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AORS: adaptive mobile data offloading based on attractor selection in heterogeneous wireless networks

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Abstract

The unforeseen mobile data explosion poses a major challenge to the performance of today’s cellular networks, and is in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an attractor-aware offloading ratio selection (AORS) algorithm, which can adaptive select an optimum offloading ratio based on attractor selection for the current networks environment. In the proposed algorithm, the throughput of AP and the cellular load corresponding to the coverage area of the AP, are mapped into the cell activity, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor for each users, the optimal offloading ratio \(\phi \), to adapt to the dynamic network environment. Hence, according to the offloading ratio \(\phi \), the part of the cellular traffic will be transmitted via WiFi networks. Through simulation, we show that the proposed AORS algorithm outperforms the existing ones with 42 % higher heterogeneous network throughput in a dense traffic environment.

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Acknowledgments

This research is supported by the WLAN achievement transformation based on SDN of Beijing Municipal Commission of Education, and the Grant Number is 201501001.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Zhiqun Hu, Xiangming Wen, Zhaoming Lu & Wenpeng Jing

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  1. Zhiqun Hu
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  2. Xiangming Wen
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  4. Wenpeng Jing
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Correspondence to Zhiqun Hu.

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Hu, Z., Wen, X., Lu, Z. et al. AORS: adaptive mobile data offloading based on attractor selection in heterogeneous wireless networks. Wireless Netw 23, 831–842 (2017). https://doi.org/10.1007/s11276-016-1195-1

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