Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks

TL;DR

This paper introduces hybrid precoder and combiner designs for decentralized parameter estimation in mmWave MIMO wireless sensor networks, leveraging sparse signal recovery techniques to improve estimation accuracy under various power constraints and noise conditions.

Contribution

It proposes a novel hybrid transceiver design framework based on MMV sparse recovery and SOMP, tailored for decentralized estimation in mmWave MIMO WSNs, with analytical bounds and low complexity.

Findings

The proposed designs achieve near-optimal estimation performance.

Simulation results confirm the effectiveness of the hybrid transceiver approach.

Analytical bounds validate the accuracy of the decentralized estimation schemes.

Abstract

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMV)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer-Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.