Primary Channel Duty Cycle Estimation Under Imperfect Spectrum Sensing Based on Mean Channel Periods

The emerged Dynamic Spectrum Access (DSA) concept based on Cognitive Radio (CR) is a promising solution to overcome the problems related to frequency spectrum scarcity. In DSA/CR systems, the inactivity patterns of the licensed frequency channels are exploited in an opportunistic and non-interfering manner by unlicensed users. Therefore, the knowledge of the occupancy rate (i.e., duty cycle) of these licensed channels is crucial for boosting the performance of the DSA/CR system. For example, it can help to select the lowest occupied channel which can offer higher opportunistic spectrum to the unlicensed users. Channel Duty Cycle (DC) is a statistical parameter about the activity of the licensed channel in time-domain, which is initially unknown to the DSA/CR system but can be estimated from the outcomes of spectrum sensing. However, spectrum sensing is imperfect in practice due to sensing errors, which in turn will provide incorrect estimation of the channel DC. In this context, this work successfully finds a novel method to accurately estimate the channel DC even under Imperfect Spectrum Sensing (ISS) without requiring any prior knowledge about the licensed channel activity. This is achieved after accurately analysing the impact of ISS on the estimation of the statistical moment (mean) of the channel activity periods, for which a closed form expression is obtained as a function of the true mean, probability of errors and sensing period. The achieved mathematical expression helps to find a novel method to accurately estimate the true mean of the channel activity periods and subsequently the channel DC based on the outcomes of the ISS.