PROJECT TITLE :

On Some Properties of the Mutual Information Between Extrinsics With Application to Iterative Decoding

ABSTRACT:

Iterative decoding is an efficient error-correction tool primarily based on the exchange of extrinsic possibilities between the constituent decoders. In this paper, the properties of the mutual info between the extrinsic LLR at the output of 2 constituent decoders are analyzed with application to turbo and LDPC codes. This is often a bridge between info-theoretic analysis and sensible implementations. It's proved here that the mutual information between extrinsics could be a lower bound of the mutual information between each extrinsic and therefore the transmitted message. Additionally, an efficient online evaluation is provided within the paper with accuracy validated through numerical experiments. As an application, the mutual info between extrinsics is used for planning efficient stopping criterion and error detection rules at the decoder facet. Two online strategies for the estimation of optimal scaling issue to be applied to the extrinsic LLR are derived. In contrast with most references, an analytical expression is obtained that does not need estimation of the actual transmitted bits. All ends up in the paper are derived for Gaussian distributed LLR with freelance mean and variance.