Neural Network Framework for Modulation and Signal Classification

The advancements in wireless communications have catapulted the usage of wireless technologies for plethora of use cases. This has led to large scale usage of different wireless signals for different tasks. The need to identify any particular signal is essential for further processing and information retrieval. Therefore, detecting the type of signal and modulation is crucial before we start processing it and working with it. In this paper, we propose a novel neural network that performs this task for us. We have proposed a model with low complexity, which produces exceptional accuracy on the dataset. Our model offers 99% prediction accuracy for modulation and signal classification for data having SNR. Besides, the number of parameters involved is also substantially low. Proposed model outperforms previous state of the art models in terms of accuracy and computational complexity.