ABSTRACT
Wireless cognitive radio (CR) is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of these bands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power and spectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, and react to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach based spectrum management. Our approach uses value functions to evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, and compare the communication performance using different sets of learning parameters. We also apply Kanerva-based function approximation to improve our approach's ability to handle large cognitive radio networks and evaluate its effect on communication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network.
- I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury. Crahns: Cognitive radio ad hoc networks. Ad Hoc Networks Journal (Elsevier), 7(5):810--836, July 2009. Google ScholarDigital Library
- I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury. Spectrum management in cognitive radio ad hoc networks. IEEE Network, 23(4):6--12, July 2009. Google ScholarDigital Library
- I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty. Next generation / dynamic spectrum access / cognitive radio wireless networks: a survey. Computer Networks Journal (Elsevier), 50:2127--2159, September 2006. Google ScholarDigital Library
- J. Albus. Brains, Behaviour, and Robotics. McGraw-Hill, 1981. Google ScholarDigital Library
- L. Baird. Residual algorithms: Reinforcement learning with function approximation. In Proc. of the 12th Intl. Conf. on Machine Learning. Morgan Kaufmann, 1995.Google ScholarDigital Library
- FCC. Spectrum policy task force report. ET Docket No. 02--155, Nov. 2002.Google Scholar
- G. J. Gordon. Stable function approximation in dynamic programming. In Proc. of Intl. Conf. on Machine Learning, 1995.Google ScholarCross Ref
- S. Haykin. Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2):201--220, February 2005. Google ScholarDigital Library
- G. Hinton. Distributed representations. Technical Report, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, 1984.Google Scholar
- IEEE Std 802.11b Specification. Ieee std 802.11b-1999/cor 1--2001, 2001.Google Scholar
- P. Kanerva. Sparse Distributed Memory. MIT Press, 1988. Google ScholarDigital Library
- J. Mitola III. Cognitive radio for flexible mobile multimedia communication. In Proc. IEEE International Workshop on Mobile Multimedia Communications (MoMuC) 1999, pages 3--10, November 1999.Google ScholarCross Ref
- J. Proakis. Digital Communications. McGraw-Hill Science/Engineering/Math, August 2000.Google Scholar
- B. Ratitch and D. Precup. Sparse distributed memories for on-line value-based reinforcement learning. In Proc. of the European Conf. on Machine Learning, 2004.Google ScholarDigital Library
- G. L. Stüber. Principles of mobile communication (2nd ed.). Kluwer Academic Publishers, 2001. Google ScholarDigital Library
- R. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Proc. of Conf. on Neural Information Processing Systems, 1995.Google Scholar
- R. Sutton and A. Barto. Reinforcement Learning: An Introduction. Bradford Books, 1998. Google ScholarDigital Library
- M. D. Waltz and K. S. Fu. A heuristic approach to reinforcment learning control systems. In IEEE Transactions on Automatic Control, 10:390--398., 1965.Google ScholarCross Ref
- C. Watkins. Learning from delayed rewards. Ph.D thesis, Cambridge Univeristy, Cambridge, England, 1989.Google Scholar
- M. Wooldridge. An introduction to multiagent systems. In John Wiley Sons Ltd, ISBN 0-471-49691-X, 2002. Google ScholarDigital Library
Index Terms
- Spectrum management of cognitive radio using multi-agent reinforcement learning
Recommendations
Spectrum management techniques with QoS provisioning in cognitive radio networks
ISWPC'10: Proceedings of the 5th IEEE international conference on Wireless pervasive computingIn this paper, we discuss the spectrum management technologies including spectrum sensing, spectrum decision, spectrum sharing, and spectrum handoff schemes in cognitive radio (CR) networks. In order to evaluate performance of different spectrum ...
Spectrum borrowing balancing-based spectrum management in cognitive radio
In Cognitive radio (CR) systems, the spectrum management of spectrum sensing and sharing for secondary users (SUs) affects the performance of secondary users and primary user. In this paper, we develop a spectrum sensing model for SUs who belong to ...
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a ...
Comments