Automatic Modulation Classification via Green Machine Learning

TL;DR

This paper introduces GAMC, an interpretable, robust, and lightweight machine learning approach for automatic modulation classification that performs well under noisy conditions and is suitable for edge AI.

Contribution

The paper presents GAMC, a novel multi-stage method combining feature extraction, supervised feature learning, and context-aware routing for efficient AMC.

Findings

GAMC reduces model parameters by 50%.

It operates at 3% to 42% of the computational cost of lightweight deep models.

GAMC maintains higher accuracy across various SNRs.

Abstract

In this work, we propose an interpretable, robust, and lightweight machine learning method for automatic modulation classification (AMC) under dynamic and noisy channel conditions. It is called green automatic modulation classification (GAMC) and targets edge artificial intelligence (AI) with low computational complexity and a small model size. GAMC operates in four stages. First, raw received I/Q signals are transformed into multi-domain representations, including constellation diagrams and spatio-temporal graphs. Second, we extract a comprehensive set of statistical and topological features from time-series signals, constellation diagrams, and graphs. Third, a supervised feature learning process leverages label guidance to project high-dimensional features into robust, discriminative low-dimensional ones. Finally, a context-aware Signal-to-Noise Ratio (SNR) soft routing mechanism ensembles predictions from downstream classifiers. Experimental results show that GAMC effectively mitigates domain shifts caused by high noise. It strikes a good balance between accuracy and efficiency, reducing the number of model parameters by $50\%$, operating at $3\%$ to $42\%$ of the computational cost of lightweight deep learning models, and maintaining higher accuracy in various SNRs.