Deep Learning-Based Decoding for Constrained Sequence Codes
This work addresses decoding challenges for constrained sequence codes in communication and data storage, offering practical implementation for capacity-achieving fixed-length codes, though it is incremental as it applies existing deep learning methods to a known bottleneck.
The paper tackles the problem of decoding constrained sequence codes in communication and data storage systems by introducing a deep learning-based approach using MLP and CNN networks, achieving low bit error rates close to MAP decoding and improving system throughput.
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.