LDPC Convolutional Decoding Comparison Program: Soft Decision vs Hard Decision

This program implements a comparative analysis of soft and hard decision decoding algorithms for LDPC convolutional codes, accompanied by graphical visualizations. The implementation begins with the soft-decision LDPC convolutional decoding algorithm, which processes probabilistic channel output values (typically log-likelihood ratios) to enhance decoding accuracy. The algorithm employs iterative belief propagation with convolutional code constraints, utilizing parity-check matrices optimized for convolutional structures. A graphical interface displays performance metrics including bit error rate (BER) curves and convergence behavior. Subsequently, the program implements hard-decision LDPC convolutional decoding, where quantized binary decisions replace probabilistic inputs. This implementation features threshold-based decision mechanisms and simplified message passing operations. The comparative analysis module systematically evaluates both approaches through Monte Carlo simulations, measuring key performance indicators such as coding gain, computational complexity, and convergence speed. The comparison reveals fundamental trade-offs: soft-decision decoding achieves approximately 2-3 dB gain in signal-to-noise ratio (SNR) but requires higher computational resources, while hard-decision offers reduced complexity at the cost of performance degradation. The program includes configurable parameters for code rate, constraint length, and iteration counts, allowing researchers to analyze various operational scenarios. This implementation provides valuable insights for coding theory research and practical communication system development, particularly in resource-constrained applications where complexity-performance trade-offs are critical.