Data-Aided Doppler Frequency Shift Estimation and Compensation for UAVs

Abstract

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for the mobile broadband service with gigabyte per second data rate in the UAV-aided fifth generation (5G) IoT system. However, Doppler frequency shift (DFS) deteriorates the link performance of UAV-aided 5G system in the highly dynamic and mobile scenarios. Therefore, a data-aided DFS estimation and compensation approach is proposed to optimize the DFS estimation process using historical estimation results, aiming to achieve a fast and accurate DFS compensation. The performance of the proposed DFS estimation algorithm is evaluated by both cost function of accuracy based on frame structure and Cramer–Rao lower bound in terms of the mean-squared error and signal-to-noise ratio. Furthermore, an adaptive frequency-domain DFS compensation algorithm is designed by leveraging DFS estimation results to enhance the quality of communication link for UAV-aided 5G system, achieving an optimal tradeoff between accuracy and complexity. Finally, both link-level simulation platform and hardware testbed are designed and developed to evaluate the performance of our proposed data-aided approach over other conventional algorithms.