DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
arXiv:2607.08031v1 Announce Type: cross Abstract: The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and physical principles, as a potential way to enhance
The increasing complexity and dynamic nature of communication environments necessitate more robust and adaptive AI models for modulation classification, pushing research into integrating domain-specific knowledge.
Improving automatic modulation classification (AMC) enhances the efficiency and reliability of military and civilian communication systems, impacting areas from intelligence gathering to spectrum management.
This research suggests a more resilient approach to AMC by combining data-driven deep learning with communication protocol prior knowledge, improving generalization across varying communication conditions.
- · Defence contractors
- · Telecommunications providers
- · Signal intelligence agencies
- · AI/ML researchers
- · Legacy signal processing methods
- · Less adaptive communication systems
Enhanced ability to classify and interpret radio frequency signals in challenging, dynamic environments.
Improved electronic warfare capabilities and cognitive radio performance due to more robust signal recognition.
Potential for more secure and efficient communication networks that can adapt to adversarial signal manipulation or rapidly changing spectral conditions.
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Read at arXiv cs.AI