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.

该研究采用基于深度学习的受限序列解码技术，在使用多层感知网络和卷积神经网络的情况下，能够实现接近于最大后验概率译码误差率的低误码率，提高系统吞吐量，实现容量实现固定长度编码，并避免基于表查找的译码方法带来的复杂性问题。