Enhancing the Performance of Spectrum Mobility in Cognitive Radio Local Area Networks Using KF-ABF-SRE Estimators

This paper aims at improving the performance of spectrum mobility in cognitive radio local area networks under a congested environment. Due to the atmospheric turbulence and random effects of fading and multipath shading, the nature of the propagation channel could be affected. For this, the link with the smallest SNR can be considered. The main purpose of the present paper is to enhance the SNR link and the end-to-end throughput, and to reduce the expected transmission time from the primary base station towards the secondary user under the used spectrum, while considering that the secondary user is in motion. To meet these objectives, three algorithms have been suggested, namely the Kalman Filter, the Alpha–Beta Filter and the Simple Recursive Estimator. The Kalman Filter and the Alpha–Beta Filter have been particularly used to estimate the path of a secondary user node, while the Simple Recursive Estimator has been employed to get better primary signal sensing in a congested environment. In the end, the simulation results obtained allowed demonstrating the effectiveness of the proposed algorithms. Note that the average expected total transmission time could be reduced to 4.1827 s, while the mean end-to-end throughput reached the value 3.67 kbps.