Improved Belief Propagation List Decoding for Polar Codes

Li, Huan; Huang, Jingxuan; Sun, Ce

doi:10.3390/electronics11152458

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…In addition, various permutation methods were introduced to find better factor graphs with which the BP decoder converged faster [7][8][9][10][11][12]. Each factor graph requires the corresponding decoder structure, and thus the complexity and the memory requirement of the BPDL hinder practical implementation.…”

Section: Belief Propagation Decodingmentioning

confidence: 99%

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Review and Evaluation of Belief Propagation Decoders for Polar Codes

et al. 2022

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The polar code has become one of the most popular and important forward error correction (FEC) coding schemes due to its symmetric characteristics of channel polarization. This paper reviews various decoding schemes for polar codes and discusses their advantages and disadvantages. After reviewing the existing performance-enhancing techniques such as belief propagation decoding with list, a new method is proposed to further improve the performance. In addition, a new complexity reduction technique based on the constituent codes is proposed, and a new scheduling scheme is introduced to reduce the decoding latency. Due to the recent development of neural networks, their applications to decoding schemes are also reviewed and evaluated. Finally, the proposed complexity-reduced technique is integrated with a neural network-based belief propagation decoding, which demonstrates performance enhancement as well as computational complexity reduction.

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Section: Belief Propagation Decodingmentioning

confidence: 99%

“…This study first reviews and analyzes a means to improve the performance of BPD, which is called BPD with list (BPDL) [5][6][7][8][9][10][11][12][13]. The BPDL operates on multiple permuted factor graphs to select the best decoding result, and eventually to improve the decoding performance.…”

Section: Introductionmentioning

confidence: 99%

Review and Evaluation of Belief Propagation Decoders for Polar Codes

et al. 2022

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Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications

Krishna Priya,

Sakhare,

Paithane

et al. 2024

Internet Technology Letters

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A Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.

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Assessing the effectiveness of polar coding with BP and BPL decoding at different Eb/No values in a BEC

Hamza,

Shandal

2024

AIP Conference Proceedings

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Improved Belief Propagation List Decoding for Polar Codes

Cited by 4 publications

References 24 publications

Review and Evaluation of Belief Propagation Decoders for Polar Codes

Review and Evaluation of Belief Propagation Decoders for Polar Codes

Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications

Assessing the effectiveness of polar coding with BP and BPL decoding at different Eb/No values in a BEC

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