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    Designing Finite Alphabet Iterative Decoders of LDPC Codes Via Recurrent Quantized Neural Networks

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    Designing Finite Alphabet Iterative ...
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    Author
    Xiao, Xin
    Vasic, Bane
    Tandon, Ravi
    Lin, Shu
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Issue Date
    2020-04-06
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    X. Xiao, B. Vasić, R. Tandon and S. Lin, "Designing Finite Alphabet Iterative Decoders of LDPC Codes Via Recurrent Quantized Neural Networks," in IEEE Transactions on Communications, vol. 68, no. 7, pp. 3963-3974, July 2020, doi: 10.1109/TCOMM.2020.2985678.
    Journal
    IEEE Transactions on Communications
    Rights
    Copyright © 2020 IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    In this paper, we propose a new approach to design finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over binary symmetric channel (BSC) via recurrent quantized neural networks (RQNN). We focus on the linear FAID class and use RQNNs to optimize the message update look-up tables by jointly training their message levels and RQNN parameters. Existing neural networks for channel coding work well over Additive White Gaussian Noise Channel (AWGNC) but are inefficient over BSC due to the finite channel values of BSC fed into neural networks. We propose the bit error rate (BER) as the loss function to train the RQNNs over BSC. The low precision activations in the RQNN and quantization in the BER cause a critical issue that their gradients vanish almost everywhere, making it difficult to use classical backward propagation. We leverage straight-through estimators as surrogate gradients to tackle this issue and provide a joint training scheme. We show that the framework is flexible for various code lengths and column weights. Specifically, in high column weight case, it automatically designs low precision linear FAIDs with superior performance, lower complexity, and faster convergence than the floating-point belief propagation algorithms in waterfall region.
    ISSN
    0090-6778
    DOI
    10.1109/tcomm.2020.2985678
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1109/tcomm.2020.2985678
    Scopus Count
    Collections
    UA Faculty Publications

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