A Quality Optimization Approach: An Application on Next Generation Networks

Authors: Gülfem I. Alptekin, S. Emre Alptekin

Abstract:

The next generation wireless systems, especially the cognitive radio networks aim at utilizing network resources more efficiently. They share a wide range of available spectrum in an opportunistic manner. In this paper, we propose a quality management model for short-term sub-lease of unutilized spectrum bands to different service providers. We built our model on competitive secondary market architecture. To establish the necessary conditions for convergent behavior, we utilize techniques from game theory. Our proposed model is based on potential game approach that is suitable for systems with dynamic decision making. The Nash equilibrium point tells the spectrum holders the ideal price values where profit is maximized at the highest level of customer satisfaction. Our numerical results show that the price decisions of the network providers depend on the price and QoS of their own bands as well as the prices and QoS levels of their opponents- bands.

Keywords: cognitive radio networks, game theory, nextgeneration wireless networks, spectrum management.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080032

References:

[1] R. Rubenstein, "Radios Get Smart", IEEE Spectrum, February 2007.
[2] Shared Spectrum Report, available online: http://www.sharedspectrum.com/measurements/download/Ireland_Spect rum_ Occupancy_Measurements_v2.pdf / .
[3] FCC, "The development of secondary markets - report and order and further notice of proposed rule making", Federal Communications Commission Report, pp. 103-113, 2003.
[4] FCC, "Promoting efficient use of spectrum through elimination of barriers to the development of secondary markets - third report and order", Federal Communications Commission Report, pp. 07-52, 2007.
[5] Qinetiq, "Cognitive Radio Technology, A Study for Ofcom - Vol. 1", available at: http://www.ofcom.org.uk/research/technology/research/ emer_tech/cograd/cograd_main.pdf, 2007.
[6] D. Niyato and E. Hossain, "Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion", IEEE Journal on Selected Areas in Communications Vol. 26 No. 1, pp. 192-202, 2008-a.
[7] D. Niyato and E. Hossain, "Market-equilibrium, competitive, and cooperative pricing for spectrum sharing in cognitive radio networks: analysis and comparison", IEEE Transactions on Wireless Communications Vol. 7 No. 11, pp. 4273-4283, 2008-b.
[8] M. Bloem, T. Alpcan and T. Başar, "A Stackelberg Game for Power Control and Channel Allocation in Cognitive Radio Networks", in Proc: 2nd International Conference on Performance Evaluation Methodologies and Tools (321), 2007.
[9] J.O. Neel, J.H. Reed and R.P. Gilles, "Convergence of Cognitive Radio Networks", in Proc: IEEE Wireless Communications and Networking Conference, Vol. 4, pp. 2250-2255, 2004.
[10] A.B. MacKenzie and L.A. DaSilva, Game Theory for Wireless Engineers, Morgan & Claypool Publishers, 2006.
[11] J. Nash, "Non-cooperative games", The Annals of Mathematics, Vol. 54 No. 2: pp. 286-295, 1951.
[12] D. Monderer and L.S. Shapley, "Potential Games", Games and Economic Behavior, Vol. 14, pp. 124-143, 1996.
[13] Y. Zhao, S. Mao, J.O. Neel and J.H. Reed, "Performance Evaluation of Cognitive Radios: Metrics, Utility Functions and Methodologies", Accepted to IEEE JSAC on Cognitive Radio, last edited 1/2/08, 2008.
[14] M. Cave, Review of Radio Spectrum Management, for Department of Trade and Industry and HM Treasury, 2002.
[15] T. Başar and G.J. Olsder, Dynamic Noncooperative Game Theory, Academic Press, second edition, 1995.