Incentive mechanisms for mobile data offloading through operator-owned WiFi access points

2020 ◽  
Vol 174 ◽  
pp. 107226 ◽  
Author(s):  
Yi Zhao ◽  
Ke Xu ◽  
Yifeng Zhong ◽  
Xiang-Yang Li ◽  
Ning Wang ◽  
...  
Keyword(s):  
Data Offloading ◽  
Incentive Mechanisms ◽  
Mobile Data ◽  
Access Points ◽  
Mobile Data Offloading

scholarly journals Mobile Data Offloading Through A Third-Party WiFi Access Point: An Operator's Perspective

2014 ◽  
Vol 13 (10) ◽  
pp. 5340-5351 ◽  
Author(s):  
Xin Kang ◽  
Yeow-Khiang Chia ◽  
Sumei Sun ◽  
Hon Fah Chong
Keyword(s):  
Access Point ◽  
Third Party ◽  
Data Offloading ◽  
Mobile Data ◽  
Mobile Data Offloading

Mobile Data Offloading for Green Wireless Networks

2017 ◽  
Vol 24 (4) ◽  
pp. 31-37 ◽  
Author(s):  
Haoran Yu ◽  
Man Hon Cheung ◽  
George Iosifidis ◽  
Lin Gao ◽  
Leandros Tassiulas ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Data Offloading ◽  
Mobile Data ◽  
Mobile Data Offloading

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

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1674 ◽  
Author(s):  
Daisuke Mochizuki ◽  
Yu Abiko ◽  
Takato Saito ◽  
Daizo Ikeda ◽  
Hiroshi Mineno
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Data Communication ◽  
Mobile Network ◽  
Utilization Efficiency ◽  
Bandwidth Utilization ◽  
Delay Tolerance ◽  
Data Offloading ◽  
Mobile Data ◽  
Mobile Data Offloading

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.

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