A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems
TL;DR
Findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.
Abstract
This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.