102,110 research outputs found
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
A Multi-variate Discrimination Technique Based on Range-Searching
We present a fast and transparent multi-variate event classification
technique, called PDE-RS, which is based on sampling the signal and background
densities in a multi-dimensional phase space using range-searching. The
employed algorithm is presented in detail and its behaviour is studied with
simple toy examples representing basic patterns of problems often encountered
in High Energy Physics data analyses. In addition an example relevant for the
search for instanton-induced processes in deep-inelastic scattering at HERA is
discussed. For all studied examples, the new presented method performs as good
as artificial Neural Networks and has furthermore the advantage to need less
computation time. This allows to carefully select the best combination of
observables which optimally separate the signal and background and for which
the simulations describe the data best. Moreover, the systematic and
statistical uncertainties can be easily evaluated. The method is therefore a
powerful tool to find a small number of signal events in the large data samples
expected at future particle colliders.Comment: Submitted to NIM, 18 pages, 8 figure
Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO
How would a cellular network designed for maximal energy efficiency look
like? To answer this fundamental question, tools from stochastic geometry are
used in this paper to model future cellular networks and obtain a new lower
bound on the average uplink spectral efficiency. This enables us to formulate a
tractable uplink energy efficiency (EE) maximization problem and solve it
analytically with respect to the density of base stations (BSs), the transmit
power levels, the number of BS antennas and users per cell, and the pilot reuse
factor. The closed-form expressions obtained from this general EE maximization
framework provide valuable insights on the interplay between the optimization
variables, hardware characteristics, and propagation environment. Small cells
are proved to give high EE, but the EE improvement saturates quickly with the
BS density. Interestingly, the maximal EE is achieved by also equipping the BSs
with multiple antennas and operate in a "massive MIMO" fashion, where the array
gain from coherent detection mitigates interference and the multiplexing of
many users reduces the energy cost per user.Comment: To appear in IEEE Journal on Selected Areas in Communications, 15
pages, 7 figures, 1 tabl
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