Maritime cognitive radio spectrum sensing based on multi-antenna cyclostationary feature detection

References

• Cabric, D., & Brodersen, R. (2007). Robust spectrum sensing techniques for cognitive radio networks. In: F. H. P. Fitzek & M. D. Katz (Eds.), Cognitive wireless networks (pp. 373–394). Berlin, Germany: Springer-Verlag.
   Google Scholar
• Chen, X., Xu, W., He, Z., & Tao, X. (2008a, March 31–April 3). Spectral correlation-based multi-antenna spectrum sensing technique. In Proceedings of IEEE Wireless Communications and Networking Conference, Las Vegas, NV.
   Google Scholar
• Chen, X., Xu, W. J., He, Z. Q., & Tao, X. F. (2008b). Spectral correlation-based multi-antenna spectrum sensing technique. In IEEE Wireless Communications and Networking Conference, Las Vegas, NV:WCNC (pp.735–740).
   Google Scholar
• Gardner, W. A. (1987). Statistical spectral analysis: An nonprobabilistic theory. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Gardner, W. A., Brown, W. A., & Chen, C.-K. (1987, June). Spectral correlation of modulated signals: Part II–Digital modulation. IEEE Transactions on Communications, 35(6), 595–601.
   Web of Science ®Google Scholar
• Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29, 317–325.
   Web of Science ®Google Scholar
• Haykin, S., Thomson, D. J., & Reed, J. H. (2009, May). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849–877.
   Web of Science ®Google Scholar
• Jeon, W. S., Jeong, D. G., Han, J. A., Ko, G., & Song, M. S. (2008, February). An efficient quiet period management scheme for cognitive radio systems. IEEE Transactions on Wireless Communications, 7(2), 505–509.
   Web of Science ®Google Scholar
• Jitvanichphaibool, K., Liang, Y.-C., & Zeng, Y. (2010, April 6–9). Spectrum sensing using multiple antennas for spatially and temporally correlated noise environments. In Proceedings of IEEE DySPAN, Singapore.
   Google Scholar
• Lam, A. Y. S., Li, V. O. K., & Yu, J. J. Q. (2013, July). Power-controlled cognitive radio spectrum allocation with chemical reaction optimization. IEEE Transactions on Wireless Communications, 12(7), 3180–3190.
   Web of Science ®Google Scholar
• LeeWC, Y. (1985). A new propagation path-loss prediction model for military mobile access. In Proceedings of IEEE Military Communications Conference (MILCOM’1985) (Vol. 2, pp. 359–368). Boston.
   Google Scholar
• Longley, A. G., & Rice, P. L.(1968). Prediction of tropospheric radio transmission loss over irregular terrain: A computer method-1968 (NTIA Technical Report ERL 79-ITS 67).
   Google Scholar
• Mo, H., Chen, B., & Shen, C. (2012). Radio propagation prediction model for maritime mobile communication. In Proceedings of IET International Conference on Wireless Communications and Applications (ICWCA’2012) (pp. 1–5). Kuala Lumpur.
   Google Scholar
• Nallagonda, S., Chandra, A., Roy, S. D., Kundu, S., Kukolev, P., & Prokes, A. (2016, February). Detection performance of cooperative spectrum sensing with hard decision fusion in fading channels. International Journal of Electronics, 103(2), 297–321.
   Web of Science ®Google Scholar
• Okumura, Y., Ohmori, E., Kawano, T. (1968). Field strength and its variability in VHF and UHF land-mobile radio service. Review of the Electrical Communications Laboratories, 16, 825–873.
   Google Scholar
• Sadeghi, H., & Azmi, P. (2008, August). A novel primary user detection method for multiple-antenna cognitive radio. In Proceedings of International Symposium on Telecommunications (pp. 188–192), Tehran, Iran.
   Google Scholar
• Semba Yawada, P., & 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). Chengdu.
   Google Scholar
• Tandra, R., & Sahai, A. (2008, February). SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing, 2(1), 4–17.
   Web of Science ®Google Scholar
• Tkachenko, A., Cabric, D., & Brodersen, R. W. (2007, April). Cyclostationary feature detector experiments using reconfigurable BEE2 (Berkeley Emulation Engine). In Proceedings of IEEE International Dynamic Spectrum Access Networks (pp. 216–219), Dublin, Ireland.
   Google Scholar
• Urriza, P., Rebeiz, E., & Cabric, D. (2013, November). Multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test. IEEE Journal on Selected Areas in Communications, 31(11), 2185–2195.
   Web of Science ®Google Scholar
• Verma, P. K., Soni, S. K., & Jain, P. (2017). On the performance of energy detection-based CR with SC diversity over IG channel. International Journal of Electronics, 104(12), 1945–1956.
   Web of Science ®Google Scholar
• Wang, B., & Liu, K. J. R. (2011, February). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.
   Web of Science ®Google Scholar
• Y¨ucek, T., & Arslan, H. (2009, March). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.
   Web of Science ®Google Scholar
• Zhang, J., Sun, Z., & Zhang, S. (2018). The improved adaptive silence period algorithm over time-variant channels in the cognitive radio system. Future Internet, 10, 12.
   Web of Science ®Google Scholar
• Zhang, J., Yu, H., & Zhang, S. (2017). An enhanced dynamic spectrum allocation algorithm based on cournot game in maritime cognitive radio communication system. Algorithms, 10, 103.
   Web of Science ®Google Scholar
• Zhang, T., Yu, G., & Sun, C. (2009). Performance of cyclostationary features based spectrum sensing method in a multiple antenna cognitive radio system. In 2009 IEEE Wireless Communications and Networking Conference (pp. 1–5). Budapest.
   Google Scholar
• Zhao, Y. W., Chi, X., & Ren, J. (2014). Maritime mobile channel transmission model based on ITM. Applied Electronic Technique, 40, 106–108.
   Google Scholar