Neural-Network-Optimized Degree-Specific Weights for LDPC MinSum Decoding

Wang, Linfang; Chen, Sean; Nguyen, Jonathan; Wesel, Richard D.

doi:10.1109/istc49272.2021.9594227

Cited by 14 publications

(5 citation statements)

References 25 publications

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“…Simulation results show that by unfolding and learning FA decoders, gains of up to 0.3 dB can be achieved for a (1296, 972) QC-LDPC code over conventional MS decoder when using 3 quantization bits. The authors in [327] proposed a neural 2D normalized MS decoder, together with various weight-sharing techniques to reduce the number of parameters that must be trained. Furthermore, the machine learning-aided decoding for protogrpah LDPC codes has been investigated in [328], along with a trajectory-based extrinsic information transfer (T-EXIT) chart developed for various decoders.…”

Section: Machine Learning-based Ldpc Decodersmentioning

confidence: 99%

Channel Coding Toward 6G: Technical Overview and Outlook

Rowshan,

Qiu,

Xie

et al. 2024

IEEE Open J. Commun. Soc.

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Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is paramount for mobile and the Internet of Things (IoT) communications. As the fifth generation (5G) of mobile communications is currently in operation and 5G-advanced is on the horizon, the objective of this paper is to assess prominent channel coding schemes in the context of recent advancements and the anticipated requirements for the sixth generation (6G). In this paper, after considering the potential impact of channel coding on key performance indicators (KPIs) of wireless networks, we review the evolution of mobile communication standards and the organizations involved in the standardization, from the first generation (1G) to the current 5G, highlighting the technologies integral to achieving targeted KPIs such as reliability, data rate, latency, energy efficiency, spectral efficiency, connection density, and traffic capacity. Following this, we delve into the anticipated requirements for potential use cases in 6G. The subsequent sections of the paper focus on a comprehensive review of three primary coding schemes utilized in past generations and their recent advancements: lowdensity parity-check (LDPC) codes, turbo codes (including convolutional codes), and polar codes (alongside Reed-Muller codes). Additionally, we examine alternative coding schemes like Fountain codes (also known as rate-less codes), sparse regression codes, among others. Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era. Lastly, we will briefly explore considerations such as higher-order modulations and waveform design, examining their contributions to enhancing key performance indicators in conjunction with channel coding schemes.

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Section: Machine Learning-based Ldpc Decodersmentioning

confidence: 99%

Channel Coding Toward 6G: Technical Overview and Outlook

Rowshan,

Qiu,

Xie

et al. 2024

IEEE Open J. Commun. Soc.

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“…The LLR BP is the paramount iterative decoding algorithm, where all the V2C messages and C2V messages are updated using Equations ( 2) and (3), respectively. Usually, in hardware implementation, Equations ( 2) and ( 3) are simplified to achieve a faster speed of iterative decoding, such as when using the min-sum decoding algorithm [23] and neural network based MS algorithms [24,25]. The informed dynamic scheduling algorithms are different from the flooding scheduling and the standard sequential scheduling [6][7][8][9][10] because of the message update order.…”

Section: Bp and Rbp Decoding For Ldpc Codesmentioning

confidence: 99%

“…The LLR BP is the paramount iterative decoding algorithm, where all the V2C messages and C2V messages are updated using Equations () and (), respectively. Usually, in hardware implementation, Equations () and () are simplified to achieve a faster speed of iterative decoding, such as when using the min‐sum decoding algorithm [23] and neural network based MS algorithms [24, 25].…”

Section: Preliminary Workmentioning

confidence: 99%

Partial IDS decoding based on the base graph of protograph LDPC codes

Liang,

Liu,

Xie

2023

IET Communications

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The residual belief propagation (RBP) algorithm, which is the most classic informed dynamic scheduling strategy, achieves outstanding performance in error correction and can drastically accelerate convergence speed. However, the greedy algorithmic property of this iterative decoding will inevitably cause loss of decoding performance. To address this, a novel algorithm called the partial average bundle residual belief propagation (PABRBP) is proposed in this paper. According to the construction characteristics of a base matrix of protograph‐LDPC codes, informed dynamic scheduling (IDS) strategies are applied to an edge bundle of base matrices for the first time. This edge bundle of the base matrix can be applied to a corresponding cyclic permutation matrix. Furthermore, the update level of each bundle is determined by the value of the Partially Average Bundle Residual (PABR). Therefore, the edge message with the maximum residual in each bundle is updated in order, and the process of iterative decoding is less likely to become trapped in a local optimum. Additionally, the generation of silent nodes is reduced as much as possible. To further improve the PABRBP decoding performance for medium and long codes over the fading channel, the adjusted compensation term is periodically introduced. Analysis and simulation results show that PABRBP demonstrates a notable convergence quality and decoding performance improvement over the fading channels compared to existing state‐of‐art IDS algorithms.

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“…Te decoding complexity can be signifcantly reduced thanks to various algorithms available for C2V messages m c i ⟶ v j updates simplifcation. Te widely used ones, in the recent works, are min-sum (MSA) and normalized min-sum (NMSA) algorithms [19][20][21]. For the MSA algorithm, the update equation became…”

Section: Llr Bp Decoding For Ldpc Codesmentioning

confidence: 99%

Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications

Benhayoun,

Razi,

Mansouri

et al. 2023

Applied Computational Intelligence and Soft Computing

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Ultra-reliable low-latency communications, URLLC, are designed for applications such as self-driving cars and telesurgery requiring a response in milliseconds and are very sensitive to transmission errors. To match the computational complexity of LDPC decoding algorithms to URLLC applications on IoT devices having very limited computational resources, this paper presents a new parallel and low-latency software implementation of the LDPC decoder. First, a decoding algorithm optimization and a compact data structure are proposed. Next, a parallel software implementation is performed on ARM multicore platforms in order to evaluate the latency of the proposed optimization. The synthesis results highlight a reduction in the memory size requirement by 50% and a three-time speedup in terms of processing time when compared to previous software decoder implementations. The reached decoding latency on the parallel processing platform is 150 μs for 288 bits with a bit error ratio of 3.410–9.

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Neural-Network-Optimized Degree-Specific Weights for LDPC MinSum Decoding

Cited by 14 publications

References 25 publications

Channel Coding Toward 6G: Technical Overview and Outlook

Channel Coding Toward 6G: Technical Overview and Outlook

Partial IDS decoding based on the base graph of protograph LDPC codes

Embedded Parallel Implementation of LDPC Decoder for Ultra-Reliable Low-Latency Communications

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