ENERGY EFFICENT AUCTION BASED DYNAMIC SPECTRUM ACCESS NETWORK
DOI:
https://doi.org/10.31436/iiumej.v18i1.682Abstract
A framework is proposed which is aimed at increasing the much needed revenue of the wireless service providers. The model uses the price paid by the wireless users to control the amount of energy consumed and the admission process based on a dynamic spectrum access network. The scheme is based on using a first price auction process with a reserve price to allocate the radio spectrum. It allows an opportunistic access to the white space in a manner that would protect the primary users in the system. The concept of green payment is used to penalise users who require high transmit power and subsidies those who require low transmit power. This work shows that with the proposed green payment in combination with the knowledge of the reserve price, the energy consumed and the delay in an auction based dynamic spectrum access network can be reduced.Downloads
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References
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[4] M. Nekovee, "Quantifying the Availability of TV White Spaces for Cognitive Radio Operation in the UK," in 2009 IEEE International Conference on Communications Workshops, 2009, pp. 1-5.
[5] I. F. Akyildiz, W. y. Lee, M. C. Vuran, and S. Mohanty, "A survey on spectrum management in cognitive radio networks," IEEE Communications Magazine, vol. 46, pp. 40-48, 2008.
[6] J. Mitola, "Cognitive radio for flexible mobile multimedia communications," in Mobile Multimedia Communications, 1999. (MoMuC '99) 1999 IEEE International Workshop on, 1999, pp. 3-10.
[7] B. G. Mölleryd, J. Markendahl, and Ö. Mäkitalo, "Analysis of operator options to reduce the impact of the revenue gap caused by flat rate mobile broadband subscriptions," in 8th Conf. on Telecom, Media & Internet Tele-Economics, 2009.
[8] J. Jia, Q. Zhang, Q. Zhang, and M. Liu, "Revenue generation for truthful spectrum auction in dynamic spectrum access," presented at the Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing, New Orleans, LA, USA, 2009.
[9] C. A. Gizelis and D. D. Vergados, "A Survey of Pricing Schemes in Wireless Networks," Communications Surveys & Tutorials, IEEE, vol. 13, pp. 126-145, 2011.
[10] J. Huang, R. A. Berry, and M. L. Honig, "Auction-based spectrum sharing," Mob. Netw. Appl., vol. 11, pp. 405-418, 2006.
[11] V. Krishna, Auction Theory: Academic Press USA, 2010.
[12] S. Gandhi, C. Buragohain, L. Cao, H. Zheng, and S. Suri, "Towards real-time dynamic spectrum auctions," Computer Networks, vol. 52, pp. 879-897, 3/14/ 2008.
[13] K. Fitchard. (2009, 28/08/2013). Wireless 2025: A look at wireless in the year 2025. Available: http://connectedplanetonline.com/wireless/news/wireless-future-year-2025-0409/
[14] A. Burr, A. Papadogiannis, and T. Jiang, "MIMO Truncated Shannon Bound for system level capacity evaluation of wireless networks," in Wireless Communications and Networking Conference Workshops (WCNCW), 2012 IEEE, 2012, pp. 268-272.
[15] A. Oloyede and D. Grace, "Energy Efficient Bid Learning Process in an Auction Based Cognitive Radio Networks," Paper accepted in Bayero Univeristy Journal of Engineering and Technology(BJET), 2016/02/02 2016.
[16] A. Oloyede and D. Grace, "Energy Efficient Soft Real Time Spectrum Auction for Dynamic Spectrum Access," presented at the 20th International Conference on Telecommunications Casablanca, 2013.
[17] A. Oloyede and D. Grace, "Energy Efficient Short Term Spectrum Auction Using the Concept of Green Payments," Wireless Personal Communications, pp. 1-28, 2016.
[18] P. Kyösti, J. Meinilä, L. Hentilä, X. Zhao, T. Jämsä, C. Schneider, et al. (2007, 1-4-2013). IST-4-027756 WINNER II D1.1.2 V1.2 WINNER II Channel Models. Available: http://www.cept.org/files/1050/documents/winner2%20-%20final%20report.pdf
