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ORS-ACSS: Optimum Relay Selection and Accurate Cooperative Spectrum Sensing for Hybrid Cognitive Radio Networks

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Abstract

In wireless spectrum, the cognitive radio network (CRN) has the capability to automatically detect the channels so that it is possible to perform concurrent communication. One of the major challenges in hybrid CRNs is effective spectrum sharing. In the interweave scheme secondary user (SU) uses the spectrum, when primary user (PU) is absent. Further in the underlay scheme SU uses the spectrum concurrently with PU along with the interference constraint. In order to utilize the advantage of both the scheme hybrid CRN has been proposed in Chu et al. (IEEE Trans Commun 62(7):2183–2197, 2014. https://doi.org/10.1109/tcomm.2014.2325041). The major challenges in hybrid CRN are to select the scheme (interweave/underlay) and use the relays accordingly. To overcome this problem, the optimum relay selection and accurate cooperative spectrum sensing scheme are proposed in this paper which improves the SU performance in terms of throughput of hybrid CRN. By accurate cooperative spectrum sensing method, the accuracy of the decision to select the underlay/overlay scheme to transmit the data is improved. The SU uses relays to minimize interference with the PU while underlay scheme is selected for transmission. Here, the relay selection is optimized by an optimum relay selection method. Numerical results show that the proposed scheme improves the throughput of hybrid CRN. The experimental results show that the proposed method is performed better in terms of delay, delivery ratio, overhead, energy consumption, and throughput.

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References

  1. Khalid, L., & Anpalagan, A. (2010). Emerging cognitive radio technology: Principles, challenges and opportunities. Computers & Electrical Engineering,36(2), 358–366. https://doi.org/10.1016/j.compeleceng.2009.03.004.

    Article  Google Scholar 

  2. Saleem, Y., & Rehmani, M. (2014). Primary radio user activity models for cognitive radio networks: A survey. Journal of Network and Computer Applications,43, 1–16. https://doi.org/10.1016/j.jnca.2014.04.001.

    Article  Google Scholar 

  3. Zhang, Y., Tay, W., Li, K., & Gaiti, D. (2014). Distributed boundary estimation for spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications,32(11), 1961–1973. https://doi.org/10.1109/jsac.2014.1411rp08.

    Article  Google Scholar 

  4. Sun, H., Nallanathan, A., Wang, C.-X., & Chen, Y. (2013). Wideband spectrum sensing for cognitive radio networks: A survey. IEEE Wireless Communications,20(2), 74–81. https://doi.org/10.1109/mwc.2013.6507397.

    Article  Google Scholar 

  5. Zhao, Q., Wu, Z., & Li, X. (2016). Energy efficiency of compressed spectrum sensing in wideband cognitive radio networks. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-016-0581-9.

    Article  Google Scholar 

  6. Li, H., Xing, X., Zhu, J., Cheng, X., Li, K., Bie, R., et al. (2016). Utility-based cooperative spectrum sensing scheduling in cognitive radio networks. IEEE Transactions on Vehicular Technology,66, 645–655.

    Google Scholar 

  7. Chen, R., Park, J.-M., Hou, Y., & Reed, J. (2008). Toward secure distributed spectrum sensing in cognitive radio networks. IEEE Communications Magazine,46(4), 50–55. https://doi.org/10.1109/mcom.2008.4481340.

    Article  Google Scholar 

  8. Sigg, S., Scholz, M., Shi, S., Ji, Y., & Beigl, M. (2014). Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Transactions on Mobile Computing,13(4), 907–920. https://doi.org/10.1109/tmc.2013.28.

    Article  Google Scholar 

  9. Unnikrishnan, J., & Veeravalli, V. (2007). Cooperative spectrum sensing and detection for cognitive radio. In IEEE GLOBECOM 2007–2007 IEEE global telecommunications conference, Washington, DC, USA (pp. 2972–2976). https://doi.org/10.1109/glocom.2007.563.

  10. Akyildiz, I., Lo, B., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication,4(1), 40–62. https://doi.org/10.1016/j.phycom.2010.12.003.

    Article  Google Scholar 

  11. He, D. (2013). Chaotic stochastic resonance energy detection fusion used in cooperative spectrum sensing. IEEE Transactions on Vehicular Technology,62(2), 620–627. https://doi.org/10.1109/tvt.2012.2224680.

    Article  Google Scholar 

  12. Axell, E., Leus, G., Larsson, E., & Poor, H. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine,29(3), 101–116. https://doi.org/10.1109/msp.2012.2183771.

    Article  Google Scholar 

  13. Zhao, Y., Kang, G., Wang, J., Liang, X., & Liu, Y. (2013). A soft fusion scheme for cooperative spectrum sensing based on the log-likelihood ratio. In 2013 IEEE 24th annual international symposium on personal, indoor, and mobile radio communications (PIMRC), London, UK (pp. 3150–3154). https://doi.org/10.1109/pimrc.2013.6666688.

  14. Ahmed, A., Hu, Y., & Noras, J. (2014). Noise variance estimation for spectrum sensing in cognitive radio networks. AASRI Procedia,9, 37–43. https://doi.org/10.1016/j.aasri.2014.09.008.

    Article  Google Scholar 

  15. Guibene, W., Turki, M., Zayen, B., & Hayar, A. (2012). Spectrum sensing for cognitive radio exploiting spectrum discontinuities detection. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/1687-1499-2012-4.

    Article  Google Scholar 

  16. Sun, M., Zhao, C., Yan, S., & Li, B. (2016). A novel spectrum sensing for cognitive radio networks with noise uncertainty. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2016.2596789.

    Article  Google Scholar 

  17. Ejaz, W., Shah, G., Hasan, N., & Kim, H. (2014). Energy and throughput efficient cooperative spectrum sensing in cognitive radio sensor networks. Transactions on Emerging Telecommunications Technologies,26(7), 1019–1030. https://doi.org/10.1002/ett.2803.

    Article  Google Scholar 

  18. Bouallegue, T., & Sethom, K. (2017). New threshold-based relay selection algorithm in dual hop cooperative network. Procedia Computer Science,109, 273–280. https://doi.org/10.1016/j.procs.2017.05.351.

    Article  Google Scholar 

  19. Li, Y., & Nosratinia, A. (2013). Spectrum sharing with distributed relay selection and clustering. IEEE Transactions on Communications,61(1), 53–62. https://doi.org/10.1109/tcomm.2012.091912.120062.

    Article  Google Scholar 

  20. Nam, S., Vu, M., & Tarokh, V. (2008). Relay selection methods for wireless cooperative communications. In 2008 42nd Annual conference on information sciences and systems, Princeton, NJ, USA (pp. 859–864). https://doi.org/10.1109/ciss.2008.4558640.

  21. Chinh Chu, T., Zepernick, H., & Phan, H. (2016). Hybrid spectrum access with relay assisting both primary and secondary networks under imperfect spectrum sensing. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-016-0700-7.

    Article  Google Scholar 

  22. Thakur, P., Kumar, A., Pandit, S., Singh, G., & Satashia, S. (2017). Advanced frame structures for hybrid spectrum access strategy in cognitive radio communication systems. IEEE Communications Letters,21(2), 410–413. https://doi.org/10.1109/lcomm.2016.2622260.

    Article  Google Scholar 

  23. Ma, Y., Guo, Y., Niu, K., & Lin, J. (2012). Transmission capacity of secondary networks in hybrid overlaid/underlaid cognitive radio systems. In 2012 IEEE 14th international conference on communication technology, Chengdu, China (pp. 397–401). https://doi.org/10.1109/icct.2012.6511250.

  24. Arjoune, Y., Mrabet, Z., Ghazi, H., & Tamtaoui, A. (2018). Spectrum sensing: Enhanced energy detection technique based on noise measurement. In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA (pp. 828–834). https://doi.org/10.1109/ccwc.2018.8301619.

  25. Cao, B., Feng, G., Li, Y., & Wang, C. (2014). Cooperative media access control with optimal relay selection in error-prone wireless networks. IEEE Transactions on Vehicular Technology,63(1), 252–265. https://doi.org/10.1109/tvt.2012.2226485.

    Article  Google Scholar 

  26. Mietzner, J., Lampe, L., & Schober, R. (2009). Distributed transmit power allocation for multihop cognitive-radio systems. IEEE Transactions on Wireless Communications,8(10), 5187–5201. https://doi.org/10.1109/twc.2009.081318.

    Article  Google Scholar 

  27. Ban, T., Choi, W., Jung, B., & Sung, D. (2009). Multi-user diversity in a spectrum sharing system. IEEE Transactions on Wireless Communications,8(1), 102–106. https://doi.org/10.1109/t-wc.2009.080326.

    Article  Google Scholar 

  28. Zou, Y., Zhu, J., Zheng, B., & Yao, Y.-D. (2010). An adaptive cooperation diversity scheme with best-relay selection in cognitive radio networks. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2010.2053708.

    Article  MathSciNet  MATH  Google Scholar 

  29. Seyfi, M., Muhaidat, S., & Liang, J. (2013). Relay selection in cognitive radio networks with interference constraints. IET Communications. https://doi.org/10.1049/iet-com.2012.0415.

    Article  Google Scholar 

  30. Zhao, Y., Adve, R., & Lim, T. J. (2007). Improving amplify-and-forward relay networks: Optimal power allocation versus selection. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2007.06026.

    Article  Google Scholar 

  31. Martínez, D., & Andrade, Á. (2014). Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks. International Journal of Communication Systems,30(1), e2907. https://doi.org/10.1002/dac.2907.

    Article  Google Scholar 

  32. Althunibat, S., Di Renzo, M., & Granelli, F. (2013). Optimizing the K-out-of-N rule for cooperative spectrum sensing in cognitive radio networks. In 2013 IEEE global communications conference (GLOBECOM), Atlanta, GA USA (pp. 1607–1611). https://doi.org/10.1109/glocom.2013.6831303.

  33. Ruby, D., Vijayalakshmi, M., & Kannan, A. (2017). Intelligent relay selection and spectrum sharing techniques for cognitive radio networks. Cluster Computing. https://doi.org/10.1007/s10586-017-1102-2.

    Article  Google Scholar 

  34. Aslam, S., & Lee, K. (2013). Spectrum sharing optimization with QoS guarantee in cognitive radio networks. Computers & Electrical Engineering,39(7), 2053–2067. https://doi.org/10.1016/j.compeleceng.2013.06.003.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology (NIT), Tiruchirappalli, Tiruchirappalli, India

    R. Rajaganapathi & P. Muthuchidambara Nathan

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  1. R. Rajaganapathi
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Correspondence to R. Rajaganapathi.

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Rajaganapathi, R., Muthuchidambara Nathan, P. ORS-ACSS: Optimum Relay Selection and Accurate Cooperative Spectrum Sensing for Hybrid Cognitive Radio Networks. Wireless Pers Commun 110, 795–813 (2020). https://doi.org/10.1007/s11277-019-06756-6

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