Two Step SOVA-Based Decoding Algorithm for Tailbiting Codes

TL;DR

This paper introduces a two-step SOVA-based decoding algorithm for tailbiting convolutional codes, achieving near-maximum likelihood performance through a novel fixed two-phase decoding process.

Contribution

It presents a new decoding method combining SOVA and Viterbi algorithms specifically for tailbiting codes, improving decoding accuracy.

Findings

Performance close to maximum-likelihood decoding

Effective two-step decoding process

Applicable to various channel conditions

Abstract

In this work we propose a novel decoding algorithm for tailbiting convolutional codes and evaluate its performance over different channels. The proposed method consists on a fixed two-step Viterbi decoding of the received data. In the first step, an estimation of the most likely state is performed based on a SOVA decoding. The second step consists of a conventional Viterbi decoding that employs the state estimated in the previous step as the initial and final states of the trellis. Simulations results show a performance close to that of maximum-likelihood decoding.