G-AMC: A Green Automatic Modulation Classification Method

TL;DR

This paper introduces G-AMC, a computationally efficient and transparent method for automatic modulation classification that reduces model size and FLOPs while maintaining high accuracy.

Contribution

It presents a novel green learning pipeline combining sparse coding, feature extraction, and hierarchical classification for AMC.

Findings

Model parameters reduced by 41% compared to lightweight deep learning models.

FLOPs are only 10^-4 of traditional blind waveform recognition.

Demonstrates high effectiveness and efficiency in classifying received signals.

Abstract

In this work, we propose an efficient and transparent green learning pipeline to address the automatic modulation classification (AMC) problem. This pipeline aims to enable receivers to blindly identify the modulation modes of the incoming signals in a computationally efficient way with a small model size. Our method includes the following steps. First, the input signal is transformed into a precise representation through the sparse coding method. Second, various features are extracted from the sparse coding representation with the statistics from the input signal. Third, the classification subspace is hierarchically partitioned with a tree structure to achieve a lightweight model size with good prediction accuracy. The experimental results demonstrate the effectiveness and efficiency in classifying the modulated features and representation of received signals. Compared to lightweight deep learning models, the number of model parameters is reduced by \textbf{41\%}, while the usage of Floating Point Operations (FLOPs) is only $\mathcal{O}(10^{-4})$ of the blind waveform recognition without pre-arranged knowledge of incoming waveforms.