Comparison of Selected Estimation Methods of Interference Level in Radio Channel

In the dynamic landscape of wireless communication, spectrum sensing algorithms playa crucial role in the evolution of Cognitive Radio Networks (CRNs). As CRNs adapt dynamically to varying spectrum conditions, robust spectrum sensing becomes pivotal. In the realm of spectrum sensing, accurate estimation of noise power is paramount for reliable decision-making. Three distinct methods for estimating noise power are: Daubechies wavelet-based methods, Bayesian methods and Median filtering which is, a non-parametric method, proves effective in estimating noise power by analyzing the central tendency of signal samples. The forthcoming analysis will contribute to the understanding of spectrum sensing algorithms within the context of CRN s, aiding in the selection of the most effective method for real-world applications.