Picocell 시스템의 보행자 통화량 모델링 및 분석

Traffic Modeling and Analysis for Pedestrians in Picocell Systems Using Random Walk Model

  • 발행 : 2003.06.30

초록

Traffic performance in a microcellular system is much more affected by cell dwell time and channel holding time in each cell. Cell dwell time of a call is characterized by its mobility pattern, i.e., stochastic changes of moving speed and direction. Cell dwell time provides important information for other analyses on traffic performance such as channel holding time, handover rate, and the average number of handovers per call. In the next generation mobile communication system, the cell size is expected to be much smaller than that of current one to accommodate the increase of user demand and to achieve high bandwidth utilization. As the cell size gets small, traffic performance is much more affected by variable mobility of users, especially by that of pedestrians. In previous work, analytical models are based on simple probability models. They provide sufficient accuracy in a simple second-generation cellular system. However, the role of them is becoming invalid in a picocellular environment where there are rapid change of network traffic conditions and highly random mobility of pedestrians. Unlike in previous work, we propose an improved probability model evolved from so-called Random walk model in order to mathematically formulate variable mobility of pedestrians and analyze the traffic performance. With our model, we can figure out variable characteristics of pedestrian mobility with stochastic correlation. The above-mentioned traffic performance measures are analyzed using our model.

키워드

참고문헌

  1. Fang, Chlamtac and Lin, Channel Occupancy Times and Handoff Rate for Mobile Computing and PCS Networks, IEEE Transactions on Computers, 47(6), pp. 679-692, 1998
  2. Guerin, R.A. (1987), Channel occupancy time distribution in a cellular radio system, IEEE Trans. Veh. Tech., 35(3), 89-99
  3. Jabbari, B., Zhou, Y. and Hiller, F. (1998), Random walk modeling of mobility in wireless networks, Proc. IEEE Veh. Technol. Conf., Ottawa, Canada, 1,639-643
  4. Kim, S. and Lee, K. O. (2001), Modelling user mobility in microcellular systems, lASTED International Journal of Modelling and Simulation, 21(2), 132-137
  5. Lee, K. O. and Kim, S. (1998), Channel holding time modeling in urban COMA networks considering random user mobility, Proc. 3rd CDMA International Conf., 1,147-151
  6. Lee, K. O. and Kim, S. (1999), Area residence time modeling in PCS networks, Proc. Korean Operations Research and Management Science Conf., 1,583
  7. Lee, K.-O. and Kim, S. (2000), Modeling and analysis of variable user mobility in future wireless personal communications, Proc. INFORMS/KORMS Joint Conf. (CD-ROM, No. 4412), Seoul Korea
  8. Lee, K. O. (2001), Stochastic optimal resource management for prioritized admission in broadband wireless communications, Ph.D. thesis, KAIST
  9. Lee, K.-O. and Kim, S. (2002), Modeling variable user mobility with stochastic correlation concept, Computer Networks, 38, pp. 603-612, 2002
  10. Marrison, P. G. and Patel, N. M. (1994), Performance Modelling of Communication Networks and Computer Architecture, Addison-Wisley
  11. Ross, S. M. (1993), Introduction to Probability Models, 5th ed., Academic Press
  12. Wolff, R. W. (1989), Stochastic Modeling and the Theory of Queues, Prentice-Hall
  13. Yeun, W. H. A. and Wong, W. S. (1998), A dynamic location area assignment algorithm for mobile cellular systems, Proc. IEEE ICT, 3, 1385-1389
  14. Yum, T.S.P. and Yeung K.L. (1995), Blocking and handoff performance analysis of directed retry in cellular mobile systems, IEEE Trans. Veh. Tech., 44(3), 645-650 https://doi.org/10.1109/25.406633
  15. Zonoozi, M.M., and Dassanayake, P. (1998), User mobility modeling and characterization of mobility pattern, IEEE J. Selected Areas Commun., 15(7), 1239-1252 https://doi.org/10.1109/49.622908