Hongwei Zhang
ABSTRACT
Unmanned aerial vehicles (UAVs) are characterized by various functions, such as flexibility and easy operation, which enable them to be widely used in certain dangerous scenarios. Considering that the cooperative working mode of multi-UAV will have an important application prospect in the future communication, this paper establishes a cognitive UAV network model by considering the shortage of spectrum resources, studies the sensing performance of multi-UAV cooperative spectrum, and proposes an optimal fusion criterion to optimize the detection performance. Finally, this paper proposes a fast and efficient cooperative spectrum sensing algorithm for large cognitive UAV network, which can minimize the number of UAV participating in cooperative sensing to assure that the total detection error rate is less than a certain threshold.
- Niu, H., Gonzalez, P. N., Heath, R. W. (2108) A UAV-based traffic monitoring system-invited paper. 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). IEEE, 1--5.Google Scholar
- Wang, Y., Luo, X., Ding, L., Fu, S., & Hu, S. (2019). Adaptive sampling for UAV tracking. Neural Computing and Applications.Google Scholar
- Senpheng, M., & Ruchanurucks, M. (2016). Automatic landing assistant system based on stripe lines on runway using computer vision. International Conference on Science & Technology. IEEE.Google Scholar
- Zhao, N., Lu, W., Sheng, M., Chen, Y., Tang, J., & Yu, F. R., et al. (2019). UAV-assisted emergency networks in disasters. IEEE Wireless Communications, 26(1), 45--51.Google ScholarCross Ref
- Wu, Q., Zeng, Y., & Zhang, R. (2017). Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Transactions on Wireless Communications, PP (99), 1--1.Google Scholar
- Tuna, G., Nefzi, B., & Conte, G. (2014). Unmanned aerial vehicle-aided communications system for disaster recovery. Journal of Network and Computer Applications, 41, 27--36.Google ScholarCross Ref
- Washburn, A., & Kress, M. (2009). Combat Modeling.Google Scholar
- Sahingoz, O. K. (2013). Flying Ad-Hoc Networks (FANETs). Elsevier Science Publishers B. V.Google Scholar
- Wang, J., Jiang, C., Han, Z., Ren, Y., & Hanzo, L. (2017). Taking drones to the next level: cooperative distributed unmanned-aerial-vehicular networks for small and mini drones. IEEE Vehicular Technology Magazine, 12(3), 73--82.Google ScholarCross Ref
- Jain, R., & Templin, F. (2012). Requirements, challenges and analysis of alternatives for wireless datalinks for unmanned aircraft systems. IEEE Journal on Selected Areas in Communications, 30(5), 852--860.Google ScholarCross Ref
- Saleem, Y., Rehmani, M. H., & Zeadally, S. (2015). Integration of cognitive radio technology with unmanned aerial vehicles. Journal of Network & Computer Applications, 50, 15--31.Google ScholarDigital Library
- Ni, L., Da, X. Y., Hu, H. et al. (2018). Outage constrained robust transmit design for secure cognitive radio with practical energy harvesting. IEEE Access, 71444--71454.Google Scholar
- Shi, B. Z., Li, L. G. (2012). Performance Analysis of Multi-hop Relay Cooperative Spectrum Sensing. Journal of Communications, 7(12), 921--927.Google Scholar
- Li, A., Han, G., Shu, L., & Guizani, M. (2016). Cooperative Secondary Users selection in Cognitive Radio Ad Hoc Networks. Wireless Communications & Mobile Computing Conference. IEEE.Google ScholarCross Ref
- Wen, J. Z., He, L., & Ming, L. J. (2019). A fusion spectrum sensing algorithm using eigenvalues. Journal on Communications, 40(9), 1--8.Google Scholar
- Liu, Y., WANG, J., SHEN, Y. (2018) Spectrum allocation for UAV-aided relative localization of ground vehicles. 2018 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 1--5.Google ScholarCross Ref
- Ghazzai, H., Ghorbel, M. B., Kadri, A., Hossain, M. J., & Menouar, H. (2017). Energy-efficient management of unmanned aerial vehicles for underlay cognitive radio systems. IEEE Transactions on Green Communications & Networking, 1(4), 434--443.Google ScholarCross Ref
- Hao, B. Y., Zhou, H., & Sun, B. (2012) Cooperative Spectrum sensing algorithm for two-UAV. Journal of Ordnance Equipment Engineering, 33(2), 114--116.Google Scholar
- Zeng, Y., Wu, Q., & Zhang, R. (2019). Accessing from the sky: a tutorial on UAV communications for 5g and beyond.Google Scholar
- Digham, F. F., Alouini, M. S., & Simon, M. K. (2003). On the energy detection of unknown signals over fading channels. IEEE International Conference on Communications. IEEE.Google ScholarCross Ref
- Nuttall, A. H. (1972). Some integrals involving the q-function. IEEE Transactions on Information Theory, 21(1), 95--96.Google ScholarDigital Library
Index Terms
- Multi-UAV cooperative spectrum sensing in cognitive UAV network
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