Compression with side information using turbo codes

We show that turbo codes can come close to the Slepian-Wolf bound in lossless distributed source coding. In the asymmetric scenario considered, X and Y are statistically dependent signals and X is encoded with no knowledge of Y. However, Y is known as side information at the decoder. We use a system based on turbo codes to send X at a rate close to H(X|Y). We apply our system to binary sequences and simulations show performance close to the information-theoretic limit. For distributed source coding of Gaussian sequences, our results show significant improvement over previous work. The scheme also performs well for joint source-channel coding.

1 Introduction

Consider a communication system with two statistically dependent signals, and (Figure 1). Assume that and come from two separate sources that cannot communicate with each other. The receiver, on the other hand, sees both encoded signals and performs joint decoding. Such a system requires distributed source co ding since the encoders are distributed and the signals are compressed independently. An example of such a system is a wireless imaging network composed of low-complexity spatially separated nodes, sending correlated image information to a central processing unit. What is the minimum encoding rate required such that both X and can be recovered perfectly at the joint decoder?