Dynamic Admission Control Based on Effective Demand for Next Generation Wireless Networks

Authors: Somenath Mukherjee, Rajdeep Ray, Raj Kumar Samanta, Mofazzal H. Khondekar, Gautam Sanyal

Abstract:

In next generation wireless networks (i.e., 4G and beyond), one of the main objectives is to ensure highest level of customer satisfaction in terms of data transfer speed, decrease in cost and delay, non-rejection and no drop of calls, availability of ‘always-on’ connectivity and services, continuity of connected services, hastle-free roaming in addition to the convenience of use of network services from anywhere and anytime. To take care of these requirements effectively, internet service providers (ISPs) and network planners have to go for major capacity enhancement of network resources and at the same time these resources are to be used effectively and efficiently to reduce cost and to increase revenue. In this work, the effective bandwidth available in a Mobile Switching Center (MSC) of a wireless network providing multi-class multimedia services is analyzed. Bandwidth requirement of the users for a customized Quality of Service (QoS) is estimated. The findings of the QoS estimation are applied for the capacity planning and admission control of the multi-class traffic flows coming into the MSC.

Keywords: Next generation wireless network, mobile switching center, multi-class traffic, quality of service, admission control, effective bandwidth.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315418

References:

[1] Anjali, T., Scoglio, C., & Uhl, G. (2003). A new scheme for traffic estimation and resource allocation for bandwidth brokers. Computer Networks, 41, 761-777.
[2] Asuquo, D. E., Williams, E. E., & Nwachukwu, E. O. (2014). A Survey of Call Admission Control Schemes in Wireless Cellular Networks. 5 (2).
[3] Bhattacharjee, P., & Sanyal, G. (2008). Design Tool for an Edge Router using Appropriate Mathematical Model. International Journal of Systematics, Cybernetics and Informatics.
[4] Bhattacharjee, P., & Sanyal, G. (2006). Study of Stoachastic Characteristic of traffic on a High Speed Network. ADCOM (pp. 648-651). IEEE Xplore.
[5] Chen, P. N. (2000). Generalization of Gartner-Ellis theorem. IEEE Transactions on Information Theory, 46 (7), 2752-2760.
[6] Chiera, B., Krzesinski, A., & Taylor, P. (2003). Some Properties of the Capacity Value Function. Melbourne, Australlia: Department of Mathematics & Statistics, The University of Melbourne, Department of Computer Science, University of Stellenbosch,.
[7] Claffy, K., Polyzos, G., & Barun, W. H. (1993). Application Of Sampling Methodologies to Network Traffic Characterization. ACM, SIGCOMM 93 (pp. 194-203). Proceedings of ACM.
[8] Dai, L. (1999). Effective Bandwidths and Performance Bounds in High Speed Communication Systems. Journal of Optimization Theory and Application, 100 (3), 549-574.
[9] Dembo, A., & Zeitouni, O. (2010). Large deviations techniques and applications (Vol. 38). Berlin: Springer-Verlag Berlin Heidelberg.
[10] Ellis, R. (1995). An Overview of the theory of large deviations and applications to statistical mechanics. Scandinavian Actuarial Journal, 1, 97-42.
[11] Fischer, W., & Hellstern, K. M. (1993). The Markov-modulated Poisson process (MMPP) cookbook. Performance Evaluation, 18 (2), 149-171.
[12] Khairnar, V. N., & Patil, D. S. (2015). QOS Evaluation of Call Admission Control for a 4G Network. 4 (6), 464-467.
[13] Kolate, V. S., Patil, G. I., & Bhide, A. S. (2012). Call Admission Control Schemes and Handoff Prioritization in 3G Wireless Mobile Networks. 1 (3), 92-97.
[14] Ma, Y., Han, J. J., & Trivedi, K. S. (2002). Call Admission Control for Reducing Dropped Calls in CDMA Cellular Systems. Computer Communications, 25 (7), 689-699.
[15] Mandjes, M., & Ridder, A. (1999). Optimal trajectory to overflow in a queue fed by a large number of sources. Queueing Systems: Theory and Applications, 31 (1/2), 137 - 170.
[16] Mitra, D., & Weinberger, P. (1984). Probablistic Models of Database locking: Solutions, Computational Algorithms and Asymptotics. Journal of the Association for Computing Machinery, 31 (4), 855-878.
[17] Mohan, N., & Ravichandran, T. (2009). An Efficient Multiclass Call Admission Control and Adaptive Scheduling for WCDMA Wireless Network. European Journal of Scientific Research, 33 (4), 718-727.
[18] Mortier, R. (2002). Internet Traffic Engineering. Cambridge: University.
[19] Pandit, C., & Meyn, S. (2006). Robust Measurement-Based Admission Control Using Markov’s Theory of Canonical Distributions. National Science Foundation under Award Nos. ECS 02 17836 and ITR 00-85929, 1-30.
[20] Rao, S. S., Chalam, S. V., & Rao, D. S. (2011). Performance analysis of Call Admission Control algorithm for Wireless Multimedia networks. Global Journal of Computer Science and Technology, 11 (8), 1-5.
[21] Reid, T. F., & Kulkarni, V. G. (2005). An Upper Bound on Overflow Probability in Transient Source Systems. Chapel Hill: Air Force Institute of Technology, University of North Carolina.
[22] Scott, S. L., & Smyth, P. (2003). The Markov Modulated Poisson Process and Markov Poisson Cascade with Applications to Web Traffic Modeling. Bayesian Statistics, 1-10.
[23] Serfozo, R. F. (1993). Queuing networks with dependent nodes and concurrent movements. Journal of Queuing System, 13 (1-3), 143-182.
[24] Veiga, M. F., García, C. L., Ardao, J. L., & González, A. S. (2003). On the effectiveness of the many-sources asymptotic for admission control. Computer Communications, 26 (12), 1376-1399.
[25] Verma, S., & Tomar, G. S. (2011). Call Admission Control and Hand Off Techniques for 3-G and Beyond Mobile Networks. 1 (1), 31-42.