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Forecasting Capital Investment for Fixed-Telephony Network Switching Equipment in Tandem with NGN Migration Using Cobb-
Douglas Model
M. Sheikhan and M. E. Kalantari
Islamic Azad University, South Tehran Branch EE Department, Khaje Nasir Toosi University
Abstract: This paper tries to estimate the capital investment required for the fixed-telephony network switching equipment as demanded by the fourth national development plan. As a first step, the Cobb-Douglas model is used as a successful demand forecasting model to estimate the demand over the target years. Then, an architectural plan is developed for the fixed-telephony switching network that takes into account the expansion of the existing exchanges as well as the addition of new ones. The number of the required ports in local exchanges, the intercity traffic (including cell phone subscribers), and the required trunks in transit exchanges are then estimated. Two scenarios are used to estimate the investment needed: expanding legacy network (circuit-based), and NGN adoption (a combination of circuit and packet-based networks). Finally, conventional pricelists from different local and foreign suppliers are used to arrive at two total investment estimates: 6,013 billion Rials and 6330 billion Rials for the two mentioned scenarios, respectively. Keywords: Fixed-telephony network, Switching equipments, Capital investment, Demand forecasting, Cobb-Douglas model,
Traffic forecasting, Next generation networks.
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1. moving average (MA) 2. exponential smoothing (ES) 3. adaptive smoothing (AS) 4. Box-Jenkins 5. auto-regressive moving average (ARMA) 6. decision science (DS) 7. forecast 8. Cobb-Douglas 9. Bass 10. loglet (Logistic + Wavelet) 11. next generation network (NGN) 12. access 13. gross domestic product (GDP)/Capita 14. ordinary least squares (OLS) 15. secondary center (SC) 16. primary center (PC) 17. international switching Center (ISC) 18. local exchange (LX) 19. terminating exchange (TX) 20. transit exchange (TR)
21. lines 22. trunks 23. erlang (E) 24. mobile switching center (MSC) 25. far-to-near 26. blocking 27. topology 28. trunk gateway (TGW) 29. time division multiplexing (TDM) 30. synchronous transmission module (STM) 31. routers 32. edge 33. core 34. multi-protocol label switching (MPLS) 35. internet protocol (IP) 36. soft switch 37. busy hour call attempt (BHCA) 38. mean holding time (MHT) 39. media gateway (MGW) 40. signaling gateway (SGW)
p:�&
1. Duffy-Deno, K. T., “Demand for Additional Telephone Lines: An Empirical Note,” Information Economics and Policy, Vol. 13, PP. 283-299, 2001.
2. Hamoudia, M., and Islam, T., “Modeling and Forecasting the Growth of Wireless Messaging,” Telektronikk, Vol. 4, PP. 64-69, 2004.
3. Forman, C., Goldfarb, A., and Greenstein, S., “Geographic Location and the Diffusion of Internet Technology,” Electronic Commerce Research and Applications, Vol. 4, PP. 1-13, 2005.
4. Armstrong, J. S., Long-Range Forecasting, From Crystal Ball to Computer, John Wiley, 1985.
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5. Leijon, H., “Basic Forecasting Theories: A brief Introduction,” PLANITU-Doc-60-E, 2002.
6. ������������������� Economics/Forecasting/ForecastX, 2002.
7. Powell, A. A., McLaren K. R., Pearson, K. R. and Rimmer, M. T., "Cobb-Douglas Utility-Eventually,” Dept. of Economics and Business Statistics, 2002 (www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2002/wp12-02.pdf).
8. Eurescom P.901 Project Report, Investment Analysis Modelling, Deliverable 2, Annex B (Market Modelling), Aug. 2000.
9. Freeman, C., Diffusion of Technologies and Social Behavior, N. Nakicenovic & A. Grubler (eds.), Springer-Verlag, 1991.
10. Mahajan, V., Muller, E. and Bass, F. M., “New Product Diffusion Models,” in Marketing, North-Holland, PP. 349-408, 1993.
11. Meade, N. and Islam, T., “Technological Forecasting-Model Selection, Model Stability and Combining Models,” Management Science, 44, PP. 1115-1130, 1998.
12. Meade, N. and Islam, T., Principles of Forecasting: A Handbook for Researchers and Practitioners,Kluwer Academic, PP. 577-595, 2001.
13. Venkatesan, R. and Kumar, V., “A Genetic Algorithm Approach to Growth Phase Forecasting of Wireless Subscribers,” International Journal of Forecasting, 18, PP. 625-646, 2002.
14. Meyer, P. S., Yung, J. W. and Ausubel, J. H., “A Primer on Logistic Growth and Substitution: The Mathematics of the Loglet Lab Software,” Technological Forecasting and Social Change, 61(3), PP. 247-271, 1999.
15. Yung, J. W., Meyer, P. S. and Ausubel, J. H., “The Loglet Lab Software: A Tutorial,” Technological Forecasting and Social Change, 61(3), PP. 273-295, 1999.
16. Eurescom P1117 Project Report, Future Access Networks (FAN), IP-based access technologies and QoS, May 2003.
17. Economides, N., “The Economics of Networks,” International Journal of Industrial Organisation, 14 (6), PP. 673-699, 1996.
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25. Sanso, B. and Soriano, P., Telecommunications Network Planning, Kluwer Academic Publishers, 2001.
26. www.erlang.com/calculator/erlb/. 27. www.analysys.com/Articles/Standard Article (The economic
case for next-generation voice, ITU Telecom World), 2003.
28. Cardone, R. P., et al., “Migrating from Legacy to IP Network Infrastructure,” 2005
(www.lucent.com/livelink/). 29. www.cisco.com/en/US/netsol/ns537/networking_solu
tions_white_paper0900aecd802e2a52.shtml (Building the carrier-class IP next generation network), 2005.
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