Abstract
We consider the distributed estimation of a random vector signal in a power constraint wireless sensor network (WSN) that follows a multiple-input and multiple-output (MIMO) coherent multiple access channel model. We design linear coding matrices based on linear minimum mean-square error (LMMSE) fusion rule that accommodates spatial correlated data. We obtain a closed-form solution that follows a water-filling strategy. We also derive a lower bound to this model. Simulation results show that when the data is more correlated, the distortion in terms of mean-square error (MSE) degrades. By taking into account the effects of correlation, observation, and channel matrices, the proposed method performs better than equal power method.
Highlights
The wireless sensor network (WSN) is a potential technology in many application areas including environmental monitoring, health, security and surveillance, and robotic exploration [1]
The distributed estimation is applied on the orthogonal multiple access channel (MAC) model [4, 6, 8] and the coherent MAC model [2, 5] that considers single-input single-output (SISO)
Compared to the existing literature, the contribution of this work lies in the following aspects: we considered multiple-input and multiple-output (MIMO) model based on the water-filling algorithm and a spatial correlated data as an extension of [2, 5]
Summary
The wireless sensor network (WSN) is a potential technology in many application areas including environmental monitoring, health, security and surveillance, and robotic exploration [1]. If the targets or the sensors are close to each other, the data will be potentially correlated Such a problem has been investigated in [3, 6, 12, 13]. The targets are observed by multiple sensors that apply an analog forwarding scheme This scheme will multiply the observed data with a designed coding matrix in each sensor, which results in encoded messages. We consider the separation of the estimation under two conditions, that is, distortion due to noisy observation and distortion due to channel noise Both of the distortions should be minimized by designing coding matrices under total power constraint. Compared to the existing literature, the contribution of this work lies in the following aspects: we consider MIMO model based on the water-filling algorithm and a spatial correlated data as an extension of [2, 5]. The operator diag(⋅), manipulates the diagonal elements of matrix or a column vector into diagonal matrix and tr(⋅) is the trace of a matrix
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call