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Estimation based cyclostationary detection for energy harvesting cooperative cognitive radio network

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Abstract

The two prime dynamics that are steering the future wireless communications are energy efficiency and efficient spectral usage. Energy harvesting (EH) when integrated with cognitive radio (CR) technology guarantees to address the challenges of limited availability of energy and spectrum. In this paper, we propose an estimation based cyclostationary spectrum sensing technique in an energy harvesting cooperative cognitive radio network (EH-CRN). The analytical model for the assessment of the performance is established under various fusion rules with varying numbers of nodes in a centralized cooperative CRN. The performance is investigated with respect to the harvested energy and overall throughput of the network. We model the analytical framework for the detection performance, energy harvesting, and throughput in an EH-CRN scenario. The implication of the various network parameters viz., detection frames, number of CR users operating in cooperation, collision probability, prediction error upon the throughput of the network is studied. A comprehensive comparative analysis is made between the performance of an estimation based cyclostationary detection (ECSD) and estimated noise power based energy detection strategy (ENP ED). The results indicate that the proposed estimation based cyclostationary approach provides a better performance regardless of the low SNR and is immune to noise uncertainty as compared to other techniques.

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References

  1. Fcc, E. (2003). Docket No 03-222 Notice of proposed rule making and order.

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

    Article  Google Scholar 

  3. 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 

  4. Xing, X., Jing, T., Cheng, W., Huo, Y., & Cheng, X. (2013). Spectrum prediction in cognitive radio networks. IEEE Wireless Communications, 20(2), 90–96.

    Article  Google Scholar 

  5. Yang, J., & Ulukus, S. (2011). Optimal packet scheduling in an energy harvesting communication system. IEEE Transactions on Communications, 60(1), 220–230.

    Article  Google Scholar 

  6. Tutuncuoglu, K., & Yener, A. (2012). Optimum transmission policies for battery limited energy harvesting nodes. IEEE Transactions on Wireless Communications, 11(3), 1180–1189.

    Article  Google Scholar 

  7. El Shafie, A., Ashour, M., Khattab, T., & Mohamed, A. (2015). On spectrum sharing between energy harvesting cognitive radio users and primary users. In 2015 International conference on computing, networking and communications (ICNC) (pp. 214–220).

  8. El Shafie, A., & Khattab, T. (2014). Maximum throughput of a cooperative energy harvesting cognitive radio user. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC) (pp. 1067–1072).

  9. 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 

  10. Park, S., & Hong, D. (2014). Achievable throughput of energy harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 13(2), 1010–1022.

    Article  Google Scholar 

  11. Park, S., & Hong, D. (2013). Optimal spectrum access for energy harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 12(12), 6166–6179.

    Article  Google Scholar 

  12. Bhowmick, A., Roy, S. D., & Kundu, S. (2016). Throughput of a cognitive radio network with energy-harvesting based on primary user signal. IEEE Wireless Communications Letters, 5(2), 136–139.

    Article  Google Scholar 

  13. Lee, S., Zhang, R., & Huang, K. (2013). Opportunistic wireless energy harvesting in cognitive radio networks. IEEE Transactions on Wireless Communications, 12(9), 4788–4799.

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Tian, Z., & Giannakis, G.B. (2007). Compressed sensing for wideband cognitive radios. In 2007 IEEE International conference on acoustics, speech and signal processing-ICASSP'07 (Vol. 4, pp. IV–1357).

  16. Ghasemi, A., & Sousa, E. S. (2007). Spectrum sensing in cognitive radio networks: The cooperation-processing tradeoff. Wireless Communications and Mobile Computing, 7(9), 1049–1060.

    Article  Google Scholar 

  17. Mishra, S.M., Sahai, A., & Brodersen, R.W. (2006). Cooperative sensing among cognitive radios. In 2006 IEEE International conference on communications (Vol. 4, pp. 1658–1663). IEEE.

  18. Dandawate, A. V., & Giannakis, G. B. (1994). Statistical tests for presence of cyclostationarity. IEEE Transactions on Signal Processing, 42(9), 2355–2369.

    Article  Google Scholar 

  19. Öner, M., & Jondral, F. (2007). Air interface identification for software radio systems. AEU-International Journal of Electronics and Communications, 61(2), 104–117.

    Article  Google Scholar 

  20. Cabric, D., Tkachenko, A., & Brodersen, R.W. (2006). Experimental study of spectrum sensing based on energy detection and network cooperation. In Proceedings of the first international workshop on Technology and policy for accessing spectrum (pp. 12–es).

  21. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  22. Gardner, W. A. (1994). Cyclostationarity in communications and signal processing. Statistical Signal Processing Inc.

    Google Scholar 

  23. Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing, 2(1), 4–17.

    Article  Google Scholar 

  24. Kumar, D., Talukdar, B., & Arif, W. (2019). Performance analysis of prediction based sensing in energy harvesting cooperative CRN. In 2019 Second international conference on advanced computational and communication paradigms (ICACCP) (pp. 1–6).

  25. Kumar, D., Talukdar, B., & Arif, W. (2019). Impact of Weibull distribution on prediction based sensing in energy harvesting cooperative CRN. In 2019 6th International conference on signal processing and integrated networks (SPIN) (pp. 704–709).

  26. Hoque, S., & Arif, W. (2018). Impact of secondary user mobility on spectrum handoff under generalized residual time distributions in cognitive radio networks. AEU-International Journal of Electronics and Communications, 86, 185–194.

    Article  Google Scholar 

  27. Awasthi, M., Nigam, M. J., & Kumar, V. (2019). Optimal sensing, fusion and transmission with primary user protection for energy-efficient cooperative spectrum sensing in CRNs. AEU-International Journal of Electronics and Communications, 98, 95–105.

    Article  Google Scholar 

  28. Nguyen, B. C., Hoang, T. M., & Tran, P. T. (2019). Performance analysis of full-duplex decode-and-forward relay system with energy harvesting over Nakagami-m fading channels. AEU-International Journal of Electronics and Communications, 98, 114–122.

    Article  Google Scholar 

  29. Hoque, S., & Arif, W. (2017). Performance analysis of cognitive radio networks with generalized call holding time distribution of secondary user. Telecommunication Systems, 66(1), 95–108.

    Article  Google Scholar 

  30. Yawada, P.S., & Wei, A.J. (2016). Cyclostationary detection based on non-cooperative spectrum sensing in cognitive radio network. In 2016 IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER) (pp. 184–187).

  31. Liu, X., Zheng, K., Chi, K., & Zhu, Y. H. (2020). Cooperative spectrum sensing optimization in energy-harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 19(11), 7663–7676.

    Article  Google Scholar 

  32. Develi, I. (2020). Spectrum sensing in cognitive radio networks: Threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking, 2020(1), 1–19.

    Article  Google Scholar 

  33. Fouda, H.S., Kabeel, A.A.E., Nasr, M.E.S., & Hussein, A.H. (2021). Multi-dimensional small-scale cooperative spectrum sensing approach for cognitive radio receivers. IEEE Access.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India

    Banani Talukdar & Wasim Arif

  2. Department of Electrical and Electronics Engineering, Indian Institute of Technology Indore, Indore, India

    Deepak Kumar

  3. Department of Electronics and Communication Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India

    Shanidul Hoque

Authors
  1. Banani Talukdar
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  2. Deepak Kumar
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  3. Shanidul Hoque
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  4. Wasim Arif
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Correspondence to Banani Talukdar.

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Talukdar, B., Kumar, D., Hoque, S. et al. Estimation based cyclostationary detection for energy harvesting cooperative cognitive radio network. Telecommun Syst 79, 133–150 (2022). https://doi.org/10.1007/s11235-021-00846-2

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