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

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• 作者：Lei Jiao ; Xuan Zhang ; B. John Oommen ; Ole-Christoffer Granmo
• 关键词：Cognitive radios ; Channel access ; Multiple users ; Potential games ; Bayesian learning automata
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：44
• 期：2
• 页码：307-321
• 全文大小：963 KB
• 参考文献：1.Granmo OC (2010) Solving two-armed Bernoulli bandit problems using a Bayesian learning automaton. International Journal of Intelligent Computing and Cybernetics 3(2):207–234CrossRef MathSciNet MATH
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  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 48–57
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  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 2372–2377
  
• 作者单位：Lei Jiao (1)
  Xuan Zhang (1)
  B. John Oommen (1) (2)
  Ole-Christoffer Granmo (1)
  
  1. Department of ICT, University of Agder, Grimstad, Norway
  2. School of Computer Science, Carleton University, Ottawa, Canada
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

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.