3,034 research outputs found
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
On Power Allocation for Distributed Detection with Correlated Observations and Linear Fusion
We consider a binary hypothesis testing problem in an inhomogeneous wireless
sensor network, where a fusion center (FC) makes a global decision on the
underlying hypothesis. We assume sensors observations are correlated Gaussian
and sensors are unaware of this correlation when making decisions. Sensors send
their modulated decisions over fading channels, subject to individual and/or
total transmit power constraints. For parallel-access channel (PAC) and
multiple-access channel (MAC) models, we derive modified deflection coefficient
(MDC) of the test statistic at the FC with coherent reception.We propose a
transmit power allocation scheme, which maximizes MDC of the test statistic,
under three different sets of transmit power constraints: total power
constraint, individual and total power constraints, individual power
constraints only. When analytical solutions to our constrained optimization
problems are elusive, we discuss how these problems can be converted to convex
ones. We study how correlation among sensors observations, reliability of local
decisions, communication channel model and channel qualities and transmit power
constraints affect the reliability of the global decision and power allocation
of inhomogeneous sensors
Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin
Rician MIMO Channel- and Jamming-Aware Decision Fusion
In this manuscript we study channel-aware decision fusion (DF) in a wireless
sensor network (WSN) where: (i) the sensors transmit their decisions
simultaneously for spectral efficiency purposes and the DF center (DFC) is
equipped with multiple antennas; (ii) each sensor-DFC channel is described via
a Rician model. As opposed to the existing literature, in order to account for
stringent energy constraints in the WSN, only statistical channel information
is assumed for the non-line-of sight (scattered) fading terms. For such a
scenario, sub-optimal fusion rules are developed in order to deal with the
exponential complexity of the likelihood ratio test (LRT) and impractical
(complete) system knowledge. Furthermore, the considered model is extended to
the case of (partially unknown) jamming-originated interference. Then the
obtained fusion rules are modified with the use of composite hypothesis testing
framework and generalized LRT. Coincidence and statistical equivalence among
them are also investigated under some relevant simplified scenarios. Numerical
results compare the proposed rules and highlight their jammingsuppression
capability.Comment: Accepted in IEEE Transactions on Signal Processing 201
Distributed detection, localization, and estimation in time-critical wireless sensor networks
In this thesis the problem of distributed detection, localization, and estimation
(DDLE) of a stationary target in a fusion center (FC) based wireless sensor network
(WSN) is considered. The communication process is subject to time-critical
operation, restricted power and bandwidth (BW) resources operating over a shared
communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm
is proposed to solve the DDLE problem consisting of two dependent stages:
distributed detection and distributed estimation. The WSN performs distributed
detection first and based on the global detection decision the distributed estimation
stage is performed. The communication between the SNs and the FC occurs over a
shared channel via a slotted Aloha MAC protocol to conserve BW.
In distributed detection, hard decision fusion is adopted, using the counting
rule (CR), and sensor censoring in order to save power and BW. The effect of
Rayleigh fading on distributed detection is also considered and accounted for by
using distributed diversity combining techniques where the diversity combining is
among the sensor nodes (SNs) in lieu of having the processing done at the FC.
Two distributed techniques are proposed: the distributed maximum ratio combining
(dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show
superior detection performance when compared to conventional diversity combining
procedures that take place at the FC.
In distributed estimation, the segmented distributed localization and estimation
(SDLE) framework is proposed. The SDLE enables efficient power and BW
processing. The SOLE hinges on the idea of introducing intermediate parameters
that are estimated locally by the SNs and transmitted to the FC instead of the
actual measurements. This concept decouples the main problem into a simpler set
of local estimation problems solved at the SNs and a global estimation problem
solved at the FC. Two algorithms are proposed for solving the local problem: a
nonlinear least squares (NLS) algorithm using the variable projection (VP) method
and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve
the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust
hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through
local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS),
NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm
combination delivers the best localization and estimation performance. In fact , the
target can be localized with less than one meter error.
The SNs send their local estimates to the FC over a shared channel using the
slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel.
However, Aloha is known for its relatively high medium access or contention delay
given the medium access probability is poorly chosen. This fact significantly
hinders the time-critical operation of the system. Hence, multi-packet reception
(MPR) is used with slotted Aloha protocol, in which several channels are used for
contention. The contention delay is analyzed for slotted Aloha with and without
MPR. More specifically, the mean and variance have been analytically computed
and the contention delay distribution is approximated. Having theoretical expressions
for the contention delay statistics enables optimizing both the medium access
probability and the number of MPR channels in order to strike a trade-off between
delay performance and complexity
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