A Strategic Bargaining Game for a Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes a strategic bargaining spectrum-sharing scheme, considering a CR-based heterogeneous WSN (HWSN). The sensors of HWSNs are discrepant and exist in different wireless environments, which leads to various signal-to-noise ratios (SNRs) for the same or different licensed users. Unlicensed users bargain with licensed users regarding the spectrum price. In each round of bargaining, licensed users are allowed to adaptively adjust their spectrum price to the best for maximizing their profits. . Then, each unlicensed user makes their best response and informs licensed users of “bargaining” and “warning”. Through finite rounds of bargaining, this scheme can obtain a Nash bargaining solution (NBS), which makes all licensed and unlicensed users reach an agreement. The simulation results demonstrate that the proposed scheme can quickly find a NBS and all players in the game prefer to be honest. The proposed scheme outperforms existing schemes, within a certain range, in terms of fairness and trade success probability.

[1]  Songlin Sun,et al.  A Stackelberg game spectrum sharing scheme in cognitive radio-based heterogeneous wireless sensor networks , 2016, Signal Process..

[2]  Xinbing Wang,et al.  Resource Pricing with Primary Service Guarantees in Cognitive Radio Networks: A Stackelberg Game Approach , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[3]  Habib F. Rashvand,et al.  A channel assignment algorithm for Cognitive Radio wireless sensor networks , 2012 .

[4]  Jianfeng Wang,et al.  Cognitive Radio Based Wireless Sensor Networks , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[5]  Zhu Han,et al.  Asymptotic optimality for distributed spectrum sharing using bargaining solutions , 2009, IEEE Transactions on Wireless Communications.

[6]  R. Venkatesha Prasad,et al.  Cognitive functionality in next generation wireless networks: standardization efforts , 2008, IEEE Communications Magazine.

[7]  Li Wang,et al.  Behavior modeling for spectrum sharing in wireless cognitive networks , 2012, Wireless Networks.

[8]  Fei Teng,et al.  Sharing of Unlicensed Spectrum by Strategic Operators , 2017, IEEE Journal on Selected Areas in Communications.

[9]  Marilda Sotomayor Game Theory, Introduction to , 2009, Encyclopedia of Complexity and Systems Science.

[10]  Guoan Bi,et al.  Distributed optimization for cognitive radio networks using Stackelberg game , 2010, 2010 IEEE International Conference on Communication Systems.

[11]  Xuezhi Tan,et al.  Spectrum Pricing Research Based on Game Theory in Cognitive Radio Networks , 2013, 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control.

[12]  Wen-Zhan Song,et al.  Cooperative Resource Sharing and Pricing for Proactive Dynamic Spectrum Access via Nash Bargaining Solution , 2014, IEEE Transactions on Parallel and Distributed Systems.

[13]  Chi Zhang,et al.  Spectrum Sharing Based on a Bertrand Game in Cognitive Radio Sensor Networks , 2017, Sensors.

[14]  Wenchao Xu,et al.  Double auction based spectrum sharing for wireless operators , 2010, 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[15]  Dusit Niyato,et al.  Market-Equilibrium, Competitive, and Cooperative Pricing for Spectrum Sharing in Cognitive Radio Networks: Analysis and Comparison , 2008, IEEE Transactions on Wireless Communications.

[16]  Jun Cai,et al.  Two-Stage Spectrum Sharing With Combinatorial Auction and Stackelberg Game in Recall-Based Cognitive Radio Networks , 2014, IEEE Transactions on Communications.

[17]  Sagar Naik,et al.  A new fairness index for radio resource allocation in wireless networks , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[18]  Andrea J. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1997, IEEE Trans. Commun..

[19]  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.

[20]  Dusit Niyato,et al.  A Game-Theoretic Approach to Competitive Spectrum Sharing in Cognitive Radio Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[21]  Rong Zheng,et al.  Repeated Auctions with Bayesian Nonparametric Learning for Spectrum Access in Cognitive Radio Networks , 2011, IEEE Transactions on Wireless Communications.

[22]  Sungwook Kim,et al.  A repeated Bayesian auction game for cognitive radio spectrum sharing scheme , 2013, Comput. Commun..

[23]  Shi-Chung Chang,et al.  Double Auction Design for Short-Interval and Heterogeneous Spectrum Sharing , 2016, IEEE Transactions on Cognitive Communications and Networking.

[24]  Gi-Hong Im,et al.  Cooperation-Based Dynamic Spectrum Leasing via Multi-Winner Auction of Multiple Bands , 2013, IEEE Transactions on Communications.

[25]  Walid Abdallah,et al.  An optimized spectrum allocation scheme for future aircraft Cognitive Radio Wireless Sensor Networks , 2014, 2014 14th International Symposium on Communications and Information Technologies (ISCIT).

[26]  Sungwook Kim Multi-leader multi-follower Stackelberg model for cognitive radio spectrum sharing scheme , 2012, Comput. Networks.

[27]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.