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Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

Graduate School of Integrated Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu-shi, Shizuoka 432-8011, Japan
NTT DOCOMO, INC., Urban Sensing Research Group, Research Laboratories, 3-6 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-8536, Japan
Authors to whom correspondence should be addressed.
Sensors 2019, 19(7), 1674;
Received: 27 February 2019 / Revised: 30 March 2019 / Accepted: 7 April 2019 / Published: 8 April 2019
(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)
PDF [3276 KB, uploaded 12 April 2019]


The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load. View Full-Text
Keywords: mobile data offloading; reinforcement learning; delay tolerant mobile data offloading; reinforcement learning; delay tolerant

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Mochizuki, D.; Abiko, Y.; Saito, T.; Ikeda, D.; Mineno, H. Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning. Sensors 2019, 19, 1674.

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