Abstract
We use multi response learning automata (MRLA) to control how secondary users should access the licensed primary channels in cognitive radio networks. We seek two aims in this paper: (1) estimating the availability probability of each primary channel and (2) admission control of secondary users to decrease the rate of collisions between them. We consider single and multiple secondary user scenarios. In the first scenario, the secondary user deploys learning automata to estimate the primary channel availability probability for efficient exploitation. In the second scenario, each secondary user deploys an algorithm based on MRLA to estimate primary traffic as well as the behavior of other secondary users in order to control the rate of collisions. Then, to have a better control on the rate of secondary collisions, when the number of secondary users is greater than the number of primary channels, we proposed an admission control scheme. In this scheme, some of secondary users are blocked in each time slot and do not have any interaction with the environment. The convergence of the proposed algorithms with and without admission schemes is analyzed. Simulation results are provided to show the improvement in the secondary users’ total throughput and switching cost while maintaining the fairness between them.
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References
Sadler B. M., Zhao Q. (2007) A survey of dynamic spectrum access. IEEE Signal Processing Magazine 24(3): 79–89. doi:10.1109/MSP.2007.361604
Haykin S. (2005) Cognitive radio: Brain-empowered wireless communication. IEEE Journal on Selected Areas in Communications 23(2): 201–220. doi:10.1109/JSAC.2004.839380
Economides A. A. (1996) Multiple response learning automata. IEEE Systems, Man, and Cybernetics Society 26(1): 153–156. doi:10.1109/3477.484448
Newman A. K. K. B., Gaeddert T. R., Menon J. K. K., Tirado R. M., Neel L., Reed J. J. Y. Z., Tranter J. H., He W. H. (2010) A survey of artificial intelligence for cognitive radios. IEEE Transactions on Vehicular Technology 59(4): 1578–1592. doi:10.1109/TVT.2010.2043968
Hecker J., Stuntebeck E., O’Shea Clancy T. (2007) Applications of machine learning to cognitive radio networks. IEEE Wireless Communications 14(4): 47–52. doi:10.1109/MWC.2007.4300983
Sutton R., Barto A. (1998) Reinforcement learning: An introduction. MIT Press, Cambridge
Tekin, C., Hong, S., & Stark, W. (2009). Enhancing cognitive radio dynamic spectrum sensing through adaptive learning. In MILCOM, IEEE, Military communications conference (pp. 1–7, 18–21). doi:10.1109/MILCOM.2009.5379925.
Cesa-Bianchi N., Freund Y., Auer R. S. P. (2003) The nonstochastic multi-armed bandit problem. SIAM Journal on Communicating 32(1): 48–77. doi:10.1137/S0097539701398375
Lai L., El Gamal H., Jiang H., Poor H. V. (2011) Cognitive medium access: Exploration, exploitation, and competition. IEEE Transactions on Mobile Computing 10(2): 239–253. doi:10.1109/TMC.2010.65
Song, Y., Fang, Y., & Zhang, Y. (2007). Stochastic channel selection in cognitive radio networks. In IEEE global telecommunications conference (pp. 4878–4882, 26–30). doi:10.1109/GLOCOM.2007.925.
Li, H., Zhu, G., Jian, L., Liang, Z., & Wang, D. (2009). Stochastic spectrum access based on learning automata in cognitive radio network. In IEEE international conference on intelligent computing and intelligent systems (Vol. 3, pp. 294–298, 20–22). doi:10.1109/ICICISYS.2009.5358183.
Tuan, T. A., Tong, L. C., & Premkumar, A. B. (2010). An adaptive learning automata algorithm for channel selection in cognitive radio network. In CMC, International conference on communications and mobile computing. (Vol. 2, pp. 159–163, 12–14). doi:10.1109/CMC.2010.328.
Narendra K. S., Thathachar M. A. L. (1989) Learning automata: An introduction. Prentice Hall, Englewood Cliffs, NJ
Bertsekas D. P., Tsitsiklis J. N. (1989) Parallel and distributed computation. Prentice Hall, Englewood Cliffs, NJ
Yates R. D. (1995) A framework for uplink power control in cellular radio systems. IEEE Journal on Selected Areas in Communications 13(7): 1341–1347. doi:10.1109/49.414651
Jain, R., Chiu, D., & Hawe, W. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. Technical Research Report TR-301, Digital Equipment Corporation.
Thathachar M. A. L., Ramakrishnan K. R. (1984) A cooperative game of a pair of learning automata. Automatica 20(6): 797–801. doi:10.1016/0005-1098
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Bizhani, H., Ghasemi, A. Joint Admission Control and Channel Selection Based on Multi Response Learning Automata (MRLA) in Cognitive Radio Networks. Wireless Pers Commun 71, 629–649 (2013). https://doi.org/10.1007/s11277-012-0834-9
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DOI: https://doi.org/10.1007/s11277-012-0834-9