Modeling Mobile Cellular Networks Based on Social Characteristics
Keywords:
social characteristics, mobile networks, spectral clustering, energy efficiency, traffic modelAbstract
Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations.References
Index, Cisco Visual Networking (2014), Global mobile data traffic forecast update, White Paper, February, 2013-2018.
P. Zerfos, X. Meng, S. HY Wong, V. Samanta, S. Lu (2006), A study of the short message service of a nationwide cellular network, Proc. of the 6th ACM SIGCOMM conference on Internet measurement, 263-268. http://dx.doi.org/10.1145/1177080.1177114
D. Willkomm, S. Machiraju, J. Bolot, A. Wolisz (2008), Primary users in cellular networks: A large-scale measurement study, New frontiers in dynamic spectrum access networks, 2008. DySPAN 2008. 3rd IEEE symposium on, 1-11.
A. Klemm, C. Lindemann, M. Lohmann (2001), Traffic modeling and characterization for UMTS networks, Global Telecommunications Conference, 2001. GLOBECOM'01. IEEE, 3: 1741-1746.
Y. Zhang, A. Ã…rvidsson (2012), Understanding the characteristics of cellular data traffic, ACM SIGCOMM Computer Communication Review, 42(4): 461-466. http://dx.doi.org/10.1145/2377677.2377764
U. Paul, A. P. Subramanian, M. M. Buddhikot, S. R. Das (2011), Understanding traffic dynamics in cellular data networks, INFOCOM, 2011 Proceedings IEEE, 882-890.
D. Lee, S. Zhou, X. Zhong, Z. Niu, X. Zhou, H. Zhang (2014), Spatial modeling of the traffic density in cellular networks, Wireless Communications, IEEE, 21(1): 80-88. 80-88.
M. C. González, C. A. Hidalgo, A. Barabási (2008), Understanding individual human mobility patterns, Nature, 453(7196): 779-782. http://dx.doi.org/10.1038/nature06958
C. Song, Z. Qu, N. Blumm, A. Barabási (2010), Limits of predictability in human mobility, Science, 327(5968): 1018-1021. http://dx.doi.org/10.1126/science.1177170
X. Zhang, Y. Zhang, R. Yu, W. Wang, M. Guizani (2014), Enhancing spectral-energy efficiency forLTE-advanced heterogeneous networks: a users social pattern perspective, Wireless Communications, IEEE, 21(2): 10-17. http://dx.doi.org/10.1109/MWC.2014.6812286
Y. Huang, X. Zhang, J. Zhang, J. Tang, Z. Su, W. Wang (2014), Energy Efficient Design in Heterogeneous Cellular Networks Based on Large-Scale User Behavior Constraints, IEEE Transactions on Wireless Communications, 13(9): 4746-4757. http://dx.doi.org/10.1109/TWC.2014.2330334
D. Hu, B. Huang, L. Tu, S. Chen (2015), Understanding Social Characteristic from Spatial Proximity in Mobile Social Network, International Journal of Computers Communications & Control, 10(4): 539-550. http://dx.doi.org/10.15837/ijccc.2015.4.1991
Z. Qiao, P. Zhang, Y. Cao, C. Zhou, L. Guo, B. Fang (2014), Combining Heterogenous Social and Geographical Information for Event Recommendation, The Twenty-eighth AAAI Conference.
I. Trestian, S. Ranjan, A. Kuzmanovic, A. Nucci (2009), Measuring serendipity: connecting people, locations and interests in a mobile 3G network, Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, 2009: 267-279. http://dx.doi.org/10.1145/1644893.1644926
U. Von Luxburg (2007), A tutorial on spectral clustering, Statistics and computing, 17(4): 395-416. http://dx.doi.org/10.1007/s11222-007-9033-z
J. Shi, J. Malik (2000), Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8): 888-905. http://dx.doi.org/10.1109/34.868688
A. Y. Ng, M. I. Jordan, Y. Weiss (2002), On spectral clustering: Analysis and an algorithm, Advances in neural information processing systems, 2: 849-856.
J. A. Hartigan, M. A. Wong (1979), Algorithm AS 136: A k-means clustering algorithm, Applied statistics, 100-108. http://dx.doi.org/10.2307/2346830
M. Greenacre (2010), Chapter 6 Measures of distance and correlation between variables, Correspondence analysis in practice.
L. Suarez, L. Nuaymi, J. Bonnin (2012), An overview and classification of research approaches in green wireless networks, EURASIP Journal on Wireless Communications and Networking, 142, DOI: 10.1186/1687-1499-2012-142. http://dx.doi.org/10.1186/1687-1499-2012-142
X. Feng, et al. (2015), Feedback analysis of interaction between urban densities and travel mode split, International Journal of Simulation Modelling, 14(2): 349-358. http://dx.doi.org/10.2507/IJSIMM14(2)CO9
X. Deng, et al. (2013), A social similarity-aware multicast routing protocol in delay tolerant networks, International Journal of Simulation and Process Modelling, 8(4):248-256. http://dx.doi.org/10.1504/IJSPM.2013.059428
M. A. Piera, R. Buil, M. M. Mota (2014), Specification of CPN models into MAS platform for the modelling of social policy issues: FUPOL project, International Journal of Simulation and Process Modelling, 9(3):195-203. http://dx.doi.org/10.1504/IJSPM.2014.064389
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