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Correlation based detectors are very popular for spectrum sensing. In these detectors, temporal correlation is often employed to discriminate the signals of primary users from noises. However, large channel delay spread can significantly decrease the correlation of neighbouring samples of received signals, inducing severe performance degradation of temporal correlation based detectors. In this work, we show that the power spectral densities of different delayed versions of a primary signal have high correlation, despite unknown delays. Then, by exploiting the correlation of power spectral densities of received signals, we propose a spectral-correlation detector. To facilitate the practical application of the proposed detector, we also derive a theoretical expression for its decision threshold to achieve a given false alarm probability. Furthermore, in order to handle the case of channels with unknown delay profile, we employ the OR rule to combine the temporal- and spectral-correlation
With the knowledge of channel occupancy rate (COR), cognitive radios can significantly improve their performance in exploring and exploiting spectrum holes. However, most existing COR estimators suffer from overestimation or underestimation even at high signal-to-noise ratios (SNRs). The iterative threshold-setting algorithm (ITA) is promising to address this issue. In this work, we revisit ITA and provide a thorough theoretical analysis of ITA. First, we prove that ITA converges to the true COR with a sufficiently large number of traffic samples. Then, we investigate its convergence when the number of traffic samples is small, and show that ITA deviates from the true COR especially at low SNRs. To address this issue, we analyze the upper bound of the number of traffic samples required to achieve a certain estimation error, and further propose an improved ITA (iITA). The proposed iITA enables us to achieve a prespecified estimation accuracy by adaptively adjusting the number of traffic