QAM Modulation and Message Enconding in Scilab

Physics/Electronics Engineering project on QAM modulation and digital data transmission. Includes RRC filtering, IQ constellation analysis, FFT-based spectral analysis, and noise evaluation, implemented in Scilab to apply core Signals and Systems concepts in a real telecom scenario. This project implements a digital communication system based on Quadrature Amplitude Modulation (QAM) using Scilab. The system simulates the complete signal processing chain, from message encoding to transmission over noisy channels and subsequent recovery.

📋 Overview

The main objective of this project is to implement the theoretical-practical concepts of the "Signals and Systems" course. It demonstrates how a text message (e.g., "Egun on") is converted into a digital signal, transmitted, and decoded back into its original form.

🛠️ Key Features

• 16-QAM Modulation: Uses a square constellation where each symbol represents 4 bits, mapping hexadecimal values to specific coordinates (I, Q).
• Signal Processing: * Upsampling: Expanding the spectrum to prepare the signal for modulation.
  • Root-Raised-Cosine (RRC) Filtering: Implemented to limit bandwidth and minimize Inter-Symbol Interference (ISI).
• Transmission Simulation:
  • Ideal Channel: Noise-free transmission to verify system logic.
  • Noisy Channel: Real-world simulation using Additive White Gaussian Noise (AWGN).
• Message Recovery: Full demodulation process, including synchronization and amplitude adjustment to restore the original ASCII message.

🚀 How it Works

1. Source Coding: Input string $\rightarrow$ ASCII $\rightarrow$ Hexadecimal $\rightarrow$ 16-QAM Mapping.
2. Filtering: The I (In-phase) and Q (Quadrature) signals are upsampled and passed through an RRC filter.
3. Modulation: Both signals are combined into a carrier signal $s(t)$ at a specific frequency $f_o$: $$s(t) = I(t) \cdot \cos(2\pi f_o t) - Q(t) \cdot \sin(2\pi f_o t)$$
4. Demodulation: The receiver separates the I and Q components using low-pass filtering and downsampling to retrieve the original symbols.

💻 Technologies Used

• Scilab: Scientific software for numerical computation.
• Core Logic: Custom functions for mapping (hex2IQ), modulation, and RRC pulse shaping.

📈 Results

The implementation confirms that in an ideal channel, the message is recovered with 100% accuracy. The project also demonstrates how filtering significantly improves signal integrity in noisy environments.

👥 Authors

• Andoni Vazquez Arza
• Beñat Arberas Larrinaga