1,020 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
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Performance analysis of energy detector over generalised wireless channels in cognitive radio
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is
widely employed to perform spectrum sensing in cognitive radio over different generalised
channel models. In this analysis, both the average probability of detection and
the average area under the receiver operating characteristic curve (AUC) are derived
using the probability density function of the received instantaneous signal to noise
ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used
to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then,
the behaviour of the energy detector over к --- µ shadowed fading channel, which is
a composite of generalized multipath/shadowing fading channel to model the lineof-
sight (LoS) communication medium is investigated. The analysis of the energy
detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to
include maximal ratio combining (MRC), square law combining (SLC) and square
law selection (SLS) with independent and non-identically (i:n:d) diversity branches.
To overcome the problem of mathematical intractability in analysing the energy
detector over i:n:d composite fading channels with MRC and selection combining
(SC), two different unified statistical properties models for the sum and the maximum
of mixture gamma (MG) variates are derived. The first model is limited by the value
of the shadowing severity index, which should be an integer number and has been
employed to study the performance of energy detector over composite α --- µ /gamma
fading channel. This channel is proposed to represent the non-linear prorogation
environment. On the other side, the second model is general and has been utilised to
analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel.
Finally, a special filter-bank transform which is called slantlet packet transform
(SPT) is developed and used to estimate the uncertain noise power. Moreover, signal
denoising based on hybrid slantlet transform (HST) is employed to reduce the noise
impact on the performance of energy detector. The combined SPT-HST approach
improves the detection capability of energy detector with 97% and reduces the total
computational complexity by nearly 19% in comparison with previously implemented
work using filter-bank transforms. The aforementioned percentages are measured at
specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio
Data Driven Quickest Detection in Networks and Its Applications in Spectrum Sensing
Cognitive radio is one of the enabling technologies considered for the next generation communication systems for many mission-critical applications. In modern cognitive ratio systems, the spectrum is becoming increasingly crowded and expensive; thus spectrum sensing becomes more important than ever before. In this dissertation, the study is focused on data driven quickest detection applied to energy detection based spectrum sensing. Firstly, a framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing where the primary user (PU) activities are heterogeneous in the space and dynamic in the time. The performance of the proposed scheme is analyzed mathematically. Using numerical simulations, detection performance measured by false alarm rate and average detection delay is obtained for different setups. Numerical simulations have demonstrated the validity of the proposed technique.Secondly, we propose a universal quickest change detection scheme based on density ratio estimation for spectrum sensing by detecting the sudden change of spectrum (e.g., the emergence of PU), where neither the pre-change nor post-change distribution (even the distribution forms) is known to secondary users (SUs), thus achieving robustness to complex spectrum environment, where SUs have no prior information about the measurement distributions. The validity of the proposed schemes has been shown by numerical simulations.Finally, we extend the detection of change in spectrum to millimeter-wave environment. As millimeter-wave is becoming part of the physical layer standard in the next-generation cellular network, it also brings about many questions and challenges. Not all the existing theories and methods for traditional wireless communication can apply directly to millimeter-wave communication because of the adoption of directional antenna and the high frequency band used. We propose a data-driven spectrum change sensing technique based on mean recurrence time to efficiently detect the PU activities which is tolerant of small fluctuations. The proposed spectrum sensing works well without a priori knowledge of the sensed signal, and doesn\u27t take assumption of independent and identically distributed random variables. It can also serve as a general framework for detection in other areas. The experimental results validate the proposed detection framework
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