Optimum Cognitive Radio Networks Performance in AWGN using Genetic Algorithm

Cognitive radio adaptively selects the operation frequency and also adjusts robustly its transmitter parameters. The main features of cognitive radios are Cognitive Capabilities and Reconfigurability. This paper is devoted to optimize the cognitive radio networks performance in AWGN using Genetic Algorithm. The optimized parameters includes number of network users, cognitive radios (CR's), Cognitive users, Threshold voltage, and signal to noise ratio (SNR), in order to achieve minimum error rate (ER), and probability of false alarm (Pfa) under different values and maximum probability of detection (PD). The results carry important aspects for optimum cooperative cognitive radio performance. The analysis of the optimized parameters demonstrate that the degradation of the error rate due to increase SNR and the optimum threshold voltage increased with the increase of the SNR which effect significantly on the probability of detection and probability of false alarm.