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In this paper, we consider a wireless powered mobile edge computing (MEC)network, where the distributed energy-harvesting wireless devices (WDs) arepowered by means of radio frequency (RF) wireless power transfer (WPT). Inparticular, the WDs follow a binary computation offloading policy, i.e., dataset of a computing task has to be executed as a whole either locally orremotely at the MEC server via task offloading. We are interested in maximizingthe (weighted) sum computation rate of all the WDs in the network by jointlyoptimizing the individual computing mode selection (i.e., local computing oroffloading) and the system transmission time allocation (on WPT and taskoffloading). The major difficulty lies in the combinatorial nature ofmulti-user computing mode selection and its strong coupling with transmissiontime allocation. To tackle this problem, we propose a joint optimization methodbased on the ADMM (alternating direction method of multipliers) decompositiontechnique. Simulation results show that the proposed method can efficientlyachieve near-optimal performance under various network setups, andsignificantly outperform the other representative benchmark methods considered.Besides, using both theoretical analysis and numerical study, we show that theproposed method enjoys low computational complexity against the increase ofnetworks size.

An ADMM Based Method for Computation Rate Maximization in Wireless Powered Mobile-Edge Computing Networks