Signal Detection of Co-Operative Cognitive Radio Network under Neural Network

Abstract

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).

Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

“…The next part of the experiment deals with FIS. The signal vectors correspoding to section 2.2 are shown in Table 4 for both H 0 and H 1 using 16-QAM signal with AWGN and Rayleigh fading of [30] at FC, and simulation is done 500 times for each hypothesis and only 9 of them are shown. The verification of Fuzzy rules is carried out against H 0 and H 1 with three numerical values for vector V as V 1 = [1 0.0198 0.0588 0.1806] and Output ≈ 0 (H 0 ); V 2 = [1 0.8039 0.6069 0.4168] and Output ≈ 1 (H 1 ); and V 3 = [0.6082 0.1989 1 0.3649] and Output ≈ 0 (H 0 ), respectively.…”