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International Conference on Wireless Communications, Networking and Mobile Computing

Iterative Decoding of L-step Majority-Logic Decodable Codes Based on Belief Propagation

Suquan Ding

Published Sep 1, 2007• 0 citations
Publisher

Abstract

Iterative decoding of L-step Majority-Logic decodable (LSMLD) codes based on belief propagation (BP) is presented. The orthogonal Tanner graph of LSMLD codes is derived based on the L-step orthogonalized parity checks. The BP decoding algorithm based on the orthogonal Tanner graph (BP-O) is also designed. As the orthogonal Tanner graph is sparse and free of 4- cycles, LSMLD codes perform well under BP-O decoding. Simulation results show that BP-O decoding of LSMLD codes achieves a good soft-decision decoding gain over hard-decision decoding and the performance of LSMLD codes under BP-O decoding is equally well as that of LDPC codes with the same block length and rate under BP decoding.

DOI
10.1109/wicom.2007.211

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Iterative decoding of L-step Majority-Logic decodable (LSMLD) codes based on belief propagation (BP) is presented. The orthogonal Tanner graph of LSMLD codes is derived based on the L-step orthogonalized parity checks. The BP decoding algorithm based on the orthogonal Tanner graph (BP-O) is also designed. As the orthogonal Tanner graph is sparse and free of 4- cycles, LSMLD codes perform well under BP-O decoding. Simulation results show that BP-O decoding of LSMLD codes achieves a good soft-decision decoding gain over hard-decision decoding and the performance of LSMLD codes under BP-O decoding is equally well as that of LDPC codes with the same block length and rate under BP decoding.
Codes constructed based on finite geometries form a large class of cyclic codes with large minimum distances which can be decoded with simple majority-logic decoding in one or multiple steps. In 2001, Kou, Lin and Fossorier showed that the one-step majority-logic decodable finite geometry codes form a class of cyclic LDPC codes whose Tanner graphs are free of cycles of length 4. These cyclic finite geometry LDPC codes perform very well over the AWGN channel using iterative decoding based on belief propagation (IDBP) and have very low error-floors. However, the standard IDBP is not effective for decoding other cyclic finite geometry codes because their Tanner graphs contain too many short cycles of length 4 which severely degrade the decoding performance. This paper investigates iterative decoding of two-step majority-logic decodable finite geometry codes. Three effective algorithms for decoding these codes are proposed. These algorithms are devised based on the orthogonal structure of the parity-check matrices of the codes to avoid or reduce the degrading effect of the short cycles of length 4. These decoding algorithms provide significant coding gains over the standard IDBP using either the sum-product or the min-sum algorithms.
Previously, the belief propagation (BP) algorithm has received a lot of attention in the coding community, mostly due to its near-optimum decoding for low-density parity check (LDPC) codes and its connection to turbo decoding. In this paper, we investigate the performance achieved by the BP algorithm for decoding one-step majority logic decodable (OSMLD) codes. The BP algorithm is expressed in terms of likelihood ratios rather than probabilities, as conventionally presented. The proposed algorithm fits better the decoding of OSMLD codes with respect to its numerical stability due to the fact that the weights of their check sums are often much higher than that of the corresponding LDPC codes. Although it has been believed that OSMLD codes are far inferior to LDPC codes, we show that for medium code lengths (say between 200-1000 bits), the BP decoding of OSMLD codes can significantly outperform BP decoding of their equivalent LDPC codes. The reasons for this behavior are elaborated.
This paper presents two iterative decoding algorithms for a class of non-binary two-step majority-logic (NB-TS-MLG) decodable cyclic codes. A partial parallel decoding scheme is also introduced to provide a balanced trade-off between decoding speed and storage requirements. Unlike non-binary one-step MLG decodable cyclic codes, the Tanner graphs of which are 4-cycle-free, NB-TS-MLG decodable cyclic codes contain a large number of short cycles of length 4, which tend to degrade decoding performance. The proposed algorithms utilize the orthogonal structure of the parity-check matrices of the codes to resolve the degrading effects of the short cycles of length 4. Simulation results demonstrate that the NB-TS-MLG decodable cyclic codes decoded with the proposed algorithms offer coding gains as much as 2.5 dB over Reed-Solomon codes of the same lengths and rates decoded with either hard-decision or algebraic soft decision decoding.
Belief propagation (BP) decoding has been recognized for its capacity-approaching performance and high throughput when decoding long low-density parity-check (LDPC) codes. However, the application of BP decoding for short codes is hindered by dense parity-check matrices (PCMs) and prevalent short cycles in the Tanner graph. In this paper, we introduce a general method to extract an optimized sparse PCM for short binary block codes, which removes length-four cycles and enhances the connectivity of short cycles to enable BP decoding with improved performance. Notably, for short binary codes with lengths up to 64, our BP decoding performance approaches the maximum likelihood bound and surpasses the best-reported BP results with reduced computational complexity. Compared with other universal decoding algorithms, BP decoding using our extracted sparse PCMs is competitive in terms of both error-rate performance and computational complexity. These promising results suggest that our method to improve BP decoding for short codes is a step toward a practical universal BP decoder for next-generation communication systems.
A novel belief propagation(BP) decoding algorithm for convolutional low-density parity-check codes is proposed.The proposed algorithm raises the efficiency of updating the variable information by applying feedback information at each decoding iteration and employs the weighting factor to reduce the error propagation caused by the cycles in the Tanner graph,thus yielding a faster convergence of the decoding.Simulation results show that an error performance better than that of the existing belief propagation algorithm can be achieved,while the 5/8 decoding delay and the computation complexity are effectively reduced.Compared with the existing BP,the proposed algorithm achieves a gain of 0.16dB at the BER of 10-6 with the same number of iterations.
In this paper, we present a novel approach to improve belief propagation (BP) decoding performance by sparsifying the Tanner graph. Our proposed method first constructs a low-weight parity-check (LWPC) matrix with increased rows based on the original parity-check matrix. Then, all 4-cycles in the LWPC matrix are eliminated by adding auxiliary variable nodes and corresponding parity-check rows, resulting in the generalized LWPC (G-LWPC) matrix. Compared to the original matrix, the sparsity is greatly improved without altering the encoding constraints. Simulation results show that BP decoding based on the G-LWPC matrix is particularly effective for short code lengths. For a (128,20) polar code with 24-bit cyclic redundancy check, our proposed G-LWPC matrix reduces the density from 17.4% to 1.3%, and with 10 iterations, BP decoding can achieve the performance of successive cancellation list decoding with a list size of 32.