ICTACT Journal on Soft Computing

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Performance Analysis of Artificial Bee Colony Algorithm in Spectrum Sensing for Cognitive Radio in Different Fading Channels


Affiliations
1 Department of Electronics and Communication Engineering, Modern Institute of Technology and Research Centre, India
     

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Recently, cognitive radio (CR) is viewed as a novel approach for improving the utilization of a radio spectrum. The cognitive radio is defined as an intelligent wireless communication system that is aware of its surrounding and uses the technique of understanding-by-learning from the environment and adapt to statistical variations in the input stimuli. Spectrum sensing is a fundamental component in a cognitive radio. This paper analyses the performance of the artificial bee colony algorithm (ABC), optimization in different fading environments.

Keywords

Cognitive Radio, Spectrum Sensing, Artificial Bee Colony.
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  • J. Mitola and G.Q. Maguire, “Cognitive Radios: making Software Radios More Personal”, IEEE Personal Communications, Vol. 6, No. 4, pp. 13-18, 1999.

  • S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications”, IEEE Transactions on Communications, Vol. 23, No. 2, pp. 201-220, 2005.

  • A. Sahai and D. Cabric, “Spectrum Sensing: Fundamental Limits and Practical Challenges”, Proceedings of IEEE International Symposium on New Frontiers Dynamic Spectrum Access Networks, pp. 1-5, 2005.

  • D. Cabric, A. Tkachenko and R. W. Brodersen, “Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection”, Proceedings of International Conference for Military Communications, pp. 1-7, 2006.

  • H.S. Chen, W. Gao and D.G. Daut, “Signature based spectrum sensing algorithms for IEEE 802.22 WRAN”, Proceedings of IEEE International Conference on Communications, pp. 1-7, 2007.

  • A. Sonnenschein and P.M. Fishman, “Radiometric Detection of Spread Spectrum Signals in Noise of Uncertainty Power”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 28, No. 3, pp. 654-660, 1992.

  • R. Tandra and A. Sahai, “Fundamental Limits on Detection in Low SNR under Noise Uncertainty”, Proceedings of International Conference on Wireless Communications, pp. 167-173, 2005.

  • Mohd Hasbullah Omar, Suhaidi Hassan, Angela Amphawan and Shahrudin AwangNor, “SVD-based Signal Detector for Cognitive Radio Networks”, Proceedings of 13th International Conference on Computer Modelling and Simulation, pp. 513-517, 2011.

  • T. Yucek and H. Arslan, “A survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE Communications Surveys and Tutorials, Vol. 11, No. 1, pp. 116-130, 2009.

  • Srdjan S. Brkic and Predrag N. Ivanis, “Energy Detector Performance in Rician Fading Channel”, Serbian Journal of Electrical Engineering, Vol. 10, No. 1, pp. 37-46, 2013.


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  • Performance Analysis of Artificial Bee Colony Algorithm in Spectrum Sensing for Cognitive Radio in Different Fading Channels

Abstract Views: 360  |  PDF Views: 0

Authors

Hina Tuteja
Department of Electronics and Communication Engineering, Modern Institute of Technology and Research Centre, India
Neeraj Jain
Department of Electronics and Communication Engineering, Modern Institute of Technology and Research Centre, India

Abstract


Recently, cognitive radio (CR) is viewed as a novel approach for improving the utilization of a radio spectrum. The cognitive radio is defined as an intelligent wireless communication system that is aware of its surrounding and uses the technique of understanding-by-learning from the environment and adapt to statistical variations in the input stimuli. Spectrum sensing is a fundamental component in a cognitive radio. This paper analyses the performance of the artificial bee colony algorithm (ABC), optimization in different fading environments.

Keywords


Cognitive Radio, Spectrum Sensing, Artificial Bee Colony.

References