Abstract
Adopting accurate and efficient spectrum sensing policy is crucial in allowing cognitive radio users to be aware of the surrounding parameters related to the radio environment characteristics. Especially, in Ad hoc networks scenario, where dynamic spectrum access is highly required, since the most of the spectrum is already assigned statistically, and the unlicensed bands are becoming overcrowded. This is due to the multiplicity of wireless communication technologies that operate in those bands, and the increasing number of connected devices. In this paper, a spectrum sensing algorithm that combines the Support Vector Machines (SVM) supervised learning technique with the Multiple Signal Characterization (MUSIC) subspace method is used. Our ultimate objective is detecting the presence of primary users (technology signals) in the band of interest. The node’s receivers which make up the network collect samples from the radio environment, estimate the number of primary user signals and the corresponding carrier frequencies. Simulations are conducted to demonstrate the efficiency of the proposed SVM based algorithm in detecting the presence of primary users based on lost-detection and false alarm probabilities evaluation.
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
Akyildiz, I., Lee, W.-Y., Chowdhury, K.: Spectrum management in cognitive radio ad hoc networks. IEEE Netw. 23, 6–12 (2009). 10.1109/MNET.2009.5191140
Akyildiz, I.F., Lee, W.-Y., Chowdhury, K.R.: CRAHNs: Cognitive radio ad hoc networks. Ad Hoc Netw. 7, 810–836 (2009). doi:10.1016/j.adhoc.2009.01.001
Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34, 276–280 (1986). doi:10.1109/TAP.1986.1143830
El Gonnouni, A., Martinez-Ramon, M., Rojo-Alvarez, J.L., Camps-Valls, G., Figueiras-Vidal, A.R., Christodoulou, C.G.: A support vector machine music algorithm. IEEE Trans. Antennas Propag. 60, 4901–4910 (2012). doi:10.1109/TAP.2012.2209195
Konishi, S., Kitagawa, G.: Biometrika Trust. 83, 875–890 (2009). doi:10.1093/biomet/81.3.425
Haardt, M., Zoltowski, M.D., Mathews, C.P., Nossek, J.: 2D unitary ESPRIT for efficient 2D parameter estimation. In: 1995 International Conference Acoustics, Speech, Signal Process, vol. 3, pp. 2096–2099 (1995). doi:10.1109/ICASSP.1995.478488
Capon, J.: High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE 57, 1408–1418 (1969). doi:10.1109/PROC.1969.7278
Gabriel, W.F.: Spectral Analysis and Adaptive Army Superresolution Techniques aglortihm. Proc. IEEE 68, 654–666 (1978). doi:10.1109/PROC.1980.11719
Ziskind, I., Wax, M.: Maximum likelihood localization of multiple sources by alternating projection. IEEE Trans. Acoust. 36, 1553–1560 (1988). doi:10.1109/29.7543
Pisarenko, V.F.: Some applications of the maximum likelihood method in seismology. Geophys. J. Roy. Astron. Soc. 21, 307–322 (1970)
El-Behery, I.N., Macphie, R.H.: Maximum likelihood estimation of the number, directions and strengths of point radio sources from variable baseline interferometer data. IEEE Trans. Antennas Propag. 75, 1928–1932 (1978)
Reddi, S.S.: Multiple source location-a digital approach. IEEE Trans. Aerosp. Electron. Syst. 15, 95–105 (1979)
Roy, R., Kailath, T.: ESPRIT - estimation of signal parameters via rotational invaraince techniques. IEEE Trans. Acoust. 37, 984–995 (1989)
Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear Prediction of Chaotic Time Series Using Support Vector Machine. IEEE NNSP (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
El Barrak, S., Lyhyaoui, A., El Gonnouni, A., Puliafito, A., Serrano, S.: Application of MVDR and MUSIC spectrum sensing techniques with implementation of node’s prototype for cognitive radio ad hoc networks. Presented at ICSDE 2017 (2017)
Bienvenu, G., Kopp, L.: Optimality of high resolution array processing using the eigensystem approach. IEEE Trans. Acoust. 31, 1235–1248 (1983)
Ramon, M.M., Xu, N., Christodoulou, C.G.: Beamforming using support vector machines. IEEE Antennas Wirel. Propag. Lett. 4, 439–442 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
El Barrak, S., Lyhyaoui, A., Gonnouni, A.E., Puliafito, A., Serrano, S. (2017). SVM-MUSIC Algorithm for Spectrum Sensing in Cognitive Radio Ad-Hoc Networks. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2017. Lecture Notes in Computer Science(), vol 10517. Springer, Cham. https://doi.org/10.1007/978-3-319-67910-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-67910-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67909-9
Online ISBN: 978-3-319-67910-5
eBook Packages: Computer ScienceComputer Science (R0)