Position Errors Analysis, Prediction and Recovery for UAV-enabled Virtual Antenna Array

Unmanned aerial vehicle (UAV) communications have a promising prospect in the next-generation network. Among various technologies, collaborative beamforming (CB) can enhance communication abilities and ranges. However, the CB method may be subject to interference issues in the physical en-vironment such as the wind and incorrect positioning, especially in outdoor conditions. In this paper, we aim to form a virtual antenna array (VAA) consists of UAV elements and simulate the interference of the physical environment on the positions of the UAV elements by considering positions errors. Then, we propose a model based on deep learning to predict the impact of errors on UAV-enabled VAA. Finally, aiming to repair the performance loss caused by the position errors of the UAV elements, we propose a performance recovery method by re-optimizing the excitation current weights of the UAV elements via the firefly algorithm (FA). Simulation results show that the position errors decrease the directivity and increase the maximum sidelobe level (SLL). Moreover, the proposed deep learning model can reflect the performance loss under different conditions. In addition, the proposed performance recovery method is effective.