Browse > Article
http://dx.doi.org/10.3837/tiis.2016.10.007

Price-based Resource Allocation for Virtualized Cognitive Radio Networks  

Li, Qun (Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications)
Xu, Ding (Wireless Communication Key Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.10, 2016 , pp. 4748-4765 More about this Journal
Abstract
We consider a virtualized cognitive radio (CR) network, where multiple virtual network operators (VNOs) who own different virtual cognitive base stations (VCBSs) share the same physical CBS (PCBS) which is owned by an infrastructure provider (InP), sharing the spectrum with the primary user (PU). The uplink scenario is considered where the secondary users (SUs) transmit to the VCBSs. The PU is protected by constraining the interference power from the SUs. Such constraint is applied by the InP through pricing the interference. A Stackelberg game is formulated to jointly maximize the revenue of the InP and the individual utilities of the VNOs, and then the Stackelberg equilibrium is investigated. Specifically, the optimal interference price and channel allocation for the VNOs to maximize the revenue of the InP and the optimal power allocation for the SUs to maximize the individual utilities of the VNOs are derived. In addition, a low‐complexity ±‐optimal solution is also proposed for obtaining the interference price and channel allocation for the VNOs. Simulations are provided to verify the proposed strategies. It is shown that the proposed strategies are effective in resource allocation and the ±‐optimal strategy achieves practically the same performance as the optimal strategy can achieve. It is also shown that the InP will not benefit from a large interference power limit, and selecting VNOs with higher unit rate utility gain to share the resources of the InP is beneficial to both the InP and the VNOs.
Keywords
Cognitive radio; resource allocation; virtualization; pricing; Stackelberg game;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. Xu and Q. Li, “Power allocation for two-user cognitive multiple access channels under primary user outage constraint,” International Journal of Communication Systems, 2015 Article (CrossRef Link)
2 N. U. Hasan, W. Ejaz, N. Ejaz, H. S. Kim, A. Anpalagan, and M. Jo, “Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks,” IEEE Access, vol. 4, pp. 980–992, 2016. Article (CrossRef Link)   DOI
3 H. Gao, W. Ejaz, and M. Jo, “Cooperative wireless energy harvesting and spectrum sharing in 5G networks,” IEEE Access, vol. 4, pp. 3647–3658, 2016. Article (CrossRef Link)   DOI
4 D. Xu and Q. Li, “Resource allocation for cognitive radio with primary user secrecy outage constraint,” IEEE Systems Journal. Article (CrossRef Link)
5 S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004. Article (CrossRef Link)
6 H. W. Kuhn, “The hungarian method for the assignment problem,” Nav Res Logist Q, vol. 2, no. 1-2, pp. 83–97, 1955. Article (CrossRef Link)   DOI
7 N. Shor, K. Kiwiel, and A. Ruszczynski, Minimization methods for non-differentiable functions. Berlin, Germany: Springer-Verlag Press, 1985. Article (CrossRef Link)
8 D. Xu, Z. Feng, and P. Zhang, “Resource allocation for heterogeneous services in multiuser cognitive radio networks,” International Journal of Communication Systems, vol. 27, no. 10, pp. 2121–2140, 2014. Article (CrossRef Link)   DOI
9 M. I. Kamel, L. B. Le, and A. Girard, “LTE multi-cell dynamic resource allocation for wireless network virtualization,” in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), pp. 966–971, 2015. Article (CrossRef Link)
10 C. Liang and F. R. Yu, “Wireless network virtualization: A survey, some research issues and challenges,” IEEE Communications Surveys & Tutorials, vol. 17, no. 1, pp. 358–380, 2015. Article (CrossRef Link)   DOI
11 B. Fan, H. Tian, and B. Liu, “Game theory based power allocation in LTE air interface virtualization,” in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), pp. 972–976, 2015. Article (CrossRef Link)
12 T. Maksymyuk, M. Kyryk, and M. Jo, “Comprehensive spectrum management for heterogeneous networks in LTE-U,” IEEE Wireless Communications, to be published.
13 F. Fu and U. C. Kozat, “Stochastic game for wireless network virtualization,” IEEE/ACM Transactions on Networking, vol. 21, no. 1, pp. 84–97, 2013. Article (CrossRef Link)   DOI
14 M. Jo, T. Maksymyuk, B. Strykhalyuk, and C.-H. Cho, “Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing,” IEEE Wireless Communications, vol. 22, no. 3, pp. 50–58, 2015. Article (CrossRef Link)   DOI
15 H. Zhang, X. Chu, W. Guo, and S. Wang, “Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum,” IEEE Communications Magazine, vol. 53, no. 3, pp. 158–164, 2015. Article (CrossRef Link)   DOI
16 L. Li and C. Xu, “On ergodic sum capacity of fading channels in OFDMA-based cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 63, no. 9, pp. 4334–4343, 2014. Article (CrossRef Link)   DOI
17 Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, “Cognitive radio networking and communications: An overview,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386–3407, 2011. Article (CrossRef Link)   DOI
18 K. Madushan Thilina, E. Hossain, and M. Moghadari, “Cellular OFDMA cognitive radio networks: Generalized spectral footprint minimization,” IEEE Transactions on Vehicular Technology, vol. 64, no. 7, pp. 3190–3204, 2015. Article (CrossRef Link)   DOI