Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Estimation over fading channels with limited feedback using distributed sensing
IEEE Transactions on Signal Processing
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
Type based estimation over multiaccess channels
IEEE Transactions on Signal Processing
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
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We consider a distributed estimation problem over fading multiple-access channels. Several sensors transmit a parameter to a fusion center using an amplify-and-forward scheme. The fusion center has multiple antennas and uses the transmissions from the sensors to estimate the parameter. In this paper we will assume that the sensors have full channel state information and formulate an optimization problem that needs to be solved to select the best sensor gains. We also propose bounds on the performance and two practical methods that can be implemented in this scenario. By comparing the performance, it is shown that though there is benefit in having multiple antennas at the fusion center, when full channel information is available at the sensors, the gain in performance is at most a factor of 2.