Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Convex Optimization
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Fundamentals of wireless communication
Fundamentals of wireless communication
Distributed Source Coding: Theory, Algorithms and Applications
Distributed Source Coding: Theory, Algorithms and Applications
A decision theoretic approach to Gaussian sensor networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Estimation Diversity and Energy Efficiency in Distributed Sensing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Performance of Distributed Estimation Over Unknown Parallel Fading Channels
IEEE Transactions on Signal Processing
Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO
IEEE Transactions on Wireless Communications
The CEO problem [multiterminal source coding]
IEEE Transactions on Information Theory
The quadratic Gaussian CEO problem
IEEE Transactions on Information Theory
Multiterminal Source–Channel Communication Over an Orthogonal Multiple-Access Channel
IEEE Transactions on Information Theory
Uncoded Transmission Is Exactly Optimal for a Simple Gaussian “Sensor” Network
IEEE Transactions on Information Theory
A tutorial on decomposition methods for network utility maximization
IEEE Journal on Selected Areas in Communications
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We address the problem of power allocation in a wireless sensor network where distributed sensors amplify and forward their partial and noisy observations of a Gaussian random source to a remote fusion center (FC). The FC reconstructs the source based on linear minimum mean-squared error (LMMSE) estimation rule. Motivated by the availability of limited energy in the sensor networks, we undertake the design of power allocation based on minimization of the reconstruction distortion subject to a constraint on the network transmit power. The design is based on the following two cases: (i) exact knowledge of the channel gains and (ii) the estimates of the channel gains. We show that the distortion can be represented as a convex function of the transmit powers of the sensors. Moreover, we show that the power allocation based on this distortion function does not bear any closed form solution. To this end, we propose a novel design based on the successive approximation of the LMMSE distortion, which turns out to be simple, computationally efficient, and exhibits excellent convergence properties. The simulation examples illustrate that the proposed design holds considerable performance gain compared to a uniform power allocation scheme.