6,206 research outputs found
Bayesian compressive sensing framework for spectrum reconstruction in Rayleigh fading channels
Compressive sensing (CS) is a novel digital signal processing technique that has found great interest in
many applications including communication theory and wireless communications. In wireless communications, CS
is particularly suitable for its application in the area of spectrum sensing for cognitive radios, where the complete
spectrum under observation, with many spectral holes, can be modeled as a sparse wide-band signal in the frequency
domain. Considering the initial works performed to exploit the benefits of Bayesian CS in spectrum sensing, the fading
characteristic of wireless communications has not been considered yet to a great extent, although it is an inherent feature
for all sorts of wireless communications and it must be considered for the design of any practically viable wireless system.
In this paper, we extend the Bayesian CS framework for the recovery of a sparse signal, whose nonzero coefficients follow
a Rayleigh distribution. It is then demonstrated via simulations that mean square error significantly improves when
appropriate prior distribution is used for the faded signal coefficients and thus, in turns, the spectrum reconstruction
improves. Different parameters of the system model, e.g., sparsity level and number of measurements, are then varied
to show the consistency of the results for different cases
Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations
Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage
Wireless visual sensor networks (VSNs) are expected to play a major role in
future IEEE 802.15.4 personal area networks (PAN) under recently-established
collision-free medium access control (MAC) protocols, such as the IEEE
802.15.4e-2012 MAC. In such environments, the VSN energy consumption is
affected by the number of camera sensors deployed (spatial coverage), as well
as the number of captured video frames out of which each node processes and
transmits data (temporal coverage). In this paper, we explore this aspect for
uniformly-formed VSNs, i.e., networks comprising identical wireless visual
sensor nodes connected to a collection node via a balanced cluster-tree
topology, with each node producing independent identically-distributed
bitstream sizes after processing the video frames captured within each network
activation interval. We derive analytic results for the energy-optimal
spatio-temporal coverage parameters of such VSNs under a-priori known bounds
for the number of frames to process per sensor and the number of nodes to
deploy within each tier of the VSN. Our results are parametric to the
probability density function characterizing the bitstream size produced by each
node and the energy consumption rates of the system of interest. Experimental
results reveal that our analytic results are always within 7% of the energy
consumption measurements for a wide range of settings. In addition, results
obtained via a multimedia subsystem show that the optimal spatio-temporal
settings derived by the proposed framework allow for substantial reduction of
energy consumption in comparison to ad-hoc settings. As such, our analytic
modeling is useful for early-stage studies of possible VSN deployments under
collision-free MAC protocols prior to costly and time-consuming experiments in
the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video
Technology, 201
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