[ Log In | Register]
! Skip Navigation Links
Skip Navigation Links
Image of Vibration Testing and System Dynamics
ISSN: 2475-4811 (print)
ISSN: 2475-482X (online)
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn

Performance Comparison of Cooperative Spectrum Sensing Models Based on Machine Learning

Journal of Vibration Testing and System Dynamics 7(2) (2023) 153--167 | DOI:10.5890/JVTSD.2023.06.004

Qian Hu, Zhong-Qiang Luo, Wen-Shi Xiao, Cheng-Jie Li

Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, China

School of Computer Science and Technology, Southwest Minzu University, Chengdu 610041, China

Download Full Text PDF

 

Abstract

So far machine learning (ML) has become increasingly significant in spectrum sensing (SS) for future intelligent wireless communications. In order to obtain an effective SS performance in fading channel, this paper proposes a GMM (Gaussian Mixture Model) based cooperative SS under the conditions of AWGN (Additive White Gaussian Noise) channel and Rayleigh flat fading channel. For evaluating the SS performance, numerous NB (Naive Bayes) and SVM (Support Vector Machine), MLP (Multi-Layer Perceptron) based ML method, as well as the traditional cooperative SS technologies (AND criterion, OR criterion, and Maximum Ration Combining (MRC)) are also investigated for performance comparison. The ROC (receiver operating characteristics) and AUC (area under the curve) performance index is used to compare performance. Simulation results and analysis show that the proposed GMM-based cooperative SS enable acquire the best performance under the Rayleigh channel.

References

  1. [1]  Chauhan, P.S., Tiwari, D., and Soni, S.K. (2019), Energy detector performance over log-normal fading channel with diversity reception, Journal of Electromagnetic Waves and Applications, 33(17), 2242-2256.
  2. [2]  Kumar, S. (2018), Performance of ED based spectrum sensing over $\alpha$--$\eta$--$\mu$ fading channel, Wireless Personal Communications, 100(4), 1845-1857.
  3. [3]  Kumar, S. (2018), Energy detection in hoyt/gamma fading channel with micro-diversity reception, Wireless Personal Communications, 101(2), 723-734.
  4. [4]  Kumar, S., Verma, P.K., and Kaur, M. (2018), On the spectrum sensing of gamma shadowed Hoyt fading channel with MRC reception, Journal of Electromagnetic Waves and Applications, 32(16), 2157-2166.
  5. [5]  Mourad, M., Hussein, A.I., and Taha, H.A. (2020), Artificial intelligence based cooperative spectrum sensing algorithm for cognitive radio networks, Procedia Computer Science, 163(C), 19-29.
  6. [6]  Chauhan, P.S., Kumar, S., and Upaddhyay, V.K. (2021), Performance analysis of ED over air-to-ground and ground-to-ground fading channels: A unified and exact solution, AEU-International Journal of Electronics and Communications, 138, 153839-153850.
  7. [7]  Yang, M., Li, Y., Liu, X., and Tang, W. (2015), Cyclostationary feature detection based spectrum sensing algorithm under complicated electromagnetic environment in cognitive radio networks, China Communications, 12(9), 35-44.
  8. [8]  Kabeel, A.A., Hussein, A.H., and Khalaf, A.A.M. (2019), A utilization of multiple antenna elements for matched filter based spectrum sensing performance enhancement in cognitive radio system, AEU-International Journal of Electronics and Communications, 107, 98-109.
  9. [9]  Tan, C.K. and Lim, W.K. (2012), Reliable and low-complexity wavelet-based spectrum sensing for cognitive radio systems at low SNR regimes, Electronics letters, 48(24), 1565-1567.
  10. [10]  Tian, J., Cheng, P., Chen, Z., Li, M., Hu, H., Li, Y., and Vucetic, B. (2019), A machine learning-enabled spectrum sensing method for OFDM systems, IEEE Transactions on Vehicular Technology, 68(11), 11374-11378.
  11. [11]  Hossain, M.A., Noor, R.M., and Yau, K.L.A. (2021), Machine learning-based cooperative spectrum sensing in dynamic segmentation enabled cognitive radio vehicular network, Energies, 14(4), 1169-1198.
  12. [12]  Ghazizadeh, E., Abbasi‐moghadam, D., and Nezamabadi‐pour, H. (2019), An enhanced two‐phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks, International Journal of Communication Systems, 32(2), 3856-3869.
  13. [13]  Hossain, M.S. and Miah, M.S. (2021), Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in Cognitive Radio-Internet of Things, Machine Learning with Applications, 5(15), 100052-100060.
  14. [14]  Khan, M.S., Khan, L., and Gul, N. (2020), Support vector machine-based classification of Malicious users in cognitive radio networks, Wireless Communications and Mobile Computing, 2020(5), 1-11.
  15. [15]  Saber, M., Abdessamad, A., and Rachid, S. (2021), Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms, Procedia Computer Science, 176, 2404-2413.
  16. [16]  Ma, Y. and Wang, Y. (2019), Cooperative spectrum sensing based on probability vector and machine learning, Journal of Sensor Technology, 32(12), 1809-1815.
  17. [17]  Tavares, C.H.A., Marinello, J.C., Proenca, Jr., M.L., and Abrao, T. (2020), Machine learning-based models for spectrum sensing in cooperative radio networks, IET Communications, 14(18), 3102-3109.
  18. [18]  Fong, K.L., Tan, C.K., and Lee, C.K. (2017), A reliable time-domain spectrum hole prediction for cognitive radio networks using regularized multi-layer perceptron, Wireless Personal Communications, 96, 647-654.
  19. [19]  Baldini, G., Chareau, J.M., and Bonavitacola, F. (2021), Spectrum sensing implemented with improved fluctuation-based dispersion entropy and machine learning, Entropy, 23(12), 1611-1635.
  20. [20]  Xing, H., Qin, H., and Luo, S. (2022), Spectrum sensing in cognitive radio: A deep learning based model, Transactions on Emerging Telecommunications Technologies, 33(1), 4388-4404.
  21. [21]  Janu, D., Singh, K., and Kumar, S. (2022), Machine learning for cooperative spectrum sensing and sharing: A survey, Transactions on Emerging Telecommunications Technologies, 33(1), 4352-4379.
  22. [22]  Chen, S., Shen, B., and Wang, X. (2019), A strong machine learning classifier and decision stumps based hybrid adaboost classification algorithm for cognitive radios, Sensors, 19(23), 5077-5091.
  23. [23]  Thilina, K.M., Choi, K.W., and Saquib, N. (2013), Machine learning techniques for cooperative spectrum sensing in cognitive radio networks, IEEE Journal on Selected Areas in Communications, 31(11), 2209-2221.
  24. [24]  Molina-Tenorio, Y., Prieto-Guerrero, A., Aguilar-Gonzalez, R., and Ruiz-Boqu{e}, S. (2019), Machine learning techniques applied to multiband spectrum sensing in cognitive radios, Sensors (Basel), 19(21), 4715-4736.
  25. [25]  Nimudomsuk, P., Sanguanwattanaraks, M., Srisomboon, K., and Lee. M. (2021), A performance comparison of spectrum sensing exploiting machine learning algorithms, 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2021, 102-105.

Copyright © 2011-2024 L & H Scientific Publishing. All rights reserved. Wildcard SSL Certificates