Automatic Modulation Classification for CR Using Deep Learning

Automatic Modulation Classification holds significant importance in the realm of wireless communication systems. The proliferation of modulation schemes, coupled with the escalating complexity of channel environments, necessitates a heightened accuracy in discerning modulated signals. Traditional methods struggle to effectively classify this array of modulation schemes. In response, this paper advocates the employment of deep learning techniques for precise modulation classification. A deep learning architecture is introduced, comprising a convolutional neural network, and a long short-term memory neural network leveraging the power of transfer learning. The convolutional neural network adeptly extracts spatial features from the received signal, while a bidirectional long short-term memory neural network is engineered to capture temporal features. Transfer learning is employed for efficient parameter sharing. Empirical evaluations were conducted using the RadioML2016.10b dataset, affirming the efficacy of the proposed mechanism.