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Multi-Layer Perceptron Based Spectrum Prediction in Cognitive Radio Network

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Abstract

The technology of cognitive radio network (CRN) appears to be an excellent alternative for the efficient allocation of the frequency spectrum between the authorized and unauthorized users, in which the spectrum sensing plays a very significant role. It is often used to identify the availability of the primary user (PU) channel in its spectrum band. The probability of PU channels in high traffic CRN is usually high. While, the secondary user senses only those channels, in the predictive manner, which remain idle after prediction. In this paper, the impact of the collision factor has been considered, and it has been observed that without prediction, the throughput decreases as the collision factor increases. Therefore, the network has been trained through the neural network, based on the multi-layer perceptron model, to reduce the prediction error. After the spectrum prediction, the obtained results have established the improvement in SU's throughput.

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References

  1. Jackson, D. S., Zang, W., Gu, Q., & Yu, M. (2015). Robust detection of rogue signals in cooperative spectrum sensing. J. Internet Serv. Inf. Secur., 5(2), 4–23.

    Google Scholar 

  2. Tragos, E. Z., & Angelakis, V. (2013). Cognitive radio inspired M2M communications. 16th International Symposium on Wireless Personal Multimedia Communications (WPMC), 1–5.

  3. Zúñiga, V., Camuñas-Mesa, L., Linares-Barranco, B., Serrano-Gotarredona, T., & Rosa, J. M. d. l. (2020). Using neural networks for optimum band selection in cognitive-radio systems. In 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 1–4.

  4. Jones, S. K., Phillips, T. W., Tuyl, H. L. V., & Weller, R. D. (2008). Evaluation of the Performance of Prototype TV-Band White Space Devices Phase II.

  5. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  6. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  7. Patel, D.K., Lopez-Benitez, M., Soni, B., & Garcia-Fernandez, A. F. (2020). Artificial neural network design for improved spectrum sensing in cognitive radio. Wireless Networks, 26, 6155–6174.

    Article  Google Scholar 

  8. Liang, Y. -C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE transactions on Wireless Communications, 7(4), 1326–1337.

    Article  Google Scholar 

  9. Naikwadi, M. H., & Patil, K. P. (2020). A survey of artificial neural network based spectrum inference for occupancy prediction in cognitive radio networks. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), 903–908.

  10. Jain, S., Goel, A., & Arora, P. (2019). Spectrum prediction using time delay neural network in cognitive radio network. In Smart Innovations in Communication and Computational Sciences (pp. 257–269). Springer.

    Chapter  Google Scholar 

  11. Bhowmick, A., Roy, S. D., & Kundu, S. (2016). Sensing throughput trade-off for an energy efficient cognitive radio network under faded sensing and reporting channel. International Journal of Communication Systems, 29(7), 1208–1218.

    Article  Google Scholar 

  12. Chatterjee, S., Maity, S. P., & Acharya, T. (2014). Energy efficient cognitive radio system for joint spectrum sensing and data transmission. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 4(3), 292–300.

    Article  Google Scholar 

  13. Yang, J., & Zhao, H. (2015). Enhanced throughput of cognitive radio networks by imperfect spectrum prediction. IEEE Communications Letters, 19(10), 1738–1741.

    Article  Google Scholar 

  14. Fong, K. L., Tan, C. K., & 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.

    Article  Google Scholar 

  15. Bhowmick, A., Yadav, K., Roy, S. D., & Kundu, S. (2017). Throughput of an energy harvesting cognitive radio network based on prediction of primary user. IEEE Transactions on Vehicular Technology, 66(9), 8119–8128.

    Article  Google Scholar 

  16. Kumar, D., Talukdar, B., & Arif, W. (2019). Performance analysis of prediction based sensing in energy harvesting cooperative CRN. In 2019 2nd International Conference on Advanced Computational and Communication Paradigms (ICACCP), 1–6.

  17. Talukdar, B., Kumar, D., & Arif, W. (2019). Analytical modelling and performance evaluation of a prediction based EH-cooperative CRN under ERLANG distribution. In 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 1–6.

  18. Thakur, P., Kumar, A., Pandit, S., Singh, G., & Satashia, S. N. (2018). Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques. Wireless Networks, 24, 2005–2015.

    Article  Google Scholar 

  19. Bhuvaneswari, B., & Meeradevi, T. (2020). An optimised neural network-based spectrum prediction scheme for cognitive radio. International Journal of Enterprise Network Management, 11(1), 76–93.

    Article  Google Scholar 

  20. Shaghluf, N., & Gulliver, T. A. (2019). Spectrum and energy efficiency of cooperative spectrum prediction in cognitive radio networks. Wireless Networks, 25, 3265–3274.

    Article  Google Scholar 

  21. Tumuluru, V. K., Wang, P., & Niyato, D. (2012). Channel status prediction for cognitive radio networks. Wireless Communications and Mobile Computing, 12, 862–874.

    Article  Google Scholar 

  22. Bhowmick, A., Prasad, B., Roy, S. D., & Kundu, S. (2016). Performance of cognitive radio network with novel hybrid spectrum access schemes. Wireless Personal Communications, 91, 541–560.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bhagalpur College of Engineering, Bhagalpur, Bihar, India

    Amit Kumar Singh

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Patna, India

    Rakesh Ranjan

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  1. Amit Kumar Singh
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  2. Rakesh Ranjan
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Correspondence to Amit Kumar Singh.

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Singh, A.K., Ranjan, R. Multi-Layer Perceptron Based Spectrum Prediction in Cognitive Radio Network. Wireless Pers Commun 123, 3539–3553 (2022). https://doi.org/10.1007/s11277-021-09302-5

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  • DOI: https://doi.org/10.1007/s11277-021-09302-5

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