Asynchronous cooperative spectrum sensing via sequential detection in fuzzy hypothesis testing under noise uncertainty in cognitive radio networks

Abstract

Spectrum sensing is a critical part in Cognitive radio networks to detect the available frequency resources, and its timeliness and accuracy are extremely important. The conventional detectors usually fail to be robust in the presence of noise uncertainty, hence an asynchronous cooperative spectrum sensing approach based on sequential probability ratio detection in fuzzy hypothesis testing is proposed. In the approach, sequential detection in fuzzy hypothesis testing is done by each cognitive radio to form local decisions, then fusion centre sequentially accumulates the local decisions of cooperative cognitive radios to make a global decision. Simulation results show that the proposed approach can reduce the average number of samples and overcome noise uncertainty when compared with conventional or selective asynchronous cooperative sensing and non‐cooperative sensing.

Vasicek and Van Es entropy‐based spectrum sensing for cognitive radios

“…This deviation results in an inaccuracy in SS, which is independent of the number of samples [22]. Hence, a robust SS technique, which is insensitive to NVU would be advantageous.…”

“…However, the estimated noise variance is observed to deviate from the exact or true value of noise power, especially at low SNR values [21]. This deviation results in an inaccuracy in SS, which is independent of the number of samples [22]. Hence, a robust SS technique, which is insensitive to NVU would be advantageous.…”