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Linear hybrid beamformer designs are conceived for the decentralizedestimation of a vector parameter in a millimeter wave (mmWave) multiple-inputmultiple-output (MIMO) Internet of Things network (IoTNe). The proposed designsincorporate both total IoTNe and individual IoTNo power constraints, while alsoeliminating the need for a baseband receiver combiner at the fusion center(FC). To circumvent the non-convexity 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 orthogonalmatching pursuit (SOMP)-based framework for obtaining the analog RF and digitalbaseband TPCs. Robust hybrid beamformers are also derived for the realisticimperfect channel state information (CSI) scenario, utilizing both thestochastic and norm-ball CSI uncertainty frameworks. The centralized MMSE boundderived in this work serves as a lower bound for the estimation performance ofthe proposed hybrid TPC designs. Finally, our simulation results quantify thebenefits of the various designs developed.

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks