Learning Cross-Domain Features With Dual-Path Signal Transformer.
IEEE Trans Neural Netw Learn Syst
; PP2024 Jan 23.
Article
em En
| MEDLINE
| ID: mdl-38261503
ABSTRACT
The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Ano de publicação:
2024
Tipo de documento:
Article