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IEEE Transactions on Broadcasting

Offset and Normalized Min-Sum Algorithms for ATSC 3.0 LDPC Decoder

Seho Myung, Sung-Ik Park, Kyung-Joong Kim, Jae-Young Lee, Sunhyoung Kwon, Jeongchang Kim

Published Apr 12, 2017• 43 citations
Publisher

Abstract

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 provide optimized offset and scaling values for those LDPC decoders based on Advanced Television Systems Committee (ATSC) 3.0 LDPC codes. Furthermore, according to extensive computer simulations, it is shown that the performance of the OMSA and NMSA with the obtained values is close to that of sum-product algorithm, even though the NMSA-based decoder may show a serious error floor due to a channel mismatch effect. Consequently, we recommend that the ATSC 3.0 transmitters use a concatenation of BCH outer code and LDPC inner code as channel coding, considering the use of NMSA for LDPC decoder in ATSC 3.0 receivers.

DOI
10.1109/tbc.2017.2686011

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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 provide optimized offset and scaling values for those LDPC decoders based on Advanced Television Systems Committee (ATSC) 3.0 LDPC codes. Furthermore, according to extensive computer simulations, it is shown that the performance of the OMSA and NMSA with the obtained values is close to that of sum-product algorithm, even though the NMSA-based decoder may show a serious error floor due to a channel mismatch effect. Consequently, we recommend that the ATSC 3.0 transmitters use a concatenation of BCH outer code and LDPC inner code as channel coding, considering the use of NMSA for LDPC decoder in ATSC 3.0 receivers.
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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.
Min sum algorithm (MSA) is widely used for decoding Low Density Parity Check (LDPC) codes in many modern Digital Video Broadcasting (DVB) as a simplification for Sum Product Algorithm (SPA) which greatly reduce the implementation complexity. This paper presents a design of modified variable scaling min sum LDPC decoding algorithm for DVB-S2/S2X/T2. The proposed design is a combination between the variable Normalized Min Sum Algorithm (NMSA) and the Offset Min Sum Algorithm (OMSA) which provides a trade-off between decoding complexity and error correcting performance. It improves the use of the Min Sum Algorithm (MSA) by 39%. A proposal for how to implement the proposed algorithm using FPGA is also introduced. The structure used is partially parallel layered decoder architecture.
It is well known that for decoding low-density parity-check (LDPC) codes, the attenuated min-sum algorithm (AMSA) and the offset min-sum algorithm (OMSA) can outperform the conventional min-sum algorithm (MSA) at low signal-to-noise-ratios (SNRs). In this paper, we demonstrate that, for quantized LDPC decoders, although the MSA achieves better high SNR performance than the AMSA and OMSA, each of the MSA, AMSA, and OMSA all suffer from a relatively high error floor. Therefore, we propose a novel modification of the MSA for decoding quantized LDPC codes with the aim of lowering the error floor. Compared to the quantized MSA, the proposed modification is also helpful at low SNRs, where it matches the waterfall performance of the quantized AMSA and OMSA. The new algorithm is designed based on the assumption that trapping/absorbing sets (or other problematic graphical objects) are the major cause of the error floor for quantized LDPC decoders, and it aims to reduce the probability that these problematic objects lead to decoding errors.
Min-sum algorithm(MSA) and its modification are used in LDPC decoder implementations because of its error performance and low computation complexity. In this paper a modified min-sum LDPC decoding algorithm through approximation enhancement using the smallest three minimum values is presented. The proposed algorithm improves error performance and reduces decoder iterations. From the simulation results for 5G NR LDPC codes, we show that the proposed algorithm can achieve about 1.6dB improvement of FER compared to the min-sum decoding algorithm and provide about 10% reduction of the average iteration numbers compare to NMSA with optimum normalization factor.
For decoding low-density parity-check (LDPC) codes, the attenuated min-sum algorithm (AMSA) and the offset min-sum algorithm (OMSA) can outperform the conventional min-sum algorithm (MSA) at low signal-to-noise-ratios (SNRs), i.e., in the “waterfall region” of the bit error rate curve. This paper demonstrates that, for quantized decoders, MSA actually outperforms AMSA and OMSA in the “error floor” region, and that all three algorithms suffer from a relatively high error floor. This motivates the introduction of a modified MSA that is designed to outperform MSA, AMSA, and OMSA across all SNRs. The new algorithm is based on the assumption that trapping sets are the major cause of the error floor for quantized LDPC decoders. A performance estimation tool based on trapping sets is used to verify the effectiveness of the new algorithm and also to guide parameter selection. We also show that the implementation complexity of the new algorithm is only slightly higher than that of AMSA or OMSA. Finally, the simulated performance of the new algorithm, using several classes of LDPC codes (including spatially coupled LDPC codes), is shown to outperform MSA, AMSA, and OMSA across all SNRs.