Digital Communication Systems Using Matlab And Simulink -

Orthogonal Frequency Division Multiplexing (OFDM) is the cornerstone of 4G, 5G, Wi-Fi, and DVB-T. MATLAB provides the 5G Toolbox, featuring:

Simulink extends this with multi-carrier synchronization, cyclic prefix insertion, and fading channel simulation for vehicular environments (e.g., TDL-A, TDL-C models).

An end-to-end BPSK simulation in MATLAB can be written in fewer than 50 lines of code:

% Parameters
M = 2;                    % BPSK modulation order
numBits = 1e5;            % Number of bits
EbNo_dB = 0:2:10;         % SNR range
ber = zeros(size(EbNo_dB));
for idx = 1:length(EbNo_dB)
% Generate random bits
data = randi([0 1], numBits, 1);
% Modulate
modSig = pskmod(data, M);
% Add AWGN
snr = EbNo_dB(idx) + 10*log10(log2(M)); % Convert Eb/No to SNR
rxSig = awgn(modSig, snr, 'measured');
% Demodulate
rxBits = pskdemod(rxSig, M);
% Compute BER
[~, ber(idx)] = biterr(data, rxBits);

end
% Plot results
semilogy(EbNo_dB, ber, 'bo-');
grid on; xlabel('Eb/No (dB)'); ylabel('BER');
title('BPSK over AWGN Channel');
hold on;
semilogy(EbNo_dB, berawgn(EbNo_dB, 'psk', M, 'nondiff'), 'r-');
legend('Simulated', 'Theoretical');
 Digital Communication Systems Using Matlab And Simulink

This script demonstrates how quickly you can validate theoretical BER curves. For more complex systems—multiple antennas, carrier offset, fading—MATLAB’s object-oriented approach (using comm objects) allows dynamic configuration and streaming.

function ber = simulate_DigitalComm(EbNo_dB, modType, codeRate)
    % modType: 'bpsk', 'qpsk', '16qam'
    % Returns BER for given EbNo
end

Objective: Build a bandwidth-efficient link with matched filtering.

MATLAB solution:

% Parameters
fs = 10000;          % Sample rate
sps = 8;             % Samples per symbol
rolloff = 0.35;      % Raised cosine rolloff
% Design filter
txfilter = comm.RaisedCosineTransmitFilter('RolloffFactor', rolloff, ...
'FilterSpanInSymbols', 10, 'OutputSamplesPerSymbol', sps);
rxfilter = comm.RaisedCosineReceiveFilter('RolloffFactor', rolloff, ...
'FilterSpanInSymbols', 10, 'InputSamplesPerSymbol', sps); end % Plot results semilogy(EbNo_dB, ber, 'bo-'); grid
% Modulate and filter
data = randi([0 1], 10000, 1);
modSig = qammod(data, 16, 'InputType', 'bit', 'UnitAveragePower', true);
txSig = txfilter(modSig);
% Add channel...
rxFiltered = rxfilter(rxSig);

Simulink alternative: Use the Raised Cosine Transmit/Receive Filter blocks, set samples per symbol = 8, rolloff = 0.35. Add a QAM Modulator Baseband with 16-point constellation. Visualize the eye diagram using Eye Diagram block.

Before diving into MATLAB tools, let’s recall the building blocks of a typical DCS:

Using MATLAB and Simulink, each of these steps can be implemented, tested, and optimized. upgrading is trivial:

Engineers rarely build systems from scratch; instead, they implement standards like IEEE 802.11 (WiFi), DVB-S2 (satellite), or 5G NR. MATLAB and Simulink provide example models and toolboxes:

For example, you can model an OFDM (Orthogonal Frequency Division Multiplexing) system with:

Simulink allows you to run BER simulations for these complex standards in minutes—a task that would take weeks using hardware alone.

One of my favorite Simulink experiments involves the Eye Diagram Block. After a raised cosine filter (Tx) and before the receiver (Rx), attach an Eye Diagram scope.

You’ll see the famous "eye opening." The wider the eye, the less ISI (Inter-Symbol Interference). Turn off the filter—the eye slams shut. That visual click is worth a hundred textbook pages.

Once your BPSK model works, upgrading is trivial:

Try doing that in Python from scratch in 10 minutes.

Digital Communication Systems Using Matlab And Simulink -

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