65 research outputs found
Adaptive Non-uniform Compressive Sampling for Time-varying Signals
In this paper, adaptive non-uniform compressive sampling (ANCS) of
time-varying signals, which are sparse in a proper basis, is introduced. ANCS
employs the measurements of previous time steps to distribute the sensing
energy among coefficients more intelligently. To this aim, a Bayesian inference
method is proposed that does not require any prior knowledge of importance
levels of coefficients or sparsity of the signal. Our numerical simulations
show that ANCS is able to achieve the desired non-uniform recovery of the
signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce
the number of required measurements significantly.Comment: 6 pages, 8 figures, Conference on Information Sciences and Systems
(CISS 2017) Baltimore, Marylan
Distributed Binary Detection over Fading Channels: Cooperative and Parallel Architectures
This paper considers the problem of binary distributed detection of a known
signal in correlated Gaussian sensing noise in a wireless sensor network, where
the sensors are restricted to use likelihood ratio test (LRT), and communicate
with the fusion center (FC) over bandwidth-constrained channels that are
subject to fading and noise. To mitigate the deteriorating effect of fading
encountered in the conventional parallel fusion architecture, in which the
sensors directly communicate with the FC, we propose new fusion architectures
that enhance the detection performance, via harvesting cooperative gain
(so-called decision diversity gain). In particular, we propose: (i) cooperative
fusion architecture with Alamouti's space-time coding (STC) scheme at sensors,
(ii) cooperative fusion architecture with signal fusion at sensors, and (iii)
parallel fusion architecture with local threshold changing at sensors. For
these schemes, we derive the LRT and majority fusion rules at the FC, and
provide upper bounds on the average error probabilities for homogeneous
sensors, subject to uncorrelated Gaussian sensing noise, in terms of
signal-to-noise ratio (SNR) of communication and sensing channels. Our
simulation results indicate that, when the FC employs the LRT rule, unless for
low communication SNR and moderate/high sensing SNR, performance improvement is
feasible with the new fusion architectures. When the FC utilizes the majority
rule, such improvement is possible, unless for high sensing SNR
Missing Spectrum-Data Recovery in Cognitive Radio Networks Using Piecewise Constant Nonnegative Matrix Factorization
In this paper, we propose a missing spectrum data recovery technique for
cognitive radio (CR) networks using Nonnegative Matrix Factorization (NMF). It
is shown that the spectrum measurements collected from secondary users (SUs)
can be factorized as product of a channel gain matrix times an activation
matrix. Then, an NMF method with piecewise constant activation coefficients is
introduced to analyze the measurements and estimate the missing spectrum data.
The proposed optimization problem is solved by a Majorization-Minimization
technique. The numerical simulation verifies that the proposed technique is
able to accurately estimate the missing spectrum data in the presence of noise
and fading.Comment: 6 pages, 6 figures, Accepted for presentation in MILCOM'15 Conferenc
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