Abstract
The main function of Cognitive Radio network is to sense the spectrum band to check whether the primary user is present or not in a given spectrum band at a place. One of the most efficient ways of spectrum sensing technique is cyclostationary feature detection. Though the computational complexity is very high in case of cyclostationary feature detection, still it is very effective in case of unknown level of noise. Here, spectral correlation function (SCF) of the received signal is determined. In this paper, SCF is calculated using Hanning, Hamming and Kaiser windows and also we have determined the cyclic periodogram of the input signal. In each case, we have considered Nakagami-m channel fading. Likelihood ratio test has been performed by varying the parameters of the channel fading distribution. Finally, the effects of the windows have been studied in the numerical section.
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Ghosh, D., Bagchi, S. (2015). Cyclostationary Feature Detection Based Spectrum Sensing Technique of Cognitive Radio in Nakagami-m Fading Environment. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_20
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DOI: https://doi.org/10.1007/978-81-322-2208-8_20
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