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。

基于深度学习的受限序列编码解码

Deep Learning-Based Decoding of Constrained Sequence Codes

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。