Abstract
Spectrum sensing is one of the major challenges for commercial development of cognitive radio systems, since the detection of the presence of a primary user is a complex task that requires high reliability. This work proposes a signal classifier capable of detecting and identifying a primary user signal on a given channel of the radio spectrum. The proposed approach combines eigen-decomposition techniques and neural networks not only to decide about the presence of a primary user, but also to identify the primary user signal type, a feature that is not encountered in the current approaches proposed in literature. Besides the advantage of identifying the primary user type, the proposed method also considerably reduces the computational cost of the detection process. The proposed classification method has been applied to the development of five primary user signal Classification Modules, which includes wireless microphone, orthogonal frequency-division multiplexing and Digital Video Broadcasting-Terrestrial signals. The results show that the proposed classifier correctly detects and identifies the primary users, even under low signal to noise ratio and multipath scenarios.
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References
Staple, G., & Werbach, K. (2004). The end of spectrum scarcity. IEEE Spectrum, 43(3), 48–52. doi:10.1109/MSPEC.2004.1270548.
Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Stockholm: KTH.
Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.
Li, P., Guo, S., Zhuang, W., & Ye, B. (2014). On efficient resource allocation for cognitive and cooperative communications. IEEE Journal on Selected Areas in Communications, 32(2), 264–273.
Wang, Q., Ye, B., Lu, S., & Guo, S. (2014). A truthful QoS-aware spectrum auction with spatial reuse for large-scale networks. IEEE Transactions on Parallel and Distributed Systems, 25(10), 2499–2508.
Wyglinski, A. M., Nekovee, A. M., & Hou, Y. T. (2010). Cognitive radio communications and networks principles and practice. Burlington: Elsevier.
Chen, C.-T. (2013). Linear system theory and design (4th ed.). Oxford: Oxford University Press.
Haykin, S. (1996). Adaptive filter theory. New Jersey: Prentice Hall.
Zeng, Y., & Liang, Y. (2007). Covariance based signal detections for cognitive radio. In IEEE 2nd international symposium on new frontiers in dynamic spectrum access networks.
Zeng, Y., & Liang, Y. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.
Clanton, C., Kenkel, M., Tang, Y. (2007). IEEE 802.22-07/0124r0: Wireless microphone signal simulation method. https://mentor.ieee.org/802.22/dcn/07/22-07-0124-00-0000-wireless-microphone-signal-simulation.doc.
Proakis, J., & Salehi, M. (2002). Communication systems engineering. New Jersey: Prentice-Hall.
European Telecommunications Standards Institute, ETSI EN 300-744: Digital video broadcasting (DVB); framing structure, channel coding and modulation for digital terrestrial television V1.6.1.
Yu, F. R. (2011). Cognitive radio mobile ad hoc networks. New York: Springer.
Akyildiz, I. F., Lo, I. F., & Balakrishnan, B. F. (2009). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4, 40–62.
Chen, K., & Prasad, R. (2009). Cognitive radio networks. Hoboken: Wiley.
Liu, R., & Wang, B. (2011). Cognitive radio networking and security—A game-theoretic view. Cambridge: Cambridge University Press.
Godara, L. C. (2001). Handbook of antennas in wireless communications. Boca Raton: CRC Press.
Krim, H., & Viberg, M. (1996). Two decades of array signal processing research: The parametric approach. IEEE Signal Processing Magazine, 13, 67–94.
Svantesson, T. (2001). Antennas and propagation from a signal processing perspective. Thesis for the degree of Doctor of Philosophy, Technical Report No. 407. Chalmers Reproservice, Sweden.
Bishop, C. (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.
Haykin, S. (1999). Neural networks. A comprehensive foundation. New Jersey: Prentice Hall International.
Signal Processing Information Base. (2006). SPIB channel impulse responses. http://spib.linse.ufsc.br/microwave.html.
Results of the Laboratory Evaluation of an 8 Mhz ADTB-T Television System For Terrestrial Broadcasting. http://www.rthk.org.hk/about/digitalbroadcasting/DSBS/TEEG_REPORT_ADTB_T_3.pdf.
Dikmese, S., J. Wong, S., Gokceoglu, A., Guzzon, E., Valkama, M., & Renfors, M. (2013). Reducing computational complexity of eigenvalue based spectrum sensing for cognitive radio. In IEEE International Conference on Cognitive Radio Wireless Networks (pp. 61–67).
Shibing, Z., Jiaojiao, Y., & Lili, G. (2012). Eigenvalue-based cooperative spectrum sensing algorithm. IEEE International Conference on Instrumentation, Measurement, Computer, Communication and Control. doi:10.1109/IMCCC.2012.92.
Zhiwen, L., Hang, Z., Shaofan, S., & Desheng, Z. (2010). Analysis and improvement of eigenvalue spectrum sensing in cognitive radio networks, 2010. IEEE International Conference on Wireless Communications and Signal. doi:10.1109/WCSP.2010.5633521.
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La Rosa Centeno, L., De Castro, F.C.C., De Castro, M.C.F. et al. Cognitive radio signal classification based on subspace decomposition and RBF neural networks. Wireless Netw 24, 821–831 (2018). https://doi.org/10.1007/s11276-016-1376-y
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DOI: https://doi.org/10.1007/s11276-016-1376-y