Abstract
This paper demonstrates a predictive channel selection method by implementing it in software-defined radio (SDR) platforms and measuring the performance using over-the-air video transmissions. The method uses both long term and short term history information in selecting the best channel for data transmission. Controlled interference is generated in the used channels and the proposed method is compared to reference methods. The achieved results show that the predictive method is a practical one, able to increase the throughput and reduce number of collisions and channel switches by using history information intelligently.
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References
Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Area Comm. 23, 201–220 (2005)
Clancy, T.C., Walker, B.D.: Predictive dynamic spectrum access. In: SDR Forum Technical Conference, Orlando (2006)
López-Benitez, M., Casadevall, F.: An overview of spectrum occupancy models for cognitive radio networks. In: Casares-Giner, V., Manzoni, P., Pont, A. (eds.) Networking Workshops 2011. LNCS, vol. 6827, pp. 32–41. Springer, Heidelberg (2011)
Gabran, W., Liu, C.H., Pawelczak, P., Cabric, D.: Primary user traffic estimation for dynamic spectrum access. IEEE J. Sel. Area Comm. 31, 544–558 (2013)
Höyhtyä, M., Pollin, S., Mämmelä, A.: Improving the performance of cognitive radios through classification, learning, and predictive channel selection. Adv. Electron. Telecommun. 2, 28–38 (2011)
Kahraman, B., Buzluka, F.: A novel channel handover strategy to improve the throughput in cognitive radio networks. In: International Wireless Communications and Mobile Computing Conference, pp. 107–112 (2011)
Zhang, C., Shin, K.G.: What should secondary users do upon incumbents return? IEEE J. Sel. Area Comm. 31, 417–428 (2013)
Shokri-Ghadikolaei, H., Fischione, C.: Analysis and optimization of random sensing order in cognitive radio networks. IEEE J. Sel. Area Comm. 33, 803–819 (2015)
Höyhtyä, M., Vartiainen, J., Sarvanko, H., Mämmelä, A.: Combination of short term and long term database for cognitive radio resource management. In: 3rd International Symposium on Applied Sciences and Communication Technologies, Rome (2010)
Höyhtyä, M., Sarvanko, H., Vartiainen, J.: Method and device for selecting one or more resources for use from among a set of resources. U. S. Pat. Appl. US20130203427 A1 (2013)
Jing, X., Mau, S.-C., Raychaudri, D., Matyas, R.: Reactive cognitive algorithms for co-existence between 802.11b and 802.16a networks. In: IEEE Global Telecommunications Conference, St. Louis, pp. 2465–2649 (2005)
Feng, S., Zhao, D.: Supporting real-time CBR traffic in a cognitive radio sensor network. In: IEEE Wireless Communications and Networking Conference, Sydney, (2010)
Ettus Research. http://www.ettus.com/
National Instruments. http://www.ni.com/
Paaso, H., Mämmelä, A., Patron, D., Dandekar, K.R.: DoA estimation through modified unitary MUSIC algorithm for CRLH leaky-wave antennas. In: 24th International Symposium on Personal Indoor and Mobile Radio Communications, London, pp. 311–315 (2013)
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Höyhtyä, M., Korpi, J., Hiivala, M. (2016). Predictive Channel Selection for over-the-Air Video Transmission Using Software-Defined Radio Platforms. In: Noguet, D., Moessner, K., Palicot, J. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-319-40352-6_47
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DOI: https://doi.org/10.1007/978-3-319-40352-6_47
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