@inproceedings{64b48aa7aa994c6b9228ab0478058b73,
title = "A learning-based expected best offloading strategy in wireless edge networks",
abstract = "Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions.",
keywords = "Energy consumption, Latency, Mobile edge computing, Non-convex optimization, Offloading, Q-learning, Resource management",
author = "Wu, {Yi Chen} and DInh, {Thinh Quang} and Yaru Fu and Che Lin and Quek, {Tony Q.S.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Global Communications Conference, GLOBECOM 2019 ; Conference date: 09-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
doi = "10.1109/GLOBECOM38437.2019.9013836",
language = "English",
series = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
booktitle = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
}