Iterative Soft Decoding of Reed-Solomon Tail-Biting Convolutional Concatenated Codes
Jianchao Ye, Ting-Yi Wu, Jiongyue Xing, Li Chen · 2020
Reed-Solomon tail-biting convolutional concatenated (RS-TBCC) codes are investigated in this paper, aiming to eliminate the rate loss caused by the tail bits of the traditional RS convolutional concatenated (RS-CC) codes. The iterative soft decoding (ISD) for the RS-TBCC code is proposed, which integrates the tail-biting maximum a posteriori (TB-MAP) decoding algorithm for the inner tail-biting convolutional (TBC) code and the adaptive belief propagation (ABP) decoding algorithm for the outer RS codes. The proposed decoding is able to obtain significant performance gains through iterations. The outer decoding output will be validated by the maximum likelihood (ML) criterion, which alters the feedback to the inner decoding. However, the effectiveness of ML assessment degrades as the outer codeword length decreases. Therefore, a modified ISD is also proposed for shorter RS-TBCC codes in which smaller RS codes are employed.