A Low-Complexity Neural Normalized Min-Sum LDPC Decoding Algorithm Using Tensor-Train Decomposition

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

Compared with traditional low-density parity-check (LDPC) decoding algorithms, the current model-driven deep learning (DL)-based LDPC decoding algorithms face the disadvantage of high computational complexity. Based on the Neural Normalized Min-Sum (NNMS) algorithm, we propose a low-complexity model-driven DL-based LDPC decoding algorithm using Tensor-Train (TT) decomposition and syndrome loss function, called TT-NNMS+ algorithm. Our experiments show that the proposed TT-NNMS+ algorithm is more competitive than the NNMS algorithm in terms of bit error rate (BER) performance, memory requirement and computational complexity.

原文English
頁(從 - 到)2914-2918
頁數5
期刊IEEE Communications Letters
26
發行號12
DOIs
出版狀態Published - 1 12月 2022

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