An Iterative Soft-Decision Decoding Algorithm with Dynamic Saturation for Short Reed-Solomon Codes

This paper proposes a new iterative soft-decision decoding algorithm which combines list decoding and adaptive belief propagation (ABP) algorithm for short Reed-Solomon (RS) codes. The proposed algorithm generates a list of codewords by restarting the decoder with log-likelihood ratio saturations to...

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Veröffentlicht in:2018 IEEE Information Theory Workshop (ITW) S. 1 - 5
Hauptverfasser: Liu, Bryan, Xie, Yixuan, Yang, Lei, Yuan, Jinhong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2018
Schlagworte:
Convergence
Correlation
Damping
Decoding
Heuristic algorithms
Iterative decoding
Signal to noise ratio
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Zusammenfassung:This paper proposes a new iterative soft-decision decoding algorithm which combines list decoding and adaptive belief propagation (ABP) algorithm for short Reed-Solomon (RS) codes. The proposed algorithm generates a list of codewords by restarting the decoder with log-likelihood ratio saturations to the dynamically selected suspicious bits based on an up-to-date best decoded codeword. The suspicious bits are selected according to a joint evaluation of the decoded codeword and the initial channel information. The damping coefficient used in the ABP decoder is set to be proportional to the channel noise variance to achieve a proper convergence speed for the decoder at different SNRs. The performance of the proposed algorithm for short RS codes is investigated. It shows that the proposed algorithm brings a considerable coding gain for short RS codes over additive white Gaussian noise channels.
DOI:10.1109/ITW.2018.8613470