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This paper considers a wireless powered multiuser mobile edge computing (MEC)system, in which a multi-antenna hybrid access point (AP) wirelessly chargesmultiple users, and each user relies on the harvested energy to executecomputation tasks. We jointly optimize the energy beamforming and remote taskexecution at the AP, as well as the local computing and task offloading, aimingto minimize the total system energy consumption over a finite time horizon,subject to causality constraints for both energy harvesting and task arrival atthe users. In particular, we consider a practical scenario with casual taskstate information (TSI) and channel state information (CSI), i.e., only thecurrent and previous TSI and CSI are available, but the future TSI and CSI canonly be predicted subject to certain errors. To solve this real-time resourceallocation problem, we propose an offline-optimization inspired online designapproach. First, we consider the offline optimization case by assuming that theTSI and CSI are perfectly known a-priori. In this case, the energy minimizationproblem corresponds to a convex problem, for which the semi-closed-form optimalsolution is obtained via the Lagrange duality method. Next, inspired by theoptimal offline solution, we propose a sliding-window based online resourceallocation design in practical cases by integrating with the sequentialoptimization. Finally, numerical results show that the proposed joint wirelesspowered MEC designs significantly improve the system's energy efficiency, ascompared with the benchmark schemes that consider a sliding window of size oneor without such joint optimization.

Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality