Generalized UAV Deployment for UAV-Assisted Cellular Networks

As appropriate deployment of unmanned aerial vehicles (UAVs) in UAV-assisted wireless networks is critical for the next-generation wireless networks, we in this paper propose centralized and decentralized UAV deployment approaches that can be applied to any UAV-assisted wireless networks for any performance metrics. The proposed centralized deployment combines the deep neural network (DNN)-based surrogate model with the zeroth-order optimization (ZOO) such that the deployment can be optimized via using the predicted network performance of the surrogate model. Since the accurate prediction of the DNN surrogate model is critical, we discuss its design and update approaches. To let UAVs update their locations for better network performance by exchanging local information with neighboring UAVs, the proposed decentralized deployment approaches combine distributed optimization frameworks with ZOO and DNN surrogate model. We conduct realistic simulations in different network scenarios with different performance metrics to evaluate our proposed approaches. Results show that our proposed can outperform all the reference schemes in all scenarios considering different performance metrics.