Access control of MTC devices using reinforcement learning approach
- Authors
- Moon, Jihun; Lim, Yujin
- Issue Date
- Jan-2017
- Publisher
- IEEE Computer Society
- Keywords
- Access class barring; LTE-A networks; machinetype communication; random access
- Citation
- International Conference on Information Networking, pp 641 - 643
- Pages
- 3
- Journal Title
- International Conference on Information Networking
- Start Page
- 641
- End Page
- 643
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8595
- DOI
- 10.1109/ICOIN.2017.7899576
- ISSN
- 1976-7684
- Abstract
- 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.
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