Description:
Automatic modulations recognition is one of the most important aspects in cognitive radios (CRs). Unlicensed users or secondary users (SUs) tend to classify the incoming signals to recognize the type of users in the system. Once the available users are detected and classified accurately, the CR can modify his transmission parameters to avoid any interference with the licensed users or primary users (PUs). In this paper, we propose a deep learning technique to detect the modulations schemes used in a number of sampled transmissions. This approach uses a deep neural network that consists of a large number of convolutional filters to extract the distinct features that separate the various modulation classes. The training is performed to improve the overall classification accuracy with a major focus on the misclassified classes. The results demonstrate that our approach outperforms the recently proposed Convolutional, Long Short Term Memory (LSTM), Deep Neural Network (CLDNN) in terms of overall classification accuracy. Moreover, the classification accuracy obtained by the proposed approach is greater than the CLDNN algorithm at the highest signal-to-noise ratio used.