Data Mining for Resource Planning and QoS Supports in GSM Networks
Abstract
Applications that run on mobile phones shaped a trendy lifestyle for many users nowadays. This led to a significant growth in the proportion of data traffic, relative to voice traffic, to be delivered in the mobile phone network such as GSM. Traditionally the underlying radio resources in GSM networks for data and voice traffic were allocated by some predefined traffic policy which was manually configured. The allocation may not be most accurate for the fact that demands for data traffic fluctuate largely and temporally. A new resource planning scheme is desired that can dynamically adjusts the resource allocations according to the latest information of the traffic statues. In order to facilitate such dynamic resource allocation, a resource management system is proposed in this paper. Data mining is used to derive rules and extract traffic patterns that reveal critical information for setting values in resource planning. Empirical testing data are used in experiments that demonstrate the efficacy of the data mining techniques.
Keywords
References
[1] S. Fong, and E. Lai, "Mobile Mini-payment Scheme Using SMS-Credit", International Conference of Computational Science and Its Applications, ICCSA 2005, Lecture Notes in Computer Science, Springer-Verlag, Vol.2., Singapore, pp. 1106-1116, 9-12 May 2005.
[2] H. Abu-Ghazaleh, A.S. Alfa, "Application of Mobility Prediction in Wireless Networks Using Markov Renewal Theory", IEEE Transactions on Vehicular Technology, Vol. 59, Iss. 2, pp.788-802, Feburary 2010.
[3] I.R. Chen, N. Verma, "Simulation Study of a Class of Autonomous Host-Centric Mobility Prediction Algorithms for Wireless Cellular and Ad Hoc Networks", ANSS '03: Proceedings of the 36th annual symposium on Simulation, IEEE Computer Society, March 2003
[4] A. Leung, S. Fong, E. Lai, "Mining Operational Data for Improving GSM Network Performance", International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age (FSKD 2002), Orchid Country Club, Singapore, ISBN 981-04-7520-9, pp.285-289, 18-22 November 2002.
[5] E. Lai, S. Fong, H. Yang, "Supporting Mobile Payment QOS by Data Mining GSM Network Traffic", The 10th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2008), Linz, Austria, ACM Press, ISBN:978-1-60558-349-5, pp.279-285, 24-26 November 2008.
[6] G. Chen, H. Liu, L. Yua, Q. Wei and X. Zhang, "A New Approach to Classification Based on Association Rule Mining", Decision Support Systems, Volume 42, Issue 2, Elsevier, pp.674-689, November 2006.
[7] A. Vaidyanathan, M. Billinghurst, H. Sirisena, "Characterizing and Visualizing Mobile Networks", NZCSRSC 2008, Christchurch, New Zealand, pp.165- 169, April 2008.
[8] R.T. Valadas, "Dimensioning and Resource Management of ATM Networks", 7th IFIP/ICCC Conference on Information Networks and Data Communications, (INDC’98), pp.209-220, 1998.
[9] A. Hushyar, "Network Traffic Clustering and Geographic Visualization", Master's Theses, San Jose State University, USA, Paper 3695, pp.1-42, 2009.
[10] U. Chaudhary, I. Papapanagiotou, M. Devetsikiotis, "Flow Classification Using Clustering And Association Rule Mining", The International Workshop on Computer-Aided Modeling Analysis and Design of Communication Links and Networks (IEEE CAMAD 2010), December 3-4, 2010.
[11] S. Fong, "Adaptive QoS Resource Management by Using Hierarchical Distributed Classification for Future Generation Networks", The Third International Conference on Computer Networks & Communications (CoNeCo-2011), Mesa, Ankara, Turkey, June 26-29, 2011
Full Text: PDF


