Analysing Primary Signal Sensing Test in Cognitive Radio Networks Using an Alpha-Beta Filter and a Neyman-Pearson Detector

Abstract

The signal strength sensing in the context of cognitive radio networks (CRNs), is very important to predict the primary signal of base station (PBS), particularly when the secondary user (SU) is in a congested environment, and also when is in motion towards the end of coverage of PBS. However, this article presents an analysis on the prediction of primary signal strength in CRNs using an Alpha-Beta Filter (ABF) and a Neyman-Pearson Detector (NPD). The challenge of this contribution is based on a realistic sensing of primary signal strength and to do that, we have assumed that the reporting channels between the SU and the PBS are composited with the shadowing and multipath fading (SMF), and the receiver noise has also added. In this regard, the obtained results were discussed through: the signal-to-noise ratio (SNR) uncertainty, the detection probability (PD) and the False Alarm Probability (PFA), where the average relative error of prediction for the PD will be equal to10-5.