Iterative Soft-Decision Decoding of Reed-Solomon Convolutional Concatenated Codes

Reed-Solomon convolutional concatenated (RSCC) code has been widely applied in wireless and space communications. However, iterative soft-decision decoding of the concatenated code is yet to be developed. This paper proposes a novel iterative soft decoding algorithm for the concatenated coding scheme. The maximum a posteriori (MAP) algorithm is used to decode the inner convolutional code. Its soft output will be deinterleaved and then passed to the soft-in-soft-out (SISO) decoding algorithm for the outer Reed-Solomon (RS) code. The outer SISO decoder integrates the adaptive belief propagation (ABP) algorithm and the Koetter-Vardy (KV) list decoding algorithm, attempting to find out the transmitted message. If it is found, the deterministic probabilities of the corresponding RS coded bits will be fed back. Otherwise, the extrinsic probabilities that are yielded by the ABP algorithm will be given as the feedback. With the proposed soft information exchange decoding mechanism, error-correction potential of the concatenated code can be better exploited. Our simulation results show that significant performance improvement can be achieved over the existing decoding algorithms.