Variable LLR scaling in LDPC min-sum decoding under horizontal shuffled structure

LDPC was first proposed in 1962, after that considerable achievements have been made both in decoding algorithms and also in the structure of the decoder. In terms of decoding algorithm, from belief propagation (BP) to min-sum (MS), then normalized MS (NMS) and finally to a more performance oriented variable MS (VMS) algorithm. In terms of the decoder structure, there are mainly two, flooding and shuffled structure (FS and SS), and flooding structure is mainly used in the past and the selection of variable factors in FS VMS has already been studied in the literature. As is known, the horizontal shuffled structure (HSS) are more hardware friendly and now widely deployed, thus to apply VMS algorithm to HSS has an important meaning for the industry. However, there is no effective method for the selection of variable factors in HSS VMS. Based on the study of FS VMS, this paper describes how to apply the generalized mutual information (GMI) based metric to HSS VMS reasonably and proposes the modified GMI based formula. Simulation results on a certain DVB-S2's LDPC code show that, by using modified formula for HSS VMS, we can obtain 0.13dB gain over HSS NMS and the performance is only 0.1dB away from that of HSS BP in terms of bit error rate (BER) at level 1e-7. And simulation results on a certain DVB-T2's LDPC code show that, by using modified formula for HSS VMS, we can obtain 0.04dB gain over HSS NMS and the performance is only 0.06dB away from that of HSS BP in terms of bit error rate (BER) at level 1e-7.

[1]  Robert G. Gallager,et al.  Low-density parity-check codes , 1962, IRE Trans. Inf. Theory.

[2]  Rüdiger L. Urbanke,et al.  The capacity of low-density parity-check codes under message-passing decoding , 2001, IEEE Trans. Inf. Theory.

[3]  Juntan Zhang,et al.  Shuffled belief propagation decoding , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[4]  Jinghu Chen,et al.  Near optimum universal belief propagation based decoding of low-density parity check codes , 2002, IEEE Trans. Commun..

[5]  Noga Alon,et al.  A linear time erasure-resilient code with nearly optimal recovery , 1996, IEEE Trans. Inf. Theory.

[6]  Bo Rong,et al.  Variable LLR Scaling in Min-Sum Decoding for Irregular LDPC Codes , 2014, IEEE Transactions on Broadcasting.

[7]  Gottfried LECHNER.  Improved Sum-Min Decoding of LDPC Codes , .

[8]  D.J.C. MacKay,et al.  Good error-correcting codes based on very sparse matrices , 1997, Proceedings of IEEE International Symposium on Information Theory.

[9]  Hideki Imai,et al.  Reduced complexity iterative decoding of low-density parity check codes based on belief propagation , 1999, IEEE Trans. Commun..

[10]  Robert Michael Tanner,et al.  A recursive approach to low complexity codes , 1981, IEEE Trans. Inf. Theory.

[11]  Alain Glavieux,et al.  Reflections on the Prize Paper : "Near optimum error-correcting coding and decoding: turbo codes" , 1998 .

[12]  Gottfried Lechner,et al.  Improved Sum-Min Decoding for Irregular LDPC Codes , 2006 .

[13]  D. Declercq,et al.  A DVB-S2 compliant LDPC decoder integrating the Horizontal Shuffle Scheduling , 2006, 2006 International Symposium on Intelligent Signal Processing and Communications.