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- W4310350438 abstract "Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained sequence codes rely on table look-up, which is prone to errors that occur during transmission. In this paper, we introduce constrained sequence decoding based on deep learning. With multiple layer perception (MLP) networks and convolutional neural networks (CNNs), we are able to achieve low bit error rates that are close to maximum a posteriori probability (MAP) decoding as well as improve the system throughput. Moreover, implementation of capacity-achieving fixed-length codes, where the complexity is prohibitively high with table look-up decoding, becomes practical with deep learning-based decoding." @default.
- W4310350438 created "2022-12-09" @default.
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- W4310350438 date "2018-09-06" @default.
- W4310350438 modified "2023-10-14" @default.
- W4310350438 title "Deep Learning-Based Decoding for Constrained Sequence Codes" @default.
- W4310350438 doi "https://doi.org/10.48550/arxiv.1809.01859" @default.
- W4310350438 hasPublicationYear "2018" @default.
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