The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2012 vol.11)
pp: 2020-2032
Alexander W. Min , Intel Labs, Hillsboro
Xinyu Zhang , The University of Michigan, Ann Arbor
Jaehyuk Choi , Kyungwon University, Seongnam
Kang G. Shin , The University of Michigan, Ann Arbor
The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order to increase their revenue and improve spectrum utilization. In DSM, determining the optimal spectrum leasing price is an important yet challenging problem that requires a comprehensive understanding of market participants' interests and interactions. In this paper, we study spectrum pricing competition in a duopoly DSM, where two wireless service providers (WSPs) lease spectrum access rights, and secondary users (SUs) purchase the spectrum use to maximize their utility. We identify two essential, but previously overlooked, properties of DSM: 1) heterogeneous spectrum resources at WSPs and 2) spectrum sharing among SUs. We demonstrate the impact of spectrum heterogeneity via an in-depth measurement study using a software-defined radio (SDR) testbed. We then study the impacts of spectrum heterogeneity on WSPs' optimal pricing and SUs' WSP selection strategies using a systematic three-step approach. First, we study how spectrum sharing among SUs subscribed to the same WSP affects the SUs' achievable utility. Then, we derive the SUs' optimal WSP selection strategy that maximizes their payoff, given the heterogeneous spectrum propagation characteristics and prices. We analyze how individual SU preferences affect market evolution and prove the market convergence to a mean-field limit, even though SUs make local decisions. Finally, given the market evolution, we formulate the WSPs' pricing strategies in a duopoly DSM as a noncooperative game and identify its Nash equilibrium points. We find that the equilibrium price and its uniqueness depend on the SUs' geographical density and spectrum propagation characteristics. Our analytical framework reveals the impact of spectrum heterogeneity in a real-world DSM, and can be used as a guideline for the WSPs' pricing strategies.
Pricing, Interference, Radio spectrum management, Signal to noise ratio, Receivers, Frequency measurement, Wireless communication, spectrum pricing, Cognitive radios, dynamic spectrum market, game theory, spectrum heterogeneity
Alexander W. Min, Xinyu Zhang, Jaehyuk Choi, Kang G. Shin, "Exploiting Spectrum Heterogeneity in Dynamic Spectrum Market", IEEE Transactions on Mobile Computing, vol.11, no. 12, pp. 2020-2032, Dec. 2012, doi:10.1109/TMC.2011.229
[1] “Mobile Broadband Capacity Constraints and the Need for Optimization,” Rysavy_Mobile_Broadband_Capacity_Constraints.pdf , 2012.
[2] FCC, “Second Memorandum Opinion and Order,” FCC 10-174, Sept. 2010.
[3] IEEE 802.22 Working Group on Wireless Regional Area Networks, http://www.ieee802.org22, 2012.
[4] IEEE 802.22 Working Group on Wireless Local Area Networks, http://www.ieee802.org11, 2012.
[5] M. Gandetto and C. Regazzoni, “Spectrum Sensing: A Distributed Approach for Cognitive Terminals,” IEEE J. Selected Areas in Comm., vol. 25, no. 3, pp. 546-557, Apr. 2007.
[6] Q. Zhao, L. Tong, A. Swami, and Y. Chen, “Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc Networks: A POMDP Framework,” IEEE J. Selected Areas in Comm., vol. 25, no. 3, pp. 589-600, Apr. 2007.
[7] A.W. Min and K.G. Shin, “An Optimal Sensing Framework Based on Spatial RSS-Profile in Cognitive Radio Networks,” Proc. IEEE CS Sixth Ann. Sensor, Mesh, and Ad Hoc Comm. and Networks (SECON), June 2009.
[8] A.W. Min, K.G. Shin, and X. Hu, “Secure Cooperative Sensing in IEEE 802.22 WRANs Using Shadow Fading Correlation,” IEEE Trans. Mobile Computing, vol. 10, no. 10, pp. 1434-1447, Oct. 2011.
[9] D. Gurney, G. Buchwald, L. Ecklund, S. Kuffner, and J. Grosspietsch, “Geo-Location Database Techniques for Incumbent Protection in the TV White Space,” Proc. IEEE Third Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Oct. 2008.
[10] R. Murty, R. Chandra, T. Moscibroda, and P. Bahl, “SenseLess: A Database-Driven White Spaces Network,” Technical Report MSR-TR-2010-127, Sept. 2010.
[11] operator , 2012.
[12] http:/, 2012.
[13] J.M. Chapin and W.H. Lehr, “The Path to Market Success for Dynamic Spectrum Access Technology,” IEEE Comm. Mag., vol. 45, no. 5, pp. 96-103, May 2007.
[14] J. Jia and Q. Zhang, “Competitions and Dynamics of Duopoly Wireless Service Providers in Dynamic Spectrum Market,” Proc. ACM MobiHoc, May 2008.
[15] H. Inaltekin, T. Wexler, and S.B. Wicker, “A Duopoly Pricing Game for Wireless IP Services,” Proc. IEEE CS Fourth Ann. Sensor, Mesh, and Ad Hoc Comm. and Networks (SECON), June 2007.
[16] D. Niyato and E. Hossain, “Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion,” IEEE J. Selected Areas in Comm., vol. 26, no. 1, pp. 192-202, Jan. 2008.
[17] D. Niyato, E. Hossain, and Z. Han, “Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach,” IEEE Trans. Mobile Computing, vol. 8, no. 8, pp. 1009-1022, Aug. 2009.
[18] L. Duan, J. Huang, and B. Shou, “Competition with Dynamic Spectrum Leasing,” Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Apr. 2010.
[19] Y. Xing, R. Chandramouli, and C. Cordeiro, “Price Dynamics in Competitive Agile Spectrum Access Markets,” IEEE J. Selected Areas in Comm., vol. 25, no. 3, pp. 613-621, Apr. 2007.
[20] A. Mas-Colell, M. Whinston, and J. Green, Microeconomic Theory. Oxford Univ., 1995.
[21] X. Zhou, S. Gandhi, S. Suri, and H. Zheng, “eBay in the Sky: Strategy-Proof Wireless Spectrum Auctions,” Proc. ACM MobiCom, Sept. 2008.
[22] G.S. Kasbekar and S. Sarkar, “Spectrum Pricing Games with Bandwidth Uncertainty and Spatial Reuse in Cognitive Radio Networks,” Proc. ACM MobiHoc, Sept. 2010.
[23] USRP: Universal Software Radio Peripheral, http:/www.ettus. com, 2012.
[24] C. Bettstetter, “On the Minimum Node Degree and Connectivity of a Wireless Multihop Network,” Proc. ACM MobiHoc, June 2002.
[25] Y. Choi and S. Choi, “Service Charge and Energy-Aware Vertical Handoff in Integrated IEEE 802.16e/802.11 Networks,” Proc. IEEE INFOCOM, May 2007.
[26] A. Goldsmith, Wireless Communications. Cambridge Univ., 2005.
[27] ITU-R Recommendation P.1546-3, Method for Point-to-Area Predictions for Terrestrial Services in the Frequency Range 30 MHz to 3000 MHz, 2007.
[28] X. Hong, C.-X. Wang, and J. Thompson, “Interference Modeling of Cognitive Radio Networks,” Proc. IEEE Vehicular Technology Conf. (VTC-Spring), May 2008.
[29] M.M. Buddhikot, “Understanding Dynamic Spectrum Access: Models, Taxonomy and Challenges,” Proc. IEEE Second Int'l Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Apr. 2007.
[30] GNU Software Radio Project,, 2012.
[31] P. Gupta and P.R. Kumar, “The Capacity of Wireless Networks,” IEEE Trans. Information Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000.
[32] K.S. Gilhousen, I.M. Jacobs, R. Padovani, A.J. Viterbi, L.A. WeaverJr, and C.E. WheatleyIII, “On the Capacity of a Cellular CDMA System,” IEEE Trans. Vehicular Technology, vol. 40, no. 2, pp. 303-312, May 1991.
[33] R. Menon, R.M. Buehrer, and J.H. Reed, “On the Impact of Dynamic Spectrum Sharing Techniques on Legacy Radio Systems,” IEEE Trans. Wireless Comm., vol. 7, no. 11, pp. 4198-4207, Nov. 2008.
[34] M. Benaim and J.-Y. L. Boudec, “A Class of Mean Field Interaction Models for Computer and Communication Systems,” Performance Evaluation, vol. 65, nos. 11/12, pp. 823-838, Apr. 2008.
[35] L. Duan, J. Huang, and B. Shou, “Cognitive Mobile Virtual Network Operator: Investment and Pricing with Supply Uncertainty,” Proc. IEEE INFOCOM, Mar. 2010.
[36] V. Gajić, J. Huang, and B. Rimoldi, “Competition of Wireless Providers for Atomic Users: Equilibrium and Social Optimality,” Proc. 47th Ann. Allerton Conf. Comm., Control, and Computing, 2009.
[37] H. Mutlu, M. Alanyali, and D. Starobinski, “On-Line Pricing of Secondary Spectrum Access with Unknown Demand Function and Call Length Distribution,” Proc. IEEE INFOCOM, Mar. 2008.
3 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool