Abstract
In the current era of big data, wide varieties of high volumes of valuable data of different veracities can be generated or collected at a high velocity. One of the popular sources of these big data is the wireless networks. Nowadays, the use of smartphones has significantly increased the traffic load in these cellular networks. Consequently, system models that are practical in real-life scenario with the significant for increasing traffic load in cellular networks have drawn attentions of researchers. Studies have been conducted to solve the related interesting research problem of user association in this complex system model. Some of these studies formulated this research problem as a many-to-one matching game, in which users and base stations evaluate each other based on well-defined utilities. In this paper, we examine how the traditional data mining techniques—in particular, the frequent pattern mining techniques—help to solve this research problem. Specifically, we examine the mining of uplink-downlink user association data in wireless heterogeneous networks.
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Acknowledgments
This project is partially supported by NSERC (Canada) and University of Manitoba. Thanks E. Hossain and S. Sekander, both from University of Manitoba, for their introduction and expertise on the uplink-downlink association problem.
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Cuzzocrea, A., Grasso, G.M., Jiang, F., Leung, C.K. (2016). Mining Uplink-Downlink User Association in Wireless Heterogeneous Networks. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_57
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DOI: https://doi.org/10.1007/978-3-319-46257-8_57
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