Performance Analysis of Channel Coding in AWGN Channel Environment

This paper presents a comprehensive performance analysis of channel coding techniques in Additive White Gaussian Noise (AWGN) channel environment. The study evaluates four distinct coding schemes: Hamming Code, Golay Code, Circular Code, and Convolutional Code. The analysis focuses on two critical performance metrics: Bit Error Rate (BER) and computational overhead (measured as execution time). Each coding algorithm is implemented with specific consideration - Hamming Code utilizes simple parity-check matrices for single-error correction, Golay Code employs its perfect (23,12) binary structure for multiple error detection/correction, Circular Code leverages cyclic properties for efficient encoding/decoding, while Convolutional Code implements trellis-based decoding using the Viterbi algorithm. Through systematic BER simulations under varying SNR conditions and runtime profiling, we assess the trade-offs between error correction capability and computational complexity. These coding techniques significantly enhance data transmission reliability and interference resistance in communication systems. Additionally, we evaluate implementation complexity and practical deployment considerations to guide optimal coding scheme selection for real-world applications based on specific performance requirements and resource constraints.