IEEE Communications Letters

Neural Min-Sum Decoding for Generalized LDPC Codes

Hee-Youl Kwak, Jae-Won Kim, Yongjune Kim, Sang‐Hyo Kim, Jong‐Seon No

Published Sep 22, 2022• 9 citations

Abstract

In this letter, we investigate the min-sum (MS) and neural MS (NMS) decoding algorithms for generalized low-density parity-check (GLDPC) codes. Although the MS decoder is much simpler than the a posteriori probability (APP) decoder commonly used for GLDPC codes, the MS decoder has not been considered mainly due to its inferior decoding performance. However, we show that the performance can be improved by i) employing the NMS decoding algorithm and ii) optimizing the component parity check matrix (PCM). For the four representative short GLDPC codes in the literature, experimental results show that the NMS decoding performance with the optimized component PCM significantly outperforms the MS decoding performance and even outperforms the APP decoding performance for some cases.

DOI: 10.1109/lcomm.2022.3208834

Related papers

Generalized LDPC Codes With Low-Complexity Decoding and Fast Convergence

Dawit Simegn, Dmitry Artemasov, Kirill Andreev +2 more

We consider generalized low-density parity-check (GLDPC) codes with component codes that are duals of Cordaro–Wagner codes. Two efficient decoding algorithms are proposed: one based on Hartmann–Rudolph processing, analogous to Sum-Product decoding, and another based on evaluating two hypotheses per bit, referred to as the Min-Sum decoder. Both algorithms are derived using latent variables and a appropriate message-passing schedule. A quantized, protograph-based density evolution procedure is used to optimize GLDPC codes for Min-Sum decoding. Compared to 5G LDPC codes, the proposed GLDPC codes offer similar performance at 50 iterations and significantly better convergence and performance at 10 iterations.

arXiv:2505.08030IEEE Wireless Communications LettersAug 20, 20250 citations

An improved normalized min-sum algorithm for LDPC codes

Jinlei Chen, Yan Zhang, Ruiyi Sun

An improved normalized min-sum (IN-MS) algorithm is proposed for decoding low-density parity-check (LDPC) codes. In this algorithm, two normalized factors are used in check node computing, one for the minimum data and another for the second minimum data, and this algorithm achieves a better approximation to the BP algorithm. Simulation results show that the decoding performance of IN-MS algorithm is better than that of the normalized min-sum (NMS) algorithm.

IEEE/ACIS International Conference on Computer and Information Science (ICIS)Jun 1, 20139 citations

Modified Normalized Min-Sum decoding of LDPC codes

Rong Chen, Yiming Wang

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.

Oct 1, 20083 citations

Neural Adjusted Min-Sum Decoding for LDPC Codes

Haochen Yu, Ming-Min Zhao, Ming Lei +1 more

In this work, we propose a neural adjusted min-sum (NAMS) decoder for low-density parity-check (LDPC) codes. In particular, we improve the traditional normalized min-sum (NMS) decoder by introducing a selection mechanism to adjust the check-node update step, where either the min-sum rule or the belief propagation (BP) rule is selected. Besides, we unfold the modified decoder into a model-driven neural network, where layer-dependent trainable parameters are introduced as weights in the Tanner graph and optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed NAMS decoder is able to provide superior error-correction performance as compared to the neural NMS decoder, with only slightly increased computational complexity. Moreover, in certain circumstances, the proposed NAMS decoder even outperforms the neural BP decoder, with much lower computational complexity.

IEEE Vehicular Technology ConferenceOct 10, 20232 citations

Low Complexity Neural Network-Aided NMS LDPC Decoder

Honghao Shi, Mingyang Zhong, Zhiyong Luo +1 more

For recent years, deep learning has been studied and utilized among decoders in wireless and optical communication. For low density parity check (LDPC) codes, most works use neural network (NN) to implement the belief propagation (BP) decoder and unfold its iterations into layers activated by different functions. However, the whole decoder composed by neural network performs lots of non-linear operations, which are time consuming and unfriendly to hardware implementation. In this work, a neural network-aided normalized min-sum (NN-aided NMS) decoder for LDPC code is proposed, in which the normalization factors are inferred from input log likelihood ratio (LLR) by apre-trained NN, while maintaining check and variable nodes processing the same as in min-sum (MS) decoder. In order to test the proposed decoder with irregular LDPC code, a rate-7/8 one was constructed using progressive edge growth (PEG) algorithm, showing better error correction capability than the standard of consultative committee for space data systems (CCSDS) LDPC (8176,7154) code by 0.08 dB with the same rate. Simulation results show that, NN-aided NMS decoder outperforms MS decoder by 0.3 dB, and its performance approaches BP decoder with a gap of 0.02 dB, which is 50% closer than the best NMS decoder using 0.7 as the normalization factor. The proposed decoder has good performance for both regular and irregular LDPC codes, and needs much less computation complexity than BP decoder. It introduces more linear computation than NMS decoder but achieves better bit error rate (BER) performance, thus becoming a good complement between BP and NMS decoder when trading off among complexity, error correction performance and hardware implementation for wireless and satellite communication.

Computing, Communications and IoT Applications (ComComAp)Nov 26, 20218 citations

Performance Enhancement of Low-Density Parity-Check Decoder Using Neural Network Optimized Parameters

Amany Sabah Hassan, Mohammed Abdullah Hussein ElSheikh

Low-density parity-check (LDPC) codes are of prime importance in achieving near-Shannon capacity in current communication systems. However, the optimal decoding process for LDPC codes is computationally intensive. This paper presents a neural normalized min-sum (NNMS) decoding network that improves error correction capabilities with minimal computational overhead. A weight-sharing approach is adopted in the NNMS model, in which the correction factors (α, β) are shared across nodes within a single layer. This approach decreases the total number of parameters in the model compared to traditional neural decoders, making it easier for hardware implementation. The NNMS model is tested using a (576, 432) LDPC code for an additive white Gaussian noise channel with binary phase shift keying modulation. Simulation results show that the NNMS model outperforms traditional normalized min-sum (NMS) decoders. At a signal-to-noise ratio of 5 dB, the NNMS model has a bit-error rate (BER) of 1.0 × 10−7, whereas traditional NMS models have a less steep slope. In particular, the NNMS model has a coding gain of 1.25 dB compared to the traditional NMS model at a BER threshold of 1.0 × 10−3. This shows that the NNMS model is an efficient solution for high-performance, real-time digital communication systems.

UHD Journal of Science and TechnologyJul 5, 20260 citations