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Access control of MTC devices using reinforcement learning approach
- Moon, Jihun;
- Lim, Yujin
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26초록
MTC (Machine Type Communication) applications are one of the promising applications in 3GPP system because it connects a huge number of devices into one network. In MTC applications, a huge number of devices attempt to access a system using contention-based random access scheme in a short period. It makes a system overloaded. To solve the overload problem, we propose an access control scheme of devices using Q-learning algorithm. Experimental results show that the scheme adaptively adjusts access control parameter. © 2017 IEEE.
키워드
Access class barring; LTE-A networks; machinetype communication; random access; Learning algorithms; Mobile telecommunication systems; Reinforcement learning; Wireless telecommunication systems; Access class; Access control schemes; LTE-A networks; Machine-type communications; Q-learning algorithms; Random access; Random access schemes; Reinforcement learning approach; Access control
- 제목
- Access control of MTC devices using reinforcement learning approach
- 저자
- Moon, Jihun; Lim, Yujin
- 발행일
- 2017-01
- 유형
- Conference Paper
- 저널명
- International Conference on Information Networking
- 페이지
- 641 ~ 643