A Reinforcement Learning Based Low-Delay Scheduling with Adaptive Transmission
- Authors
- Zhao, Yu; Lee, Joohyun
- Issue Date
- Oct-2019
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- adaptive transmission; delay-power tradeoff; infinite-horizon Markov Decision Process; Reinforcement Learning
- Citation
- ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, pp.916 - 919
- Indexed
- SCIE
SCOPUS
- Journal Title
- ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
- Start Page
- 916
- End Page
- 919
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/4550
- DOI
- 10.1109/ICTC46691.2019.8939680
- ISSN
- 2162-1233
- Abstract
- As modern communication systems become indispensable, the requirements for communication systems such as delay and power get more stringent. In this paper, we adopt a Reinforcement Learning (RL) based approach to obtain the optimal trade-off between delay and power consumption for a given power constraint in a communication system whose conditions (e.g., channel conditions, traffic arrival rates) can change over time. To this end, we first formulate this problem as an infinite-horizon Markov Decision Process (MDP) and then Q-learning is adopted to solve this problem. To handle the given power constraint, we apply the Lagrange multiplier method that transforms a constrained optimization problem into a non-constrained problem. Finally, via simulation, we show that Q-learning achieves the optimal policy. © 2019 IEEE.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/4550)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.