Equalize BSPK Signal
Equalize a BPSK signal using a linear equalizer with an least mean square (LMS) algorithm.
Generate random binary data and apply BPSK modulation.
data = randi([0 1],1000,1); modData = pskmod(data,2);
Apply two-tap static fading to the modulated signal.
rxSig = conv(modData,[0.5 0.05]);
Create an LMS adaptive algorithm object with a step size of 0.06.
alg = lms(0.06);
Warning: LMS will be removed in a future release. Use comm.LinearEqualizer or comm.DecisionFeedbackEqualizer instead. See <a href="matlab:helpview(fullfile(docroot, 'toolbox','comm', 'comm.map'), 'REPLACE_udd_equalizer')">Communications Toolbox Release Notes</a> for more information.
Create a linear equalizer object having 8 taps using the previously created algorithm object. Set the reference tap index to 4.
eqlms = lineareq(8,alg);
Warning: LINEAREQ will be removed in a future release. Use comm.LinearEqualizer instead. See <a href="matlab:helpview(fullfile(docroot, 'toolbox','comm', 'comm.map'), 'REPLACE_udd_equalizer')">Communications Toolbox Release Notes</a> for more information.
eqlms.RefTap = 4;
Equalize the received signal, rxSig, while using the first 200 data bits as a training sequence.
trSeq = data(1:200); [eqSig,~,e] = equalize(eqlms,rxSig,trSeq);
Warning: EQUALIZE will be removed in a future release. Use comm.LinearEqualizer or comm.DecisionFeedbackEqualizer instead. See <a href="matlab:helpview(fullfile(docroot, 'toolbox','comm', 'comm.map'), 'REPLACE_udd_equalizer')">Communications Toolbox Release Notes</a> for more information.
Filter and plot the power of the received (nonequalized) signal. The magnitude of the signal has been attenuated by the channel.
rxSigPwr = filter(0.1*ones(10,1),1,abs(rxSig)).^2; plot(rxSigPwr) title('Received Signal') xlabel('Bits') ylabel('Power (W)')
Plot the equalized signal. The signal reaches the intended power level of 1 W.
eqSigPwr = filter(0.1*ones(10,1),1,abs(eqSig)).^2; plot(eqSigPwr) title('Equalized Signal') xlabel('Bits') ylabel('Power (W)')
Plot the magnitude of the error estimate, e. The error decreases until it is nearly zero after 400 bits.
plot(abs(e)) title('Error Estimate') xlabel('Bits') ylabel('Amplitude (V)')
