Regression-based beam training for UAV mmWave communications

Abstract

For unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication systems, efficient and accurate beam training is urgently required to overcome beam misalignment. By taking into account the mmWave propagation environment, a three-dimensional (3D) intelligent beam training strategy that leverages the polynomial regression (PR) model and optimized beam patterns is proposed in this paper. We treat mmWave beam selection as a PR problem. By using machine learning (ML), the regression function is determined. The training dataset applied in the ML method consists of measured power and estimated angles and is obtained by carefully designed beam patterns. Furthermore, a noise suppression method involving the use of a denoising autoencoder (DAE) is developed to overcome the noise sensitivity of the proposed regression model. The numerical simulation results demonstrate that our proposed beam training strategy is capable of obtaining the same precision as an exhaustive search with a shorter time.