A game based energy sensitive spectrum auction model and bid learning process for cognitive radio systems
Автор: Oloyede Abdulkarim Ayopo
Журнал: Журнал Сибирского федерального университета. Серия: Техника и технологии @technologies-sfu
Статья в выпуске: 6 т.11, 2018 года.
Бесплатный доступ
An auction based bid learning process for cognitive radio networks, where the users and the service providers are learning about each other to maximise each other’s utility is examined. A game model is formulated to allow players to learn depending on their priority. This enables users to learn different parameters such as the best offered bid price and the appropriate time to participate in the auction process. The performance of the system is examined based on the developed utility function. The results show that the blocking probability, utility function and the energy consumed is better with the learning users when compared to the non-learning users. Results also show that provided learning is taking place in the system, Nash Equilibrium can be established.
Spectrum auction, dynamic spectrum access, learning based auction, utility function
Короткий адрес: https://sciup.org/146279385
IDR: 146279385 | DOI: 10.17516/1999-494X-0086
Список литературы A game based energy sensitive spectrum auction model and bid learning process for cognitive radio systems
- Patil K., Prasad R. and Skouby K. A Survey of Worldwide Spectrum Occupancy Measurement Campaigns for Cognitive Radio, iDevices and Communications (ICDeCom), 2011 International Conference on, 2011, 1-5.
- Wang Z. and Salous S. Spectrum occupancy statistics and time series models for cognitive radio, Journal of Signal Processing Systems, 2011, 62, 145-155.
- Jinzhao S., Jianfei W. and Wei W. Dynamic spectrum allocation for heterogeneous cognitive radio networks from auction perspective, iCognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2011 Sixth International ICST Conference on, 2011, 176-180.
- Yao L., Hao H., Jun W., and L. Shaoqian. Energy-efficient dynamic spectrum access using no-regret learning, Information, Communications and Signal Processing, 2009. ICICS 2009. 7th International Conference on, 2009, 1-5.
- Gu, x, G. r, Alago, x, and F. z, Green wireless communications via cognitive dimension: an overview, Network, IEEE, 2011, 25, 50-56.
- Zhu J. and Liu K.J.R. Cognitive radios for dynamic spectrum access -Dynamic Spectrum Sharing: A Game Theoretical Overview, Communications Magazine, IEEE, 2007, 45, 88-94.
- Iosifidis G. and Koutsopoulos I. Challenges in auction theory driven spectrum management, Communications Magazine, IEEE, 2011, 49, 128-135.
- Grace A.O. a. D. Energy Efficient Dynamic Spectrum Pricing for Cognitive Radio Based Cellular Systems Using The Concept of Green Payments, Paper under review. Submitted to Journal of Wireless and Personal Communications on 21st November 2014, 2014.
- Chunchun W., Sheng Z., and Guihai C. A strategy-proof spectrum auction for balancing revenue and fairness, Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th, 2014, 827-832.
- Kelly F.P., Maulloo A.K. and Tan D.K. Rate control for communication networks: shadow prices, proportional fairness and stability, Journal of the Operational Research society, 1998, 49, 237-252.
- Sengupta S. and Chatterjee M. An Economic Framework for Dynamic Spectrum Access and Service Pricing, Networking, IEEE/ACM Transactions on, 2009, 17, 1200-1213.
- Haitao L., Chatterjee M., Das S.K. and Basu K. ARC: an integrated admission and rate control framework for competitive wireless CDMA data networks using noncooperative games, Mobile Computing, IEEE Transactions on, 2005, 4, 243-258.
- Marbach P. Pricing differentiated services networks: bursty traffic, INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001, 2, 650-658.
- Moore H.L. Empirical laws of demand and supply and the flexibility of prices, Political Science Quarterly, 1919, 34, 546-567.
- Oloyede A. and Grace D. 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.
- Nan F., Siun-Chuon M. and Mandayam N.B. Pricing and power control for joint network-centric and user-centric radio resource management, Communications, IEEE Transactions on, 2004, 52, 1547-1557.
- Oloyede A. and Grace D. Energy efficient learning based auction process for cognitive radio systems, Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th, 2014, 35-40.
- Zhu H., Rong Z., and Poor H.V. Repeated Auctions with Bayesian Nonparametric Learning for Spectrum Access in Cognitive Radio Networks, Wireless Communications, IEEE Transactions on, 2011, 10, 890-900.
- Oloyede A. and Grace D. Energy Efficient Soft Real Time Spectrum Auction for Dynamic Spectrum Access, presented at the 20th International Conference on Telecommunications Casablanca, 2013.
- Youping Z., Shiwen M., J. Neel O. and Reed J.H. Performance Evaluation of Cognitive Radios: Metrics, Utility Functions, and Methodology, Proceedings of the IEEE, 2009, 97, 642-659.
- Oloyede A. and Dainkeh A. Energy efficient soft real-time spectrum auction, Advances in Wireless and Optical Communications (RTUWO), 2015, 113-118.
- Kyösti P., Meinilä J., Hentilä L., Zhao X., Jämsä T., Schneider C. et al. IST-4-027756 WINNER II D1.1.2 V1.2 WINNER II Channel Models, 2007. Access: http://www.cept.org/files/1050/documents/winner2%20-%20final%20report.pdf
- Burr A., Papadogiannis A., and Jiang T. MIMO Truncated Shannon Bound for system level capacity evaluation of wireless networks, Wireless Communications and Networking Conference Workshops (WCNCW), 2012 IEEE, 2012, 268-272.
Статья научная
