Efficient list decoding for parallel concatenated convolutional codes

The focus of this research work is the sub-optimal list decoding algorithms for parallel concatenated convolutional codes (PCCCs) which improve the frame error rate (FER) performance. Error events and weight spectra for convolutional codes and PCCCs are analyzed with emphasis on their effects on list decoding. We explain the inefficiencies of list decoding algorithms for PCCCs that use a list generated from the component codes, and introduce a new algorithm based on the sub-block structure that generates a list directly for the PCCC. The additional complexity of the new algorithm is low and does not depend on the complexity of the component code. Simulations on the additive white Gaussian noise (AWGN) channel show that the new algorithm can lower the frame error floor by more than one order of magnitude.

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