Energy Consumption Minimization for UAV-Mounted Active RIS-Assisted Mobile Edge Computing

In this paper, we propose a UAV-mounted active reconfiguration intelligent surface (U-ARIS)-assisted mobile edge computing (MEC) system, where multiple users transmit data to the edge server with the assistance of an U-ARIS. Our goal is to minimize the overall energy consumption (EC) by jointly optimizing the UAV trajectory and amplification/reflection coefficients of the ARIS. Due to the highly coupled optimization variables, the formulated problem is non-convex and hard to be optimally solved. To address these challenges, we leveraged the block coordinate descent (BCD) and successive convex approximation (SCA) algorithm to iteratively solve the original problem for obtaining a local optimum. Numerical results demonstrate that the proposed algorithm can significantly reduce the EC of the U-ARIS-assisted MEC system when compared to other benchmark solutions.