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A Low-Complexity Neural Normalized Min-Sum LDPC Decoding Algorithm Using Tensor-Train Decomposition
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DOI：10.1109/LCOMM.2022.3207506.png)
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Abstract
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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.
Keywords:
Model-driven LDPC decoding
tensor-train decomposition
neural normalized min-sum
syndrome loss
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