4,030 research outputs found
Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI
This paper investigates the problem of adaptive power allocation for
distributed best linear unbiased estimation (BLUE) of a random parameter at the
fusion center (FC) of a wireless sensor network (WSN). An optimal
power-allocation scheme is proposed that minimizes the -norm of the vector
of local transmit powers, given a maximum variance for the BLUE estimator. This
scheme results in the increased lifetime of the WSN compared to similar
approaches that are based on the minimization of the sum of the local transmit
powers. The limitation of the proposed optimal power-allocation scheme is that
it requires the feedback of the instantaneous channel state information (CSI)
from the FC to local sensors, which is not practical in most applications of
large-scale WSNs. In this paper, a limited-feedback strategy is proposed that
eliminates this requirement by designing an optimal codebook for the FC using
the generalized Lloyd algorithm with modified distortion metrics. Each sensor
amplifies its analog noisy observation using a quantized version of its optimal
amplification gain, which is received by the FC and used to estimate the
unknown parameter.Comment: 6 pages, 3 figures, to appear at the IEEE Military Communications
Conference (MILCOM) 201
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Distributed Detection and Estimation in Wireless Sensor Networks
Wireless sensor networks (WSNs) are typically formed by a large number of densely deployed, spatially distributed sensors with limited sensing, computing, and communication capabilities that cooperate with each other to achieve a common goal. In this dissertation, we investigate the problem of distributed detection, classification, estimation, and localization in WSNs. In this context, the sensors observe the conditions of their surrounding environment, locally process their noisy observations, and send the processed data to a central entity, known as the fusion center (FC), through parallel communication channels corrupted by fading and additive noise. The FC will then combine the received information from the sensors to make a global inference about the underlying phenomenon, which can be either the detection or classification of a discrete variable or the estimation of a continuous one.;In the domain of distributed detection and classification, we propose a novel scheme that enables the FC to make a multi-hypothesis classification of an underlying hypothesis using only binary detections of spatially distributed sensors. This goal is achieved by exploiting the relationship between the influence fields characterizing different hypotheses and the accumulated noisy versions of local binary decisions as received by the FC, where the influence field of a hypothesis is defined as the spatial region in its surrounding in which it can be sensed using some sensing modality. In the realm of distributed estimation and localization, we make four main contributions: (a) We first formulate a general framework that estimates a vector of parameters associated with a deterministic function using spatially distributed noisy samples of the function for both analog and digital local processing schemes. ( b) We consider the estimation of a scalar, random signal at the FC and derive an optimal power-allocation scheme that assigns the optimal local amplification gains to the sensors performing analog local processing. The objective of this optimized power allocation is to minimize the L 2-norm of the vector of local transmission powers, given a maximum estimation distortion at the FC. We also propose a variant of this scheme that uses a limited-feedback strategy to eliminate the requirement of perfect feedback of the instantaneous channel fading coefficients from the FC to local sensors through infinite-rate, error-free links. ( c) We propose a linear spatial collaboration scheme in which sensors collaborate with each other by sharing their local noisy observations. We derive the optimal set of coefficients used to form linear combinations of the shared noisy observations at local sensors to minimize the total estimation distortion at the FC, given a constraint on the maximum average cumulative transmission power in the entire network. (d) Using a novel performance measure called the estimation outage, we analyze the effects of the spatial randomness of the location of the sensors on the quality and performance of localization algorithms by considering an energy-based source-localization scheme under the assumption that the sensors are positioned according to a uniform clustering process
Performance Analysis and Design of Maximum Ratio Combining in Channel-Aware MIMO Decision Fusion
In this paper we present a theoretical performance analysis of the maximum
ratio combining (MRC) rule for channel-aware decision fusion over
multiple-input multiple-output (MIMO) channels for (conditionally) dependent
and independent local decisions. The system probabilities of false alarm and
detection conditioned on the channel realization are derived in closed form and
an approximated threshold choice is given. Furthermore, the channel-averaged
(CA) performances are evaluated in terms of the CA system probabilities of
false alarm and detection and the area under the receiver operating
characteristic (ROC) through the closed form of the conditional moment
generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC)
quadrature rules. Furthermore, we derive the deflection coefficients in closed
form, which are used for sensor threshold design. Finally, all the results are
confirmed through Monte Carlo simulations.Comment: To appear in IEEE Transactions on Wireless Communication
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