Iterative Soft Decoding of Reed-Solomon Convolutional Concatenated Codes

Abstract

Reed-Solomon convolutional concatenated (RSCC) code is a popular coding scheme whose application can be found in wireless and space communications. However, iterative soft decoding of the concatenated code is yet to be developed. This paper proposes a novel iterative soft decoding algorithm for the concatenated code, aiming to better exploit its error-correction potential. The maximum a posteriori (MAP) algorithm is used to decode the inner convolutional code. Its soft output will be deinterleaved and then given to the soft-in-soft-out (SISO) decoder of the outer Reed-Solomon (RS) code. The RS SISO decoder integrates the adaptive belief propagation (ABP) algorithm and the Koetter-Vardy (KV) list decoding algorithm, attempting to find out the transmitted message. It feeds back both the deterministic and the extrinsic probabilities of RS coded bits, enabling the soft information to be exchanged between the inner and outer decoders. An extrinsic information transfer (EXIT) analysis of the proposed algorithm is presented, analyzing its iterative decoding behavior for RSCC codes. The EXIT analysis also leads to the design insight of inner code in the concatenation. Computational complexity of the proposed algorithm is also analyzed. Finally, the iterative decoding performance is shown and its advantage over the existing decoding algorithms is demonstrated.