Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A simple remedy for the exaggerated extrinsic information produced by the SOVA algorithm
IEEE Transactions on Wireless Communications
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
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In this paper the issue of improving the performance of iterative decoders based on sub-optimal calculation of the messages exchanged during iterations (L-values) is addressed. It is well known in the literature that a simple-yet very effective-- way to improve the performance of suboptimal iterative decoders is based on applying a scaling factor to the L-values. In this paper, starting with a theoretical model based on the so-called consistency condition of a random variable, we propose a methodology for correcting the L-values that relies only on the distribution of the soft information exchanged in the iterative process. This methodology gives a clear explanation of why the well-known linear scaling factor provides a very good performance. Additionally, the proposed methodology allows us to avoid the exhaustive search required otherwise. Numerical simulations show that for turbo codes the scaling factors found closely follow the optimum values, which translates to a close-to-optimal BER performance. Moreover, for LDPC codes, the proposed methodology produces a better BER performance compared with the known method in the literature.