ADMM penalized decoding method based on improved penalty function for LDPC codes in the IoTs

Wang, Biao; Wang, Zhongfei

doi:10.1016/j.comcom.2020.02.063

Cited by 5 publications

(8 citation statements)

References 29 publications

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“…This paper extends the work by designing a new ADMM penalized decoding method with all of the reducing the number of Euclidean projections [11], horizontal layered scheduling [12], and improved penalty function [19]. In addition, an algorithmic complexity comparison of the proposed method and the decoding method in [23] is illustrated in this paper.…”

Section: Introductionmentioning

confidence: 77%

“…By combining the 1 l and 2 l penalty functions, Wang et al proposed an improved penalty function as [19]…”

Section: Preliminariesmentioning

confidence: 99%

“…This result occurs because the iterations needed to converge to a valid codeword is much lower for the layered scheduling wh i ch le a d s to a mea n in g f u l co m p u t at i o n sav i n g compared with the flooding scheduling. By adopting the 1 l penalty function on 1 (0, ) [19]. Therefore, the reducing the number of Euclidean projections, choosing a suitable scheduling strategy, and devising an improved penalty function are three different but effective techniques that can speed up the ADMM penalized decoding.…”

Section: A Motivationmentioning

confidence: 99%

“…An improved ADMM penalized decoder, which significantly improved the performance, is devised based on the weighted penalized factor [18]. With the improved penalty function combined by the 1 l and 2 l penalty functions, the decoder obtains better performance and faster convergence rate than those with the 1 l and 2 l penalty functions [19]. By exploiting the quadratic programming (QP) model's inside structure, Bai et al developed an efficient QP ADMM decoding algorithm that attains better performance [20].…”

Section: Introductionmentioning

confidence: 99%

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Horizontal Layered Scheduling ADMM Penalized Decoder Based on the Improved Penalty Function for LDPC

Wang

2022

IEEE Access

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For low-density parity-check (LDPC) codes, reducing the number of Euclidean projections, choosing a suitable scheduling strategy, and devising an improved penalty function are three effective ways to increase the alternating direction method of multipliers (ADMM) penalized decoding speed. By combining the above three ways, this paper proposes a horizontal layered scheduling ADMM penalized decoder based on the improved penalty function to speed up decoding. The experimental data indicate that the proposed decoder can obtain better performance and lower average number of iterations than the existing ADMM penalized decoders.INDEX TERMS Horizontal layered scheduling, alternating direction method of multipliers, penalized decoding, low-density parity-check codes

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Section: Introductionmentioning

confidence: 77%

“…By combining the 1 l and 2 l penalty functions, Wang et al proposed an improved penalty function as [19]…”

Section: Preliminariesmentioning

confidence: 99%

Section: A Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Horizontal Layered Scheduling ADMM Penalized Decoder Based on the Improved Penalty Function for LDPC

Wang

2022

IEEE Access

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“…A novel ADMM-penalized decoding method that significantly improves the decoding performance is also devised based on the weighted penalized factor [16]. e performance of the ADMM-penalized decoder with the improved penalty function devised by combining the l 1 and l 2 penalty functions can also be improved [17].…”

Section: Introductionmentioning

confidence: 99%

Novel Early Termination Method of an ADMM-Penalized Decoder for LDPC Codes in the IoT

Wang

2022

Security and Communication Networks

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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.

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A Comparative Review of Recent Advances in Decoding Algorithms for Low-Density Parity-Check (LDPC) Codes and Their Applications

Roberts¹,

Anguraj²

2020

Arch Computat Methods Eng

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ADMM penalized decoding method based on improved penalty function for LDPC codes in the IoTs

Cited by 5 publications

References 29 publications

Horizontal Layered Scheduling ADMM Penalized Decoder Based on the Improved Penalty Function for LDPC

Horizontal Layered Scheduling ADMM Penalized Decoder Based on the Improved Penalty Function for LDPC

Novel Early Termination Method of an ADMM-Penalized Decoder for LDPC Codes in the IoT

A Comparative Review of Recent Advances in Decoding Algorithms for Low-Density Parity-Check (LDPC) Codes and Their Applications

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