3,281 research outputs found
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
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
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