Adaptive Two-stage Spectrum Sensing under Noise Uncertainty in Cognitive Radio Networks

Abstract

To utilize licensed spectrum bands efficiently, spectrum sensing needs to be accurate and fast. The occurrence of noise uncertainty and the lower in received PU signal power due to the distance between the transmitter and the receiver, path loss, are the main challenges that has a great impact on the accuracy of spectrum sensing.

In this paper, we propose a new scheme of two-stage spectrum sensing, “Adaptive Two-stage Spectrum Sensing (ATSS)”, under noise uncertainty environment. ATSS is a modified of a conventional two-stage spectrum sensing where the decision threshold of both stages are adapted on the distance, estimated noise variance and calculated noise uncertainty interval. Therefore, ATSS improves the detection performance of the existing spectrum sensing and is robust to noise uncertainty. The contribution of this paper is three-fold. First, an unreliable detection and wasted stage activation of a conventional two-stage spectrum sensing are reduced. Second, noise uncertainty is addressed. Third, a new parameter, critical distance ( ), is proposed in order to reduce computational burden and sensing time of the first stage.