Abstract
In this paper we propose and investigate a novel technique to enhance the performance of parametric classifiers for cognitive radio spectrum sensing application under slowly fading Rayleigh channel conditions. While trained conventional parametric classifiers such as the one based on K-means are capable of generating excellent decision boundary for data classification, their performance could degrade severely when deployed under time varying channel conditions due to mobility of secondary users in the presence of scatterers. To address this problem we consider the use of Kalman filter based channel estimation technique for tracking the temporally correlated slow fading channel and aiding the classifiers to update the decision boundary in real time. The performance of the enhanced classifiers is quantified in terms of average probabilities of detection and false alarm. Under this operating condition and with the use of a few collaborating secondary devices, the proposed scheme is found to exhibit significant performance improvement with minimal cooperation overhead.
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
Preview
Unable to display preview. Download preview PDF.
References
Haykin, S.: Cognitive Radio: Brain-empowered Wireless Communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)
Akyildiz, I.F., Lo, B.F., Balakrishnan, R.: Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey. Phys. Commun. J. 4(1), 40–62 (2011)
Yucek, T., Huseyin, A.: A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications. IEEE Commun. Surv. Tut. 11(1), 116–130 (2009)
Awe, O.P., Zhu, Z., Lambotharan, S.: Eigenvalue and support vector machine techniques for spectrum sensing in cognitive radio networks. In: Conf. Technol. Appl. Artif. Intell., Taipei, Taiwan, pp. 223–227 (2013)
Thilina, K., Saquib, N., Hossain, E.: Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks. IEEE J. Sel. Areas Commun. 31(11), 2209–2221 (2013)
Awe, O.P., Naqvi, S.M., Lambotharan, S.: Variational bayesian learning technique for spectrum sensing in cognitive radio networks. In: 2nd IEEE Glob. Conf. Signal Inf. Process., Atlanta, GA, USA, pp. 1353–1357 (2014)
Bkassiny, M., Li, Y., Jayaweera, S.K.: A Survey on Machine Learning Techniques in Cognitive Radios. IEEE Commun. Surv. Tut. 15(3), 1136–1159 (2013)
Biao, C., Ruixiang, J., Kasetkasem, T., Varshney, P.K.: Fusion of decisions transmitted over fading channels in wireless sensor networks. In: Conf. Rec. Thirty-Sixth Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, USA, pp. 1184–1188 (2002)
Wang, T., Song, L., Han, Z., Saad, W.: Overlapping coalitional games for collaborative sensing in cognitive radio networks. In: IEEE Wirel. Commun. Netw. Conf., Shanghai, China, pp. 4118–4123 (2013)
Kondareddy, Y., Agrawal, P.: Enforcing cooperative spectrum sensing in cognitive radio networks. In: IEEE Glob. Tel. Conf., Houston, USA, pp. 1–6 (2011)
Liang, Y., Zeng, Y.: Sensing-Throughput Tradeoff for Cognitive Radio Networks. IEEE Trans. Wirel. Commun. 7(4), 1326–1337 (2008)
Urkowitz, H.: Energy Detection of Unknown Deterministic Signals. Proc. IEEE 55(4), 523–531 (1967)
Liu, Y., Ning, P., Dai, H.: Authenticating primary users’ signals in cognitive radio networks via integrated cryptographic and wireless link signatures. In: IEEE Symp. Secur. Priv., Oakland, CA, USA, pp. 286–301 (2010)
Gerzaguet, R., Ros, L.: Self-adaptive stochastic rayleigh flat fading channel estimation. In: 18th Int. Conf. Digit. Signal Process., Fira, Greece, pp. 1–6 (2013)
Kim, S.J., DallAnese, E., Giannakis, G.B.: Cooperative Spectrum Sensing for Cognitive Radios Using Kriged Kalman Filtering. IEEE J. Sel. Top. Signal Process. 5(1), 24–36 (2011)
Ros, L., Simon, E.P., Shu, H.: Third-order Complex Amplitudes Tracking Loop for Slow Flat Fading Channel Online Estimation. IET Commun. J. 8, 360–371 (2014)
Baddour, K.E., Beaulieu, N.C.: Autoregressive Modeling for Fading Channel Simulation. IEEE Trans. Wirel. Commun. 4(4), 1650–1662 (2005)
Kay, S.M.: Fundamentals of Statistical Signal Processing, Vol. II: Detection Theory.Signal Process, Up. Saddle River, NJ Prentice (1998)
Dent, P., Bottomley, G.E., Croft, T.: Jakes Fading Model Revisited. Electron. Lett. 29(13), 1162–1163 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Awe, O.P., Naqvi, S.M., Lambotharan, S. (2015). Kalman Filter Enhanced Parametric Classifiers for Spectrum Sensing Under Flat Fading Channels. In: Weichold, M., Hamdi, M., Shakir, M., Abdallah, M., Karagiannidis, G., Ismail, M. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-319-24540-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-24540-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24539-3
Online ISBN: 978-3-319-24540-9
eBook Packages: Computer ScienceComputer Science (R0)