1. Guide Overview

Objective: Build a complete digital transceiver (source to sink) using MATLAB (scripting/data analysis) and Simulink (system-level modeling).

Key Topics:

• Random data source generation
• Source coding (optional compression)
• Channel coding (error control)
• Modulation (BPSK, QPSK, QAM)
• Pulse shaping & matched filtering
• Channel models (AWGN, multipath fading)
• Synchronization (carrier, timing)
• Bit error rate (BER) simulation
• Simulink model architecture

A. Source Coding and Generation

The process begins with generating a digital signal.

• MATLAB Implementation: Using functions like randi() to generate random integers or randerr() to introduce specific bit errors.
• Simulink Implementation: Using the Bernoulli Binary Generator block to create a random bit stream.

E. Receiver and Performance Analysis

The receiver attempts to recover the original message.

• Key Metric: Bit Error Rate (BER).
• Analysis: MATLAB’s bertool is a powerful utility that simulates the system across a range of SNR values and plots the BER curve, allowing direct comparison against theoretical limits (e.g., the waterfall curve of QPSK).

Part 2: MATLAB for Digital Communication – Algorithmic Power

The Gap Between Math and Airwaves

If you’ve ever taken a course in digital communications, you know the drill. You start with Bernoulli’s theorem, move through line coding, wrestle with QAM constellations, and eventually cry over a Rayleigh fading channel—all on paper.

But there is a massive difference between calculating a bit error rate (BER) on a whiteboard and watching actual bits get mangled by noise in real-time.

This is where MATLAB and Simulink shine. They don’t just help you pass an exam; they help you see the signal.

In this post, I’ll walk through the high-level workflow of building a digital communication system using these tools—without getting buried in code.