100 research outputs found
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
On the Combined Effect of Directional Antennas and Imperfect Spectrum Sensing upon Ergodic Capacity of Cognitive Radio Systems
We consider a cognitive radio system, consisting of a primary transmitter
(PUtx), a primary receiver (PUrx), a secondary transmitter (SUtx), and a
secondary receiver (SUrx). The secondary users (SUs) are equipped with
steerable directional antennas. We assume the SUs and primary users (PUs)
coexist and the SUtx knows the geometry of network. We find the ergodic
capacity of the channel between SUtx and SUrx , and study how spectrum sensing
errors affect the capacity. In our system, the SUtx first senses the spectrum
and then transmits data at two power levels, according to the result of
sensing. The optimal SUtx transmit power levels and the optimal directions of
SUtx transmit antenna and SUrx receive antenna are obtained by maximizing the
ergodic capacity, subject to average transmit power and average interference
power constraints. To study the effect of fading channel, we considered three
scenarios: 1) when SUtx knows fading channels between SUtx and PUrx, PUtx and
SUrx, SUtx and SUrx, 2) when SUtx knows only the channel between SUtx and SUrx,
and statistics of the other two channels, and, 3) when SUtx only knows the
statistics of these three fading channels. For each scenario, we explore the
optimal SUtx transmit power levels and the optimal directions of SUtx and SUrx
antennas, such that the ergodic capacity is maximized, while the power
constraints are satisfied
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
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