A novel D-CNL-R classifier approach for automatic modulation classification

Automatic Modulation Classification (AMC) is a significant decision-making process in non-cooperative, 5G, and beyond communication systems. Advancements in Artificial Intelligence (AI) led to the implementation of Deep Learning (DL) to provide superior performance over the feature extraction and offline training process of AMC. In this work, we proposed a hybrid modulation classification architecture by integrating Convolutional Neural Networks (CNN) with ML classifiers such as Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). The Radio Frequency Signal Classification (RFSC) dataset consists of data collected under different Signal-to-Noise Ratio (SNR) scenarios to analyze the resilience of the classifiers. Among all architectures, the Deep Convolutional Layer based RF (D-CNL-R) model achieved superior modulation recognition accuracy due to its enhanced capability to learn complex nonlinear feature distributions. We observed that the training overhead of the proposed D-CNL-R reduced to $\simeq 1\times$ with better classification accuracy performance. We also presented an experimental approach for the prediction performance of real-time signals for indoor and outdoor scenarios.