Cramér-Rao Bound for Drone-Assisted MIMO-OFDM IoT Systems Based on PARATUCK Tensor Model

In the internet of things (IoT) communication systems, channel modeling and channel estimation are the focus of research, especially for the acquisition of channel parameters such as angles of departure (AoDs), angles of arrival (AoAs), and time delays. In this paper, we investigate the performance lower bound of channel parameter estimation algorithms associated with the IoT systems. Based on the high maneuverability of drone and the cost-effectiveness of reconfigurable intelligent surface (RIS), we consider a drone-assisted millimeter-wave (mmWave) multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) IoT system with RIS installed on the drone. Then, the received signal is constructed as a PARATUCK tensor model and the Cramér-Rao bound (CRB) for each channel parameter is derived to predict the behavior of channel parameter estimation algorithms. Moreover, the impact of different numbers of paths, RIS elements, and time resources on the CRB is also analyzed.