Digital Communication Systems Using — Matlab And Simulink

Digital Communication Systems Using MATLAB and Simulink

A Practical Approach to Modern Telecommunications Theory

4.2 Low-Density Parity Check (LDPC) and Polar Codes

Modern systems like 5G and DVB-S2 rely on advanced FEC. The Communications Toolbox includes standardized LDPC and polar encoder/decoder objects, complete with:

Simulink allows you to integrate these into an iterative receiver model—for example, a turbo equalizer with LDPC feedback.

2. The Simulation Environment: A Tale of Two Tools

3.2 Building a QPSK System with Simulink: A Step-by-Step Overview

Consider designing a QPSK-based digital modem:

Step 1: Source Coding – Generate random bits using a Bernoulli Binary Generator.

Step 2: Modulation – Map each pair of bits to a complex symbol using the QPSK Modulator Baseband block. Set average power to 1. Digital Communication Systems Using Matlab And Simulink

Step 3: Pulse Shaping – Insert a Raised Cosine Transmit Filter with 50% roll-off. Oversample by 8 to avoid aliasing.

Step 4: Channel – Add AWGN with desired (E_b/N_0). If modeling multipath, insert a Multipath Rayleigh Fading block before AWGN.

Step 5: Receiver Front-End – The received signal passes through a Raised Cosine Receive Filter (matched filter). Then timing recovery (using Mueller & Muller or Gardner algorithm) corrects symbol timing offset.

Step 6: Demodulation & Decoding – The synchronized symbols enter a QPSK Demodulator Baseband block. Hard or soft decisions can be output.

Step 7: BER Analysis – Compare original bits with demodulated bits using the BER Calculator block. Export results to MATLAB workspace using an "To Workspace" block. Digital Communication Systems Using MATLAB and Simulink A

Example Simulink Model Structure:

[Bernoulli Gen] → [QPSK Mod] → [Tx Filter] → [AWGN] → [Rx Filter] → [Timing Rec] → [QPSK Demod] → [BER Calc]

Visual scopes can be placed at each stage to observe the constellation before/after filtering and the eye diagram after matched filtering.

Abstract

The study of digital communications has traditionally been divided between rigorous mathematical theory and hardware implementation. However, the gap between abstract equations and real-world systems is bridged effectively through simulation. "Digital Communication Systems Using MATLAB and Simulink" represents a methodology where theoretical concepts—such as modulation, coding, and error analysis—are modeled, visualized, and tested in a software environment before any hardware is built. This write-up explores the synergy between communication theory and simulation tools, highlighting how MATLAB and Simulink serve as the industry standard for prototyping modern communication systems.

2.1 Basic Digital Communication Chain in MATLAB Script

% Parameters
numBits = 1e5;          % Number of bits
EbNo_dB = 0:2:10;       % SNR range
M = 2;                  % Modulation order (BPSK)

% Generate random bits
dataBits = randi([0 1], numBits, 1);


% Modulation (BPSK)
txSymbols = 2*dataBits - 1;   % map 0->-1, 1->+1 Simulink allows you to integrate these into an


% AWGN channel simulation
for idx = 1:length(EbNo_dB)
% Add noise (complex for general modulations)
snr = EbNo_dB(idx) + 10*log10(log2(M));
rxSymbols = awgn(txSymbols, snr, 'measured');


% Demodulation
rxBits = rxSymbols > 0;
% BER calculation
[~, ber(idx)] = biterr(dataBits, rxBits);



end


% Plot results
semilogy(EbNo_dB, ber, 'b-o');
grid on; xlabel('E_b/N_o (dB)'); ylabel('BER');
title('BPSK over AWGN');

1. Introduction

In the modern era, communication systems range from simple text messaging to complex 5G networks and satellite links. The theoretical foundation of these systems relies heavily on probability, stochastic processes, and signal processing. While textbooks provide the mathematical derivations, true understanding often requires observing how signals behave in the presence of noise and interference.

MATLAB (Matrix Laboratory) provides a text-based scripting environment ideal for matrix manipulations and algorithm development. Simulink, an extension of MATLAB, offers a graphical block-diagram environment for modeling dynamic systems. Together, they allow engineers to simulate the entire communication chain—from the information source to the destination receiver—offering a "virtual laboratory" for experimentation.