54,416 research outputs found
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms
Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to
exploit the sparse nature of the occupation of the primary users. Also,
distributed spectrum sensing has been proposed to tackle the wireless channel
problems, like node or link failures, rather than the common (centralized
approach) for spectrum sensing. In this paper, we propose a distributed
spectrum sensing framework based on consensus algorithms where SU nodes
exchange their binary decisions to take global decisions without a fusion
center to coordinate the sensing process. Each SU will share its decision with
its neighbors, and at every new iteration each SU will take a new decision
based on its current decision and the decisions it receives from its neighbors;
in the next iteration, each SU will share its new decision with its neighbors.
We show via simulations that the detection performance can tend to the
performance of majority rule Fusion Center based CRNs
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
Sparse Signal Recovery under Poisson Statistics
We are motivated by problems that arise in a number of applications such as
Online Marketing and explosives detection, where the observations are usually
modeled using Poisson statistics. We model each observation as a Poisson random
variable whose mean is a sparse linear superposition of known patterns. Unlike
many conventional problems observations here are not identically distributed
since they are associated with different sensing modalities. We analyze the
performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting
involves a non-linear optimization but yet is computationally tractable. We
derive fundamental sample complexity bounds for sparse recovery when the
measurements are contaminated with Poisson noise. In contrast to the
least-squares linear regression setting with Gaussian noise, we observe that in
addition to sparsity, the scale of the parameters also fundamentally impacts
sample complexity. We introduce a novel notion of Restricted Likelihood
Perturbation (RLP), to jointly account for scale and sparsity. We derive sample
complexity bounds for regularized ML estimators in terms of RLP and
further specialize these results for deterministic and random sensing matrix
designs.Comment: 13 pages, 11 figures, 2 tables, submitted to IEEE Transactions on
Signal Processin
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs
with increased portability, higher levels of sensing capabilities, and more
powerful autonomy. These features make them attractive for many recent
applica-tions, potentially increasing the shortage of spectrum resources. In
this paper, wideband spectrum sensing augmented technology is discussed for
distributed UAV swarms to improve the utilization of spectrum. However, the
sub-Nyquist sampling applied in existing schemes has high hardware complexity,
power consumption, and low recovery efficiency for non-strictly sparse
conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the
distributed UAV swarms, which can theoretically achieve full-band spectrum
detection and reception using a single analog-to-digital converter (ADC) at low
speed for all circuit components. There is a focus on the sensing model of two
multichannel scenarios for the distributed UAV swarms, one with a complete
functional receiver for the UAV swarm with RIS, and another with a
decentralized UAV swarm equipped with a complete functional receiver for each
UAV element. The key issue is to consider whether the application of RIS
technology will bring advantages to spectrum sensing and the data fusion
problem of decentralized UAV swarms based on the NYFR architecture. Therefore,
the property for multiple pulse reconstruction is analyzed through the
Gershgorin circle theorem, especially for very short pulses. Further, the block
sparse recovery property is analyzed for wide bandwidth signals. The proposed
technology can improve the processing capability for multiple signals and wide
bandwidth signals while reducing interference from folded noise and subsampled
harmonics. Experiment results show augmented spectrum sensing efficiency under
non-strictly sparse conditions
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