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The Scientific World Journal
Volume 2014 (2014), Article ID 960584, 22 pages
http://dx.doi.org/10.1155/2014/960584
Research Article

Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks

1Department of Computer Science and Networked Systems, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor, Malaysia
2Wireless Network and Protocol Research Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia

Received 13 April 2014; Accepted 31 May 2014; Published 16 July 2014

Academic Editor: Su Fong Chien

Copyright © 2014 Hasan A. A. Al-Rawi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. A. A. Al-Rawi and K.-L. A. Yau, “Routing in distributed cognitive radio networks: a survey,” Wireless Personal Communications, vol. 69, no. 4, pp. 1983–2020, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. H. A. A. Al-Rawi, M. A. Ng, and K.-L. A. Yau, “Application of reinforcement learning to routing in distributed wireless netowrks: a review,” Artificial Intelligence Review, 2013. View at Publisher · View at Google Scholar
  3. P. Derakhshan-Barjoei, G. Dadashzadeh, F. Razzazi, and S. M. Razavizadeh, “Power and time slot allocation in cogitive relay networks using particle swarm optimization,” The Scientific World Journal, vol. 2013, Article ID 424162, 9 pages, 2013. View at Publisher · View at Google Scholar
  4. J. Zhao and J. Yuan, “An improved centralized cognitive radio network spectrum allocation algorithm based on the allocation sequence,” nternational Journal of Distributed Sensor Networks, vol. 2013, Article ID 875342, 13 pages, 2013. View at Publisher · View at Google Scholar
  5. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, Mass, USA, 1998.
  6. M. Bowling and M. Veloso, “Multiagent learning using a variable learning rate,” Artificial Intelligence, vol. 136, no. 2, pp. 215–250, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  7. Q. Guan, F. R. Yu, S. Jiang, and G. Wei, “Prediction-based topology control and routing in cognitive radio mobile ad hoc networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 9, pp. 4443–4452, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. K. R. Chowdhury and I. F. Akyildiz, “CRP: a routing protocol for cognitive radio ad hoc networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 4, pp. 794–804, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. Q. Zhu, Z. Yuan, J. B. Song, Z. Han, and T. Başar, “Dynamic interference minimization routing game for on-demand cognitive pilot channel,” in Proceedings of the 53rd IEEE Global Communications Conference (GLOBECOM '10), pp. 1–6, Miami, Fla, USA, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Xie, W. Zhang, and K. Wong, “A geometric approach to improve spectrum efficiency for cognitive relay networks,” IEEE Transactions on Wireless Communications, vol. 9, no. 1, pp. 268–281, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Xia, M. H. Wahab, Y. Yang, Z. Fan, and M. Sooriyabandara, “Reinforcement learning based spectrum-aware routing in multi-hop cognitive radio networks,” in Proceedings of the 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM '09), pp. 1–5, Hannover, Germany, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Oužecki and D. Jevtić, “Reinforcement learning as adaptive network routing of mobile agents,” in Proceedings of the 33rd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO '10), pp. 479–484, Opatija, Croatia, May 2010. View at Scopus
  13. J. Dowling, E. Curran, R. Cunningham, and V. Cahill, “Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., vol. 35, no. 3, pp. 360–372, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. J. A. Boyan and M. L. Littman, “Packet routing in dynamically changing networks: a reinforcement learning approach,” Advances in Neural Infomation Processing Systems, vol. 6, pp. 671–678, 1994. View at Google Scholar
  15. Y. Zhang and M. Fromherz, “Constrained flooding: a robust and efficient routing framework for wireless sensor networks,,” in Proceedings of 20th IEEE International Conference on Advanced Information Networking and Applications (AINA '20), IEEE, Vienna, Austria, 2006.
  16. H. A. Al-Rawi, K.-L. A. Yau, H. Mohamad, N. Ramli, and W. Hashim, “A reinforcement learning–based routing scheme for cognitive radio ad hoc networks,” in Proceedings of the 7th IFIP Wireless and Mobile Networking Conference (WMNC '14), May 2014.
  17. G. E. Box and M. E. Mullerm, “A note on the generation of random normal deviates,” The Annals of Mathematical Statistics (Institute of Mathematical Statistics), vol. 29, no. 2, pp. 610–611, 1958. View at Google Scholar
  18. A. Förster and A. L. Murphys, “FROMS: feedback routing for optimizing multiple sinks in WSN with reinforcement learning,” in Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP '07), pp. 371–376, IEEE, Melbourne, Australia, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. A. McAuley, K. Sinkar, L. Kant, C. Graff, and M. Patel, “Tuning of reinforcement learning parameters applied to OLSR using a cognitive network design tool,” in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '12), pp. 2786–2791, IEEE, Shanghai, China, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Nurmi, “Reinforcement learning for routing in ad hoc networks,” in Proceedings of the 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt '07), pp. 1–8, Limassol, Cyprus, April 2007. View at Publisher · View at Google Scholar · View at Scopus