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Cooperative Spectrum Sensing Scheme using Fuzzy Logic Technique


Affiliations
1 Department of Electronics and Communications Engineering, Pune Institute of Computer Technology, India
     

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In communication system spectrum has a crucial role and wireless technologies are increasing rapidly it is required to make efficient use of spectrum to satisfy the spectrum scarcity problem. Using spectrum efficiently can be done by cognitive radio because of its ability to sense surrounding environment. Cognitive radio sense unoccupied spectrum by detecting the primary users’ presence or absence in the spectrum. Using more than one cognitive radio in the detection process will increase the efficiency of spectrum usage and prevent interference between signals. Using machine learning techniques for the implementation of intelligent cognitive radio increase efficiency and detection performance and detect signal at low SNR condition. Fuzzy logic machine learning technique is implemented which is based on fuzzy membership functions and fusion centre. Energy detection is used to classify signal and noise at each cognitive radio after that each cognitive radio output information convert into membership function and apply fuzzy rules such as algebraic sum, the algebraic product give a final decision about the signal presence or absence. Simulation results show that the proposed system gives much better results compared to the conventional energy detection system and improves the performance of the system.

Keywords

Cognitive Radio, Energy Detection, Cooperative Spectrum Sensing, Fuzzy Logic, Membership Functions.
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  • N. Muchandi and R. Khanai, “Cognitive Radio Spectrum Sensing: A Survey”, Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques, pp. 3233-3237, 2016.
  • M.Z. Alom, T.K. Godder and M.N. Morshed, “A Survey of Spectrum Sensing Techniques in Cognitive Radio Network”, Proceedings of International Conference on Advances in Electrical Engineering, pp. 161-164, 2015.
  • M. Bkassiny, Y. Li and S. Jayaweera, “A Survey on Machine-Learning Techniques in Cognitive Radios”, IEEE Communications Surveys and Tutorials, Vol. 15, No. 3, pp. 1136-1159, 2013.
  • N. Swetha, P.N. Sastry and Y.R. Rao, “Analysis of Spectrum Sensing Based on Energy Detection Method in Cognitive Radio Networks”, Proceedings of International Conference on IT Convergence and Security, pp. 1-4, 2014.
  • I. Akyildiz, W. Lee, M. Vuran and S. Mohanty, “Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey”, Computer Networks, Vol. 50, No. 13, pp. 2127-2159, 2006.
  • N. Zhao, F. Yu, H. Sun and A. Nallanathan, “A Energy-Efficient Cooperative Spectrum Sensing Schemes for Cognitive Radio Networks”, EURASIP Journal on Wireless Communications and Networking, Vol. 6, No. 1, pp. 1-7, 2013.
  • Kuldeep Kaur and Inderdeep Kaur Aulakh, “Optimization of Cooperative Spectrum Sensing using Fuzzy Logic”, International Journal of Application or Innovation in Engineering and Management, Vol. 3, No. 11, pp. 1-8, 2014.
  • N. Abbas, Y. Nasser and K. Ahmad, “Recent Advances in Artificial Intelligence and Learning Techniques in Cognitive Radio Networks”, EURASIP Journal on Wireless Communications and Networking, Vol. 8, No. 1, pp. 23-28, 2015.
  • A. Mohammadi and Taban, “Cooperative Spectrum Sensing using Fuzzy Membership Function of Energy Statistics”, AEU-International Journal of Electronics and Communications, Vol. 70, No. 3, pp. 234-240, 2016.
  • C. Satrio and J. Jaeshin, “Two-Stage Spectrum Sensing Scheme using Fuzzy Logic for Cognitive Radio Networks”, Journal of Information and Communication Convergence Engineering, Vol. 14, No. 1, pp. 1-8, 2016.
  • N. Arora and R. Mahajan, “Cooperative Spectrum Sensing using Hard Decision Fusion Scheme”, International Journal of Engineering Research and General Science, Vol. 2, No. 4, pp. 1-8, 2014.
  • T. Kieu-Xuan, “A Cooperative Spectrum Sensing Scheme using Fuzzy Logic for Cognitive Radio Networks”, KSII Transactions on Internet and Information Systems, Vol. 4, No. 3, pp. 289-304, 2010.
  • D. Teguig, B. Scheers and V. Le Nir, “Data Fusion Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks”, Proceedings of IEEE International Conference on Military Communications and Information Systems, pp. 881-887, 2012.

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  • Cooperative Spectrum Sensing Scheme using Fuzzy Logic Technique

Abstract Views: 281  |  PDF Views: 1

Authors

Rohit Kantikar
Department of Electronics and Communications Engineering, Pune Institute of Computer Technology, India
R. G. Yelalwar
Department of Electronics and Communications Engineering, Pune Institute of Computer Technology, India

Abstract


In communication system spectrum has a crucial role and wireless technologies are increasing rapidly it is required to make efficient use of spectrum to satisfy the spectrum scarcity problem. Using spectrum efficiently can be done by cognitive radio because of its ability to sense surrounding environment. Cognitive radio sense unoccupied spectrum by detecting the primary users’ presence or absence in the spectrum. Using more than one cognitive radio in the detection process will increase the efficiency of spectrum usage and prevent interference between signals. Using machine learning techniques for the implementation of intelligent cognitive radio increase efficiency and detection performance and detect signal at low SNR condition. Fuzzy logic machine learning technique is implemented which is based on fuzzy membership functions and fusion centre. Energy detection is used to classify signal and noise at each cognitive radio after that each cognitive radio output information convert into membership function and apply fuzzy rules such as algebraic sum, the algebraic product give a final decision about the signal presence or absence. Simulation results show that the proposed system gives much better results compared to the conventional energy detection system and improves the performance of the system.

Keywords


Cognitive Radio, Energy Detection, Cooperative Spectrum Sensing, Fuzzy Logic, Membership Functions.

References