Abstract
Natural calamities are increasing every year and communication plays a major role in post disaster measures to save human lives. This work utilizes the adaptation of the emerging dynamic radio technology called cognitive radio networks over Unmanned Aerial vehicles (UAV). Enhancing emergency communication over disaster affected zones where the mobile network base stations are completely destroyed is enabled by mounting drones with an omni antenna base station. This chapter analyses the cooperative spectrum sensing (CSS) technique of the intelligent radio to study incoming primary user (PU) when the available spectrum consists of multiple secondary users (SUs). A deep learning based technique called SpecCNN (Spectrum sensing Convolutional Neural Network) is proposed for performing intelligent spectrum sensing by analysing hidden cyclostationary features from drone data (image) of disastrous areas.
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References
A. Trotta, Re-establishing network connectivity in post-disaster scenarios through mobile cognitive radio networks, in 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 2013. ISBN: 978-1-4799-1004-5
M.H. Rehmani, A.C. Viana, H. Khalife, S. Fdida, A Cognitive Radio Based Internet Access Framework for Disaster Response Network Deployment. [Research Report] RR-7285, INRIA, 2010
K. Namuduri, S. Chaumette, J. Kim, J. Sterbenz (eds.), UAV Networks and Communications (Cambridge University Press, Cambridge, 2017). https://doi.org/10.1017/9781316335765
N. Islam, G.S. Shaikh, Towards a Disaster Response System Based on Cognitive Radio Ad Hoc Networks, 2017, arXiv:1710.02404 [cs.NI]
R.D. Grodi, Design, Analysis and Evaluation of Unmanned Aerial Vehicle Ad hoc Network for Emergency Response Communications, Electronic Theses & Dissertations, 2016
M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, M. Debbah, A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems, 2018, arXiv:1803.00680
W. Lee, M. Kim, D.-H. Cho, R. Schober, D. Sensing, Cooperative Spectrum Sensing Based on Convolutional Neural Networks, 2017, arXiv:1705.08164v1
K. Namuduri, Flying cell towers to the rescue. IEEE Spectr. 54(9), 38–43 (2017). https://doi.org/10.1109/mspec.2017.8012238
V.Q. Do, I. Koo, Learning frameworks for cooperative spectrum sensing and energy-efficient data protection in cognitive radio networks. Appl. Sci. 8, 722 (2018). https://doi.org/10.3390/app8050722
A. Fotouhi, M. Ding, M. Hassan, Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells, 2017, arXiv:1704.01244v1 [cs.IT]
F. Paisana, A. Selim, M. Kist, P. Alvarez, J. Tallon, C. Bluemm, A. Puschmann, L. DaSilva, Context-aware cognitive radio using deep learning, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2017, 978-1-5090-2830-6/17
P. Rungsawang, A. Khawne, The implementation of spectrum sensing and spectrum allocation on cognitive radio, in 19th International Conference on Advanced Communication Technology (ICACT), 2017, https://doi.org/10.23919/ICACT.2017.7890206
M. Mozaffar, W. Saad, M. Bennis, Y.-H. Nam, M. Debbah, A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems, 2018, arXiv:1803.00680v1 [cs.IT]
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Suriya, M., Sumithra, M.G. (2020). Enhancing Cooperative Spectrum Sensing in Flying Cell Towers for Disaster Management Using Convolutional Neural Networks. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_18
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DOI: https://doi.org/10.1007/978-3-030-19562-5_18
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