An SDR implementation of reliable spectrum sensing using fuzzy logic and genetic algorithm

被引：1
作者：
Ponnusamy V. [1 ]
Thejaswi K. [1 ]
Sushmita B. [1 ]
机构：
[1] ECE Department, SRM Institute of Science and Technology, Kattankulathur
来源：
International Journal of Systems, Control and Communications | 2021年 / 12卷 / 02期
关键词：
Adaptive spectrum sensing; Cognitive radio; Cyclo-stationary detection; Decision making system; Energy detection; Feature detection; Fuzzy logic; Genetic algorithm; Optimal spectrum sensing; SDR implementation; Spectrum sensing;
D O I：
10.1504/IJSCC.2021.114599
中图分类号：
学科分类号：
摘要：
This paper presents the design of a reliable spectrum sensing method using fuzzy logic and genetic algorithm. Fuzzy logic acts as a decision-making system. It is used to select an optimal sensing method based on the current wireless channel state, the required accuracy of sensing, and computational complexity. Three sensing algorithms named as energy detection, feature detection and cyclo-stationary detection are used. The fuzzy decision-making system analyses the present state of SNR, availability information about the primary user, application demand on delay and accuracy. It selects optimal spectrum sensing techniques based on that analysis result. A genetic algorithm is used to choose optimal parameter selection for the selected optimal algorithm to improve the reliability further. The entire scheme is implemented on the USRP RIO SDR hardware. Performance evaluation on experiments shows that the proposed system outperforms with the probability of detection 99.7%. Copyright © 2021 Inderscience Enterprises Ltd.
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页码：148 / 157
页数：9
相关论文
共 11 条
  • [1] Cichon K., Kliks A., Bogucka H., Energy-efficient cooperative spectrum sensing: a survey, IEEE Communications Surveys & Tutorials, 18, 3, pp. 1861-1886, (2016)
  • [2] Li B., Et al., Energy detection based spectrum sensing for cognitive radios over time-frequency doubly selective fading channels, IEEE Transactions on Signal Processing, 63, 2, pp. 402-417, (2014)
  • [3] Mabrook M.M., Khalil H.A., Hussein A.I., Artificial intelligence based cooperative spectrum sensing algorithm for cognitive radio networks, Procedia Computer Science, 163, pp. 19-29, (2019)
  • [4] Matinmikko M., Et al., Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems, IEEE Journal on Selected Areas in Communications, 31, 11, pp. 2173-2184, (2013)
  • [5] Quan Z., Et al., Optimal spectral feature detection for spectrum sensing at very low SNR, IEEE Transactions on Communications, 59, 1, pp. 201-212, (2010)
  • [6] Stoianovici V.C., Et al., A software-defined radio approach to spectrum sensing systems' architecture, Bulletin of the Transilvania University of Brasov, Engineering Sciences, Series I, 4, 1, (2011)
  • [7] Vijayakumar P., Malarvizhi S., Fuzzy logic based decision system for context aware cognitive waveform generation, Wireless Personal Communications, 94, 4, pp. 2681-2703, (2017)
  • [8] Vijayakumar P., Malarvizhi S., Wide band full duplex spectrum sensing with self-interference cancellation - an efficient SDR implementation, Mobile Networks and Applications, 22, 4, pp. 702-711, (2017)
  • [9] Yang M., Et al., Cyclostationary feature detection based spectrum sensing algorithm under complicated electromagnetic environment in cognitive radio networks, China Communications, 12, 9, pp. 35-44, (2015)
  • [10] Yucek T., Arslan H., A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Communications Surveys & Tutorials, 11, 1, pp. 116-130, (2009)
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