Tracking Position and Orientation through Millimeter Wave Lens MIMO in 5G Systems

TL;DR

This paper introduces a novel millimeter-wave lens MIMO method for 5G systems that enables accurate user position and orientation tracking using a single transmitter, leveraging sparse signal recovery techniques.

Contribution

It proposes a support detection-based channel training method for mm-wave lens MIMO, improving sparse detection probability over compressed sensing and enabling joint position and orientation tracking.

Findings

High accuracy in position and orientation estimation using single transmitter signals.

Enhanced sparse detection probability compared to traditional compressed sensing methods.

Effective joint beamformer design for tracking based on initial channel estimates.

Abstract

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and time-of-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.