A Near Maximum Likelihood Decoding Algorithm for Convolutionally Coded Relay Channels

Abstract:
In many real-world wireless communication systems, the cooperative technique is used as an efficient tool to improve the reliability of the networks. The main advantage of this technique is to achieve the spatial diversity gain through forming virtual antenna arrays. In this paper, we consider the classical three-node Gaussian relay channel by using the decodeand-forward strategy. Assuming perfect receiver channel state information, we propose a near maximum likelihood decoding algorithm at the destination side, based on the trellis structure of the underlying convolutionally coded data. Particularly, our algorithm makes the least approximation to the optimal maximum likelihood decoder, and can be computed efficiently. It is empirically demonstrated that our algorithm can offer significant performance improvement over the conventional decoding algorithms. To this end, our results can indicate an important performance benchmark for this channel model.