RS Convolutional Codes Implementation in Gaussian Channels

This study investigates the implementation of RS convolutional codes in Gaussian channel environments. The performance evaluation incorporates both soft-decision and hard-decision decoding algorithms for comprehensive bit error rate analysis. The simulation framework is constructed using Simulink block diagrams, where key components include random data generation modules, RS convolutional encoding blocks, and Gaussian channel simulation. The MATLAB m-file scripting interface orchestrates the simulation workflow by configuring parameters, executing iterative runs, and processing output data. Implementation details feature convolutional encoding with constraint length specifications and Viterbi decoding algorithms with adjustable threshold settings for soft/hard decision comparisons. The simulation protocol involves generating randomized input sequences, applying RS convolutional encoding with predefined generator polynomials, transmitting through additive white Gaussian noise channels with configurable SNR levels, and performing parallel decoding operations using both decision methods. Performance metrics are calculated through systematic comparison of original and recovered data sequences, demonstrating significant error correction improvement with soft-decision decoding under high-noise conditions through quantitative BER versus SNR plots.