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Performance Validation of Spectrum Sensing Using Kernelized Support Vector Machine Transformation

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Abstract

Due to the increasing interest in wireless networks, the availability of spectrum has become a challenge. With the help of cognitive radio, a promising technology, can be overcome this issue. One of the most challenging tasks in this technique is finding the available spectrum holes. The increasing interest in machine learning techniques for spectrum sensing (SS) has led to the development of several novel methods. In this paper, we use the support vector machine with the kernel transformation that are designed to improve the performance of SS, such as such as Linear kernel, Radial Basis Function or Gaussian kernel, Polynomial kernel and Sigmoid kernel. One of the main reasons why the kernel functions are used is due to the possibility of having a non-linear dataset. The performance of kernel functions is compared in terms of accuracy, precision, recall, f1_score and confusion matrix for different number of users such as 100, 500 and 1000. Among all these, Polynomial kernel SVM has shown better performance of 96%, 97% and 100% accuracy for 100, 500 and 1000 number of users. In addition, this paper presents a comparison of the proposed and existing methods, where the proposed method has shown a better performance.

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No datasets are used in this paper.

References

  1. Prasad, R., & Rohokale, V. (2020). Cyber security: The lifeline of information and communication technology. Springer.

    Book  Google Scholar 

  2. Gupta, M. S., & Kumar, K. (2019). Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues. Journal of Network and Computer Applications, 143, 47–76.

    Article  Google Scholar 

  3. Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2209–2221.

    Article  Google Scholar 

  4. Eidelman, A. (2020). Python data science handbook by jake VANDERPLAS (2016). Statistique et Société, 8(2), 45–47.

    Google Scholar 

  5. Pati, B. M., Kaneko, M., & Taparugssanagorn, A. (2020). A deep convolutional neural network based transfer learning method for non-cooperative spectrum sensing. IEEE Access, 8, 164529–164545.

    Article  Google Scholar 

  6. Chen, Y., et al. (2019). Optimized non-cooperative spectrum sensing algorithm in cognitive wireless sensor networks. Sensors, 19(9), 2174.

    Article  Google Scholar 

  7. Wang, J., & Liu, B. (2021). A brief review of machine learning algorithms for cooperative spectrum sensing. Journal of Physics: Conference Series., 1852(4), 042094.

    Google Scholar 

  8. Sun, W., Liu, J., & Yue, Y. (2019). AI-enhanced offloading in edge computing: When machine learning meets industrial IoT. IEEE Network, 33(5), 68–74.

    Article  Google Scholar 

  9. Zeng, D., et al. (2019). Resource management at the network edge: A deep reinforcement learning approach. IEEE Network, 33(3), 26–33.

    Article  Google Scholar 

  10. Gu, L., et al. (2020). Intelligent VNF orchestration and flow scheduling via model-assisted deep reinforcement learning. IEEE Journal on Selected Areas in Communications, 38(2), 279–291.

    Article  Google Scholar 

  11. Zhou, Z., et al. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.

    Article  Google Scholar 

  12. Kouziokas, G. N. (2020). SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting. Applied Soft Computing, 93, 106410.

    Article  Google Scholar 

  13. Hou, S., & Qiu, R. C. (2011). Spectrum sensing for cognitive radio using kernel-based learning. arXiv preprint arXiv:1105.2978

  14. Kouziokas, G. N. (2020). A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting. Engineering Applications of Artificial Intelligence, 92, 103650.

    Article  Google Scholar 

  15. Tang, Q., Qiu, W., & Zhou, Y. (2019). Classification of complex power quality disturbances using optimized S-transform and kernel SVM. IEEE Transactions on Industrial Electronics, 67(11), 9715–9723.

    Article  Google Scholar 

  16. Arjoune, Y., & Kaabouch, N. (2019). A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions. Sensors, 19(1), 126.

    Article  Google Scholar 

  17. Siva Kumar Reddy, B. (2019). Experimental validation of spectrum sensing techniques using software-defined radio. In Nanoelectronics, circuits and communication systems (pp. 97–103). Springer.

  18. Siva Kumar Reddy, B., Modi, D., & Upadhyay, S. (2019). Performance evaluation of various digital modulation techniques using GNU radio. In Innovations in infrastructure (pp. 13–20) Springer.

  19. Patil, R. B., Kulat, K. D., & Gandhi, A. S. (2018). SDR based energy detection spectrum sensing in cognitive radio for real time video transmission. Modelling and Simulation in Engineering, 2018, 2424305.

    Article  Google Scholar 

  20. Siva Kumar Reddy, B., & Lakshmi, B. (2014). BER analysis of energy detection spectrum sensing in cognitive radio using GNU radio. International Journal of Computer and Information Engineering, 8(11), 1699–1705.

    Google Scholar 

  21. Kumar, M. A., et al. (2021). Comprehensive analysis of cyclo-stationary feature detection technique for efficient spectrum usage: Future research and recent advantages. Turkish Journal of Physiotherapy and Rehabilitation, 32, 3.

    Google Scholar 

  22. Sheykhmousa, M., et al. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325.

    Article  Google Scholar 

  23. Tavara, S. (2019). Parallel computing of support vector machines: A survey. ACM Computing Surveys (CSUR), 51(6), 1–38.

    Article  Google Scholar 

  24. Ghosh, S., Dasgupta, A., & Swetapadma, A. (2019). A study on support vector machine based linear and non-linear pattern classification. In 2019 International conference on intelligent sustainable systems (ICISS). IEEE.

  25. Rizwan, A., et al. (2021). WR-SVM model based on the margin radius approach for solving the minimum enclosing ball problem in support vector machine classification. Applied Sciences, 11(10), 4657.

    Article  Google Scholar 

  26. Saber, M., et al. (2019). Artificial neural networks, support vector machine and energy detection for spectrum sensing based on real signals. International Journal of Communication Networks and Information Security, 11(1), 52–60.

    Google Scholar 

  27. Ahmad, H. B. (2019). Ensemble classifier based spectrum sensing in cognitive radio networks. Wireless Communications and Mobile Computing, 2019, 9250562.

    Article  Google Scholar 

  28. Varun, M., & Annadurai, C. (2021). PALM-CSS: A high accuracy and intelligent machine learning based cooperative spectrum sensing methodology in cognitive health care networks. Journal of Ambient Intelligence and Humanized Computing, 12(5), 4631–4642.

    Article  Google Scholar 

  29. Shen, Z., & Wang, Q. (2020). A high-precision spectrum-detection algorithm based on the normalized variance of nonreconstruction compression sensing. Mathematical Problems in Engineering, 2020, 9867823.

    Article  Google Scholar 

  30. Nasser, A., et al. (2022). Intelligent reflecting surfaces and spectrum sensing for cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 8, 1497.

    Article  Google Scholar 

  31. Düntsch, I., & Gediga, G. (2019). Confusion matrices and rough set data analysis. Journal of Physics: Conference Series., 1229(1), 012055.

    Google Scholar 

  32. Al Azies, H., Trishnanti, D., & Elvira Mustikawati, P. H. (2019). Comparison of kernel support vector machine (SVM) in classification of human development index (HDI). IPTEK Journal of Proceedings Series, 6, 53–57.

    Article  Google Scholar 

  33. Panja, S., Chatterjee, A., & Yasmin, G. (2019). Kernel functions of SVM: A comparison and optimal solution. In Advanced informatics for computing research: Second international conference, ICAICR 2018, Shimla, India, July 14–15, 2018, Revised Selected Papers, Part I 2. Springer.

  34. Yekkehkhany, B., et al. (2014). A comparison study of different kernel functions for SVM-based classification of multi-temporal polarimetry SAR data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2), 281.

    Article  Google Scholar 

  35. Shanei, A., et al. (2022). Comparison of different kernels in a support vector machine to classify prostate cancerous tissues in T2-weighted magnetic resonance imaging. Exploratory Research and Hypothesis in Medicine, 8(1), 25.

    Google Scholar 

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Electronics and Communication Engineering, Technology and Advanced Studies (VISTAS), Vels Institute of Science, Chennai, India

    S. Lakshmikantha Reddy & M. Meena

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  1. S. Lakshmikantha Reddy
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  2. M. Meena
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SLR and Dr. MM. The first draft of the manuscript was written by SLR and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to S. Lakshmikantha Reddy.

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Reddy, S.L., Meena, M. Performance Validation of Spectrum Sensing Using Kernelized Support Vector Machine Transformation. Wireless Pers Commun 132, 1293–1306 (2023). https://doi.org/10.1007/s11277-023-10662-3

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  • DOI: https://doi.org/10.1007/s11277-023-10662-3

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