LAUSR

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter-wave (mmWave) multiple-input–multiple-output (MIMO) Internet of Things Network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoT node power constraints, while also eliminating the need for a baseband receiver combiner at the fusion center (FC). To circumvent the nonconvexity of the hybrid beamformer design problem, the proposed approach initially determines the minimum mean-square error (MMSE) digital transmit precoder (TPC) weights followed by a simultaneous orthogonal matching pursuit (SOMP)-based framework for obtaining the analog RF and digital baseband TPCs. Robust hybrid beamformers are also derived for the realistic imperfect channel state information (CSI) scenario, utilizing both the stochastic and norm ball CSI uncertainty frameworks. The centralized MMSE bound derived in this work serves as a lower bound for the estimation performance of the proposed hybrid TPC designs. Finally, our simulation results quantify the benefits of the various designs developed.