@inproceedings{a9756ae2567e410b8d82767240d09b04,
title = "Deep reinforcement learning and optimization based green mobile edge computing",
abstract = "In mobile edge computing (MEC) networks, by offloading tasks (partially or completely) to the MEC server, it becomes possible to complete computation-intensive and latency-critical applications without communicating with the cloud center, resulting in dramatic reduction both in latency and energy consumption. Performance improvements depend on the offloading decisions at the user equipments (UEs) and computational resource allocation at the MEC server. In this paper, we aim to optimize the UE offloading data ratios and MEC computational resource allocation under delay constraints with the goal to minimize the global energy consumption. Both conventional optimization method and learning-based approach are studied. Simulation results are provided to compare the performances of different schemes.",
keywords = "Data offloading, Deep learning, Energy consumption, Latency, Mobile edge computing (MEC), Optimization",
author = "Yang Yang and Yulin Hu and Gursoy, {M. Cenk}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 ; Conference date: 09-01-2021 Through 13-01-2021",
year = "2021",
month = jan,
day = "9",
doi = "10.1109/CCNC49032.2021.9369566",
language = "English (US)",
series = "2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021",
}