182 research outputs found
Side-Information Coding with Turbo Codes and its Application to Quantum Key Distribution
Turbo coding is a powerful class of forward error correcting codes, which can
achieve performances close to the Shannon limit. The turbo principle can be
applied to the problem of side-information source coding, and we investigate
here its application to the reconciliation problem occurring in a
continuous-variable quantum key distribution protocol.Comment: 3 pages, submitted to ISITA 200
Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources
This correspondence considers the use of punctured
quasi-arithmetic (QA) codes for the Slepian–Wolf problem. These
entropy codes are defined by finite state machines for memoryless and
first-order memory sources. Puncturing an entropy coded bit-stream leads
to an ambiguity at the decoder side. The decoder makes use of a correlated
version of the original message in order to remove this ambiguity. A
complete distributed source coding (DSC) scheme based on QA encoding
with side information at the decoder is presented, together with iterative
structures based on QA codes. The proposed schemes are adapted to
memoryless and first-order memory sources. Simulation results reveal
that the proposed schemes are efficient in terms of decoding performance
for short sequences compared to well-known DSC solutions using channel
codes.Peer ReviewedPostprint (published version
Spatially-Coupled LDPC Codes for Decode-and-Forward Relaying of Two Correlated Sources over the BEC
We present a decode-and-forward transmission scheme based on
spatially-coupled low-density parity-check (SC-LDPC) codes for a network
consisting of two (possibly correlated) sources, one relay, and one
destination. The links between the nodes are modeled as binary erasure
channels. Joint source-channel coding with joint channel decoding is used to
exploit the correlation. The relay performs network coding. We derive
analytical bounds on the achievable rates for the binary erasure time-division
multiple-access relay channel with correlated sources. We then design bilayer
SC-LDPC codes and analyze their asymptotic performance for this scenario. We
prove analytically that the proposed coding scheme achieves the theoretical
limit for symmetric channel conditions and uncorrelated sources. Using density
evolution, we furthermore demonstrate that our scheme approaches the
theoretical limit also for non-symmetric channel conditions and when the
sources are correlated, and we observe the threshold saturation effect that is
typical for spatially-coupled systems. Finally, we give simulation results for
large block lengths, which validate the DE analysis.Comment: IEEE Transactions on Communications, to appea
Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources
As the use of Internet of Things (IoT) devices for monitoring purposes
becomes ubiquitous, the efficiency of sensor communication is a major issue for
the modern Internet. Channel coding is less efficient for extremely short
packets, and traditional techniques that rely on source compression require
extensive signaling or pre-existing knowledge of the source dynamics. In this
work, we propose an encoding and decoding scheme that learns source dynamics
online using a Hidden Markov Model (HMM), puncturing a short packet code to
outperform existing compression-based approaches. Our approach shows
significant performance improvements for sources that are highly correlated in
time, with no additional complexity on the sender side.Comment: Preprint version of the paper published in IEEE Communications
Letter
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