Moghimi F, Nasri A, Schober R (2011)
Publication Type: Journal article
Publication year: 2011
Book Volume: 59
Pages Range: 1934-1945
Article Number: 5772061
Journal Issue: 7
DOI: 10.1109/TCOMM.2011.051311.090588
In cognitive radio (CR) systems, reliable spectrum sensing techniques are required in order to avoid interference to the primary users of the spectrum. Whereas most of the existing literature on spectrum sensing considers impairment by additive white Gaussian noise (AWGN) only, in practice, CRs also have to cope with various types of nonGaussian noise such as manmade impulsive noise, cochannel interference, and ultrawideband interference. In this paper, we propose an adaptive Lp-norm detector which does not require any a priori knowledge about the primary user signal and performs well for a wide range of circularly symmetric nonGaussian noises with finite moments. We analyze the probabilities of false alarm and missed detection of the proposed detector in Rayleigh fading in the low signaltonoise ratio regime and investigate its asymptotic performance if the number of samples available for spectrum sensing is large. Furthermore, we consider the deflection coefficient for optimization of the Lp-norm parameters and discuss its connection to the probabilities of false alarm and missed detection. Based on the deflection coefficient an adaptive algorithm for online optimization of the L p-norm parameters is developed. Analytical and simulation results show that the proposed Lp-norm detector yields significant performance gains compared to conventional energy detection in nonGaussian noise and approaches the performance of the locally optimal detector which requires knowledge of the noise distribution. © 2011 IEEE.
APA:
Moghimi, F., Nasri, A., & Schober, R. (2011). Adaptive Lp-Norm spectrum sensing for cognitive radio networks. IEEE Transactions on Communications, 59(7), 1934-1945. https://doi.org/10.1109/TCOMM.2011.051311.090588
MLA:
Moghimi, Farzad, Amir Nasri, and Robert Schober. "Adaptive Lp-Norm spectrum sensing for cognitive radio networks." IEEE Transactions on Communications 59.7 (2011): 1934-1945.
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