Access control of MTC devices using reinforcement learning approach

Moon, Jihun; Lim, Yujin

doi:10.1109/ICOIN.2017.7899576

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Access control of MTC devices using reinforcement learning approach

Moon, Jihun;
Lim, Yujin

Citations

SCOPUS

26

초록

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

DOI: 10.1109/ICOIN.2017.7899576

발행일: 2017-01

유형: Conference Paper

저널명: International Conference on Information Networking

페이지: 641 ~ 643

언어: ENG

출판사: IEEE Computer Society

발행국가: 미국

분량: 3 페이지

ISSN: P 1976-7684