Custom ANN: An Approach for Efficient Modulation Classification

Modulation is a fundamental technique for transmitting signals between a transmitter and receiver in wired and wireless scenarios. In a non-cooperative communication scenario, the receiver should be capable of classifying the modulation type efficiently. Automatic Modulation Classification (AMC) is designed to monitor the radio-frequency spectrum and for making effective transmission decisions. Earlier AMC methods relied on feature extraction and likelihood-based approaches. However, with the rise of Deep Learning (DL), AMC has gained momentum due to its superior predictive capabilities. In this work, we propose a custom artificial neural network (ANN) for modulation classification. We analyze the impact of a deep learning model on AMC and how different modulation techniques influence its accuracy.