A clustering algorithm that maximizes throughput in 5G heterogeneous F-RAN networks

IEEE International Conference on Communications

Volumn 2018-May, Issue , 2018, Pages

  Balevi, Eren ^a   Gitlin, Richard D ^a  

^a University of South Florida   (United States)

Author keywords

Clustering; Fog computing; HetNets

Indexed keywords

5G MOBILE COMMUNICATION SYSTEMS; CELLS; CYTOLOGY; FOG; FOG COMPUTING; THROUGHPUT;

5G WIRELESS NETWORKS; CLUSTERING; HARD CLUSTERING ALGORITHMS; HET-NETS; MAXIMIZE THROUGHPUT; NUMERICAL RESULTS; PRIORI INFORMATION; VORONOI TESSELLATIONS;

CLUSTERING ALGORITHMS;

EID: 85051445028     PISSN: 15503607     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICC.2018.8422151     Document Type: Conference Paper

References (18)

1. A. Damnjanovi, J. Montojo, Y. Wei, T. Ji, T. Luo, M. Vajapeyam, T. Yoo, O. Song, and D. Malladi, "A survey on 3gpp heterogeneous networks", IEEE Wireless Communications, vol. 18, no. 3, pp. 10-21, June 2011.
2. F. Bonomi et al., "Fog Computing and its Role in the Internet of Things", Proc. Wksp. Mobile Cloud Computing, Helsinki, Finland, pp. 1316, Aug. 2012.
3. M. Chiang and T. Zhang, "Fog and IoT: An overview of research opportunities", IEEE Internet Things J., vol. PP, no. 99, pp. 11, 2016.
4. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, Aug. 2006.
5. S. Wang, et al, "A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications", IEEE Access, vol.5, pp. 6757-6779, March 2017.
6. S. Yi, C. Li, Q. Li, "A survey of fog computing: Concepts applications and issues", Proceedings of the 2015 Workshop on Mobile Big Data, 2015.
7. L. M. Vaquero, L. Rodero-Merino, "Finding your way in the fog: Towards a comprehensive definition of fog computing", ACM SIGCOMM CCR, 2014.
8. E. Balevi and R. D. Gitlin, "Unsupervised Machine Learning in 5G Networks for Low Latency Communications", IEEE International Performance Computing and Communications Conference (IPCCC), 2017.
9. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, New York: Plenum, 1981.
10. R. Krishnapuram and J. Keller, "A possibilistic approach to clustering", IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98110, Apr. 1993.
11. N. R. Pal, K. Pal, and J. C. Bezdek, "A mixed c-means clustering model", in IEEE Int. Conf. Fuzzy Systems, pp. 1121, 1997.
12. N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, "A possibilistic fuzzy c-means clustering algorithm", IEEE Trans. Fuzzy Syst., vol. 13, pp. 517-530, Aug. 2005.
13. S. Boyd and L. Vandenberghe, Convex Optimization., 2004.
14. W. Yu, W. Rhee, S. Boyd, and J. Cioffi, "Iterative water-filling for Gaussian vector multiple-Access channels", IEEE Trans. Inf. Theory, vol. 50, no. 1, pp. 145152, Jan. 2004.
15. N. Jindal, W. Rhee, S. Vishwanath, S. A. Jafar, and A. Goldsmith, "Sum power iterative waterfilling for multi-Antenna Gaussian broadcast channels", IEEE Trans. Inf. Theory, vol. 51, pp. 15701580, Apr. 2005.
16. M. Kobayashi, G. Caire, "An iterative water-filling algorithm for maximum weighted sum-rate of Gaussian MIMO-BC", IEEE J. Select. Areas Commun., vol. 24, no. 8, pp. 1640-1646, Aug. 2006.
17. E. Balevi and R. D. Gitlin, "Optimizing the Number of Fog Nodes for Cloud-Fog-Thing Networks", submitted for publication. See https://arxiv.org/pdf/1801.00831v1.pdf
18. N. McKeown et al., "OpenFlow: Enabling innovation in campus networks", SIGCOMM Comput. Commun. Rev., vol. 38, no. 2, pp.69-74, Mar.2008.