Open Access
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
Article Number 03014
Number of page(s) 5
Section Digital Signal and Image Processing
Published online 19 June 2018
  1. Elgala, H., Elgala, H., Jungnickel, V., Jungnickel, V., Little, T., & Shao, S., et al. 2016. Coexistence of wifi and lifi toward 5g: concepts, opportunities, and challenges. IEEE Communications Magazine, 54(2), 64-71. [Google Scholar]
  2. Du, Z., Wu, Q., Yang, P., Xu, Y., Wang, J., & Yao, Y. D. 2015. Exploiting user demand diversity in heterogeneous wireless networks. IEEE Transactions on Wireless Communications, 14(8), 4142-4155. [CrossRef] [Google Scholar]
  3. Du, Z., Wu, Q., Yang, P., & Xu, Y. 2014. User-demand-aware wireless network selection: a localized cooperation approach. IEEE Transactions on Vehicular Technology, 63(9), 4492-4507. [CrossRef] [Google Scholar]
  4. Olivas, E. S., Guerrero, J. D. M., Sober, M. M., Benedito, J. R. M., & Lopez, A. J. S. 2009. Handbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques. Information Science Reference - Imprint of: IGI Publishing. [Google Scholar]
  5. Boran, M., Gönen, F., & Cetin, S. 2013. Matching with Externalities for Context-Aware User-Cell Association in Small Cell Networks. IEEE Global Communications Conference (Vol.113, pp.4483-4488). IEEE. [Google Scholar]
  6. Du, Z., Wu, Q., & Yang, P. 2014. Dynamic user demand driven online network selection. IEEE Communications Letters, 18(3), 419-422. [CrossRef] [Google Scholar]
  7. Wu, Q., Du, Z., Yang, P., Yao, Y. D., & Wang, J. 2016. Traffic-aware online network selection in heterogeneous wireless networks. IEEE Transactions on Vehicular Technology, 65(1), 381-397. [CrossRef] [Google Scholar]
  8. Xu, Y., Wang, J., Wu, Q., & Du, Z. 2015. A game-theoretic perspective on self-organizing optimization for cognitive small cells. IEEE Communications Magazine, 53(7), 100-108. [CrossRef] [Google Scholar]
  9. IrDA Standards. [Google Scholar]
  10. Bianchi, G. 2000. Performance analysis of the ieee 802.11 distributed coordination function. IEEE J. Sel. Areas Commun, vol. 18(3), 535-547. [CrossRef] [Google Scholar]
  11. Basnayaka, D. A., & Haas, H. 2015. Hybrid RF and VLC Systems: Improving User Data Rate Performance of VLC Systems. IEEE, Vehicular Technology Conference (pp.1-5). IEEE. [Google Scholar]
  12. Kavehrad, M. 2010. Sustainable energy-efficient wireless applications using light. Communications Magazine IEEE, 48(12), 66-73. [CrossRef] [Google Scholar]
  13. Kaelbling, L. P., Littman, M. L., & Moore, A. W. 1996. Reinforcement learning: a survey. Journal of Artificial Intelligence Research, 4(1), 237--285. [CrossRef] [Google Scholar]
  14. Lee, D., Zhou, S., Zhong, X., Niu, Z., Zhou, X., & Zhang, H. 2014. Spatial modeling of the traffic density in cellular networks. IEEE Wireless Communications, 21(1), 80-88. [CrossRef] [Google Scholar]
  15. Ibrahim, M., Khawam, K., & Tohme, S. 2010. Congestion Games for Distributed Radio Access Selection in Broadband Networks. Global Telecommunications Conference (Vol.45, pp.1-5). IEEE. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.