Current Result Document : 23 / 23
| 한글제목(Korean Title) |
An Optimized Computation Offloading and Resource Allocation Strategy in Mobile Edge Computing Using Deep Reinforcement Learning |
| 영문제목(English Title) |
An Optimized Computation Offloading and Resource Allocation Strategy in Mobile Edge Computing Using Deep Reinforcement Learning |
| 저자(Author) |
Yu Qiao
Md. Shirajum Munir and Choong Seon Hong
|
| 원문수록처(Citation) |
VOL 49 NO. 01 PP. 0190 ~ 0192 (2022. 06) |
한글내용
(Korean Abstract) |
In this paper, we study the computation offloading strategy selection and computation rate maximization problems in a wireless powered mobile edge computing networks (MEC). Firstly, a mixed integer nonlinear programming (MINLP) problem is proposed to model the problem with jointly optimizing computation offloading and computation rate. Secondly, a computation mode selection method based on deep reinforcement learning (DRL) is proposed to decouple the joint optimization problem. Finally, with the given mode selection strategy, the MINLP problem can be converted into a convex problem while correspondingly obtaining the maximized computation rate. The results of simulation show that the proposed method is significantly improved compared with other baseline methods. |
영문내용
(English Abstract) |
|
| 키워드(Keyword) |
|
| 파일첨부 |
PDF 다운로드
|
