Convergence improvement of iterative decoders
Iterative decoding techniques shaked the waters of the error correction and communications field in general. Their amazing compromise between complexity and performance offered much more freedom in code design and made highly complex codes, that were being considered undecodable until recently, part of almost any communication system. Nevertheless, iterative decoding is a sub-optimum decoding method and as such, it has attracted huge research interest. But the iterative decoder still hides many of its secrets, as it has not been possible yet to fully describe its behaviour and its cost function. This work presents the convergence problem of iterative decoding from various angles and explores methods for reducing any sub-optimalities on its operation. The decoding algorithms for both LDPC and turbo codes were investigated and aspects that contribute to convergence problems were identified. A new algorithm was proposed, capable of providing considerable coding gain in any iterative scheme. Moreover, it was shown that for some codes the proposed algorithm is sufficient to eliminate any sub-optimality and perform maximum likelihood decoding. Its performance and efficiency was compared to that of other convergence improvement schemes. Various conditions that can be considered critical to the outcome of the iterative decoder were also investigated and the decoding algorithm of LDPC codes was followed analytically to verify the experimental results.