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Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang (2009)
Languages: English
Types: Article
Subjects: 1711, 1708, 2208
Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run. Copyright (C) 2009 Xianfu Chen et al.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Federal Communications Commission, “Spectrum Policy Task Force,” Tech. Rep. ET Docket 02-135, November 2002.
    • [2] J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, 1999.
    • [3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, 2005.
    • [4] T. Chen, H. Zhang, G. M. Maggio, and I. Chlamtac, “CogMesh: a cluster-based cognitive radio network,” in Proceedings of the 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '07), pp. 168-178, April 2007.
    • [5] Y. Shi and Y. T. Hou, “A distributed optimization algorithm for multi-hop cognitive radio networks,” in Proceedings of the 27th IEEE Communications Society Conference on Computer Communications (INFOCOM '08), pp. 1292-1300, Phoenix, Ariz, USA, April 2008.
    • [6] L. Zhang, Y. Xin, and Y.-C. Liang, “Power allocation for multiantenna multiple access channels in cognitive radio networks,” in Proceedings of the 41st Annual Conference on Information Sciences and Systems (CISS '07), pp. 351-356, Baltimore, Md, USA, March 2007.
    • [7] F. Wang, M. Krunz, and S. Cui, “Price-based spectrum management in cognitive radio networks,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 1, pp. 74-87, 2008.
    • [8] W. Zhang and U. Mitra, “A spectrum-shaping perspective on cognitive radio,” in Proceedings of the 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '08), pp. 1-12, Chicago, Ill, USA, October 2008.
    • [9] C. E. Shannon, “Two-way communication channels,” in Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 611-644, 1961.
    • [10] R. Ahlswede, N. Cai, S.-Y. R. Li, and R. W. Yeung, “Network information flow,” IEEE Transactions on Information Theory, vol. 46, no. 4, pp. 1204-1216, 2000.
    • [11] S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft, “XORs in the air: practical wireless network coding,” in Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '06), Pisa, Italy, September 2006.
    • [12] S. Katti, I. Maric´, A. Goldsmith, D. Katabi, and M. Medard, “Joint relaying and network coding in wireless networks,” in Proceedings of the IEEE International Symposium on Information Theory (ISIT '07), pp. 1101-1105, Nice, France, June 2007.
    • [13] Y. Wu, P. A. Chou, and S.-Y. Kung, “Minimum-energy multicast in mobile ad hoc networks using network coding,” IEEE Transactions on Communications, vol. 53, no. 11, pp. 1906-1918, 2005.
    • [14] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, Mass, USA, 1998.
    • [15] L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: a survey,” Journal of Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.
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