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As radio access technology has been greatly developed nowadays, dynamic spectrum allocation (DSA), with which secondary users (SUs) are able to access the spectrum allocated to primary users becomes an important method for service providers to efficiently utilize limited wireless resources. Meanwhile, call admission control of SUs in an environment with multiple possible heterogenous wireless networks supporting DSA is also evolving. In this paper, we propose a pricing-based resource allocation scheme for multiple cooperative providers. According to this scheme, SUs are charged based on current system state by service provider to join one of several radio access networks (RANs). We integrate stochastic call admission control with dynamic pricing and formulate the problem of maximizing the expectation of system revenue in the long run as an infinite horizon average reward problem which can be solved by stochastic dynamic programming. Characteristics of the optimal pricing policy are analyzed based on series of computation results under various system conditions. Simulations carried out for a small network model show that a maximum profit can be achieved under our scheme and this maximum value varies with primary user arriving rates, primary user blocking punishments and channel capacities. Using our scheme, the system income could be improved by nearly 60 % in particular cases. Also, if each RAN’s probability of admitting a SU is carefully chosen, the system profit can be maximized and primary user blocking count minimized.
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FCCSPT: Report of the spectrum efficiency working group. Technical Report. November 2002.
Norton, M. (2007). Dynamic Spectrum Management.
Haykin, S. (2007). Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220. CrossRef
MacKie-Mason, J. K., & Varian, H. R. (1995). Pricing the internet. Public access to the internet (pp. 269–314).
Courcoubetis, C., Kelly, F. P., Siris, V. A., & Weber, R. (2000). A study of simple usage-based charging schemes for broadband networks. Telecommunication Systems, 15(3), 323–343. CrossRef
Courcoubetis, C., & Weber, R. (2003). Pricing communication networks. Wiley Online Library, vol 2.
Hou, J., Yang, J., & Papavassiliou, S. (2002). Integration of pricing with call admission control to meet qos requirements in cellular networks. IEEE Transactions on Parallel & Distributed Systems, 13(9), 898–910. CrossRef
Paschalidis, I. C., & Tsitsiklis, J. N. (2000). Congestion-dependent pricing of network services. IEEE/ACM Transactions on Networking, 8(2), 171–184. CrossRef
Paschalidis, I. C., & Liu, Y. (2002). Pricing in multiservice loss networks: Static pricing, asymptotic optimality, and demand substitution effects. IEEE/ACM Transactions on Networking, 10(3), 425–438. CrossRef
Chen, I. R., Yilmaz, O., & Yen, I. L. (2006). Admission control algorithms for revenue optimization with QoS guarantees in mobile wireless networks. Wireless Personal Communication, 38(3), 357–376. CrossRef
Zachariadis, G., & Barria, J. A. (2008). Dynamic pricing and resource allocation using revenue management for multiservice networks. IEEE Transactions on Network & Service Management, 5(4), 215–226. CrossRef
Elias, J., & Martignon, F. (May 2010). Joint spectrum access and pricing in cognitive radio networks with elastic traffic. IEEE international conference on communications, ICC 2010 (pp. 1–5).
Gizelis, C. A., & Vergados, D. D. (2011). A survey of pricing schemes in wireless networks. Communications Surveys & Tutorials, IEEE, 13(1), 126–145. CrossRef
Mutlu, H., Alanyali, M., & Starobinski, D. (2009). Spot pricing of secondary spectrum usage in wireless cellular networks. IEEE/ACM Transactions on Networking, 17(6), 1794–1804. CrossRef
Niyato, D., & Hossain, E. (2008). Competitive pricing for spectrum sharing in cognitive radio networks: Dynamic game, inefficiency of nash equilibrium, and collusion. IEEE Journal on Selected Areas in Communications, 26(1), 192–202. CrossRef
Khan, M. A., Toker, A. C., Sivrikaya, F., & Albayrak, S. (2011). Cooperation-based resource allocation and call admission for wireless network operators. Telecommunication Systems, 51, 1–13.
Wang, X., Zhuo, C., Xu, Y., & Wang, R. (November 2007). A new stochastic admission control scheme for wireless networks.In Proceedings of IEEE GLOBECOM (pp. 807–811).
Fang, Y., & Zhang, Y. (2002). Call admission control schemes and performance analysis in wireless mobile networks. IEEE Transactions on Vehicular Technology, 51(2), 371–382. CrossRef
Akl, R. G., Hegde, M. V., & Naraghi-Pour, M. (2005). Mobility-based CAC algorithm for arbitrary call-arrival rates in CDMA cellular systems. IEEE Transactions on Vehicular Technology, 54(2), 639–651. CrossRef
Yilmaz, O., Furuskar, A., Pettersson, J., & Simonsson, A. (2005). Access selection in WCDMA and WLAN multi-access networks. Vehicular Technology Conference, 4, 2220–2224.
Gelabert, X., Pérez-Romero, J., Sallent, O., & Agustí, R. (2008). A Markovian approach to radio access technology selection in heterogeneous multiaccess/multiservice wireless networks. IEEE Transactions on Mobile Computing, 7, 1257–1270. CrossRef
Lai, J., Dutkiewicz, E., Liu, R., Vesilo, R., & Fang, G. (2011). Network selection in cooperative cognitive radio networks. IEEE 11th international symposium on communications and information technologies (ISCIT) (pp. 378–383).
Lai, J., Dutkiewicz, E., Liu, R., & Vesilo, R. (2011). Joint admission control for cooperative cognitive radio networks. IEEE 6th international ICST conference on cognitive radio oriented wireless networks and communications (CROWNCOM) (pp. 276–280).
Bertsekas, D. (1995). Dynamic programming and optimal control. MA, Belmont: Athena Scientific Belmont.
- Cooperative optimal pricing for stochastic access control in overlaid radio access networks
- Springer US