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In cognitive radio systems, the random nature of primary users’ activities heavily influences the performance of spectrum sensing techniques. Considering both of the primary user random arrival and departure in one model is essential for mitigating the performance degradation in spectrum sensing. In this paper, we apply the probability based weighting energy detector (ED) to overcome the effect of random arrival and departure of primary user (PU), on a single carrier-frequency division multiple access (SC-FDMA) signal used in LTE uplink physical channel. Then compare the performance of weighted ED to the second order Cyclostationarity detector with/without the random arrival and departure of primary user signal during the secondary user’s sensing period. The simulation shows that the weighting ED approaches the performance of Cyclostationarity detector in the normal case, where there are no random activities of primary user signals during the secondary user’s sensing period, with less computational complexity. Moreover, the weighted ED outperforms the Cyclostationarity detector in the case of random arrival and departure of PU.

Energy vs. Cyclostationarity-based Detection of Random Arrival and Departure of LTE SC-FDMA Signals for Cognitive Radio Systems