Advertisement

Applied Intelligence

, Volume 44, Issue 2, pp 307–321 | Cite as

Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach

  • Lei Jiao
  • Xuan ZhangEmail author
  • B. John Oommen
  • Ole-Christoffer Granmo
Article

Abstract

Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called “Exact Potential” game. We also formally state the relationship between the global optimal point and the Nash Equilibrium (NE) point as far as system capacity is concerned. Thereafter, to facilitate the SU to select a proper channel in the game in a distributed manner, we design a Bayesian Learning Automaton (BLA)-based approach. Unlike many other Learning Automata (LA), a key advantage of the BLA is that it is learning-parameter free. The performance of the BLA-based approach is evaluated through rigorous simulations and this has been compared with the competing LA-based solution reported for this application, whence we confirm the superiority of our BLA approach.

Keywords

Cognitive radios Channel access Multiple users Potential games Bayesian learning automata 

References

  1. 1.
    Granmo OC (2010) Solving two-armed Bernoulli bandit problems using a Bayesian learning automaton. International Journal of Intelligent Computing and Cybernetics 3(2):207–234CrossRefMathSciNetzbMATHGoogle Scholar
  2. 2.
    Granmo OC, Glimsdal S (2013) “Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore game. Applied Intelligence 38(4):479–488CrossRefGoogle Scholar
  3. 3.
    IEEE 802.22 WG (2011) IEEE standard for wireless regional area networks—Part 22: Cognitive wireless RAN medium access control (MAC) and physical layer (PHY) specifications, Policies and procedures for operation in the TV bands, IEEE StdGoogle Scholar
  4. 4.
    Jiao L, Zhang X, Granmo OC, Oommen BJ (2014) A Bayesian Learning Automata-based distributed channel selection scheme for cognitive radio networks. In: Proceedings of IEA/AIE 14, the 2014 International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Kaohsiung, Taiwan, pp 48–57Google Scholar
  5. 5.
    Monderer D, Shapley LS (1996) Potential games. Games and Economic Behavior 14:124–143CrossRefMathSciNetzbMATHGoogle Scholar
  6. 6.
    Lakshmivarahan S (1981) Learning Algorithms Theory and Applications. Springer-Verlag, New YorkCrossRefzbMATHGoogle Scholar
  7. 7.
    Liang YC, Chen KC, Li GY, Mahönen P (2011) Cognitive radio networking and communications: An overview. IEEE Trans Veh Technol 60(7):3386–3407CrossRefGoogle Scholar
  8. 8.
    Narendra KS, Thathachar MAL (1989) Learning Automata: An Introduction. Prentice HallGoogle Scholar
  9. 9.
    Song Y, Fang Y, Zhang Y (2007) Stochastic channel selection in cognitive radio networks. In: IEEE Global Telecommunications Conference, Washington DC, USA, Nov, pp 4878–4882Google Scholar
  10. 10.
    Thompson WR (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–294CrossRefzbMATHGoogle Scholar
  11. 11.
    Tuan TA, Tong LC, Premkumar AB (2010) An adaptive learning automata algorithm for channel selection in cognitive radio network. In: Proceedings of the IEEE international conference on communications and mobile computing, Shenzhen, China, pp 159–163Google Scholar
  12. 12.
    Xu Y, Wang J, Wu Q, Anpalagan A, Yao Y-D (2012) Opportunistic spectrum access in unknown dynamic environment: A game-theoretic stochastic learning solution. IEEE Trans Wirel Commun 11(4):1380–1391CrossRefGoogle Scholar
  13. 13.
    Zhang X, Jiao L, Granmo OC, Oommen BJ (2013) Channel selection in cognitive radio networks: A switchable Bayesian Learning Automata approach. In: Proceedings of PIMRC’13, the 2013 IEEE international symposium on personal, indoor and mobile radio communications, London, UK , pp 2372–2377Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Lei Jiao
    • 1
  • Xuan Zhang
    • 1
    Email author
  • B. John Oommen
    • 1
    • 2
  • Ole-Christoffer Granmo
    • 1
  1. 1.Department of ICTUniversity of AgderGrimstadNorway
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada

Personalised recommendations