Delay-Sensitive Task Offloading for Internet of Things in Nonorthogonal Multiple Access MEC Networks
- Authors
- Tuong Van Dat; Truong Thanh Phung; Tran, Anh-Tien; Masood, Arooj.; Lakew, Demeke Shumeye; Lee, Chunghyun.; Lee, Yunseong.; Cho, Sungrae
- Issue Date
- Oct-2020
- Publisher
- IEEE Computer Society
- Keywords
- Delay-sensitive task offloading; mobile edge computing; nonorthogonal multiple access; reinforcement learning; resource allocation
- Citation
- International Conference on ICT Convergence, v.2020, no.October, pp 597 - 599
- Pages
- 3
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2020
- Number
- October
- Start Page
- 597
- End Page
- 599
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44062
- DOI
- 10.1109/ICTC49870.2020.9289406
- ISSN
- 2162-1233
- Abstract
- With the rapid development of the Internet of Things (IoTs), the fifth-generation (5G) networks need to serve massive connection and accommodate ultra-low delay. In response to these challenges, mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) have been considered as the promising solutions. In this paper, we investigate the joint optimization problem of computation offloading and resource allocation in NOMA MEC networks to minimize the delay to complete tasks of all users. Different from the conventional optimization approach, we propose and develop an online solution based on deep reinforcement learning (DRL) algorithm, which can fit with dynamic networks with time-varying channels. In particular, we employ deep neural networks (DNNs) to process the raw state inputs and then output the computation offloading decision and resource allocation at different times. The weights of DNNs are continuously trained with the observed data via interactions with the environment. Simulation results reveal that our proposed algorithm achieves higher delay reduction compared to the existing strategies. © 2020 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44062)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.