The Transactions of The Korean Institute of Electrical Engineers

Single-Step Adaptive Offset Min-Sum Algorithm for Decoding LDPC Codes

Xiaoju Lin, Gansuren Baasantseren, Hae-Kee Lee, Sung‐Soo Kim

Published Jan 1, 2010• 0 citations

Abstract

Low-density parity-check (LDPC) codes with belief-propagation (BP) algorithm achieve a remarkable performance close to the Shannon limit at reasonable decoding complexity. Conventionally, each iteration in decoding process contains two steps, the horizontal step and the vertical step. In this paper, an efficient implementation of the adaptive offset min-sum (AOMS) algorithm for decoding LDPC codes using the single-step method is proposed. Furthermore, the performances of the AOMS algorithm compared with belief-propagation (BP) algorithm are investigated. The algorithms using the single-step method reduce the implementation complexity, speed up the decoding process and have better efficiency in terms of memory requirements.

DOI: 10.5370/kieep.2010.59.1.053

Related papers

Improved quantized adaptive-offset min-sum algorithm for low-density parity-check codes

Dong Tongxi

In order to reduce the implementation complexity of the quantized decoding algorithm for the Low-Density Parity-Check(LDPC)codes, an improved 4-bit quantized Adaptive-Offset Min-Sum(AOMS)algorithm is designed. The improved AOMS algorithm is obtained by introducing a pre-determined iteration number as the condition of adaptive selecting the offset factors. Furthermore, a 4-bit nonuniform quantization scheme is designed, which can guarantee the dynamic range of the extrinsic information and easily utilize the optimized quantized offset factors. The simulation results show that, compared with the floating-point decoding algorithms, the performance degradation of the improved 4-bit nonuniform quantized AOMS algorithm can be neglected.

Computer Engineering and Applications JournalJan 1, 20140 citations

Improved LDPC decoding algorithms based on min-sum algorithm

Y.V. A. C. Kumara, Chandika Bandara Wavegedara

Low-Density Parity check (LDPC) codes offer high-performance error correction near the Shannon limit which employs large code lengths and some iterations in the decoding process. The conventional decoding algorithm of LDPC is the Log Likelihood Ratio based Belief Propagation (LLR BP) which is also known as the `Sum-Product algorithm' which gives the best decoding performance and requires the most computational complexity and implementations with increased hardware complexity. Another simpler variant of this algorithm is used which is known as `min-sum algorithm' which reduces computational complexity as well as hardware complexity but with reduced accuracy. This paper analyzes the reason min-sum algorithm is more prone to errors when compared to the sum-product algorithm, and puts forward two improved algorithms which improve the performance of the min-sum algorithm with comparable algorithmic complexity.

Moratuwa Engineering Research Conference (MERCon)Apr 1, 20168 citations

A modified Offset Min-Sum decoding algorithm for LDPC codes

Meng Xu, Jianhui Wu, Meng Zhang

In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.

Jul 1, 201029 citations

Layered Offset Min-Sum Decoding for Low Density Parity Check Codes

Nejwa El Maammar, Seddik Bri, Jaouad Foshi

Offset Min-Sum Algorithm (OMSA) and Normalized Min-Sum Algorithm (NMSA) are widely used in commercial LDPC decoders due to low complexity and reasonable performance. In this paper we proposes an Adaptive Layered Offset min- sum algorithm for the decoding of LDPC codes, which utilizes an adaptive offset factor to improve the accuracy of the soft information transferred during the iterative decoding process, and it provides good performance accordingly. The performed simulations showed that the layered offset min-sum decoding for LDPC codes can provide significant improvement compared to existing MS based algorithm, OMSA and NMSA.

International Symposium on Advanced Electrical and Communication Technologies (ISAECT)Nov 1, 201810 citations

Modified min-sum decoding algorithm for LDPC codes based on classified correction

Zhong Zhou, Yunzhou Li, Xiang Chen +2 more

In this paper, a modified min-sum decoding algorithm based on classified correction is proposed for low density parity check (LDPC) codes. Different from the single correction in the normalized belief propagation (BP)-based and offset BP-based algorithms [4], the proposed algorithm utilizes two corrections for both minimum and sub-minimum magnitudes of input messages in check nodes. These two correction factors can be obtained by analyzing the offset of updated messages in check nodes between the BP and the min-sum algorithms associated with check node degree. Simulation results show that the proposed algorithm can achieve performance very close to that of the BP algorithm. Furthermore, the FPGA implementation of this algorithm can reach a throughput of 200 Mbps at BER=10-6with lower complexity and fewer resources than the BP algorithm.

International Conference on Communications and Networking in China (ChinaCom)Aug 1, 20089 citations

An optimized min-sum variable node layering for LDPC decoding

Nassib Laouini, Larbi Ben Hadj Slama, Ammar Bouallègue

Layered decoding is well appreciated in Low-Density Parity-Check (LDPC) decoder implementation since it provides efficient and high-throughput implementation of LDPC decoders. Variable-Node Layered Belief Propagation (VL-BP) algorithm and their reduced-complexity derivatives for LDPC codes are presented. The VL-BP algorithm is a modification of Belief Propagation algorithm (BP), where the variable nodes are divided in subgroups called layers and each iteration is broken into multiple sub-iterations. Some simplifications can also be made to lower the complexity of both BP and CL-BP algorithms, and particularly the complexity of the check node update rule. In this paper, we consider Variable Node Layered BP (VL-BP) algorithm and propose an optimized min-sum VL-BP (MS VL-BP) algorithm for decoding LDPC code which has better performance not only from MS VL-BP algorithm but also from BP algorithm. In this decoding method, unlike other layered algorithms, we consider for the first layer a set of variable nodes that has a low value of the intrinsic information. Simulation results show that good performance can be achieved, and which can even be improved by the addition of either a normalization term or an additive offset term.

Apr 1, 20141 citation