Convolutional Code Encoding with Viterbi Decoding and Soft Decision

In this documentation, I strongly recommend using convolutional code encoding, Viterbi decoding, and soft decision techniques. These encoding and decoding methods effectively handle errors in information transmission. Convolutional code encoding is a reliable method that transforms input data into a sequence of codewords, thereby enhancing data reliability through continuous memory-based encoding. The implementation typically uses shift registers and polynomial generators to create redundant bits for error correction. Viterbi decoding is a widely-used decoding algorithm that reconstructs original data from received codewords and corrects transmission errors by finding the most likely path through a trellis diagram using dynamic programming principles. The algorithm efficiently handles both hard and soft decision inputs through path metric calculations and traceback operations. Soft decision is a statistical judgment technique that improves the reliability estimation of each bit in the codeword by using probability metrics instead of binary decisions, typically implemented through log-likelihood ratio (LLR) calculations. This approach significantly enhances decoding performance by providing more granular information about bit confidence levels. The provided code offers a straightforward implementation featuring clear structure with key functions for encoding polynomials, trellis generation, and metric computations. You can easily adapt this code to implement convolutional code encoding, Viterbi decoding, and soft decision techniques in your projects. I hope these technologies and code examples prove valuable for your work!