Assignment of Spectrum Demands by Merits via Analytic Hierarchy Process and Integer Programming

Authors

  • Razamin Ramli School of Quantitative Sciences, UUM College of Arts and Sciences, Malaysia
  • Faathirah Jamaluddin School of Quantitative Sciences, UUM College of Arts and Sciences, Malaysia
  • Engku Muhammad Nazri Engku Abu Bakar School of Quantitative Sciences, UUM College of Arts and Sciences, Malaysia
  • Mohamad Yusoff Alias Faculty of Engineering, Multimedia University, Malaysia
  • Nor Idayu Mahat School of Quantitative Sciences, UUM College of Arts and Sciences, Malaysia
  • Mohd Zaini Abdul Karim School of Economics, Finance and Islamic Banking UUM College of Business, Universiti Utara Malaysia 06010 Sintok, Kedah, Malaysia

DOI:

https://doi.org/10.32890/jict2013.12.3

Keywords:

Spectrum assignment problem, analytic hierarchy process, integer programming, multi-criteria evaluation, integrated approach

Abstract

Demand for wireless services is ever increasing due to wireless technology advances, which directly relates to the increasing demand for spectrum bandwidths. Due to the high demand, thus spectrum has become scarce. As a result, spectrum bandwidths have become important and need to be efficiently assigned to potential and demanding service providers. Thus, this paper proposes an integrated approach to solve a spectrum assignment problem. The integrated model is developed to efficiently determine the optimal spectrum assignment for the purpose of satisfying the identified relevant requirements and constraints or criteria. The development of the model has also taken into consideration the situation when the spectrum regulator wishes to choose the best provider or licensee based on merits. For that purpose, suitable weights based on qualitative criteria were obtained via Analytic Hierarchy Process and subsequently used as coefficients in the respective Integer Programming formulation. The integrated optimization model is able to tackle a multi-criteria spectrum problem and then solve the assignment problem by way of maximizing the total efficiency. The results exhibits a more efficient alternative as compared to the existing qualitative approach, as it is able to combine subjective judgments computation with a mathematically formulated approach to produce a systematic and consistent results.

 

References

Andersson, P., Hulten, S., & Valiente, P. (2005). Beauty contest licensing lessons from the 3G process in Sweden. Telecommunication Policy, 29, 577-593.

Babich, F., & Deotto, L., (2002). Modeling and performances analysis of resource allocation strategies for real-time services in UMTS using TIPP tool. Elsevier Science B.V. Performance Evaluation 50, 101-128.

Baijal, Pradip. (2004). Efficient utilization, spectrum allocation, and spectrum pricing, Consultation Paper no. 11/2004. Telecom Regulatory Authority of India.

Binmore, K., & Klemperer, P. (2001). The biggest auction ever: the sale of the British 3G telecom licenses. Economic Journal,112(478), C74-C96. http://jict.uum.edu.my/ Journal of ICT, 12, 2013, pp: 39–

Braglia, M., Gabbrielli, R., & Miconi, D. (2001). Material handling device selection in cellular manufacturing. Journal of Multi-criteria Decision Analysis, 10(6), 303-315.

Bultez, A., & Naert, P. (1988). SHARP: Shelf allocation for retailers’ profit. Marketing Science, 7(3), 211-231.

Chang, K. N., & Kim, S. (1997). Channel allocation in cellular radio networks. Computer Ops Res.,24(9), 849-860.

Cramtom, P. (2002). Spectrum auction. In Cave, M., Majumdar, S., & Vogelsang, I. (Eds.), Handbook of telecommunications economics (pp. 605-639). Amsterdam: Elsevier Science.

Davis, L. (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold. Engku Muhammad Nazri Engku Abu Bakar, Razamin Ramli, Ang Chooi Leng, Nor Idayu Mahat, Mohd Zaini Abdul Karim, & Mohamad Yusoff

Alias. (2009). Strategic models for spectrum allocation and spectrum pricing. Research Report, Universiti Utara Malaysia, Malaysia.

Fette, B. (2006). Cognitive radio technology. Handbook of communications engineering series United Kingdom. UK: Elsevier Science.

Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimize resource allocation for emergency response after earthquake disasters. Safety Science, 35, 41-57.

Gardent, P. B. & Reeves, S. A. (2009). Ethics conflict in rural communities: Allocation of scarce resources. In W. A. Nelson, Handbook for rural health care ethics: A practical guide for professional (Chapter 9, pp. 164-185). Hanover, NH: University Press of New England.

Gilbert, K. C., Holmes, D. D., & Rosenthal, R. E. (1985). A multiobjective discrete optimization model for land allocation. Management Science, 31(2), 1509-1522.

Huang, Y., Fan, Y., & Cheu, R. L. (2006, August). Optimal allocation of multiple emergency service resources for critical transportation infrastructure protection. Proceedings of the 86th Annual Meeting of the Transportation Research Board and Publication in Transportation Research Record, Davis, CA. http://jict.uum.edu.my/ Journal of ICT, 12, 2013, pp: 39–

Jain, A., & Kumar, A. M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592.

Fathirah Jamaluddin. (2011). Integrated models of analytic hierarchy process and integer programming for allocation of spectrum demands (Unpublished master’s thesis). Universiti Utara Malaysia.

Kalvenes, J., Kennington, J., & Olinick, E. (2005). Hierarchical cellular network design with channel allocation. European Journal of Operational Research, 160, 3–18.

Klemperer, P. (2000): Why every economist should learn some auction theory. Invited Lecture to the 8th World Congress of the Econometric Society. Kriesche, H. U. M., Schold, J. D., Gaston, R. S., Wadstrom, J., & Kaplan,

B. (2005). Kidneys from deceased donors: Maximizing the value of a scarce resource. American Journal of Transplantation, 5, 1725-1730.

Krishna, P. V., Misra, S., Obaidat, M. S., & Saritha, V. (2009). An efficient approach for distributed dynamic channel allocation with queues for real-time and non-real-time traffic in cellular networks. The Journal of Systems and Software, 82, 1112-1124.

Kulkarni, R., & Zekavat, S. A. (2006). Traffic-aware inter-vendor dynamic spectrum allocation: Performance in multi-vendor environments. In S., Onoe, M.,Guizani, H. H., Chen, & M. Sawahashi (Eds.), Proceedings of the International Conference on Wireless Communications and Mobile Computing (pp.85-89). Vancouver, British Columbia, Canada.

Lashine, S. H., Fattouh, M., & Issa, A. (2006). Location/allocation and routing decisions in supply chain network design. Journal Modelling in Management, 1(2), 173-183.

Madas, M. A., & Zografos, K. G. (2010). Airport slot allocation: A time for change. Transport Policy 17, 274–285.

Matlin, S. (1970). A review of literature on the missile allocation problem. Operation Research, 18(2),334-373.

Malczewski, J., Moreno-Sanchez, R., Bojo´rquez-Tapia, L.A., & OngayDelhumeau, E., (1997). Multicriteria group decision making model for environmental conflict analysis in the Cape Region, Mexico. Journal of Environmental Planning and Management, 40(3), 349–374. http://jict.uum.edu.my/ Journal of ICT, 12, 2013, pp: 39–

McMilan, J. (1995). Why auction the spectrum. Telecommunications Policy, 19, 191-199. Maznah Mat Kasim, Haslinda Ibrahim, & Mahmoud Salem Barakat Bataineh. (2011). Multi-criteria decision making methods for determining computer preference index. Journal of Information and Communication Technology, 10, 137-148.

Morris, A.C. (2005). Spectrum auctions: Distortionary input tax or efficient revenue instruments. Telecommunications Policy, 29, 687–709.

Pattavina, A., Quadri, S., & Trecordi, V. (1999). Reuse partitioning in cellular networks with dynamic channel allocation. Wireless Networks, 5, 299–309. Razamin Ramli, Fathirah Jamaludin, Engku Muhammad Nazri Engku Abu Bakar, Mohd Yusof Alias, Nor Idayu Mahat, Mohd Zaini Abdul Karim, & Ang Chooi Leng. (2011). Modelling of spectrum demands through hybrids of analytic hierarchy process and integer programming, International Journal of Modelling and Optimization, 1(5), December 2011, 368-374.

Sofianopoulou, S. (1992). The process allocation problem: A survey of the application graph-theoretic and integer programming approaches. The Journal of the Operational Research Society, 43(5), 407–413.

Sekiguchi, H., Ishikawa, H., Koyama, M., & Sawada, H. (1986). Increasing frequency spectrum utilization in mobile systems. IEEE AES Magazine, 1-5.

Taylor III, B.W. (2010). Introduction to management science (10th ed.). New Jersey: Pearson Prentice Hall. Tzifa, E. C., Demestichas, P. P., Louta, M. D., Tsouka, E. S., Theologuo, M.

E., & Anagnostou, M. E. (1999). Adaptive radio spectrum allocation through mid-term reconfigurations for cellular communications systems. Computer Communication, 22, 361–375.

Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Operational Research, 107, 507–529.

Zhao, Z., Peng, Z., Zheng, S., & Shang, J. (2009). Cognitive radio spectrum allocation using evolutionary algorithms. IEEE Transactions on Wireless Communications, 8(9), 4421–4425.

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Published

23-04-2013

Issue

Vol. 12 (2013): Journal of Information and Communication Technology (JICT) Vol. 12, 2013

Section

Articles

How to Cite

Ramli, R., Jamaluddin, F., Engku Abu Bakar, E. M. N., Alias, M. Y., Mahat, N. I., & Abdul Karim, M. Z. (2013). Assignment of Spectrum Demands by Merits via Analytic Hierarchy Process and Integer Programming. Journal of Information and Communication Technology, 12, 39-53. https://doi.org/10.32890/jict2013.12.3

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2013.12.3

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