Levenberg marquedet lion based artificial neural network for cooperative spectrum sensing in cognitive radio

Cognitive Radio (CR) is a promising wireless communication system that allows the user in wireless environment to communicate with wide band of frequencies. Spectrum sensing is the most vital function of CR that plays significant role in identifying the available channel in CR environment. Hence, this work introduces the novel cooperative spectrum sensing approach based on the proposed optimal fusion score that enhances spectrum sensing performance. The optimal fusion score finds the required channel availability by training the proposed Levenberg Marquedet (LM), and the Lion Optimization Algorithm (LOA) based Neural Network (LML-NN) classifier and the fuzzy classifier. The inputs for the classifier training are test statistics based on the Energy Detection (ED), and the Generalized Likelihood Ratio Test (GLRT). These test statistics are derived using the eigenvalues extracted from a received signal covariance matrix. The performance analysis of the system is done by varying the number of sensors in CR and threshold values. The system performance is quantified in terms of probability of detection, probability of false alarm and receiver operating curves. The simulation results confirm that proposed LML-NN algorithm has achieved an improved probability of detection and reduced false alarm rate as compared to the existing eigenvalue based CSS model.