Incentivizing spectrum sensing in database-driven dynamic spectrum sharing

The legacy concept of exclusion zones (EZs) is inept at enabling efficient utilization of fallow spectrum by secondary users (SUs), since legacy EZs are static and overly-conservative. The notion of a static EZ implies that it has to protect incumbent users (IUs) from the union of likely interference scenarios, leading to a worst-case, conservative solution. In this paper, we propose the concept of dynamic, multi-tier EZs, which takes advantage of participatory spectrum sensing carried out by SUs to support efficient database-driven spectrum sharing while protecting IUs against SU-induced aggregate interference. Specifically, the database directly incentivizes SUs to participate in spectrum sensing, which augments geolocation database by defining smaller EZs with dynamic boundaries and creating additional spectrum access opportunities for SUs. We propose an incentive mechanism based on a two-level game-theoretic model, in which the database conducts dynamic pricing in a first-level Stackelberg game in the presence of SUs who strategically contribute to spectrum sensing in a second-level stochastic game. The existence of an equilibrium solution is proven. According to our findings, the proposed incentive mechanism for the concept of dynamic, multi-tier EZs is effective to improve spectrum utilization efficiency while guaranteeing incumbent protection.

[1]  L. Fenton The Sum of Log-Normal Probability Distributions in Scatter Transmission Systems , 1960 .

[2]  G. Ryzin,et al.  Optimal dynamic pricing of inventories with stochastic demand over finite horizons , 1994 .

[3]  Eric Maskin,et al.  Markov Perfect Equilibrium: I. Observable Actions , 2001, J. Econ. Theory.

[4]  Pinar Keskinocak,et al.  Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions , 2003, IEEE Engineering Management Review.

[5]  B. Stengel,et al.  Leadership with commitment to mixed strategies , 2004 .

[6]  Yong-Pin Zhou,et al.  Strategic Consumer Response to Dynamic Pricing of Perishable Products , 2006 .

[7]  Jeffrey H. Reed,et al.  Chapter 11 – Network Support: The Radio Environment Map , 2006 .

[8]  Yoav Shoham,et al.  Essentials of Game Theory: A Concise Multidisciplinary Introduction , 2008, Essentials of Game Theory: A Concise Multidisciplinary Introduction.

[9]  J. Grosspietsch,et al.  Geo-Location Database Techniques for Incumbent Protection in the TV White Space , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

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

[11]  Jeffrey H. Reed,et al.  Network Support: The Radio Environment Map , 2009 .

[12]  Jeff McGill,et al.  Optimal Dynamic Pricing of Perishable Items by a Monopolist Facing Strategic Consumers , 2010 .

[13]  Paramvir Bahl,et al.  SenseLess: A database-driven white spaces network , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[14]  U. Rieder,et al.  Theory of Infinite Horizon Markov Decision Processes , 2011 .

[15]  Cheng-Xiang Wang,et al.  Aggregate Interference Modeling in Cognitive Radio Networks with Power and Contention Control , 2012, IEEE Transactions on Communications.

[16]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[17]  Jean C. Walrand,et al.  Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing , 2012, 2012 Proceedings IEEE INFOCOM.

[18]  Mihaela van der Schaar,et al.  Reputation-based incentive protocols in crowdsourcing applications , 2011, 2012 Proceedings IEEE INFOCOM.

[19]  Jonathan Rodriguez,et al.  Testbed for combination of local sensing with geolocation database in real environments , 2012, IEEE Wireless Communications.

[20]  Xuhang Ying,et al.  Exploring indoor white spaces in metropolises , 2013, MobiCom.

[21]  Tan Zhang,et al.  Inaccurate spectrum databases?: public transit to its rescue! , 2013, HotNets.

[22]  Tuna Tugcu,et al.  Radio environment map as enabler for practical cognitive radio networks , 2013, IEEE Communications Magazine.

[23]  Athanasios V. Vasilakos,et al.  TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[24]  Bo Gao,et al.  A credit-token-based spectrum etiquette framework for coexistence of heterogeneous cognitive radio networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[25]  Bo Gao,et al.  Supporting mobile users in database-driven opportunistic spectrum access , 2014, MobiHoc '14.

[26]  Ayon Chakraborty,et al.  Measurement-Augmented Spectrum Databases for White Space Spectrum , 2014, CoNEXT.

[27]  Hwee Pink Tan,et al.  Profit-maximizing incentive for participatory sensing , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[28]  Bo Gao,et al.  Uplink Soft Frequency Reuse for Self-Coexistence of Cognitive Radio Networks , 2014, IEEE Transactions on Mobile Computing.

[29]  Chantal Marlats A Folk theorem for stochastic games with finite horizon , 2015 .

[30]  Xiaohua Tian,et al.  Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing , 2015, IEEE Transactions on Vehicular Technology.

[31]  Behnam Bahrak,et al.  Multi-tier exclusion zones for dynamic spectrum sharing , 2015, 2015 IEEE International Conference on Communications (ICC).

[32]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.