, intercepting the last N/2bit of the previous frame and the first N/2bit of the next frame, and adding the Mbit of the current frame, an N + Mbit output signal y can be obtained; the front Nbit data of the input signal of the next frame is unknown, but the frame header information is the same, so the frame header of the restored signal x' can be used for replacing the data;
the influence zb of the previous frame on the current frame is subtracted from the output signal y, and then the influence za of the next frame on the current frame is subtracted, so that the influence value of the adjacent frame on the current frame can be eliminated, and a frame of linear convolution signal frame is obtained.
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