三种均衡器的MATLAB实现

%%第一种

clear

clc
% Build a set of test data.
hMod = comm.BPSKModulator; % BPSKModulator System object
x = step(hMod,randi([0 1],1000,1)); % BPSK symbols
rxsig = conv(x,[1 0.8 0.3]);  % Received signal
% Create an equalizer object.
eqlms = lineareq(8,lms(0.03));
% Change the reference tap index in the equalizer.
eqlms.RefTap = 4;
% Apply the equalizer object to a signal.

y = equalize(eqlms,rxsig,x(1:200));

%%第二种

clear
clc
% Set up parameters and signals.
M = 4; % Alphabet size for modulation
msg = randi([0 M-1],1500,1); % Random message
hMod = comm.QPSKModulator('PhaseOffset',pi/4);
modmsg = step(hMod,msg); % Modulate using QPSK.
trainlen = 500; % Length of training sequence
chan = [.986; .845; .237; .123+.31i]; % Channel coefficients
filtmsg = filter(chan,1,modmsg); % Introduce channel distortion.


% Equalize the received signal.
eq1 = lineareq(8, lms(0.01)); % Create an equalizer object.
eq1.SigConst = step(hMod,(0:M-1)')'; % Set signal constellation.
[symbolest,yd] = equalize(eq1,filtmsg,modmsg(1:trainlen)); % Equalize.


% Plot signals.
h = scatterplot(filtmsg,1,trainlen,'bx'); hold on;
scatterplot(symbolest,1,trainlen,'g.',h);
scatterplot(eq1.SigConst,1,0,'k*',h);
legend('Filtered signal','Equalized signal',...
   'Ideal signal constellation');
hold off;


% Compute error rates with and without equalization.
hDemod = comm.QPSKDemodulator('PhaseOffset',pi/4);
demodmsg_noeq = step(hDemod,filtmsg); % Demodulate unequalized signal.
demodmsg = step(hDemod,yd); % Demodulate detected signal from equalizer.
hErrorCalc = comm.ErrorRate; % ErrorRate calculator
ser_noEq = step(hErrorCalc, ...
    msg(trainlen+1:end), demodmsg_noeq(trainlen+1:end));
reset(hErrorCalc)
ser_Eq = step(hErrorCalc, msg(trainlen+1:end),demodmsg(trainlen+1:end));
disp('Symbol error rates with and without equalizer:')
disp([ser_Eq(1) ser_noEq(1)])

%%第三种

clear
clc


M = 4; % Alphabet size for modulation
msg = randi([0 M-1],1500,1); % Random message
hMod = comm.QPSKModulator('PhaseOffset',pi/4);
modmsg = step(hMod,msg); % Modulate using QPSK.
trainlen = 500; % Length of training sequence
chan = [.986; .845; .237; .123+.31i]; % Channel coefficients
filtmsg = filter(chan,1,modmsg); % Introduce channel distortion.


% Set up equalizer.
eqlms = lineareq(8, lms(0.01)); % Create an equalizer object.
eqlms.SigConst = step(hMod,(0:M-1)')'; % Set signal constellation.
% Maintain continuity between calls to equalize.
eqlms.ResetBeforeFiltering = 0;


% Equalize the received signal, in pieces.
% 1. Process the training sequence.
s1 = equalize(eqlms,filtmsg(1:trainlen),modmsg(1:trainlen));
% 2. Process some of the data in decision-directed mode.
s2 = equalize(eqlms,filtmsg(trainlen+1:800));
% 3. Process the rest of the data in decision-directed mode.
s3 = equalize(eqlms,filtmsg(801:end));
s = [s1; s2; s3]; % Full output of equalizer