Construct decision-feedback equalizer object
eqobj = dfe(nfwdweights,nfbkweights,alg)
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst)
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst,nsamp)
eqobj = dfe(nfwdweights,nfbkweights,alg) constructs a decision feedback equalizer object. The equalizer's feedforward and feedback filters have nfwdweights and nfbkweights symbol-spaced complex weights, respectively, which are initially all zeros. alg describes the adaptive algorithm that the equalizer uses; you should create alg using any of these functions: lms, signlms, normlms, varlms, rls, or cma. The signal constellation of the desired output is [-1 1], which corresponds to binary phase shift keying (BPSK).
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst,nsamp) constructs a DFE with a fractionally spaced forward filter. The forward filter has nfwdweights complex weights spaced at T/nsamp, where T is the symbol period and nsamp is a positive integer. nsamp = 1 corresponds to a symbol-spaced forward filter.
The table below describes the properties of the decision feedback equalizer object. To learn how to view or change the values of a decision feedback equalizer object, see Accessing Properties of an Equalizer.
|EqType||Fixed value, 'Decision Feedback Equalizer'|
|AlgType||Name of the adaptive algorithm represented by alg|
|nWeights||Number of weights in the forward filter and the feedback filter, in the format [nfwdweights, nfbkweights]. The number of weights in the forward filter must be at least 1.|
|nSampPerSym||Number of input samples per symbol (equivalent to nsamp input argument). This value relates to both the equalizer structure (see the use of K in Decision-Feedback Equalizers) and an assumption about the signal to be equalized.|
|RefTap (except for CMA equalizers)||Reference tap index, between 1 and nfwdweights. Setting this to a value greater than 1 effectively delays the reference signal with respect to the equalizer's input signal.|
|SigConst||Signal constellation, a vector whose length is typically a power of 2.|
|Weights||Vector that concatenates the complex coefficients from the forward filter and the feedback filter. This is the set of wi values in the schematic in Decision-Feedback Equalizers.|
|WeightInputs||Vector that concatenates the tap weight inputs for the forward filter and the feedback filter. This is the set of ui values in the schematic in Decision-Feedback Equalizers.|
|ResetBeforeFiltering||If 1, each call to equalize resets the state of eqobj before equalizing. If 0, the equalization process maintains continuity from one call to the next.|
|NumSamplesProcessed||Number of samples the equalizer processed since the last reset. When you create or reset eqobj, this property value is 0.|
|Properties specific to the adaptive algorithm represented by alg||See reference page for the adaptive algorithm function that created alg: lms, signlms, normlms, varlms, rls, or cma.|
If you change nWeights, MATLAB maintains consistency in the equalizer object by adjusting the values of the properties listed below.
|StepSize (Variable-step-size LMS equalizers)||InitStep*ones(1,sum(nWeights))|
|InvCorrMatrix (RLS equalizers)||InvCorrInit*eye(sum(nWeights))|
An example illustrating relationships among properties is in Linked Properties of an Equalizer Object.
Apply a decision feedback equalizer (DFE) to an 8-PSK modulated signal impaired by a frequency selective channel. The DFE uses 600 training symbols.
Create a PSK modulator System object™ and set the modulation order to 8.
hMod = comm.PSKModulator('ModulationOrder',8);
Create a column vector of 8-ary random integer symbols.
data = randi([0 7],5000,1);
Modulate the random data by calling the step function of the comm.PSKModulator System object.
modData = step(hMod,data);
Create a Rayleigh channel System object to define a static frequency selective channel with four taps. Use the step function to pass the modulated data through the channel object.
hChan = comm.RayleighChannel('SampleRate',1000, ... 'PathDelays',[0 0.002 0.004 0.008],'AveragePathGains',[0 -3 -6 -9]); rxSig = step(hChan,modData);
Create a DFE equalizer that has 10 feed forward taps and five feedback taps. The equalizer uses the LMS update method with a step size of 0.01.
numFFTaps = 10; numFBTaps = 5; equalizerDFE = dfe(numFFTaps,numFBTaps,lms(0.01));
Set the SigConst property of the DFE equalizer to match the 8-PSK modulator reference constellation. The reference constellation is determined by using the constellation method. For decision directed operation, the DFE must use the same signal constellation as the transmission scheme.
equalizerDFE.SigConst = constellation(hMod).';
Equalize the signal to remove the effects of channel distortion. Use the first 600 symbols to train the equalizer.
trainlen = 600; [eqSig,detectedSig] = equalize(equalizerDFE,rxSig, ... modData(1:trainlen));
Plot the received signal, equalizer output after training, and the ideal signal constellation.
h = scatterplot(rxSig,1,trainlen,'bx'); hold on scatterplot(eqSig,1,trainlen,'g.',h); scatterplot(equalizerDFE.SigConst,1,0,'m*',h); legend('Received signal','Equalized signal',... 'Ideal signal constellation'); hold off
Create a PSK demodulator System object to demodulate the received signal before and after equalization. Use the step function to demodulate the signals.
hDemod = comm.PSKDemodulator('ModulationOrder',8); demodSig = step(hDemod,rxSig); demodEqualizedSig = step(hDemod,detectedSig);
Compute the error rates for the two demodulated signals and compare the results.
hErrorCalc = comm.ErrorRate; nonEqualizedSER = step(hErrorCalc,data(trainlen+1:end), ... demodSig(trainlen+1:end)); reset(hErrorCalc) equalizedSER = step(hErrorCalc, data(trainlen+1:end), ... demodEqualizedSig(trainlen+1:end)); disp('Symbol error rates with and without equalizer:') disp([equalizedSER(1) nonEqualizedSER(1)])
Symbol error rates with and without equalizer: 0 0.8909
The equalizer helps eliminate the distortion introduced by the frequency selective channel and reduces the error rate.