Abstract
This paper considers a price-based power control problem in the cognitive radio networks (CRNs). The primary user (PU) can admit secondary users (SUs) to access if their interference powers are all under the interference power constraint. In order to access the spectrum, the SUs need to pay for their interference power. The PU first decides the price for each SU to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on a non-cooperative game. The interaction between the PU and the SUs is modeled as a Stackelberg game. Using the backward induction, a revenue function of the PU is expressed as a non-convex function of the transmit power of the SUs. To find the optimal price for the PU, we rewrite the revenue maximization problem of the PU as a monotone optimization by variable substitution. Based on the monotone optimization, a novel price-based power control algorithm is proposed. Simulation results show the convergence and the effectiveness of the proposed algorithm compared to the non-uniform pricing algorithm.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Federal Communications Commission. Spectrum policy task force report, 02-155 [R]. Washington DC, USA: Federal Communications Commission, 2002.
Liang Y C, Chen K C, Li G Y, et al. Cognitive radio networking and communications: An overview [J]. IEEE Transactions on Vehicular Technology, 2011, 60(7): 3386–3407.
Zhao Q, Sadler B M. A survey of dynamic spectrum access [J]. IEEE Signal Processing Magazine, 2007, 24: 79–89.
Yücek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications [J]. IEEE Communications Surveys & Tutorials, 2009, 11(1): 116–130.
Haykin S. Cognitive radio: Brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201–220.
Wang F, Krunz M, Cui S G. Price-based spectrum management in cognitive radio networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 74–87.
Yu H, Gao L, Li Z, et al. Pricing for uplink power control in cognitive radio networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1769–1778.
Wu Y, Zhang T, Tsang D H K. Joint pricing and power allocation for dynamic spectrum access networks with Stackelberg game model [J]. IEEE Transactions on Wireless Communications, 2011, 10(1): 12–19.
Wang Z Q, Jiang L G, He C. A novel price-based power control algorithm in cognitive radio networks [J]. IEEE Communications Letters, 2013, 17(1): 43–46.
Kang X, Zhang R, Motani M. Price-based resource allocation for spectrum-sharing femtocell networks: A Stackelberg game approach [J]. IEEE Journal on Selected Areas in Communications, 2012, 30(3): 538–549.
Fudenberg D, Tirole J. Game theory [M]. Cambridge, England: The MIT Press, 1993.
Boyd S, Vandenberghe L. Convex optimization [M]. Cambridge, England: Cambridge University Press, 2003.
Tuy H. Monotonic optimization: Problems and solution approaches [J]. SIAM Journal on Optimization, 2000, 11(2): 464–494.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: the National Natural Science Foundation of China (Nos. 61172067 and 61371086), and the National High Technology Research and Development Program (863) of China (No. 2014AA01A701)
Rights and permissions
About this article
Cite this article
Wang, Zq., Jiang, Lg. & He, C. Price-based power control algorithm in cognitive radio networks based on monotone optimization. J. Shanghai Jiaotong Univ. (Sci.) 20, 654–659 (2015). https://doi.org/10.1007/s12204-015-1673-0
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-015-1673-0