Improved soft-decision decoding of RSCC codes

Abstract

Reed-Solomon convolutional concatenated (RSCC) codes are a popular coding scheme for wireless communications. However, the current decoding algorithm for the outer code, i.e., the Reed-Solomon (RS) code, employs hard-decision decoding and cannot make full use of the soft information provided by the decoder of the inner code. Consequently, the concatenated code's error-correction potential is not fully exploited. This paper proposes an improved soft-decision decoding algorithm for the RSCC codes. The maximum a posteriori (MAP) algorithm is applied to decode the inner code, providing soft information for the outer code. The iterative decoding algorithm that can approach the maximum likelihood (ML) decoding performance for RS codes is applied to decode the outer code, exploiting the benefits of the soft output of the inner decoder. The iterative decoding of RS codes integrates the adaptive belief propagation (ABP) algorithm and the Koetter-Vardy (KV) list decoding algorithm, namely the ABPKV algorithm. Our performance analysis shows that sizable error-correction performance gains can be achieved over the conventional decoding scheme. The complexity of the proposed decoding scheme will also be presented, discussing the implementation cost for achieving the performance improvement.