Accurate estimation of primary user traffic based on periodic spectrum sensing
Ahmed Al-Tahmeesschi, Miguel López‐Benítez, Janne J. Lehtomäki, Kenta Umebayashi · 2018
An accurate estimation of the primary statistics is essential for Cognitive Radio (CR) systems. This knowledge can be exploited to enhance CR performance and reduce the interference with the primary users. In this work, we propose a method based on the Method of Moments (MoM) to improve the distribution estimation. A Modified Method of Moments (MMoM) with a correction factor is proposed to improve the estimation of moments and thus the resulting primary distribution. The simulation and experimental results show that the MMoM approach is notably more accurate. Finally, we study the importance of having a sufficiently large sample space and the effect of sample size on the moments and the primary distribution estimation.