Energy Efficient Non-Cooperative Methods for Resource Allocation in Cognitive Radio Networks

Enrico Del Re, Renato Pucci, Luca Simone Ronga

CNIT, Florence Research Unit, Florence, Italy.
University of Florence, Florence, Italy.

DOI: 10.4236/cn.2012.41001                

Abstract

In a cognitive radio network wherein primary and secondary users coexist, an efficient power allocation method represents one of the most important key aspects. This paper provides a novel approach based on a game theory framework to solve this problem in a distributed and fair way. Formulated as an optimization problem, the resource allocation problem between secondary users and primary users can be modeled and investigated with the Game Theory, and in particular S-Modular Games, since they provide useful tools for the definition of multi objective distributed algorithms in the context of radio communications. This paper provides also a performance comparison among the proposed game and two other algorithms, frequently used in this context: Simulated Annealing and Water Filling.

Keywords

Cognitive Radio Networks; Resource Allocation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	L. S. Ronga and E. Del Re, “S-Modular Games for Distributed Power Allocation in Cognitive Radio Systems,” Proceedings of the PIMRC 2009, Tokyo, 13-16 September 2009.
[2]	D. M. Topkis, “Equilibrium Points in Nonzero-Sum N-Person Submodular Games,” SIAM Journal on Control and Optimization, Vol. 17, No. 5, 1979, pp. 773-778. doi:10.1137/0317054
[3]	B. Wang, Y. Wu and K. J. R. Liu, “Game Theory for Cognitive Radio Networks: An Overview,” Computer Networks, Vol. 54, No. 14, 2010, pp. 2537-2561. doi:10.1016/j.comnet.2010.04.004
[4]	R. Luo and Z. Yan, “Power Allocation Using Non-Cooperative Game Theoretic Analysis in Cognitive Radio Networks,” Proceedings of WiCOM, Chengdu, 23-25 September, 2010.
[5]	D. Li, X. Dai and H. Zhang, “Game Theoretic Analysis of Joint Rate and Power Allocation in Cognitive Radio Networks,” International Journal of Communications, Network and System Sciences, Vol. 1, 2009, pp. 1-89. doi:10.4236/ijcns.2009.21001
[6]	G. Bansal, Md. J. Hossian and V. K.Bhargava, “Adaptive Power Loading for OFDM-Based Cognitive Radio Systems,” IEEE Conference on Communications, Glasgow, 24-28 June 2007, pp. 5137-5142. doi:10.1109/ICC.2007.849
[7]	J. Huang, R. Berry and M. L. Honig, “Auction-Based Spectrum Sharing,” ACM/Springer Mobile Networks, 2010.
[8]	C. U. Saraydar, N. B. Mandayam and D. Goodman, “Efficient Power Control via Pricing in Wireless Data Networks,” IEEE Transaction on Communications, Vol. 50, No. 2, 2002, pp. 291-303. doi:10.1109/26.983324
[9]	R. D. Yates, “A Framework for Uplink Power Control in Cellular Radio Systems,” IEEE Journal on Selected Area of Communications, Vol. 13, No. 7, 1995, pp. 1341-1347. doi:10.1109/49.414651
[10]	NATO IST-077 (RTG-035), “Final Technical Activity Report of IST077-RTG035,” Cognitive Radio in NATO, NATO, 2011.
[11]	F. Meshkati, H. V. Poor and S. C. Schwartz, “Energy Efficiency-Delay Tradeoffs in CDMA Networks: A Game Theoretic Approach,” IEEE Transactions on Information Theory, Vol. 55, No. 7, 2009, pp. 3220-3228. doi:10.1109/TIT.2009.2021374
[12]	D. Wu, D. Yu and Y. Cai, ‘Subcarrier and Power Allocation in Uplink OFDMA Systems Based on Game Theory,” Proceedings of IEEE International Conference Neural Networks & Signal Processing, Zhenjiang, 7-11 June 2008.
[13]	C. K. Tan, M. L. Sim and T. C. Chuah, “Fair Power Control for Wireless Ad Hoc Networks Using Game Theory with Pricing Scheme,” IET Communications, Vol. 4, No. 3, 2008, pp. 1751-8628.
[14]	H. Arslan, “Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems,” Springer, Berlin, 2007. doi:10.1007/978-1-4020-5542-3
[15]	D. Fudenberg and J. Tirole, “Game Theory,” MIT press, Cambridge, 1991.
[16]	F. Nan, M. Siun-Chuon and N. B. Mandayam, “Pricing and Power Control for Joint Network-Centric and User-Centric Radio Resource Management,” IEEE Transactions on Communications, Vol. 52, No. 9, 2004, pp. 1547-1557. doi:10.1109/TCOMM.2004.833191
[17]	H. Zhu, Z. Ji and K. J. R. Liu, “Fair Multiuser Channel Allocation for OFDMA Networks Using Nash Bargaing Solutions and Coalitions,” IEEE Transactions on Communications, Vol. 53, No. 8, 2005, pp.1366-1376. doi:10.1109/TCOMM.2005.852826
[18]	H. Jianwei, R. A. Berry and M. L. Honig, “Game Theoretic Model for Radio Resource Management in HIPERLAN Type 2 Networks,” IEEE Journal in Selected Areas in Communications, Vol. 24, No. 6, 2006, pp. 1074-1084.
[19]	S. Kirkpatrick, et al., “Optimization by Simulated Annealing,” Science, New Series, Vol. 220, No. 4598, 1983, pp. 671-680.