Identify the Primary Signal Buried Under Impulsive Noise Channel in Cognitive Radio Networks Using MAF-KF-NPD

The sensing method of spectrum resources in wireless networks has become a very special challenge for the future generations of mobile communications, such as: the radio frequencies (RF) and the millimeter waves 5G. However, the awareness of the current spectral situation is very important in wireless networks and this to identify the spectrum scarcity with accurately. In this paper, we propose an original contribution in the spectrum sensing context for the cognitive radio networks (CRNs). The sensing of the primary signals for each primary base station (PBS) in the CRNs can be very difficult when the Gaussian noise of the channel becomes impulsive noise, i.e. the noise is non-stationary. To remedy this issue and optimized the Conventional Sensing Methods (CSMs), the Moving Average Filter (MAF) and the Kalman Filter (KF) have been used. In addition, to quantify the primary signal sensing, the Neyman-Pearson Detector (NPD) is proposed. Finally, the sensing parameter optimizations under an impulsive noise channel are confirmed by the numerical results.