620 research outputs found
On some new approaches to practical Slepian-Wolf compression inspired by channel coding
This paper considers the problem, first introduced by Ahlswede and Körner in 1975, of lossless source coding with coded side information. Specifically, let X and Y be two random variables such that X is desired losslessly at the decoder while Y serves as side information. The random variables are encoded independently, and both descriptions are used by the decoder to reconstruct X. Ahlswede and Körner describe the achievable rate region in terms of an auxiliary random variable. This paper gives a partial solution for the optimal auxiliary random variable, thereby describing part of the rate region explicitly in terms of the distribution of X and Y
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
Orthogonal Multiple Access with Correlated Sources: Feasible Region and Pragmatic Schemes
In this paper, we consider orthogonal multiple access coding schemes, where
correlated sources are encoded in a distributed fashion and transmitted,
through additive white Gaussian noise (AWGN) channels, to an access point (AP).
At the AP, component decoders, associated with the source encoders, iteratively
exchange soft information by taking into account the source correlation. The
first goal of this paper is to investigate the ultimate achievable performance
limits in terms of a multi-dimensional feasible region in the space of channel
parameters, deriving insights on the impact of the number of sources. The
second goal is the design of pragmatic schemes, where the sources use
"off-the-shelf" channel codes. In order to analyze the performance of given
coding schemes, we propose an extrinsic information transfer (EXIT)-based
approach, which allows to determine the corresponding multi-dimensional
feasible regions. On the basis of the proposed analytical framework, the
performance of pragmatic coded schemes, based on serially concatenated
convolutional codes (SCCCs), is discussed
Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression
This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “source-splitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance
Multi-Way Relay Networks: Orthogonal Uplink, Source-Channel Separation and Code Design
We consider a multi-way relay network with an orthogonal uplink and
correlated sources, and we characterise reliable communication (in the usual
Shannon sense) with a single-letter expression. The characterisation is
obtained using a joint source-channel random-coding argument, which is based on
a combination of Wyner et al.'s "Cascaded Slepian-Wolf Source Coding" and
Tuncel's "Slepian-Wolf Coding over Broadcast Channels". We prove a separation
theorem for the special case of two nodes; that is, we show that a modular code
architecture with separate source and channel coding functions is
(asymptotically) optimal. Finally, we propose a practical coding scheme based
on low-density parity-check codes, and we analyse its performance using
multi-edge density evolution.Comment: Authors' final version (accepted and to appear in IEEE Transactions
on Communications
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