Deep Learning Based Multiple Beamforming for 5G UAV IoT Networks
This article develops a novel hierarchical 5G Internet of Things network with unmanned aerial vehicles (UAVs) in the sky. In the proposed system, the leader UAV plays a vital role in the communication with ground base stations and other UAVs. The leader UAV relies on multiple beamforming to establish and maintain reliable broadband connections, which requires the location and altitude information of the UAV. Therefore, we propose a novel deep learning algorithm based on gated recurrent units and autoencoder for trajectory prediction and pose estimation. Simulation results show that this algorithm greatly improves the performance of the entire system, and has obvious advantages compared to traditional methods.
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