Robust Linear Transceiver Designs for Vector Parameter Estimation in MIMO Wireless Sensor Networks Under CSI Uncertainty

This work conceives the robust linear transceivers for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) multi-sensor network under imperfect channel state information (CSI) at the fusion center (FC). Both the popular stochastic (S-) and norm ball CSI uncertainty (N-CSIU) models are considered for robust design. The proposed techniques are based on two design criterion, the first being, minimizing the mean squared error (MSE) of the estimate at the FC subject to total network power or individual sensor power constraints. Second, minimizing the total power consumption in the network while meeting a predefined level of MSE performance. Furthermore, the framework for precoder and combiner optimization is based on results from majorization theory, which leads to non-iterative closed-form solutions for the transceivers. While the most general scenario with correlated parameters and arbitrary observation SNR is considered to begin with, scenarios with uncorrelated parameters and high observation SNR are also considered as special cases, which makes the analysis comprehensive. Simulation results are presented to demonstrate the efficacy of the proposed schemes.

Automated summary

This study proposes robust linear transceivers for estimating unknown vector parameters in a coherent multiple access channel (MAC) based multiple-input multiple-output (MIMO) multi-sensor network with imperfect channel state information (CSI) at the fusion center (FC). The techniques aim to minimize estimation error subject to power constraints, with non-iterative closed-form solutions based on majorization theory, considering scenarios with correlated and uncorrelated parameters and different observation signal-to-noise ratios (SNR). Simulation results demonstrate the effectiveness of the proposed schemes.