Power allocation in vector estimation systems with the impact of wireless channel uncertainty

Summary Motivated by the fact that wireless channel uncertainty always exists and influences the distributed estimation system, this paper proposes power allocation schemes for linear minimum mean square error estimation. We consider training‐based vector systems and investigate how the power allocation ratio between training and transmitting is influenced by the system information, assuming that the sum of training and data transmitting power is fixed. We propose to use the average mean square error as the distortion measure so as to fulfill the statistical characteristics of channel estimation. We derive the closed‐form solutions to the optimal power allocation ratio, which is the function of system parameters, such as the vector signal's size, channel covariance, and noise covariance. Monte Carlo simulations are carried out to verify the performance of the proposed methods. Simulation results show that (i) in vector estimation system, the power allocation set to be 0.5 is usually not optimal; (ii) compared with training based, the equal power allocation system, the newly proposed methods could significantly improve the estimation performance. Copyright © 2015 John Wiley & Sons, Ltd. We have proposed a two‐step vector estimation approach for power allocation. In the first step, training‐based channel estimation is carried out at the fusion center. In the second step, the fusion center uses the data received from sensors to obtain the vector estimation. The problem we deal with is how to obtain the optimal power allocation between step one and step two. The closed‐form solution to the optimal power allocation ratio is derived to obtain the optimal vector estimation performance.