Abstract
This paper presents new spectrum sensing algorithms based on the cumulative power spectral density (CPSD). The proposed detectors examine the CPSD of the received signal to make a decision on the absence/presence of the primary user (PU) signal. Those detectors require the whiteness of the noise in the band of interest. The false alarm and detection probabilities are derived analytically and simulated under Gaussian and Rayleigh fading channels. Our proposed detectors present better performance than the energy (ED) or the cyclostationary detectors (CSD). Moreover, in the presence of noise uncertainty (NU), they are shown to provide more robustness than ED, with less performance loss. In order to neglect the NU, we modified our algorithms to be independent from the noise variance.
Highlights
Due to an increasing demand of wireless devices and the limitation of natural spectrum resources, the cognitive radio (CR) has been introduced to optimize the use of the available spectrum [1]
8 Conclusions In this paper, we proposed spectrum sensing detectors based on the cumulative power spectral density (CPSD)
Our proposed detectors verify the linearity of the CPSD shape of the received signal
Summary
Due to an increasing demand of wireless devices and the limitation of natural spectrum resources, the cognitive radio (CR) has been introduced to optimize the use of the available spectrum [1]. Many factors (such as low signal to noise ratio (SNR), shadowing, channel fading, etc.) lead to a situation where SU is no longer able to correctly diagnose the status of the PU To overcome this problem, new techniques for cooperative spectrum sensing (CSS) have been proposed [2, 3]. This detector performs the spectrum sensing without the knowledge of the cyclic frequencies. The corresponding test statistic combines linearly the autocorrelation measures for different non-zero lags before making a decision on the PU status The performance of this algorithm increases with Ns. In this paper, we propose new spectrum sensing detectors based mainly on the cumulative sum of the power spectral density (PSD) of the received signal. Since w(n) is a circular symmetric process, its DFT W (k) becomes a zero mean circular symmetric process [21–23]
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