SHS Web Conf.
Volume 144, 20222022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
|Number of page(s)||5|
|Section||Application of Artificial Intelligence Technology and Machine Learning Algorithms|
|Published online||26 August 2022|
Computation for Reinforcement Learning at the Mobile Edge Network
School of Electrical and Information engineering, Northeast Agriculture University, Harbin, Heilongjiang province, China, 150006
* Corresponding author. Email: firstname.lastname@example.org
Wireless Mobile Edge Computing (MEC) is one of several promising models emerging in recent years. A wireless powered MEC network is researched in this paper. The proposed online offload framework based on deep reinforcement learning (DROO) draws lessons from the previous offload experience. As such, it fulfills the need to solve complex problems like MIP. The computational complexity will not surge with the increase of network size. DROO decomposes the original optimization problem into a secondary problem from design to unloading decision and a resource allocation sub-problem. It is suitable for the space without interruption, and there is no need to discretize the channel gain, so as to avoid the disaster caused by the problem of dimension. By studying the simulation results, the DROO algorithm is found that it can achieve almost perfect performance by designing and calculating the method at the current stage, but it also needs to reduce CPU execution latency by at least one order of magnitude, so as to ensure that the realtime system optimization is fading. It is really feasible to use MEC networks when wirelessly powered in the environment.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.