Autonomous Learning based Proactive Deployment for UAV Assisted Wireless Networks

Unmanned aerial vehicle (UAV) assisted wireless network is emerging as a promising technology to address the extremely high and dynamic traffic demands in future communication systems. In this paper, we investigate the on-demand deployment of UAV assisted wireless networks (OWN) problem. We propose an efficient autonomous learning framework (ALF), for learning a proactive and optimal on-demand deployment policy to complement terrestrial networks. In ALF, the OWN problem is solved in two co-related stages: the demand prediction stage and the proactive deployment stage. We first design a dual transformer network (DTN) to forecast the wireless traffic in the demand prediction stage. To decrease the complexity of DTN, we employ a patch embedding method and a modified self-attention scheme to improve the efficiency. With the predicted traffic demands, we jointly optimize the UAVs' location and wireless resource allocation by formulating it as a non-convex mixed integer nonlinear programming (MINLP) problem in the proactive deployment stage. To provide an efficient guaranteed solution to the MINLP problem, a multi-cut general benders decomposition algorithm is proposed to decompose the optimization problem into two subproblems. We theoretically prove that the proposed algorithm can achieve a T-optimal convergence. Extensive simulation results show the proposed solution outperforms existing baselines.