CN  Vol.5 No.3 , August 2013
Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology
Abstract: Unmanned Aircraft System networks are a special type of networks where high speeds of the nodes, long distances and radio spectrum scarcity pose a number of challenges. In these networks, the strength of the transmitted/received signals varies due to jamming, multipath propagation, and the changing distance among nodes. High speeds cause another problem, Doppler Effect, which produces a shifting of the central frequency of the signal at the receiver. In this paper we discuss a modular system based on cognitive to enhance the reliability of UAS networks.
Cite this paper: H. Reyes and N. Kaabouch, "Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology," Communications and Network, Vol. 5 No. 3, 2013, pp. 225-230. doi: 10.4236/cn.2013.53027.

[1]   E. W. Frew and T. X. Brown, “Networking Issues for Small Unmanned Aircraft Systems,” Journal of Intelligent and Robotic Systems, Vol. 54, No. 1-3, 2009, pp. 21-37.

[2]   R. K. Barnhart, “The Future of Unmanned Aircraft Systems,” In: R. K. Barnhart, R. Powers and E. Shapee, Eds., Introduction to Unmanned Aircraft Systems, CRC Press, Boca Raton, 2011, p. 181.

[3]   K. Dalamagkidis, K. Valavanis and L. Piegl, “On Unmanned Aircraft Systems Issues, Challenges and Operational Restrictions Preventing Integration into the National Airspace System,” Progress in Aerospace Sciences, Vol. 44, No. 7, 2008, pp. 503-519.

[4]   S. A. Cambone, “Unmanned Aircraft Systems Roadmap 2005-2030,” 2005.

[5]   R. Jain and F. Templin, “Requirements, Challenges and Analysis of Alternatives for Wireless Datalinks for Unmanned Aircraft Systems,” IEEE Journal on Selected Areas in Communications, Vol. 30, No. 5, 2012, pp. 852-860.

[6]   A. Azarfar, J.-F. Frigon and B. Sanso, “Improving the Reliability of Wireless Networks Using Cognitive Radios,” Communications Surveys & Tutorials, Vol. 14, No. 2, 2012, pp. 338-354.

[7]   P. Fahlstrom and T. Gleason, “Introduction to UAV Systems,” 2012.

[8]   T. H. Eggen, J. C. Preisig and A. B. Baggeroer, “Communication over Doppler Spread Channels. II. Receiver Characterization and Practical Results,” IEEE Journal of Oceanic Engineering, Vol. 26, No. 4, 2001, pp. 612-621.

[9]   J. Mitola III and G. Q. Maguire Jr., “Cognitive Radio: Making Software Radios More Personal,” Personal Communications, Vol. 6, No. 4, 1999, pp. 13-18.

[10]   L. Doyle, “Essentials of Cognitive Radio,” 2009.

[11]   T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” Communications Surveys & Tutorials, Vol. 11, No. 1, 2009, pp. 116-130.

[12]   P. Demestichas, A. Katidiotis, K. A. Tsagkaris, E. F. Adamopoulou and K. P. Demestichas, “Enhancing Channel Estimation in Cognitive Radio Systems by Means of Bayesian Networks,” Wireless Personal Communications, Vol. 49, No. 1, 2009, pp. 87-105. doi:10.1007/s11277-008-9559-1

[13]   C. Tepedelenlioglu, A. Abdi, G. B. Giannakis and M. Kaveh, “Estimation of Doppler Spread and Signal Strength in Mobile Communications with Applications to Handoff and Adaptive Transmission,” Wireless Communications and Mobile Computing, Vol. 1, No. 2, 2001, pp. 221-242. doi:10.1002/wcm.1

[14]   A. F. Molisch, “Wireless Communications,” 2007.

[15]   S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, 2005, pp. 201-220.

[16]   H. V. Poor, “An Introduction to Signal Detection and Estimation,” Springer-Verlag, New York, 1988. doi:10.1007/978-1-4757-3863-6

[17]   S. Kay, “Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)” 1993.

[18]   A. P. Sage and J. L. Melsa, “Estimation Theory with Applications to Communications and Control,” McGrawHill, New York, 1971.

[19]   E. Hossain, D. Niyato and Z. Han, “Dynamic Spectrum Access and Management in Cognitive Radio Networks,” 2009.

[20]   A. He, K. K. Bae, T. R. Newman, J. Gaeddert, K. Kim, R. Menon, L. Morales-Tirado, J. J. Neel, Y. Zhao and J. H. Reed, “A Survey of Artificial Intelligence for Cognitive Radios,” IEEE Transactions on Vehicular Technology, Vol. 59, No. 4, 2010, pp. 1578-1592.

[21]   T. W. Rondeau, “Application of Artificial Intelligence to Wireless Communications,” 2007.

[22]   A. F. Cattoni, M. Ottonello, M. Raffetto and C. S. Regazzoni, “Neural Networks Mode Classification Based on Frequency Distribution Features,” The 2nd International Conference on Cognitive Radio Oriented Wireless Network and Communications, Orlando, 1-3 August 2007, pp. 251-257.

[23]   A. Fehske, J. Gaeddert and J. Reed, “A New Approach to Signal Classification Using Spectral Correlation and Neural Networks,” The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Network, Baltimore, 8-11 November 2005, pp. 144-150.

[24]   Y. Zhao, J. Gaeddert, L. Morales, K. Bae, J. S. Um and J. H. Reed, “Development of Radio Environment Map Enabled Case-and Knowledge-Based Learning Algorithms for IEEE 802.22 WRAN Cognitive Engines,” The 2nd International Conference on Cognitive Radio Oriented Wireless Network and Communications, Orlando, 1-3 August 2007, pp. 44-49.

[25]   Z. Zhang and X. Xie, “Intelligent Cognitive Radio: Research on Learning and Evaluation of CR Based on Neural Network,” The 15th International Conference on Information and Communications Technology, Cairo, 16-18 December 2007, pp. 33-37.

[26]   I. A. Akbar and W. H. Tranter, “Dynamic Spectrum Allocation in Cognitive Radio Using Hidden Markov Models: Poisson Distributed Case,” Proceedings of IEEE, Richmond, 22-25 March 2007, pp. 196-201.

[27]   C. H. Park, S. W. Kim, S. M. Lim and M. S. Song, “HMM Based Channel Status Predictor for Cognitive Radio,” Asia-Pacific Microwave Conference, Bangkok, 11-14 December 2007, pp. 1-4.

[28]   T. R. Newman, B. A. Barker, A. M. Wyglinski, A. Agah, J. B. Evans and G. J. Minden, “Cognitive Engine Implementation for Wireless Multicarrier Transceivers,” Wireless Communications and Mobile Computing, Vol. 7, No. 9, 2007, pp. 1129-1142. doi:10.1002/wcm.486

[29]   L. Yong, J. Hong and H. Y. Qing, “Design of Cognitive Radio Wireless Parameters Based on Multi-Objective Immune Genetic Algorithm,” Vol. 1, WRI International Conference on Communications and Mobile Computing, Yunnan, 6-8 January 2009, pp. 92-96.