Convolutional neural network-based transmit antenna selection for UAV-ground station communications with time-varying channels

Abstract

This study proposes a novel transmit antenna selection (TAS) method for improving communications between the unmanned aerial vehicle (UAV) and ground station (GS). By selecting an appropriate UAV antenna, the signal-to-noise ratio (SNR) at the GS can be significantly enhanced. However, obtaining the necessary channel state information between the UAV and GS is challenging due to the UAV’s movement and resulting channel variations. To overcome this challenge, we propose an innovative approach that leverages a convolutional neural network to predict SNRs and a missing SNR completion method. The numerical evaluation demonstrates that the proposed method can effectively enhance TAS accuracy.