Joint Optimal Placement and Dynamic Resource Allocation for multi-UAV Enhanced Reconfigurable Intelligent Surface Assisted Wireless Network

In this paper, the optimal placement and dynamic resource allocation problem has been investigated for multi-UAV enhanced reconfigurable intelligent surface (RIS) assisted wireless network with uncertain time-varying wireless channels. This paper aims to stimulate the potential of RIS by adding mobility to RIS through unmanned aerial vehicles (UAV). A novel UAV optimal placement and dynamic resource allocation technique needs to be developed jointly. A novel online rein-forcement learning based optimal resource allocation algorithm has been designed. Firstly, a deep Q-learning based K-means clustering algorithm is utilized to optimize the deployment of the multi-UAV. Then, an online actor-critic reinforcement learning algorithm is developed to learn the optimal transmit power control as well as mobile RIS phase shift control policy. Compared with conventional learning algorithms, the developed algorithm can learn the optimal resource allocation and multi-UAV placement for mobile RIS-assisted wireless networks in real-time even with uncertain and time-varying wireless channels. Eventually, numerical simulations are provided to demonstrate the effectiveness of developed schemes.