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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 6474768, 7 pages
Research Article

A Reinforcement Learning Approach to Access Management in Wireless Cellular Networks

1Department of Computer Science, University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong, Gyeonggi-do 445-743, Republic of Korea
2Department of Information Technology Engineering, Sookmyung Women’s University, 100 Cheongpa-ro 47-gil, Yongsan-gu, Seoul 04310, Republic of Korea

Correspondence should be addressed to Yujin Lim;

Received 25 February 2017; Accepted 9 April 2017; Published 14 May 2017

Academic Editor: Syed Hassan Ahmed

Copyright © 2017 Jihun Moon and Yujin Lim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In smart city applications, huge numbers of devices need to be connected in an autonomous manner. 3rd Generation Partnership Project (3GPP) specifies that Machine Type Communication (MTC) should be used to handle data transmission among a large number of devices. However, the data transmission rates are highly variable, and this brings about a congestion problem. To tackle this problem, the use of Access Class Barring (ACB) is recommended to restrict the number of access attempts allowed in data transmission by utilizing strategic parameters. In this paper, we model the problem of determining the strategic parameters with a reinforcement learning algorithm. In our model, the system evolves to minimize both the collision rate and the access delay. The experimental results show that our scheme improves system performance in terms of the access success rate, the failure rate, the collision rate, and the access delay.