BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Abstract

SummaryThe vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy‐stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought‐in to create an optimal energy solution and bivariate Lévy‐stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi‐taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal‐to‐noise ratio is accomplished to the extent.