IRS/UAV-Based Edge-Computing/Traffic-Offloading Over RF-Powered 6G Mobile Wireless Networks
Fei Wang, Xi Zhang
Abstract
As widely recognized 6G promising techniques, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have recently attracted much research attention in both academia and industries. In this paper, we propose the schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over the radio-frequency (RF)-powered 6G mobile wireless networks, which consists of an UAV serving as an MEC-server to collect/receive data from multiple ground users (GUs) and several sets of IRSs to significantly enhance the simultaneous wireless data and energy transmissions. To overcome the difficulties of on-board energy limitation significantly affecting UAV’s sustainability and performances, we propose the schemes to minimize UAV’s total flying-time while enabling all GUs’ data to be collected/received. We formulate a processing-time minimization problem which jointly optimize IRSs’ phase shifts, UAV’s control in trajectory, flying-time, and resource-allocation, and scheduling of GUs. Since our joint-optimization problem is non-convex, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which thus can be iteratively solved. Finally, we validate and evaluate our proposed schemes through the conducted numerical analyses, showing that the total UAV flying-time can be significantly saved by around 20% under our proposed IRS/UAV-based schemes simultaneously supporting both wireless data and energy transmissions.