In this paper the analytical and simulation results of probability of detection and false alarm of a cooperative cognitive radio network are compared under both awgn and Rayleigh fading environment. After getting the confidence level of above 95% from the simulation, a neural network (NN) is trained with simulation data where the analytical result is given as the target of the NN. Finally the results are verified with the profile of MSE (mean square error) of three data set (train, validation and test), regression on data set, confusion matrices and error histogram. Here we use Backpropagation algorithm and Hopfield model, all the results yield error of less than 4.5%. The concept of paper is applicable at fusion center (FC) to make proper judgment of presence of primary user (PU).
To enhance the reliability of long wireless link, a relay is placed between transmitter and receiver which break the link into two parts: transmitter to relay called 1 st hop and relay to receiver called 2 nd hop; such arrangement is called two hop wireless links. In two-hop wireless communication sometimes 1 st and 2 nd hops are affected by different types of fading called asymmetric fading environment. In this paper we consider four different combinations of Rayleigh and Nakagami-m fading on 1 st and 2 nd hop to observe the profile of combined pdf (probability density function) and cdf (cumulative distribution function). With the help of cdf we determine the variation of outage probability of mixed fading. Finally we plot the SER (symbol error rate) against the SNR (signal to noise ratio) of the 2 nd hop for all possible combinations of the two fading taking the SNR of 1 st hop as a parameter. Although recent literature deals with asymmetric fading environment but in this paper instead of details statistical model of such fading we provide the relative scenario of mixed fading on 2-hop wireless link.
<span>The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy <em>c</em>-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.</span>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.