Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916907801640960 |
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| author | Zhang, Bojun |
| author_facet | Zhang, Bojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning Zhang, Bojun Networking and Internet Architecture 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. |
| title | Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.13474 |