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IEEE International Conference on Computer and Communications (ICCC)

A Multi-parameter Compensated Min-Sum Based LDPC Decoding Algorithm

Shuxun He, Cheng Wang, Songsong Cai, Weidong Wang

Published Dec 8, 2023• 1 citation
Publisher

Abstract

The Minimum Sum (MS) algorithm is a standard Low-Density Parity-Check Codes (LDPC) decoding algorithm. Compared with traditional algorithms, it uses approximate calculations to reduce the computational complexity of the algorithm when calculating parity nodes, resulting in performance loss issues. To improve the LDPC decoding performance, the LDPC decoding algorithm of Multi-parameter Compensated Min-Sum (MCMS) is proposed. This algorithm introduces relevant information, such as channel and code length, into calculating update verification nodes. Introducing multiplication and correction factors to reduce errors in validation node calculations through multi-parameter compensation. Simulation results show that the MCMS algorithm has superior error rate performance compared with the MS algorithm.

DOI
10.1109/iccc59590.2023.10507301

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The Minimum Sum (MS) algorithm is a standard Low-Density Parity-Check Codes (LDPC) decoding algorithm. Compared with traditional algorithms, it uses approximate calculations to reduce the computational complexity of the algorithm when calculating parity nodes, resulting in performance loss issues. To improve the LDPC decoding performance, the LDPC decoding algorithm of Multi-parameter Compensated Min-Sum (MCMS) is proposed. This algorithm introduces relevant information, such as channel and code length, into calculating update verification nodes. Introducing multiplication and correction factors to reduce errors in validation node calculations through multi-parameter compensation. Simulation results show that the MCMS algorithm has superior error rate performance compared with the MS algorithm.
In modern communication technology, low-density parity-check codes (LDPC) are widely used as an extremely important channel coding method, and it has significant performance advantages when dealing with long codes. However, the complexity of LDPC decoding algorithms is high. In response to this phenomenon, based on MCMS decoding algorithm, this paper proposes an Improved Multi-parameter Compensated MinSum (IMCMS) decoding algorithm. This algorithm adds a threshold near zero at the minimum value and chooses a locally optimal algorithm when the minimum value is less than the threshold to avoid the zero return loss of MCMS decoding algorithm. On this basis, a layered scheduling strategy is applied to propose a Layered Improved Multi-parameter Compensated Min-Sum (LIMCMS) decoding algorithm. Simulation results show that the decoding algorithm proposed in this paper not only effectively improves bit error performance but also maintains low computational complexity and significantly accelerates the convergence speed of decoding in academic papers.
In order to reduce the performance loss of SMMS( Single-Minimum,Min-Sum) algorithm for decoding LDPC( LowDensity Parity-Check) codes,the ACMS( adaptive configurable Min-Sum) algorithm is proposed in this paper. Only the absolute minimum is used in this algorithm to update the check-node message and the second minimum is determined by a configurable estimation weight factor based on the iteration and a correction factor based on the number of error bits in each hard decision. Compared to the original SMMS and VWMS algorithm,the simulation result shows the proposed ACMS algorithm reduce the performance loss in general by introducing only a little additional complexity.
In this paper, we present an improvement on the normalized min-sum (NMS) decoding algorithm for low density parity check codes. The modified NMS (M-NMS) algorithm introduces a more efficient adjustment for check node update computation. Taking the sub-minimum value among extrinsic information from the variable nodes into consideration, we make the correction factor varies with different iterations. Results show that, compared with standard min-sum (MS) and NMS decoding algorithms, the proposed method not only offers a better performance but also achieves a lower error floor in the high SNR region.
Low Density Parity Check Codes (LDPC) give groundbreaking performance which is known to approach Shannon's limits for sufficiently large block length. Historically and recently, LDPC have been known to give superior performance than concatenated coding. In the following paper, a proposal to modify the standard Min-Sum (MS) algorithm for decoding LDPC codes is presented. This is done by introduction of a difference to sum ratio factor, κ in the check to bit node updating process. The algorithm is further extended by implementing hard decision of the Bit-Flipping (BF) algorithm over the soft decision of MS algorithm. Simulation results demonstrate that the proposed algorithms are effective in imparting a better performance in terms of a lower bit error rate (BER) at medium to high signal to noise ratio (SNR) when compared to the traditional MS algorithm while adding fair amount of complexity.
To improve the decoding performance of Low Density Parity Check(LDPC) code,an efficient decoding algorithm based on min-sum algorithm is proposed. The proposed algorithm analyzes the check node update equation and at the view of probability to form an approximation min-sum equation. It adopts a dynamic normalization factor to improve the decoding performance. Detailed simulation results and comparisons with Belief Propagation(BP) algorithm show that,with little decoding performance loss,the proposed algorithm not only decreases computational load,but also improves decoding efficiency.