On efficient soft-input soft-output encoding of convolutional codes

We study efficient algorithms for soft-input soft-output (SISO) encoding of convolutional codes. While the BCJR algorithm has been suggested for SISO encoding, we show that a forward recursion on the code's trellis is sufficient to compute the a posteriori probabilities of the code bits. We further propose a shift-register based SISO encoding algorithm for non-recursive convolutional encoders, whose complexity scales linearly with constraint length and block length. Finally, we assess the complexity of the proposed algorithms and we discuss approximations to facilitate practical implementation.

1. INTRODUCTION

Soft information processing is a key ingredient of many advanced receiver concepts we know today. The ubiquity of soft-input soft-output (SISO) processing is due to pioneering work, e.g., by Viterbi [1], Bahl et al. [2], and Hagenauer [3], [4]. This work has paved the way for turbo codes [5] and numerous iterative receiver algorithms which have revolutionized communication theory since 1993.