RF Signal Automatic Modulation Classifier Using AI: Technical Review and Implementation Methodology

Electronic Warfare (EW) and Signal Intelligence (SIGINT) have become increasingly vital components in modern military operations.Despite this, research in artificial intelligence (AI) has predominantly focused on image recognition and classification, particularly following the success of AlexNet in the field of computer vision.In the short term, advances in AI-powered communications are expected to enable the rapid detection and analysis of adversarial SIGINT, even under harsh EW conditions.In the long term, the deployment of AI-driven auto-encoding and decoding systems could significantly reduce the manpower required for spectrum sensing missions while improving operational speed and accuracy.The primary objective of this study is to examine the evolution of automatic modulation classification (AMC) technologies and to compare the performance of traditional expert feature-based methods with that of recent CNN-based automatic feature extraction approaches.Notably, deep learning-based classifiers exhibit superior performance, particularly in low signal-to-noise ratio (SNR) environments where conventional approaches degrade significantly.Furthermore, this study emphasizes the necessity of constructing a military-specific RF signal learning infrastructure-referred to as Military RadioNet-modeled after ImageNet in the field of computer vision.Such an infrastructure is expected to serve as a foundational platform for the future development of RF recognition technologies tailored to tactical military operations, and to contribute to enhancing the situational awareness, decision-making capabilities, and overall combat effectiveness of modern armed forces.