Transactions Papers Two Reliability-Based Iterative Majority-Logic Decoding Algorithms for LDPC Codes

Qin Huang, Jaewook Kang, Li Guo Zhang +2 more

This paper presents two novel reliability-based iterative majority-logic decoding algorithms for LDPC codes. Both algorithms are binary message-passing algorithms and require only logical operations and integer additions. Consequently, they can be implemented with simple combinational logic circuits. They either outperform or perform just as well as the existing weighted bit-flipping or other reliability-based iterative decoding algorithms for LDPC codes in error performance with a faster rate of decoding convergence and less decoding complexity. Compared to the sum-product algorithm for LDPC codes, they offer effective trade-offs between performance and decoding complexity.

Chen Haiqian

Michael Lentmaier, Dmitri V. Truhachev, Daniel J. Costello +1 more

Qin Huang, Jingyu Kang, Li Guo Zhang +2 more

This paper presents two novel iterative reliability-based majority-logic algorithms for decoding LDPC codes. Both algorithms are binary message-passing algorithms and require only logical operations and integer additions. Consequently, they can be implemented with simple combinational logic circuits. They either outperform or perform just well as the existing weighted bit-flipping or other reliability-based decoding algorithms for LDPC codes in error performance with a faster rate of decoding convergence and less decoding complexity. Compared to the sum-product algorithm for LDPC codes, they offer effective trade-offs between performance and decoding complexity.

Minghua Liu, Lijun Zhang

An attenuated iterative reliability-based majority-logic (AIML) decoding algorithm for low-density parity-check (LDPC) codes is proposed. This novel algorithm is devised based on the orthogonal check-sums of the one-step majority-logic decoding algorithm in conjunction with certain of reliability measures of the received symbols. The computation of reliability measure of the syndrome sum is refined by introducing an attenuation factor. Simulation results show that in binary input-additive white Gaussian noise channel, the AIML algorithm outperforms other popular iterative reliability-based majority-logic decoding algorithms with a slight increase in computational complexity. With maximum iterations five and fifty, the AIML algorithm can achieve almost identical error performance for LDPC codes. No error floor effect can be observed for AIML algorithm down to the bit error rate (BER) of 10−8, while error floor appears for sum product algorithm (SPA) around the BER of 10−7even with maximum iteration 100. The inherent feature of parallel decoding for AIML algorithm enforces the decoding speed in contrast to those serial decoding schemes, such as weighted bit-flipping algorithm.

David Burshtein, Gadi Miller