Abstract
The actual implementation of an intelligent system that can well manage and utilize the scarce spectrum is a major difficulty towards cognitive radio deployment. By integrating spectrum usage characteristics in Uganda, we develop a hybrid protocol to select optimal channels for use by the cognitive radio. It uses physical layer characteristics of signal to interference and noise ratio and interference power to legacy users to achieve a higher layer goal of maximizing network throughput. The fuzzy logic approach effectively reduces the protocol stack to a hybrid form that considers only the parameters that directly impact on the desired goal. The multiple pertinent variables can be suitably represented in a common linguistic language and solved as a multi-objective optimization problem. The resulting hybrid protocol shows high efficiency in selecting the channel while also maximizing the network throughput.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
FCC. ET Docket No. 03-322 Notice of Proposed Rule Making and Order (2003)
Akyildiz, I.F., Lee, W.Y., Vuran, M.C., Mohanty, S.: Cognitive radio communications and networks. IEEE Commun. Mag. 46, 40–48 (2008)
Hossain, E., Niyato, D., Han, Z.: Dynamic Spectrum Access and Management in Cognitive Radio Networks, pp. 35, 186, 310. Cambridge University Press, Cambridge (2009)
Kagarura, G.M., Okello, D.K., Akol, R.N.: Evaluation of spectrum occupancy: a case for cognitive radio in uganda. In: IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks (MSN) (2013)
Kawadia, V., Kumar, P.R.: A cautionary perspective on cross-layer design. IEEE Wirel. Commun. 12(1), 3–11 (2005)
Baldo, N., Zorzi, M.: Fuzzy logic for cross-layer optimization in cognitive radio networks. IEEE Commun. Mag. 46, 64–71 (2008)
Fangwen, V.M., Schaar, D.: Learning for cross-layer optimization. In: Proceedings of Cognitive Information Processing, Santorini, Greece, pp. 69–74. (2008)
Mahajan, R.: Cross layer optimization: system design and simulation methodologies. Masters Thesis. Virginia Polytechnic Institute and State University, USA (2003)
Sooriyabandara, M., Quadri, S. (eds.): Adaptive reconfigurable access and generic interfaces for optimization in radio networks. ARAGORN (2008)
Kolar, V., Mahonen, P., Petrova, M., Sooriyabandara, M., Riihiarvi, J., Farnham, T.: A case for generic interfaces in cognitive radio networks. In: ICT- Mobile Summit Conference Proceedings (2009)
Razzaque, M.A., Dobson, S., Nixon, P.: Cross-layer architectures for autonomic communications. J. Netw. Syst. Manage. 15(1), 13–27 (2006)
Wang, J., Korhonen, T., Zhao, Y.: Cross layer optimization for fairness balancing based on adaptively weighted utility functions in OFDMA systems. In: World Academy of Science, Engineering and Technology (2007)
Ding, L., Melodia, T., Batalama, S.N., Matyjas, J.D., Medley, M.J.: Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Trans. Veh. Technol. 10(10) (2010)
Bogatinovski, M., Gavrilovska, L.: Overview of cross-layer optimization methodologies for cognitive radio. In: 16th Telecommunications Forum TELFOR, pp. 254–257 (2008)
Matinmikko, M., Rauma, T., Mustonen, M., Harjula, I., Sarvanko, H., Mamella, A.: Application of fuzzy logic to cognitive radio systems. IEICE Trans. Commun. E92-B(12), 3572–3580 (2009)
Tabakovic, Z., Grgic, S.: Fuzzy logic power control in cognitive radio. In: 16th International Conference on Systems, Signals and Image Processing, pp. 1–5 (2009)
Ma, M., Tsang, D.H.K.: Cross-layer throughput optimization in cognitive radio networks with SINR constraints. IJDMB, 2010, 13pp
Shi, Y., Kompella, Y.T., Sherali, H.D.: Maximizing capacity in multihop cognitive radio networks under the SINR model. IEEE Trans. Mob. Comput. 10(7), 954–967 (2011)
Ejaz, W., Hasan, N.U., Awais, M., Kim, H.S.: Improved local spectrum sensing for cognitive radio networks. EURASIP J. Adv. Signal Process. (2012)
Ozhathil, D.G.: A fuzzy Logic based channel allocation scheme for cognitive radio networks. Masters thesis, Makerere University, College of Engineering, Design, Art and Technology (Unpublished) (2014)
Ross, T.J.: Decision making with fuzzy information. In: Fuzzy Logic with Engineering Applications. pp. 320–323. Wiley, New York (2004)
Mueck, M., Nokia, R.C., Piipponen, A., Kalliojarvi, K., Dimitrakopoulos, G., Tsagkaris, K., Demestichas, P., Casadevall, F., Perez-Romero, J., Sallent, O., Baldini, G., et al.: ETSI reconfigurable radio systems: status and future directions on software defined radio and cognitive radio standards. Commun. Mag. IEEE, vol. 48, no. 9, pp. 78–86 (2010)
Cordeiro, C., Challapali, K., Birru, D.: IEEE 802. 22: an introduction to the first wireless standard based on cognitive radios. Networks 1(1), 38–47 (2006)
Song, Y., Xie, J.: On the Spectrum Handoff for Cognitive Radio Ad Hoc Networks without Common Control. Spectr. [arXiv] (2011)
Acknowledgements
We acknowledge the Millennium Science Initiative (MSI) for the research scholarship in Adaptive Bandwidth Management and equipment used for spectral analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ozhathil, D.G., Kagarura, G.M., Okello, D.K., Akol, R.N. (2015). Towards a Practical Cognitive Channel Allocation Scheme. In: Nungu, A., Pehrson, B., Sansa-Otim, J. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-16886-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-16886-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16885-2
Online ISBN: 978-3-319-16886-9
eBook Packages: Computer ScienceComputer Science (R0)