Hardware-Aware Joint Localization-Synchronization and Tracking Using Reconfigurable Intelligent Surfaces in 5G and Beyond

This work investigates joint localization-synchronization and tracking in 5G and beyond. In particular, we target realistic circumstances where the theoretical assumptions of a perfect synchronous system and ideal transceivers no longer exist. We take a close look at the multiple-input single-output (MISO) millimeter-wave
$(mm$
wave) system that employs the orthogonal frequency-division multiplexing (OFDM), with the existence of the reconfigurable intelligent surface (RIS). Given the known positions of the RIS and the base station (BS), the single antenna mobile station (MS) can estimate its position and jointly synchronize itself with the multiple antennas BS. This can be accomplished using a maximum likelihood estimator (MLE) whose cost function accounts for the transceivers’ hardware impairments (HWIs). In our tracking scenario, the Kalman filter based tracker (KFT) follows the MLE by paying attention to HWIs-driven accuracy degradation. We then present the theoretical bounds of the tracking accuracy, expressed in terms of the Bayesian Cramer-Rao bound (BCRB). Finally, we conduct computer simulations to demonstrate the adverse deterioration in the joint localization-synchronization process accuracy as well as the accuracy of tracking due to the HWIs.