Abstract
This paper proposes enhancements to the channel(-state) estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.
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References
European Radiocommunications Committee (ERC) (2002). European table of frequency allocations and utilizations frequency range 9 kHz to 275 GHz. ERC Report 25, January 2002.
Mitola J., Maguire G. Jr. (1999) Cognitive radio: making software radios more personal. IEEE Personal Communications Magazine 6(6): 13–18
Haykin S. (2005) Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas In Communications 23(2): 201–220
Balamuralidhar P., Prasad R. (2008) A context driven architecture for cognitive radio nodes. Wireless Personal Communications 45(3): 423–434
End-to-End Efficiency (E3) project (2008). https://ict-e3.eu/.
Demestichas, P., Boscovic, D., Stavroulaki, V., Lee, A., & Strassner, J. (2006). m@ANGEL: Autonomic management platform for seamless wireless cognitive connectivity. IEEE Communications Magazine, 44(6).
Strassner, J. (2005). Policy-based network management: Solutions for the next generation. Morgan Kaufmann (Series in networking).
Neapolitan R.E. (2003) Learning Bayesian networks. Prentice-Hall, NJ
Pearl J. (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Francisco
Jensen F. (2001) Bayesian networks and decision graphs. Springer-Verlag, NY, USA
Qi Jiang, Joachim Speidel, Chunming Zhao (2008) A joint OFDM channel estimation and ici cancellation for double selective channels. Wireless Personal Communications 45(1): 131–143
Morelli M., Mengali U. (2001) A comparison of pilot-aided channel estimation methods for OFDM systems. IEEE Transactions on Signal Processing 49(12): 3065–3073
Heath R.W., Giannakis G.B. (1999) Exploiting input cyclostationarity for blind channel identification in OFDM systems. IEEE Transactions on Signal Processing 47(3): 848–856
Zhou S., Giannakis G.B. (2001) Finite-alphabet based channel estimation for OFDM and related multicarrier systems. IEEE Transactions on Communications 49(8): 1402–1414
Zhou S., Muquet B., Giannakis G.B. (2002) Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM. IEEE Transactions on Signal Processing 50(5): 1215–1228
Petropulu A., Zhang R. (2004) Blind OFDM channel estimation through simple linear precoding. IEEE Transactions on Wireless Communications 3(2): 647–655
Rashad, I., Budiarjo, I., & Nikookar, H. (2007). Efficient pilot pattern for OFDM-based cognitive radio channel estimation—Part 1. In Communications and Vehicular Technology in the Benelux, 2007 14th IEEE Symposium on (pp. 1–5, 15–15). November 2007.
Budiarjo, I., Rashad, I., & Nikookar, H. (2007). Efficient pilot pattern for OFDM-based cognitive radio channel estimation—Part 2. In Communications and Vehicular Technology in the Benelux, 2007 14th IEEE Symposium on (pp.1–5, 15–15). November 2007.
Soysal, A., Ulukus, S., & Clancy, C. (2008). Channel estimation and adaptive M-QAM in cognitive radio links. In Proc. IEEE International Conference on Communications (ICC) 08’, Beijing, China, 19th–23rd May, 2008.
Heckerman, D. (1995). A tutorial on learning with Bayesian networks. In Report No. MSR-TR-95–06, Microsoft Research.
Tetko I.V., Livingstone D.J., Luik A.I. (1995) Neural network studies. 1. Comparison of overfitting and overtraining. Journal of Chemical Information and Computer Sciences 35: 826–833
Khanafer, R., Moltsen, L., Dubreil, H., Altman, Z., & Barco, R. (2006). A Bayesian approach for automated troubleshooting for UMTS networks. In Proc. 17th IEEE Int’l Symp. Personal, Indoor and Mobile Radio Comm. (PIMRC ’06), August 2006.
Barco R., Wille V., Dı’ez L. (2005) System for automated diagnosis in cellular networks based on performance indicators. European Trans. Telecommunications 16(5): 399–409
Barco, R., Lázaro, P., Díez, L., & Wille, V. (2008). Continuous versus discrete model in auto-diagnosis systems for wireless networks. IEEE Transactions on Mobile Computing, in press.
Koutsorodi A., Adamopoulou E., Demestichas K., Theologou M. (2007) Service configuration and user profiling in 4G terminals. Wireless Personal Communications 43(4): 1303–1321
Demestichas, K., Koutsorodi, A., Adamopoulou, E., & Theologou, M. (2007). Modelling user preferences and configuring services in B3G devices. Wireless Networks in press.
Bauer, E., Koller, D., & Singer, Y. (1997). Update rules for parameter estimation in Bayesian networks. In Proceedings of the 13th Annual Conference on Uncertainty in AI (1997), pp. 3–13.
Zhang, S. Z., Yu, H., Ding, H., Yang, N. H., & Wang, X. K. (2003). An application of online learning algorithm for Bayesian network parameter. In 2003 International Conference on, Machine Learning and Cybernetics, Vol. 1, pp. 153–156, November 2003.
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Demestichas, P., Katidiotis, A., Tsagkaris, K.A. et al. Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks. Wireless Pers Commun 49, 87–105 (2009). https://doi.org/10.1007/s11277-008-9559-1
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DOI: https://doi.org/10.1007/s11277-008-9559-1