Approaching the ML performance with iterative decoding

The paper presents a method to significantly improve the convergence of iteratively decoded concatenated schemes and reduce the gap between iterative and maximum likelihood (ML) decoding. It is shown that many of the error blocks produced by the iterative decoder can be corrected by modifying a single critical coordinate (channel value) of the received vector and repeating the decoding. This is the basis of the RVCM (received vector coordinate modification) algorithm. Its description, performance and drawbacks are discussed later on. The paper also presents a practically obtained lower bound on ML performance based on the Euclidean distances of the transmitted and the iteratively decoded codewords from the received vector. At low SNR this bound is assuming an unrealistic perfect code, while at high SNR the approximations are getting closer to the real characteristics of the code and the RVCM iterative decoder is shown to achieve the ultimate ML performance.