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Cognitive MAC Designs: Hopping Transmission Strategy

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Cognitive MAC Designs for OSA Networks

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

This chapter studies and develops cognitive MAC protocols in OSA networks for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band, taking into account the time-varying and dynamic behavior of primary users (PUs). Aiming to reduce the effects of collision between PUs and SUs due to the PU random return, a transmission strategy is proposed for SUs to dynamically hop over multiple idle frequency slots, each with an adaptive activity factor to be determined. Taking into account the spectrum sharing among SUs, the dynamic PU activity and channel characteristics, the SU activity factor optimization problem is formulated for maximizing the overall SU throughput. Based on the dual decomposition method, the optimal MAC algorithm is presented. Subsequently, a fully distributed learning-based OSA algorithm is developed in which each SU independently adapts its activity factors to the optimal values over time by learning other SUs' behavior from locally available information. The convergence and convergence rate that characterize its asymptotic behavior and efficiency are analyzed.

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References

  1. Q. Zhao, L. Tong, A. Swami, and Y. Chen, “Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework,” IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 589–600, Apr. 2007.

    Google Scholar 

  2. Q. Zhao, S. Geirhofer, L. Tong, and B. M. Sadler, “Opportunistic spectrum access via periodic channel sensing,” IEEE Trans. Signal Process., vol. 56, no. 2, pp. 785–796, Feb. 2008.

    MathSciNet  Google Scholar 

  3. 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 (Elsevier), vol. 50, no. 13, pp. 2127–2159, Sep. 2006.

    Article  MATH  Google Scholar 

  4. I. F. Akyildiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio networks,” IEEE Commun. Mag., vol. 46, no. 4, pp. 40–48, Apr. 2008.

    Google Scholar 

  5. Q. Zhao and B. M. Sadler, “A survey of dynamic spectrum access: Processing, networking, and regulatory policy,” IEEE Signal Process. Mag., vol. 24, no. 3, pp. 79–89, May 2007.

    Google Scholar 

  6. B. Jabbari, R. Pickholtz, and M. Norton, “Dynamic spectrum access and management,” IEEE Wireless Commun. Mag., vol. 17, no. 4, pp. 6–15, Aug. 2010.

    Google Scholar 

  7. L. Berlemann and S. Mangold, Cognitive Radio and Dynamic Spectrum Access. Wiley, 2009.

    Google Scholar 

  8. E. Hossain, D. Niyato, and Z. Han, Dynamic Spectrum Access and Management in Cognitive Radio Networks. Cambridge, 2009.

    Google Scholar 

  9. Y. Chen, Q. Zhao, and A. Swami, “Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors,” IEEE Trans. Inf. Theory, vol. 54, no. 5, pp. 2053–2071, May 2008.

    MathSciNet  Google Scholar 

  10. Q. Zhao, B. Krishnamachari, and K. Liu, “On myopic sensing for multichannel opportunistic access: Structure, optimality, and performance,” IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 5431–5440, Dec. 2008.

    Google Scholar 

  11. L. Lai, H. E. Gamal, H. Jiang, and H. Poor, “Cognitive medium access: Exploration, exploitation and competition,” IEEE Trans. Mobile Comput., vol. 10, no. 2, pp. 239–253, Feb. 2011.

    Google Scholar 

  12. K. Liu, Q. Zhao, and B. Krishnamachari, “Dynamic multichannel access with imperfect channel state detection,” IEEE Trans. Signal Process., vol. 58, no. 5, pp. 2795–2808, May 2010.

    MathSciNet  Google Scholar 

  13. S. Geirhofer, L. Tong, and B. M. Sadler, “Dynamic spectrum access in WLAN channels: Empirical model and its stochastic analysis,” in Proc. Int. Workshop on Technology and Policy for Accessing Spectrum (TAPAS), New York, NY, USA, Aug. 2006.

    Google Scholar 

  14. H. Zheng and C. Peng, “Collaboration and fairness in opportunistic spectrum access,” in Proc. IEEE Intl. Conf. Commun. (ICC), Seoul, Korea, May 2005.

    Google Scholar 

  15. H. Su and X. Zhang, “Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks,” IEEE J. Sel. Areas Commun., vol. 26, no. 1, pp. 118–129, Jan. 2008.

    Google Scholar 

  16. Y. C. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, Apr. 2008.

    Google Scholar 

  17. Y. Xu, Y. Sun, Y. Li, Y. Zhao, and H. Zou, “Joint sensing period and transmission time optimization for energy-constrained cognitive radios,” EURASIP J. Wireless Commun. Netw., vol. 2010, no. 92, pp. 1–16, Apr. 2010.

    Google Scholar 

  18. P. Tehrani, K. Liu, and Q. Zhao, “Opportunistic spectrum access in unslotted primary systems,” Journal of the Franklin Institute (Elsevier), vol. 349, no. 3, pp. 985–1010, 2012.

    Article  MATH  MathSciNet  Google Scholar 

  19. S. Shetty, M. Song, C. Xin, and E. K. Park, “A learning-based multiuser opportunistic spectrum access approach in unslotted primary networks,” in Proc. IEEE Intl. Conf. on Computer Commun. (INFOCOM), Rio de Janeiro, Brazil, Apr. 2009.

    Google Scholar 

  20. D. Palomar and M. Chiang, “A tutorial on decomposition methods for network utility maximization,” IEEE J. Sel. Areas Commun., vol. 24, no. 8, pp. 1439–1451, Aug. 2006.

    Google Scholar 

  21. P. W. Glynn and P. Heidelberger, “Bias properties of budget constrained simulations,” Operations Research, vol. 38, no. 5, pp. 801–814, Oct. 1990.

    Article  MATH  MathSciNet  Google Scholar 

  22. H. Kushner and G. Yin, \textitStochastic Approximation and Recursive Algorithms and Applications. New York: Springer, 2003.

    MATH  Google Scholar 

  23. J. C. Spall, Introduction to Stochastic Search and Optimization. Willey, 2003.

    Google Scholar 

  24. D. P. Bertsekas and J. N. Tsitsiklis, “Gradient convergence in gradient methods with errors,” SIAM Journal on Optimization, vol. 10, no. 3, pp. 627–642, Feb. 2000.

    Article  MATH  MathSciNet  Google Scholar 

  25. V. B. Tadić, “Convergence and convergence rate of stochastic gradient search in the case of multiple and non-isolated extrema,” in Proc. IEEE Conf. Decision and Control (CDC), Atlanta, GA, USA, Dec. 2010.

    Google Scholar 

  26. B. Polyak, \textitIntroduction to Optimization. New York: Optimization Software, Inc., 1987.

    Google Scholar 

  27. J. Sacks, “Asymptotic distribution of stochastic approximation procedures,” The Annals of Mathematical Statistics, vol. 29, no. 2, pp. 373–405, Jun. 1958.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

    Mahsa Derakhshani

  2. Department of Electrical and Computer Engineering, McGill University, Montreal, Québec, Canada

    Tho Le-Ngoc

Authors
  1. Mahsa Derakhshani
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  2. Tho Le-Ngoc
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Correspondence to Mahsa Derakhshani .

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Derakhshani, M., Le-Ngoc, T. (2014). Cognitive MAC Designs: Hopping Transmission Strategy. In: Cognitive MAC Designs for OSA Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-12649-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-12649-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12648-7

  • Online ISBN: 978-3-319-12649-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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