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

TL;DR

This paper proposes robust hybrid beamforming strategies for mmWave MIMO IoT networks that operate effectively despite imperfect channel knowledge, optimizing estimation accuracy without requiring a baseband combiner at the fusion center.

Contribution

It introduces a novel hybrid beamforming design framework that accounts for imperfect CSI and eliminates the need for a baseband receiver at the fusion center, using a two-step optimization approach.

Findings

Robust beamformers improve estimation accuracy under CSI uncertainty.

The proposed designs outperform non-robust counterparts in simulations.

The centralized MMSE bound provides a benchmark for performance evaluation.

Abstract

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 IoTNo power constraints, while also eliminating 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 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.