QoS‐driven spectrum sharing game in cognitive radio systems: a variational inequality approach

Cognitive radio CR technology aspires to the efficient usage of the limited and underutilised radio spectrum resources. Constituting a flexible and intelligent network, the unlicensed secondary users SUs may coexist with the licensed primary users PUs, under the prerequisite not to disturb their communication. A spectrum interweave CR system is considered, where SUs coexist with a single PU pair in a fading environment. The SUs are allowed to transmit their data 'peacefully', only with the absence of PU's transmission, by exploiting spectrum holes of the licensed band. A quality of service QoS-driven spectrum sharing scheme is proposed. Specifically, the problem of licensed spectrum sharing among SUs is formulated as a novel non-cooperative game with a view of maximising the effective capacity of each SU that has specific delay QoS requirements, subject to the coupled constraint of the available bandwidth. Because this game constitutes a generalised Nash equilibrium problem, we apply the variational inequality framework to solve it. The variational solution is proven to be unique, and it is obtained through a distributed iterative algorithm. The performance of the proposed scheme is evaluated through numerical simulations, and the results confirm the importance of incorporating the delay QoS factor in spectrum sharing studies. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Philip Constantinou,et al.  Effects of spatial correlation on QoS-driven power allocation over Nakagami-m fading channels in cognitive radio systems , 2015, Trans. Emerg. Telecommun. Technol..

[2]  Francisco Facchinei,et al.  Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach , 2010, IEEE Transactions on Signal Processing.

[3]  Francisco Facchinei,et al.  Monotone Games for Cognitive Radio Systems , 2012 .

[4]  Eitan Altman,et al.  Equilibrium selection in interference management non-cooperative games in femtocell networks , 2012, 6th International ICST Conference on Performance Evaluation Methodologies and Tools.

[5]  Moshe T. Masonta,et al.  Spectrum Decision in Cognitive Radio Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[6]  Francisco Facchinei,et al.  Convex Optimization, Game Theory, and Variational Inequality Theory , 2010, IEEE Signal Processing Magazine.

[7]  Farshad Lahouti,et al.  Effective capacity optimization for multiuser diversity systems with adaptive transmission , 2012, Trans. Emerg. Telecommun. Technol..

[8]  K. J. Ray Liu,et al.  Cognitive Radio Networking and Security: Preface , 2010 .

[9]  Mohamed-Slim Alouini,et al.  Coded Communication over Fading Channels , 2005 .

[10]  Dapeng Wu,et al.  Effective capacity: a wireless link model for support of quality of service , 2003, IEEE Trans. Wirel. Commun..

[11]  Pramod Viswanath,et al.  Cognitive Radio: An Information-Theoretic Perspective , 2009, IEEE Transactions on Information Theory.

[12]  Jia Tang,et al.  Quality-of-Service Driven Power and Rate Adaptation over Wireless Links , 2007, IEEE Transactions on Wireless Communications.

[13]  Wessam Ajib,et al.  Game theory based resource allocation for cognitive radio networks , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[14]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[15]  Mohamed-Slim Alouini,et al.  Digital Communication Over Fading Channels: A Unified Approach to Performance Analysis , 2000 .

[16]  George Mastorakis,et al.  Efficient radio resource management algorithms in opportunistic cognitive radio networks , 2014, Trans. Emerg. Telecommun. Technol..

[17]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[18]  F. Facchinei,et al.  Finite-Dimensional Variational Inequalities and Complementarity Problems , 2003 .

[19]  Mingyi Hong,et al.  Competitive sharing of the spectrum in cognitive radio network: A market equilibrium framework , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[20]  Guevara Noubir,et al.  Game theory-based resource management strategy for cognitive radio networks , 2012, Multimedia Tools and Applications.

[21]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[22]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[23]  M. J. Omidi,et al.  Spectrum Sharing in Cognitive Radio Networks , 2009 .

[24]  Sherali Zeadally,et al.  Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey , 2013, IEEE Communications Surveys & Tutorials.

[25]  Dusit Niyato,et al.  Competitive spectrum sharing in cognitive radio networks: a dynamic game approach , 2008, IEEE Transactions on Wireless Communications.