Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Principles of mobile communication (2nd ed.)
Principles of mobile communication (2nd ed.)
Convex Optimization
Estimation over fading channels with limited feedback using distributed sensing
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Estimation Diversity and Energy Efficiency in Distributed Sensing
IEEE Transactions on Signal Processing
Linear Coherent Decentralized Estimation
IEEE Transactions on Signal Processing
Performance of Distributed Estimation Over Unknown Parallel Fading Channels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
IEEE Transactions on Signal Processing
To code, or not to code: lossy source-channel communication revisited
IEEE Transactions on Information Theory
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We study linear distributed estimation with coherent multiple access channel model and MMSE fusion rule. The flat fading channels are assumed unknown at the fusion center and need to be estimated. We adopt a two-phase approach, which first estimates channels and then estimates the source signal, to minimize the MSE of the estimated signal. We study optimal power allocation under a total network power constraint. We consider the optimal power allocation scheme in which training power and data power for each sensor are optimized, and the equal power allocation scheme in which training power is optimized while data power for each sensor is set equal. In both schemes, the problem is formulated as a constrained optimization problem and analytical closed-form solution is obtained. Analytic results reveal that (i) with estimated channels, the MSE approaches to a finite nonzero value as the number of sensors increases; (ii) the optimal training powers are the same in both schemes; (iii) the MSE performance compared with the case when channels are known shows the penalty caused by channel estimation becomes worse as the number of sensors increases. Simulation results verify our findings.