Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning

TL;DR

This paper introduces SEL-AMR, a novel deep learning approach that models communication systems as subgraphs to achieve robust automatic modulation recognition across various channels and systems, outperforming existing methods.

Contribution

Proposes the first Subgraph Embedding Learning structure for adaptive and robust automatic modulation recognition in dynamic communication environments.

Findings

Outperforms state-of-the-art algorithms by up to 20% in recognition precision.

Achieves up to 30% improvement in recognition accuracy.

Demonstrates robustness across five public datasets and simulated data.

Abstract

Automatic Modulation Recognition (AMR) detects modulation schemes of received signals for further processing of signals without any priori information, which is critically important for civil spectrum regulation, information countermea sures, and communication security. Due to the powerful feature extraction and classification capabilities of Deep Learning (DL), DL-based AMR algorithms have achieved excellent performance gains compared with traditional modulation detection algorithms. However, all existing DL-based AMR algorithms, to the best of our knowledge, are designed for specific channels and systems, because data dimension of the used training dataset is fixed. To this end, we takes the first step to propose a Subgraph Embedding Learning (SEL) structure to address the classical AMR problem, and the proposed algorithm is called SEL-AMR. Our algorithm treats the communication system as a subgraph and uses the relationship between samples to smooth the effects brought by noise and different channels to extract robust features. Thus, the proposed SEL-AMR algorithm can adapt to any dynamic channels and systems. We use 5 public real datasets and a small amount of simulation data to evaluate our SEL-AMR algorithm. Experimental results reveal that SEL-AMR can well adapt to different channels and systems, and always outperforms the state of-the-art algorithms by improving up to 20% macro-average recognition precision and 30% recognition accuracy.