摘要：With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.
关键词：mobile edge computing; wireless power transmission; convex optimization; augmented Lagrangian method mobile edge computing ; wireless power transmission ; convex optimization ; augmented Lagrangian method
