  Mobile-edge computing (MEC) and wireless power transfer (WPT) have been
recognized as promising techniques in the Internet of Things (IoT) era to
provide massive low-power wireless devices with enhanced computation capability
and sustainable energy supply. In this paper, we propose a unified MEC-WPT
design by considering a wireless powered multiuser MEC system, where a
multi-antenna access point (AP) (integrated with an MEC server) broadcasts
wireless power to charge multiple users and each user node relies on the
harvested energy to execute computation tasks. With MEC, these users can
execute their respective tasks locally by themselves or offload all or part of
them to the AP based on a time division multiple access (TDMA) protocol.
Building on the proposed model, we develop an innovative framework to improve
the MEC performance, by jointly optimizing the energy transmit beamformer at
the AP, the central processing unit (CPU) frequencies and the numbers of
offloaded bits at the users, as well as the time allocation among users. Under
this framework, we address a practical scenario where latency-limited
computation is required. In this case, we develop an optimal resource
allocation scheme that minimizes the AP's total energy consumption subject to
the users' individual computation latency constraints. Leveraging the
state-of-the-art optimization techniques, we derive the optimal solution in a
semi-closed form. Numerical results demonstrate the merits of the proposed
design over alternative benchmark schemes.
