Constrained sequence (CS) codes, including fixed-length CS codes and
variable-length CS codes, have been widely used in modern wireless
communication and data storage systems. Sequences encoded with constrained
sequence codes satisfy constraints imposed by the physical channel to enable
efficient and reliable transmission of coded symbols. In this paper, we propose
using deep learning approaches to decode fixed-length and variable-length CS
codes. Traditional encoding and decoding of fixed-length CS codes rely on
look-up tables (LUTs), which is prone to errors that occur during transmission.
We introduce fixed-length constrained sequence decoding based on multiple layer
perception (MLP) networks and convolutional neural networks (CNNs), and
demonstrate that 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. Further, implementation of capacity-achieving fixed-length codes,
where the complexity is prohibitively high with LUT decoding, becomes practical
with deep learning-based decoding. We then consider CNN-aided decoding of
variable-length CS codes. Different from conventional decoding where the
received sequence is processed bit-by-bit, we propose using CNNs to perform
one-shot batch-processing of variable-length CS codes such that an entire batch
is decoded at once, which improves the system throughput. Moreover, since the
CNNs can exploit global information with batch-processing instead of only
making use of local information as in conventional bit-by-bit processing, the
error rates can be reduced. We present simulation results that show excellent
performance with both fixed-length and variable-length CS codes that are used
in the frontiers of wireless communication systems.

本文提出使用深度学习方法解码固定长度和可变长度的约束序列码，其中基于多层感知机（MLP）网络和卷积神经网络（CNN）的解码固定长度的约束序列码和使用 CNN 对可变长度的约束序列码进行批处理解码，可以提高系统吞吐量和降低误比特率。