CN117040976B - Dual-mode joint blind equalization method and blind equalizer under non-cooperative communication condition - Google Patents
Dual-mode joint blind equalization method and blind equalizer under non-cooperative communication condition Download PDFInfo
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Abstract
The invention belongs to the technical field of signal processing, and provides a blind equalizer and a dual-mode joint blind equalization method under a non-cooperative communication condition, wherein the blind equalizer comprises an improved normal mode correction algorithm equalizer, a cluster modulation decision device and a decision-directed equalizer; after a received signal sequence passes through a IMCMA equalizer, the signal is subjected to preliminary equalization, a cluster modulation decision device gives a modulation type decision result based on IMCMA preliminary equalization result, at the moment, the DD equalizer is converted into common blind equalization, and the DD equalizer is guided to re-equalize by using modulation information given by the cluster modulation decision device, so that the overall equalization effect is improved; the invention has better equalization effect on the signal with unknown modulation mode, and has obvious steady-state error performance improvement compared with the single IMCMA equalizer.
Description
Technical Field
The invention belongs to the technical field of signal processing, and particularly relates to a dual-mode joint blind equalization method and a blind equalizer under a non-cooperative communication condition.
Background
The signal transmission process is often affected by multipath effect, additive noise and other factors in the channel, and the signal received by the receiving end has intersymbol interference, which affects the communication reliability. The equalization technology can effectively weaken inter-code crosstalk, and high-quality communication is realized by compensating channels.
The traditional equalization technology utilizes a signal transmitting end to transmit a known training sequence, utilizes adjustment of equalizer tap iteration to enable equalizer output to approach the transmitting sequence, when training is completed, the equalizer has minimum output error, the equalizer is also adjusted to the parameters which are most suitable for the current channel, and the equalizer can well counteract the influence of signal multipath effect and the like on signals. The blind equalization technology equalizes the signal without using a training sequence at the receiving end, so that the frequency band resource is saved, and the adaptive adjustment can be performed aiming at the change of the channel.
The traditional blind equalization algorithm can well realize equalization of signals, but the cost function of the traditional blind equalization algorithm depends on modulation information of received signals, so that the application of the traditional blind equalization algorithm under the non-cooperative communication condition is limited. Conventional blind equalization algorithms are a prerequisite for modulation recognition as blind equalization algorithms, however, in the face of increasingly complex communication channels, the effects of multipath effects, additive noise and clutter make modulation recognition difficult, and current modulation recognition is mostly based on equalization. Thus, modulation identification and blind equalization under non-cooperative communication conditions become a pair of contradictions to each other. Aiming at the contradiction difficulty existing at present, a dual-mode joint blind equalization method is provided, and the signal blind equalization under the condition of lack of modulation prior information is effectively solved.
Disclosure of Invention
Aiming at the signal blind equalization problem of lack of prior information such as a modulation mode under a non-cooperative communication condition, the invention provides a dual-mode joint blind equalization method and a blind equalizer under the non-cooperative communication condition aiming at Multi-system phase shift keying (Multi-ARY PHASE SHIFT KEYING, MPSK) and Multi-system quadrature amplitude modulation (Multi-ary Quadrature Amplitude Modulation, MQAM) constant amplitude phase modulation signals.
The invention adopts the technical scheme that the blind equalizer under the non-cooperative communication condition comprises an improved correction modulus algorithm (Improved Modified Constant Modulus Algorithm, IMCMA) equalizer, a cluster modulation Decision device and a Decision Directed (DD) equalizer; after a received signal sequence passes through a IMCMA equalizer, the signal is subjected to preliminary equalization, a cluster modulation decision device gives a modulation type decision result based on the IMCMA preliminary equalization result, at the moment, the DD equalizer is converted into common blind equalization, and the DD equalizer is guided to re-equalize by utilizing modulation information given by the cluster modulation decision device, so that the overall equalization effect is improved.
The above-described blind equalization has the following advantages over conventional blind equalizers: firstly, the problem that the traditional blind equalizer needs modulation information as auxiliary information in the use process rather than can not provide modulation information under the cooperative communication condition is solved, the function of synchronous completion of equalization and modulation identification is realized by a method of combining the equalizer and a modulation decision device, and the faster convergence speed and lower steady-state error compared with the traditional blind equalizer are realized by a dual-mode equalization mode.
The invention also provides a dual-mode joint blind equalization method based on the blind equalizer under the non-cooperative communication condition, aiming at MPSK and MQAM constant amplitude phase modulation signals, the method comprises the following steps:
The first step, preprocessing the received signal, the method is:
When the received signal is a single-channel complex signal y (k), directly entering the next step, and if the received signal is IQ data, combining is needed: let the I-path received signal be y I (k) and the Q-path received signal be y Q (k), the signal sequence after combining is:
y(k)＝yQ(k)+yI(k)·i (1)
Where i denotes a complex unit and k denotes a length of a received signal sequence.
Inputting the signal sequence y (k) into IMCMA equalizer for preliminary blind equalization to obtain preliminary equalized output sequence z n (k) and IMCMA equalizer tap coefficient w n; the method comprises the following steps:
2.1 initializing parameters of IMCMA equalizer, setting IMCMA equalizer default length and tap coefficient w n. Setting IMCMA the equalizer length to 2L+1, the tap coefficient w n is expressed as
wn＝[wn(-L),wn(-L+1),wn(-L+2),…,wn(0),...wn(L-2),wn(L-1),wn(L)], Typically set to w n (0) =1, the remainder set to 0.
2.2 The real part Re (y (k)) and the imaginary part Im (y (k)) of y (k) are calculated.
2.3 Calculating a first statistic R pr and a second statistic R pi:
Wherein E (-) represents the mean value.
2.4 Calculate the autocorrelation matrix R y for y (k):
wherein, Represents the transpose of y (k).
2.5 Calculate trace tr for R y (R y):
Wherein R ll represents the diagonal element of the autocorrelation matrix R y;
2.6, calculating an initial step size:
Wherein, the value range of the step length parameter alpha is more than or equal to 1 and less than or equal to 10.
2.7 Sets the number of iterations n=1.
2.8 Calculation IMCMA equalizer output z n (k):
zn(k)＝y(k)*wn (6)
2.9 calculation error e (n):
Where z r,n and z i,n represent the real and imaginary parts, respectively, of the IMCMA equalizer output z n (k).
2.10 Calculate the current step size μ (n):
Wherein, the step size parameter beta is more than or equal to 1, exp (·) represents an exponential function based on natural logarithms.
2.11 Iterative update of equalizer tap coefficients:
Where w n+1 represents the equalizer tap coefficients at n+1 iterations.
2.12 Iteration number +1.
2.13 If e (n) -e (n-1) < th, indicating that the equalizer is going to steady state, proceed to 2.14, otherwise repeat steps 2.8-2.12. Where th is a steady state determination parameter, typically 10 -3～10-5.
2.14 Output IMCMA the equalization result z n (k) and equalizer tap coefficient w n after n iterations of the equalizer;
third, judging the modulation parameter M and judging the confidence coefficient, wherein the method is as follows:
Inputting IMCMA equalizer equalization results z n (K) into a cluster modulation decision device, and judging whether the z n (K) belongs to the same cluster or not by adopting K-means clustering (Lloyd,Stuart P."Least Squares Quantization in PCM."IEEE Transactions on Information Theory.Vol.28,1982,pp.129–137.), in a constellation diagram mode through the distance between different constellation points obtained through calculation; the method comprises the following steps:
3.1 setting the clustering center number as kn E [2,256], clustering z n (K) by using a K-means algorithm, wherein all sample points in the same cluster as z n (K) are denoted as z C (K), and sample points clustered as the j-th cluster and not in the same cluster as z n (K) are denoted as
3.2 Calculating a first profile factor D a (k) and a second profile factor D b (k), respectively:
Da(k)＝E(||zn(k)-zC(k)||) (10)
Where D a (k) represents the average distance of all sample points from z n (k) to z C (k), the smaller the D a (k) value, the closer z n (k) is to other sample points of the same cluster, the greater the likelihood that z n (k) belongs to z C (k); d b (k) represents the minimum value of the average distance of z n (k) to other clusters, and the larger the D b (k) value, which means that the further z n (k) is from other clusters, the greater the likelihood that z n (k) belongs to z C (k).
3.3 Calculating the contour coefficients D k (k) of all possible cluster center numbers kn according to equation (12), and calculating the average value thereof, to obtain the overall contour coefficient D kmean:
Dkmean＝E(Dk(k)) (13)
the closer the overall profile coefficient D kmean∈[-1,1],Dkmean is to 1, the better the clustering effect is under the clustering center number.
And 3.4, taking the maximum value in D kmean, taking the corresponding cluster center number kn as a judgment result of the modulation parameter, and marking the judgment result as M.
3.5 Checking the confidence level of the modulation parameter M according to equation (14).
The confidence degree of the modulation parameter is judged based on the overall contour coefficient D kmean, a threshold value rho is set, the value range of the threshold value rho is between 0.5 and 1, and the closer to 1, the higher the confidence degree requirement on the clustering judgment is indicated. When D kmean is more than or equal to ρ, the reliability of the returned modulation parameter is considered to be high, the output modulation parameter M enters the fourth step, otherwise, the confidence of the modulation parameter given by the modulation decision device is considered to be low, the equalizing effect of the IMCMA equalizer is considered to be poor, and the second step is returned.
The modulation parameter M corresponds to the modulation scheme one by one, for example, when m=4, the modulation scheme is QPSK, when m=8, the modulation scheme is 8psk, and when m=16, the modulation scheme is 16QAM (Proakis, john g.digital communications.4th.new York: MCGRAW HILL, 2001).
Fourth step, DD re-equalization
The IMCMA equalizer has the problem of larger steady state error, and in order to reduce the steady state error and improve the equalization effect, a DD equalizer is introduced to form a dual-mode equalizer. And the DD equalizer adjusts the decision guiding criterion according to the modulation parameter M given in the third step and performs DD re-equalization. Here the decision guiding criteria are:
Under the condition of known modulation modes, the constellation diagram of each modulation mode is a fixed pattern, the z n (k) output by the IMCMA equalizer is guided by judgment according to the modulation mode and a minimum distance criterion, and each point judgment is guided to be the nearest constellation point; the method comprises the following steps:
4.1 initializing a DD equalizer; setting DD equalizer length, tap coefficient v n and step size μ D: the DD equalizer length is 2l+1, then tap coefficient v n is denoted vn＝[vn(-L),vn(-L+1),vn(-L+2),…vn(0),...vn(L-2),vn(L-1),vn(L)],, typically v n (0) =1, the remaining tap coefficients are 0, and step size μ D is set to between 10 -4-10-7.
4.2 Calculating the decision guide results
Wherein DL (·) represents a decision criterion, and the employed decision criterion is a minimum distance constellation mapping criterion: based on the modulation mode corresponding to the modulation parameter M given in the third step, guiding the decision of the input sequence z n (k) into the following by adopting the principle of minimum distance of constellation points according to the constellation diagram of the modulation mode given by the decision(Wu Bingsheng, zhiyong, wu Bingya, et al. A blind equalization algorithm combining DDLMS and CMA methods [ J ]. Instructions on circuits and systems, 2003 (01): 81-84.).
4.3 Let n=1.
4.4 Calculating DD equalizer output:
zD,n(k)＝zn(k)*vn (16)
Where z D,n (k) represents the output result of the nth iteration of the DD equalizer.
4.5 Calculating error function e D (n):
4.6 updating DD equalizer tap coefficients:
Where v n denotes the tap coefficients of the DD equalizer at the nth iteration, v n+1 denotes the tap coefficients of the DD equalizer at the n +1 th iteration, Representing the transpose of z n (k);
4.7, the iteration times are n+1;
4.8 if e D(n)-eD (n-1) < th, go to 4.9, otherwise repeat steps 4.4-4.7. Where th is a steady state determination parameter, typically 10 -3-10-5.
4.9 Output equalization result z D,n (k) and equalizer tap coefficient v n.zD,n (k) are the blind equalization result of input signal y (k).
The invention has the following beneficial effects:
1, solving the blind equalization problem of modulation information deletion under the non-cooperative communication condition;
2. The recognition result of the modulation mode is given at the same time of signal equalization;
3. Compared with the traditional equalization method, the method provided by the invention has the advantages of faster convergence speed and lower equalization error.
Drawings
FIG. 1 is an equalization system model;
Fig. 2 is a zero pole diagram of a channel;
FIG. 3 is a channel amplitude-frequency response;
Fig. 4 is a received signal constellation;
fig. 5 is IMCMA equalized signal constellation;
FIG. 6 is IMCMA equalization signal residual inter-code crosstalk;
FIG. 7 is a cluster modulation decision performance graph;
Fig. 8 is a dual mode joint blind equalization signal constellation
Fig. 9 is the IMCMA algorithm and the dual-mode joint blind equalization method residual inter-code crosstalk.
Detailed Description
The invention is explained and illustrated in detail below with reference to the drawings and the examples.
Fig. 1 is a block diagram of a dual mode joint blind equalizer, consisting of a three part equalizer, IMCMA equalizer, clustered modulation decision device, and DD equalizer, with a feedback loop.
Taking multipath Gaussian channel signal blind equalization as an example, a dual-mode joint blind equalization process of a signal in a16 QAM modulation mode under a non-cooperative communication condition is simulated, and the implementation process is as follows:
and generating a first step of signal generation. A16 QAM modulation mode is adopted to generate a random sequence with a signal to noise ratio of 25dB and a symbol length of 2000, the zero pole diagram of the channel is shown in figure 2, and the amplitude-frequency response is shown in figure 3 after the random sequence passes through the channel h= [ 0.2.0.5-0.1 ]. This channel is a non-minimum phase system and fading is severe. The signal y (k) is received through channel simulation, the constellation diagram of the signal y (k) is shown in fig. 4, the signal constellation points are disordered, and the modulation mode cannot be recognized at all. The received signal is preprocessed, and the received signal is a single-channel complex signal and directly enters the next step.
A second step IMCMA performs preliminary blind equalization.
2.1 Set IMCMA equalizer length to 11 and initialize equalizer tap coefficients to 0,0,0,0,0,1,0,0,0,0,0.
2.2 The real part Re (y (k)) and the imaginary part Im (y (k)) of y (k) are calculated.
2.3 To calculate a first statistic R pr = 14.5487 and a second statistic R pi = 14.2017.
2.4 Calculate the autocorrelation matrix R y of y (k).
2.5 Calculate trace tr for R y (R y).
Taking α=2 for 2.6, the initial step μ (1) =2.4×10 -6 is obtained.
2.7 Let n=1.
2.8 Calculate IMCMA equalizer output z n (k).
2.9 Calculating error e (n).
2.10 Calculate the current step size μ (n).
2.11 Update IMCMA equalizer tap coefficients.
2.12 Let iteration number n+1.
2.13 Let th=10 -3, if e (n) -e (n-1) < th then proceed to the next step, otherwise return to 2.8 for iteration until e (n) -e (n-1) < th.
2.14 To IMCMA the equalizer equalization result z n (k) and equalizer tap coefficients [0.010,-0.049,0.065,0.090,-0.607,1.165,0.213,0.045,0.010,-0.001,0.003]+[0.009,0.001,0.006,-0.002,0.002,0,0.004,-0.007,0.004,-0.001,0.007]×i. are shown in fig. 5 as constellations of equalization result z n (k), and fig. 6 shows the variation of the residual inter-code crosstalk during equalization. It can be seen that the signal constellation after IMCMA equalization is significantly improved, and the residual inter-symbol interference gradually decreases as the number of equalizer iterations increases.
And thirdly, judging the modulation parameter M and judging the confidence level.
And 3.1, judging the steady-state error change condition while carrying out the second step, and sending the equalization result into a cluster modulation decision device when the steady-state error tends to be stable. And setting a clustering center number kn epsilon [2,256], and clustering the equalization result z n (K) by using a K-means algorithm.
3.2 Calculating a first profile factor D a (k) and a second profile factor D b (k).
3.3 Calculating the overall profile coefficient D kmean for all possible cluster centers,
The judgment accuracy of the cluster modulation judgment device is shown in figure 7, and the recognition performance under different signal to noise ratios is tested for signals of different modulation modes.
3.4 Takes the maximum value max of D kmean (D kmean) =0.92, corresponding to the cluster center number m=16.
3.5 Judging the confidence coefficient of the modulation parameter, setting a threshold value rho=0.7 and setting an overall profile coefficient D kmean =0.92 > rho of M=16, so that the result of cluster modulation identification is considered to be reliable, the modulation mode is 16QAM, and the next step is carried out.
And step four, DD re-equalizes.
4.1 Initializing DD equalizer, setting DD equalizer length to 11, initializing equalizer tap coefficient to [0,0,0,0,0,1,0,0,0,0,0], setting step size mu D to 10 -5.
And 4.2, carrying out decision guiding on the input signal based on the modulation decision result given in the third step. And judging each point of the input sequence based on a minimum distance constellation mapping method, and balancing the judging result sequence serving as a balancing target.
4.3 Let n=1.
4.4 Calculate DD equalizer output z D,n (k).
4.5 Calculate the error function e D (n).
4.6 Update DD equalizer tap coefficients based on a minimum variance criterion.
4.7 Iteration number n+1.
4.8 Let th=10 -5, if e (n) -e (n-1) < th, then go on to the next step, otherwise go back to 4.4 for iteration until the DD equalizer goes into steady state or the maximum number of iterations is reached. When the DD equalizer iteration number is 325, e (937) -e (936) = 0.00000996 < th=10 -5, go to the next step.
4.9 Output DD equalization result z D,n (k) and DD equalizer tap coefficient vn＝[0.002,-0.001,0.001,-0.004,0,0.872,-0.001,-0.003,0,0.001,-0.002]+[-0.007,0,-0.003,0.003,-0.003,0,-0.002,0.005,-0.004,-0.001,-0.005]×i.zD,n(k) are the results after the received signal y (k) dual mode joint blind equalization.
As shown in fig. 8, which is a constellation diagram of DD re-equalization results, the residual inter-code crosstalk is significantly reduced compared to the previous IMCMA equalization results (fig. 5). Fig. 9 shows the comparison of the residual inter-code crosstalk between the IMCMA equalizer and the dual-mode joint blind equalization method, and it can be seen that after the IMCMA equalizer reaches a steady state, the dual-mode joint blind equalization method starts cluster modulation decision and guides the cluster modulation decision to enter DD for re-equalization, thereby achieving the effect of further reducing the residual inter-code crosstalk. The residual inter-code crosstalk is increased from-36 dB to-43 dB, and the performance is improved by 7 dB.
In summary, the dual-mode joint blind equalization method and the blind equalizer under the non-cooperative communication condition better solve the blind equalization problem of the amplitude-phase modulation signal with unknown modulation mode, and the joint processing of blind equalization and modulation identification reduces the workload of subsequent signal processing.
Claims (8)
1. A dual-mode joint blind equalization method under a non-cooperative communication condition aims at a multi-system phase shift keying (MPSK) and multi-system quadrature amplitude modulation (MQAM) amplitude-phase modulation signal, and is characterized by comprising the following steps:
The first step, preprocessing the received signal, the method is:
When the received signal is a single-channel complex signal y (k), directly entering the next step, and if the received signal is IQ data, combining is needed: let the I-path received signal be y I (k) and the Q-path received signal be y Q (k), the signal sequence after combining is:
y(k)＝yQ(k)+yI(k)·i (1)
Wherein i represents a complex unit, and k represents the length of the received signal sequence;
Secondly, inputting the signal sequence y (k) into an improved correction modulus algorithm IMCMA equalizer for preliminary blind equalization to obtain a preliminary equalized output sequence z n (k) and IMCMA equalizer tap coefficients w n; the method comprises the following steps:
2.1 initializing parameters of IMCMA equalizer, setting IMCMA equalizer default length and tap coefficient w n: setting IMCMA the equalizer length to 2L+1, the tap coefficient w n is expressed as wn＝[wn(-L),wn(-L+1),wn(-L+2),…,wn(0),…wn(L-2),wn(L-1),wn(L)];
2.2 Calculating the real part Re (y (k)) and the imaginary part Im (y (k)) of y (k);
2.3 calculating a first statistic R pr and a second statistic R pi:
wherein E (·) represents taking the mean;
2.4 calculate the autocorrelation matrix R y for y (k):
wherein, Represents a transpose of y (k);
2.5 calculate trace tr for R y (R y):
Wherein R ll represents the diagonal element of the autocorrelation matrix R y;
2.6, calculating an initial step size:
Alpha is a step size parameter;
2.7 setting the iteration number n=1;
2.8 calculation IMCMA equalizer output z n (k):
zn(k)＝y(k)*wn (6)
2.9 calculation error e (n):
Wherein z r,n and z i,n represent the real and imaginary parts, respectively, of the IMCMA equalizer output z n (k);
2.10 calculate the current step size μ (n):
Wherein exp (·) represents an exponential function with natural logarithm as a base, and β is a step parameter;
2.11 iterative update of equalizer tap coefficients:
Where w n+1 represents the equalizer tap coefficients at n+1 iterations;
2.12 times of iteration +1;
2.13 if e (n) -e (n-1) < th, th is a steady state decision parameter, indicating that the equalizer enters steady state, then proceeding to 2.14, otherwise repeating steps 2.8-2.12;
2.14 output IMCMA the equalization result z n (k) and equalizer tap coefficient w n after n iterations of the equalizer;
third, judging the modulation parameter M and judging the confidence coefficient, wherein the method is as follows:
Inputting IMCMA equalizer equalization results z n (K) into a cluster modulation decision device, wherein the cluster modulation decision device performs K-means clustering on z n (K) in a constellation diagram mode, and judging whether the z n (K) belongs to the same cluster or not through the distance between different constellation points obtained through calculation; the method comprises the following steps:
3.1 setting the clustering center number as kn E [2,256], clustering z n (K) by using a K-means algorithm, wherein all sample points in the same cluster as z n (K) are denoted as z C (K), and sample points clustered as the j-th cluster and not in the same cluster as z n (K) are denoted as
3.2 Calculating a first profile factor D a (k) and a second profile factor D b (k), respectively:
Da(k)＝E(||zn(k)-zC(k)||) (10)
Where D a (k) represents the average distance of all sample points from z n (k) to z C (k), the smaller the D a (k) value, the closer z n (k) is to other sample points of the same cluster, the greater the likelihood that z n (k) belongs to z C (k); d b (k) represents the minimum value of the average distance of z n (k) to other clusters, the larger the D b (k) value, which means that the further z n (k) is from other clusters, the greater the likelihood that z n (k) belongs to z C (k);
3.3 calculating the contour coefficients D k (k) of all possible cluster center numbers kn according to equation (12), and calculating the average value thereof, to obtain the overall contour coefficient D kmean:
Dkmean＝E(Dk(k)) (13)
The closer the overall profile coefficient D kmean∈[-1,1],Dkmean is to 1, the better the clustering effect is under the clustering center number;
3.4, taking the maximum value in D kmean, taking the corresponding cluster center number kn as a judging result of the modulation parameter, and marking as M;
3.5 checking the confidence level of the modulation parameter M according to equation (14):
The judgment of the confidence coefficient of the modulation parameter is based on the overall contour coefficient D kmean, a threshold value rho is set, and the closer the threshold value rho is to 1, the higher the confidence coefficient requirement for clustering judgment is indicated; when D kmean is more than or equal to ρ, the reliability of the returned modulation parameter is considered to be high, the output modulation parameter M enters the fourth step, otherwise, the confidence of the modulation parameter given by the modulation decision device is considered to be low, the equalizing effect of the IMCMA equalizer is considered to be poor, and the second step is returned;
Fourth step, decision-directed DD re-equalization
Introducing a decision-directed DD equalizer to form a dual-mode equalizer; the DD equalizer adjusts the decision guiding criterion according to the modulation parameter M given in the third step, and performs DD re-equalization; here the decision guiding criteria are:
Under the condition of known modulation modes, the constellation diagram of each modulation mode is a fixed pattern, the z n (k) output by the IMCMA equalizer is guided by judgment according to the modulation mode and a minimum distance criterion, and each point judgment is guided to be the nearest constellation point; the method comprises the following steps:
4.1 initializing a DD equalizer; setting DD equalizer length, tap coefficient v n and step size μ D: if the DD equalizer is 2l+1 in length, the tap coefficient v n is vn＝[vn(-L),vn(-L+1),vn(-L+2),…vn(0),…vn(L-2),vn(L-1),vn(L)],, v n (0) =1, the remaining tap coefficients are 0, and the step size μ D is set to be between 10 -4-10-7;
4.2 calculating the decision guide results
Wherein DL (·) represents a decision criterion, and the employed decision criterion is a minimum distance constellation mapping criterion: based on the modulation mode corresponding to the modulation parameter M given in the third step, guiding the decision of the input sequence z n (k) into the following by adopting the principle of minimum distance of constellation points according to the constellation diagram of the modulation mode given by the decision
4.3 Setting the iteration number n=1;
4.4 calculating DD equalizer output:
zD,n(k)＝zn(k)*vn (16)
Wherein z D,n (k) represents the output result of the nth iteration of the DD equalizer;
4.5 calculating error function e D (n):
4.6 updating DD equalizer tap coefficients:
Where v n denotes the tap coefficients of the DD equalizer at the nth iteration, v n+1 denotes the tap coefficients of the DD equalizer at the n +1 th iteration, Representing the transpose of z n (k);
4.7, the iteration times are n+1;
4.8 if e D(n)-eD (n-1) < th, then 4.9 is performed, otherwise, repeating steps 4.4-4.7;
4.9 output equalization result z D,n (k) and equalizer tap coefficient v n,zD,n (k) are the blind equalization result of input signal y (k).
2. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: in 2.1, w n (0) =1 is set in the tap coefficients, and the rest are set to 0.
3. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: 2.6, the value range of the step parameter alpha is more than or equal to 1 and less than or equal to 10.
4. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: in 2.10, the step size parameter beta is more than or equal to 1.
5. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: in 2.13, the steady state determination parameter th is 10 -3～10-5.
6. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: in 3.5, the value of the threshold value rho is in the range of 0.5-1.
7. A dual mode joint blind equalization method based on the non-cooperative communication condition of claim 1, characterized in that: in 4.8, the steady state determination parameter th is 10 -3-10-5.
8. A blind equalizer based on the dual mode joint blind equalization method of any one of claims 1 to 7, characterized by: the method comprises an improved correction modulus algorithm IMCMA equalizer, a cluster modulation decision device and a decision directed DD equalizer; after the received signal sequence passes through a IMCMA equalizer, the signal is subjected to preliminary equalization, and the cluster modulation decision device gives a modulation type decision result based on the preliminary equalization result of the correction modulus algorithm.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101309244A (en) * | 2008-06-27 | 2008-11-19 | 南京邮电大学 | Constant modular complete blind detection equalizing method for phase modulation signal |
| CN103763228A (en) * | 2014-01-07 | 2014-04-30 | 南京信息工程大学 | Combination optimization self-adaptive frequency domain blind equalization method and system |
| CN104158633A (en) * | 2014-09-09 | 2014-11-19 | 电子科技大学 | Maximum likelihood modulation recognition method based on Gaussian mixture model |
| CN107566307A (en) * | 2017-08-31 | 2018-01-09 | 北京睿信丰科技有限公司 | Blind equalizing apparatus and method, data modulation system and method |
| CN110266388A (en) * | 2019-06-18 | 2019-09-20 | 北京邮电大学 | A kind of PMD equalization methods, device, electronic equipment and storage medium |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101309244A (en) * | 2008-06-27 | 2008-11-19 | 南京邮电大学 | Constant modular complete blind detection equalizing method for phase modulation signal |
| CN103763228A (en) * | 2014-01-07 | 2014-04-30 | 南京信息工程大学 | Combination optimization self-adaptive frequency domain blind equalization method and system |
| CN104158633A (en) * | 2014-09-09 | 2014-11-19 | 电子科技大学 | Maximum likelihood modulation recognition method based on Gaussian mixture model |
| CN107566307A (en) * | 2017-08-31 | 2018-01-09 | 北京睿信丰科技有限公司 | Blind equalizing apparatus and method, data modulation system and method |
| CN110266388A (en) * | 2019-06-18 | 2019-09-20 | 北京邮电大学 | A kind of PMD equalization methods, device, electronic equipment and storage medium |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
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