Title :
High SNR Linear Estimation of Vector Sources
Author :
Behbahani, Alireza S. ; Eltawil, Ahmed M. ; Jafarkhani, Hamid
Author_Institution :
Center for Pervasive Commun. & Comput., Univ. of California, Irvine, Irvine, CA, USA
Abstract :
In this letter, we extend our prior work and consider decentralized estimation of unknown random vectors under high observation signal-to-noise ratio (SNR). A linear model is considered for decentralized estimation of vector sources. Observation models and sensor operations are both linear. Furthermore, the channel between the wireless sensors and fusion center (FC) is a coherent multiple access channel (MAC). Each sensor observes a different vector source. Sensors are designed to minimize the total mean square error (MSE) at the FC subject to the individual transmit power constraints at the sensors. We first provide the solution for scalar sources under high observation SNR regime. Then, we use the provided solution for scalar sources and extend it to the case of vector sources.
Keywords :
mean square error methods; sensor fusion; wireless channels; wireless sensor networks; FC; coherent MAC; coherent multiple access channel; fusion center; power transmission; scalar source; signal-to-noise ratio; total MSE minimization; total mean square error minimization; unknown random vector source decentralized estimation; vector source high SNR linear estimation; wireless sensor network; Algorithm design and analysis; Covariance matrices; Estimation; Noise measurement; Signal to noise ratio; Wireless sensor networks; Distributed estimation; multiple access channel; wireless sensor networks;
Journal_Title :
Wireless Communications Letters, IEEE
DOI :
10.1109/LWC.2014.2359210
