Improved SOVA and APP decoding algorithms for concatenated codes

In this thesis, we propose a novel and simple approach for dealing with the exaggerated extrinsic information produced by the soft-output Viterbi algorithm (SOVA). We identify the reason behind producing these exaggerated values and propose a simple remedy for it. We argue that what leads to these optimistic extrinsic information is the inherent strong correlation between the intrinsic information (input to the SOVA) and extrinsic information (output of the SOVA). The proposed remedy is based on mathematical analysis and it involves using two attenuators, one applied to the immediate output of the SOVA and another applied to the extrinsic information before it is passed to the other decoder (assuming iterative decoding). We examine the modified SOVA (MSOVA) on various channel models including additive white Gaussian noise (AWGN) channels, flat fading channels, and storage channels for both parallel concatenated codes (PCCs) and serial concatenated codes (SCCs