An Approach for Maximizing Computation Bits in UAV-Assisted Wireless Powered Mobile Edge Computing Networks

With the development of the Internet of Things (IoT), IoT nodes with limited energy and computing capability are no longer able to address increasingly complex computational tasks. To address this issue, an Unmanned Aerial Vehicle (UAV)-assisted Wireless Power Transfer (WPT) Mobile Edge Computing (MEC) system is proposed in this study. By jointly optimizing variables such as energy harvesting time, user transmission power, user offloading time, CPU frequency, and UAV deployment location, the system aims to maximize the number of computation bits by the users. This optimization yields a challenging non-convex optimization problem. To address these issues, a two-stage alternating method based on the Lagrangian dual method and the Successive Convex Approximation (SCA) method is proposed to decompose the initial problem into two sub-problems. Firstly, the UAV position is fixed to obtain the optimal values of other variables, and then the UAV position is optimized based on the solved variables. Finally, this iterative process continues until the algorithm convergences, and the optimal solution for the given problem is obtained. The simulation results indicate that the proposed algorithm exhibits good convergence. Compared to other benchmark solutions, the proposed approach performs optimally in maximizing computation bits.