Low Complexity Iterative Decoding Of Reed-Solomon Convolutional Concatenated Codes

The power limitation is the predominant challenge for achieving a reliable communication transmission for aerospace systems. Therefore, it is appealing to use robust channel coding techniques with a low decoding complexity. The robust forward error correction scheme that uses a Reed-Solomon as an outer code concatenated with a convolutional code as an inner code is an attractive scheme whose applications are widely used in wireless and space communications. However, iterative soft-decision decoding of that concatenated code is still an open research challenge. This paper proposes a reduced complexity iterative decoding algorithm for this concatenated coding scheme. The soft-output adaptive Viterbi algorithm with a dynamic discarding threshold has been adopted to decode the inner convolutional code while the outer decoder will be based on a bit-level modified Chase algorithm. We have used the Hamming metric instead of the Euclidean metric, which is not only much less complex but also overcomes the lack of channel information on the outer decoder input. Simulation results using the proposed soft information exchange decoding mechanism show that a considerable performance enhancement over the classical decoding scheme as well as a significant reduction in complexity over the existing decoding algorithms that use an iterative process to decode this concatenated coding scheme. The adaptive decoding of the inner convolutional code can gain a complexity reduction of 90% after 5 iterations compared to the soft-output Viterbi algorithm while maintaining the small performance loss from the maximum a posteriori decoding algorithm.