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Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Group-Based Layered Scheduling of ADMM Decoding for LDPC Codes

Xinglong Zhang, Meng Niu, Luo-Hui Su, Song Ke-pu

Published Jan 1, 2018• 2 citations
Publisher
DOI
10.1007/978-3-319-78139-6_22

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This paper proposes a general framework aiming at describing and comparing LDPC decoders based on the ADMM approach. ADMM is a linear programming decoding method. When applied on LDPC codes, it acts as an iterative message passing technique and enhances the error correction performance compared with the usual belief propagation based decoders. In this paper, three ADMM decoding algorithms classified according to their scheduling type are described and compared. Simulation results show that the layered scheduling is efficient to enhance the error correction performance while decreasing the average number of decoding iterations for the considered LDPC codes.
As a critical communication technology, low-density parity-check (LDPC) codes are widely concerned with the Internet of things (IoT). To increase the convergence rate of the alternating direction method of multiplier (ADMM)-penalized decoder for LDPC codes, a novel early termination (ET) method is presented by computing the average sum of the hard decision (ASHD) during each ADMM iteration. In terms of the flooding scheduling and layered scheduling ADMM-penalized decoders, the simulation results show that the proposed ET method can significantly reduce the average number of iterations at low signal-to-noise ratios (SNRs) with negligible decoding performance loss.
For low-density parity-check (LDPC) codes, the penalized decoding method based on the alternating direction method of multipliers (ADMM) can improve the decoding performance at low signal-to-noise ratios and also has low decoding complexity. There are three effective methods that could increase the ADMM penalized decoding speed, which are reducing the number of Euclidean projections in ADMM penalized decoding, designing an effective penalty function and selecting an appropriate layered scheduling strategy for message transmission. In order to further increase the ADMM penalized decoding speed, through reducing the number of Euclidean projections and using the vertical layered scheduling strategy, this paper designs a fast converging ADMM penalized decoding method based on the improved penalty function. Simulation results show that the proposed method not only improves the decoding performance but also reduces the average number of iterations and the average decoding time.