2 research outputs found
Properties and constructions of constrained codes for DNA-based data storage
We describe properties and constructions of constraint-based codes for
DNA-based data storage which account for the maximum repetition length and
AT/GC balance. We present algorithms for computing the number of sequences with
maximum repetition length and AT/GC balance constraint. We describe routines
for translating binary runlength limited and/or balanced strings into DNA
strands, and compute the efficiency of such routines. We show that the
implementation of AT/GC-balanced codes is straightforward accomplished with
binary balanced codes. We present codes that account for both the maximum
repetition length and AT/GC balance. We compute the redundancy difference
between the binary and a fully fledged quaternary approach
Deep Learning-Based Decoding of Constrained Sequence Codes
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.Comment: 12 pages, 9 figures, accepted by IEEE Journal on Selected Areas in
Communications (JSAC) - Machine Learning in Wireless Communications. arXiv
admin note: substantial text overlap with arXiv:1809.0185