The optimal macro control strategies of service providers and micro service selection of users: quantification model based on synergetics
- 98 Downloads
Multiple wireless technologies, possibly administered by same or different service providers (SPs), are expected to coexist in the rapidly-expanding heterogeneous networks. It is also economically beneficial for all SPs to cooperate with each other. In such model, one SP temporally shares a portion of its spectrum with other SPs at a certain price to allow better utilization of the available spectrum . To this end, users can make service selection decisions dynamically at micro level according to the performance satisfaction level and cost, depending on the pricing plans and spectrum sharing strategies of SPs at macro level. On the other hand, the users’ service selection population states in turn influence the control strategies of SPs. To model this dynamic interactive decision making problem, a novel approach based on synergetics is presented in this paper. The motion equations of population state decided by pricing and spectrum sharing are derived . Then, by taking into account the population state, an optimization problem is formulated to determine the optimal dynamic pricing and open access ratio for the SPs in order to maximize their respective benefits. A closed-loop feedback equilibrium is obtained as the solution of the formed optimization problem. Numerical results demonstrate the effectiveness and advantages of the proposed model in terms of dynamic control of the spectrum sharing and pricing as well as dynamic analysis of service selection.
KeywordsHeterogeneous networks Spectrum sharing Pricing Service selection Synergetics
This work was supported by Natural Science Foundation of China (61372125), 973 project (2013CB329104), and the open research fund of National Mobile Communications Research Laboratory, Southeast University (2013D01).
- 1.FCC. (2003). Promoting efficient use of spectrum through elimination of barriers to the development of secondary markets, Technical Report FCC:03-113.Google Scholar
- 2.Ofcom. (2011). Simplifying spectrum trading, spectrum leasing and other market enhancements, Final Statement Report, June 2011.Google Scholar
- 3.New Zealand Government. (2016). Radio spectrum management. Available: http://www.rsm.govt.nz/cms.
- 4.Australia Government. (2014). ACMA. Available: http://www.acma.gov.au/web/homepage/pc=home.
- 6.Bari, F., & Leung, V. C. M. (2007). Multi-attribute network selection by iterative TOPSIS for heterogeneous wireless access. In Proceedings of IEEE Consumer Communications and Networking Conference (CCNC), (pp. 808–812).Google Scholar
- 12.Chang, H. B., & Chen, K. C. (2011). Cooperative spectrum sharing economy for heterogeneous wireless networks. In IEEE GLOBECOM Workshops (pp. 458–463). Dec 2011.Google Scholar
- 13.Bennis, M., Lasaulce, S., & Debbah, M. (2009). Inter-operator spectrum sharing from a game theoretical perspective. EURASIP Journal of Signal Processing, 2009, 295739. doi: 10.1155/2009/295739.
- 17.Guerrero-Iban, A., Flores-Corte, C., Barba, A., & Reyes, A. (2010). A quality of service-enabled pricing approach for heterogeneous wireless access networks. In 2010 Sixth International Conference on Intelligent Environments (IE), (pp. 231–236).Google Scholar
- 20.Krishnaswamy, D. (2010). Network economics considerations for incremental data services in heterogeneous wireless wide area networks. In VehicularTechnology Conference Fall (VTC 2010-Fall), (pp. 1–5).Google Scholar
- 27.Badia, L., Lindstrom, M., Zander & J., Zorzi, M. (2003). Demand and pricing effects on the radio resource allocation of multimedia communication systems. In IEEE GLOBECOM, (vol. 7, pp. 4116–4121).Google Scholar