eqberdemo.m

Adaptive Filter. This script shows the BER performance of several types of equalizers in a static ch

%% BER Performance of Several Equalizer Types
% This script shows the BER performance of several types of equalizers in a
% static channel with a null in the passband.  The script constructs and
% implements a linear equalizer object and a decision feedback equalizer (DFE)
% object.  It also initializes and invokes a maximum likelihood sequence
% estimation (MLSE) equalizer.  The MLSE equalizer is first invoked with perfect
% channel knowledge, then with a straightforward but imperfect channel
% estimation technique.
%
% As the simulation progresses, it updates a BER plot for comparative analysis
% between the equalization methods.  It also shows the signal spectra of the
% linearly equalized and DFE equalized signals.  It also shows the relative
% burstiness of the errors, indicating that at low BERs, both the MLSE algorithm
% and the DFE algorithm suffer from error bursts.  In particular, the DFE error
% performance is burstier with detected bits fed back than with correct bits fed
% back.  Finally, during the "imperfect" MLSE portion of the simulation, it
% shows and dynamically updates the estimated channel response.
%
% This script relies on several other scripts and functions to perform link
% simulations over a range of Eb/No values.  These files are as follows:
%
% <eqber_adaptive.html eqber_adaptive> - a script that runs link
% simulations for linear and DFE equalizers
%
% <eqber_mlse.html eqber_mlse> - a script that runs link simulations
% for ideal and imperfect MLSE equalizers
%
% <eqber_siggen.html eqber_siggen>   - a script that generates a BPSK
% signal with no pulse shaping, then processes it through the channel and adds
% noise
%
% eqber_graphics - a function that generates and updates plots showing the
% performance of the linear, DFE, and MLSE equalizers.  Type "edit
% eqber_graphics" at the MATLAB command line to view this file.
%
% The scripts eqber_adaptive and eqber_mlse illustrate how to use adaptive and
% MLSE equalizers across multiple blocks of data such that state information is
% retained between data blocks.
%
% To experiment with this demo, you can change such parameters as the channel
% impulse response, the number of equalizer tap weights, the recursive least
% squares (RLS) forgetting factor, the least mean square (LMS) step size, the
% MLSE traceback length, the error in estimated channel length, and the maximum
% number of errors collected at each Eb/No value.

%   Copyright 1996-2004 The MathWorks, Inc.
%   $Revision: 1.1.4.1 $  $Date: 2004/06/30 23:03:12 $


%% Signal and channel parameters
% Set parameters related to the signal and channel.  Use BPSK without any pulse
% shaping, and a 5-tap real-valued symmetric channel impulse response.  (See
% section 10.2.3 of Digital Communications by J. Proakis for more details on the
% channel.)  Set initial states of data and noise generators.  Set the Eb/No
% range.

% System simulation parameters
Fs      = 1;      % sampling frequency (notional)
nBits   = 2048;   % number of BPSK symbols per vector
maxErrs = 50;     % target number of errors at each Eb/No
maxBits = 1e8;    % maximum number of symbols at each Eb/No

% Modulated signal parameters
M          = 2;            % order of modulation
Rs         = Fs;           % symbol rate
nSamp      = Fs/Rs;        % samples per symbol
Rb         = Rs * log2(M); % bit rate
dataState  = 999983;       % initial state of data generator

% Channel parameters
chnl       = [0.227 0.460 0.688 0.460 0.227]';  % channel impulse response
chnlLen    = length(chnl);      % channel length, in samples
EbNo       = 0:14;              % in dB
BER        = zeros(size(EbNo)); % initialize values
noiseState = 999917;            % initial state of noise generator


%% Adaptive equalizer parameters
% Set parameter values for the linear and DFE equalizers.  Use a 31-tap linear
% equalizer, and a DFE with 15 feedforward and feedback taps.  Use the recursive
% least squares (RLS) algorithm for the first block of data to ensure rapid tap
% convergence.  Use the least mean square (LMS) algorithm thereafter to ensure
% rapid execution speed.

% Linear equalizer parameters
nWts         = 31;       % number of weights
algType1     = 'rls';    % RLS algorithm for first data block at each Eb/No
forgetFactor = 0.999999; % parameter of RLS algorithm
algType2     = 'lms';    % LMS algorithm for remaining data blocks
stepSize     = 0.00001;  % parameter of LMS algorithm

% DFE parameters - use same update algorithms as linear equalizer
nFwdWts      = 15;       % number of feedforward weights 
nFbkWts      = 15;       % number of feedback weights


%% MLSE equalizer and channel estimation parameters, and initial visualization
% Set the parameters of the MLSE equalizer.  Use a traceback length of six times
% the length of the channel impulse response.  Initialize the equalizer states.
% Set the equalization mode to "continuous", to enable seamless equalization
% over multiple blocks of data.  Use a cyclic prefix in the channel esimation
% technique, and set the length of the prefix.  Assume that the estimated length
% of the channel impulse response is one sample longer than the actual length.

% MLSE equalizer parameters
tbLen      = 30;                 % MLSE equalizer traceback length
numStates  = M^(chnlLen-1);      % number of trellis states
[mlseMetric, mlseStates, mlseInputs] = deal([]);
const      = pskmod(0:M-1, M);   % signal constellation
mlseType   = 'ideal';            % perfect channel estimates at first
mlseMode   = 'cont';             % no MLSE resets

% Channel estimation parameters
chnlEst = chnl;         % perfect estimation initially
prefixLen = 2*chnlLen;  % cyclic prefix length
excessEst = 1;          % length of estimated channel impulse response
                        % beyond the true length

% Initialize the graphics for the simulation.  Plot the unequalized channel
% frequency response, and the BER of an ideal BPSK system.
idealBER = berawgn(EbNo, 'psk', M, 'nondiff');

[hBER, hLegend, legendString, hLinSpec, hDfeSpec, hErrs, ...
    hText1, hText2, hFit, hEstPlot] = eqber_graphics('init', chnl, EbNo, ...
                                               idealBER, nBits);


%% Construct RLS and LMS linear and DFE equalizer objects.
% The RLS update algorithm is used to initially set the weights, and the LMS
% algorithm is used thereafter for speed purposes.
alg1 = eval([algType1 '(' num2str(forgetFactor) ')']);
linEq1 = lineareq(nWts, alg1);
alg2 = eval([algType2 '(' num2str(stepSize) ')']);
linEq2 = lineareq(nWts, alg2);
[linEq1.RefTap, linEq2.RefTap] = ...
    deal(round(nWts/2));    % Set reference tap to center tap
[linEq1.ResetBeforeFiltering, linEq2.ResetBeforeFiltering] = ...
    deal(0);                % Maintain continuity between iterations

dfeEq1 = dfe(nFwdWts, nFbkWts, alg1);
dfeEq2 = dfe(nFwdWts, nFbkWts, alg2);
[dfeEq1.RefTap, dfeEq2.RefTap] = ...
    deal(round(nFwdWts/2)); % Set reference tap to center forward tap
[dfeEq1.ResetBeforeFiltering, dfeEq2.ResetBeforeFiltering] = ...
    deal(0);                % Maintain continuity between iterations


%% Linear equalizer
% Run the linear equalizer, and plot the equalized signal spectrum, the BER, and
% the burst error performance for each data block.  Note that as the Eb/No
% increases, the linearly equalized signal spectrum has a progressively deeper
% null.  This highlights the fact that a linear equalizer must have many more
% taps to adequately equalize a channel with a deep null.  Note also that the
% errors occur with small inter-error intervals, which is to be expected at such
% a high error rate.
%
% See <eqber_adaptive.html eqber_adaptive> for a listing of the simulation code
% for the adaptive equalizers.
firstRun = true;  % flag to ensure known initial states for noise and data
eqType = 'linear';
eqber_adaptive;


%% Decision feedback equalizer
% Run the DFE, and plot the equalized signal spectrum, the BER, and the burst
% error performance for each data block.  Note that the DFE is much better able
% to mitigate the channel null than the linear equalizer, as shown in the
% spectral plot and the BER plot.  The plotted BER points at a given Eb/No value
% are updated every data block, so they move up or down depending on the number
% of errors collected in that block.  Note also that the DFE errors are somewhat
% bursty, due to the error propagation caused by feeding back detected bits
% instead of correct bits. The burst error plot shows that as the BER decreases,
% a significant number of errors occurs with an inter-error arrival of five bits
% or less.  (If the DFE equalizer were run in training mode at all times, the
% errors would be far less bursty.)  
%
% For every data block, the plot also indicates the average inter-error interval
% if those errors were randomly occurring.
%
% See <eqber_adaptive.html eqber_adaptive> for a listing of the simulation code
% for the adaptive equalizers.
eqType = 'dfe';
eqber_adaptive;
 

%% Ideal MLSE equalizer, with perfect channel knowledge
% Run the MLSE equalizer with a perfect channel estimate, and plot the BER and
% the burst error performance for each data block.  Note that the errors occur
% in an extremely bursty fashion.  Observe, particularly at low BERs, that the
% overwhelming percentage of errors occur with an inter-error interval of one or
% two bits.
%
% See <eqber_mlse.html eqber_mlse> for a listing of the simulation code
% for the MLSE equalizers.
eqType = 'mlse';
mlseType = 'ideal';
eqber_mlse;


%% MLSE equalizer with an imperfect channel estimate
% Run the MLSE equalizer with an imperfect channel estimate, and plot the BER
% and the burst error performance for each data block.  These results align
% fairly closely with the ideal MLSE results.  (The channel estimation algorithm
% is highly dependent on the data, such that an FFT of a transmitted data block
% has no nulls.)  Note how the estimated channel plots compare with the actual
% channel spectrum plot.
%
% See <eqber_mlse.html eqber_mlse> for a listing of the simulation code
% for the MLSE equalizers.
mlseType = 'imperfect';
eqber_mlse;