PROJECT TITLE :
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading - 2018
Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of the many low-power wireless networks, e.g., wireless sensor networks and Web of Things. The recent development of radio frequency-primarily based wireless power transfer (WPT) and mobile edge computing (MEC) technologies offer a promising answer to completely remove these limitations so as to attain sustainable device operation and enhanced computational capability. In this Project, we tend to consider a multi-user MEC network powered by the WPT, where every energy-harvesting WD follows a binary computation offloading policy, i.e., the info set of a task should be executed as a full either domestically or remotely at the MEC server via task offloading. In specific, we have a tendency to are inquisitive about maximizing the (weighted) add computation rate of all the WDs within the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and therefore the system transmission time allocation (on WPT and task offloading). The major problem lies in the combinatorial nature of the multi-user computing mode selection and its robust coupling with the transmission time allocation. To tackle this problem, we tend to initial contemplate a decoupled optimization, where we assume that the mode selection is given and propose a easy bi-section search algorithm to get the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode choice. The methodology is simple in implementation however may suffer from high computational complexity in a very massive-size network. To handle this drawback, we any propose a joint optimization technique primarily based on the alternating direction methodology of multipliers (ADMM) decomposition technique, that enjoys a a lot of slower increase of computational complexity because the networks size will increase. Extensive simulations show that each the proposed strategies can efficiently achieve a near-optimal performance underneath varied network setups, and considerably outperform the other representative benchmark strategies thought-about.
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