1. Cisco visual networking index: Global mobile data traffic forecast update, 2016-2021. San Jose, CA, USA: CISCO. 2017.
2. Andrews J G. Seven ways that HetNets are a cellular paradigm shift. IEEE Communications Magazine, 2013, 51(3): 136-144.
3. Lu X, Wetter E, Bharti N, et al. Approaching the limit of predictability in human mobility. Scientific Reports, 2013, 3 (Article 2923).
4. Qiao Y Y, Cheng Y H, Yang J, et al. A Mobility analytical framework for big mobile data in densely populated area. IEEE Transactions on Vehicular Technology, 2017, 66(2): 1443-1455.
5. Song L, Kotz D, Jain R, et al. Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Transactions on Mobile Computing, 2007, 5(12): 1633-1649.
6. Wickramasuriya D S, Perumalla C A, Davaslioglu K, et al. Base station prediction and proactive mobility management in virtual cells using recurrent neural networks. Proceedings of the IEEE 18th Wireless and Microwave Technology Conference (WAMICON’17), Apr 24-25, 2017, Cocoa Beach, FL, USA. Piscataway, NJ, USA: IEEE, 2017: 6p.
7. Cheikh A B, Ayari M, Langar R, et al. Optimized handoff with mobility prediction scheme using HMM for femtocell networks. Proceedings of the 2015 IEEE International Conference on Communications (ICC’15), Jun 8-12, 2015, London, UK. Piscataway, NJ, USA: IEEE, 2015: 3448-3453.
8. Farooq H, Asghar A, Imran A. Mobility prediction-based autonomous proactive energy saving (AURORA) framework for emerging ultra-dense networks. IEEE Transactions on Green Communications and Networking, 2018, 2(4): 958-971.
9. Yang P B, Li X, Ji H, et al. A novel mobility prediction scheme for outdoor crowded scenario using fuzzy C-means. Proceedings of the IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC’17), Oct 8-13, 2017, Montreal, Canada. Piscataway, NJ, USA: IEEE, 2017: 5p.
10. Lü Q J, Qiao Y Y, Ansari N, et al. Big data driven hidden Markov model based individual mobility prediction at points of interest. IEEE Transactions on Vehicular Technology, 2017, 66(6): 5204-5216.
11. Isaacman S, Becker R, Cáceres R, et al. Identifying important places in people's lives from cellular network data. Pervasive Computing: Proceedings of the 9th International Conference on Pervasive Computing (Pervasive’11), Jun 12-15, 2011, San Francisco, CA, USA. LNCS 6696. Berlin, Germany: Springer-Verlag, 2011: 133-151.
12. Zhang Y. User mobility from the view of cellular data networks. Proceedings of the 2014 IEEE Conference on Computer Communications (INFOCOM’14), Apr 27-May 2, 2014, Toronto, Canada. Piscataway, NJ, USA: IEEE, 2014: 1348-1356.
13. Si H B, Wang Y, Yuan J, et al. Mobility prediction in cellular network using hidden Markov model. Proceedings of the 7th IEEE Consumer Communications and Networking Conference, Jan 9-12, 2010, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2010: 5p.
14. Ashbrook D, Starner T. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 2003, 7(5): 275-286. |