@inproceedings{d287e23e27654791a4f392c1a3d671bc,
title = "Computation Efficiency Maximization for Wireless-Powered Mobile Edge Computing",
abstract = "Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.",
keywords = "Computation efficiency, edge computing, fairness, non-linear energy harvesting model, resource allocation",
author = "Fuhui Zhou and Haijian Sun and Zheng Chu and Hu, {Rose Qingyang}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Communications Conference, GLOBECOM 2018 ; Conference date: 09-12-2018 Through 13-12-2018",
year = "2018",
doi = "10.1109/GLOCOM.2018.8647509",
language = "English",
series = "2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings",
address = "United States",
}
