A novel two-stage dynamic spectrum sharing scheme in cognitive radio networks

In order to enhance the efficiency of spectrum utilization and reduce communication overhead in spectrum sharing process, we propose a two-stage dynamic spectrum sharing scheme in which cooperative and non-cooperative modes are analyzed in both stages. In particular, the existence and the uniqueness of Nash Equilibrium (NE) strategies for noncooperative mode are proved. In addition, a distributed iterative algorithm is proposed to obtain the optimal solutions of the scheme. Simulation studies are carried out to show the performance comparison between two modes as well as the system revenue improvement of the proposed scheme compared with a conventional scheme without a virtual price control factor.

[1]  Dusit Niyato,et al.  Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion , 2008, IEEE Journal on Selected Areas in Communications.

[2]  Sudharman K. Jayaweera,et al.  Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games , 2009, IEEE Transactions on Wireless Communications.

[3]  Yan Zhang,et al.  Economic Approaches for Cognitive Radio Networks: A Survey , 2011, Wirel. Pers. Commun..

[4]  Feng-Tsun Chien,et al.  Bandwidth-constrained multistage Bayesian game for spectrum trading in cognitive radio networks , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[5]  Rajarathnam Chandramouli,et al.  Price dynamics in competitive agile spectrum access markets , 2007, IEEE Journal on Selected Areas in Communications.

[6]  Xiaoying Gan,et al.  Spectrum Trading in Cognitive Radio Networks: An Agent-Based Model under Demand Uncertainty , 2011, IEEE Transactions on Communications.

[7]  Shuguang Cui,et al.  Price-Based Spectrum Management in Cognitive Radio Networks , 2008, IEEE J. Sel. Top. Signal Process..

[8]  Lijie Wang,et al.  Utility based cooperative spectrum leasing in cognitive radio networks , 2012, 2012 International Symposium on Wireless Communication Systems (ISWCS).

[9]  A. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1996, Proceedings of Vehicular Technology Conference - VTC.

[10]  Zhu Han,et al.  Dynamic Spectrum Access and Management in Cognitive Radio Networks: References , 2009 .

[11]  Tiankui Zhang,et al.  Stackelberg Game Based Cooperative User Relay Assisted Load Balancing in Cellular Networks , 2013, IEEE Communications Letters.

[12]  Zhu Han,et al.  Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach , 2009, IEEE Transactions on Mobile Computing.

[13]  Jianwei Huang,et al.  Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator's Perspective , 2009, IEEE Transactions on Mobile Computing.

[14]  K. J. Ray Liu,et al.  Multi-Stage Pricing Game for Collusion-Resistant Dynamic Spectrum Allocation , 2008, IEEE Journal on Selected Areas in Communications.

[15]  Liang Dong,et al.  Spectrum Sharing in MIMO Cognitive Radio Networks Based on Cooperative Game Theory , 2014, IEEE Transactions on Wireless Communications.

[16]  Dong In Kim,et al.  Game Theoretic Approaches for Multiple Access in Wireless Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

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

[18]  Xinbing Wang,et al.  Spectrum trading with insurance in cognitive radio networks , 2012, 2012 Proceedings IEEE INFOCOM.

[19]  Jun Sun,et al.  An improved spectrum sharing algorithm in cognitive radio based on game theory , 2010, 2010 IEEE 12th International Conference on Communication Technology.