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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
A Unified Backbone--Expert Framework with Relation-Token and Residual--Classifier Interfaces for Automatic Modulation Recognition
TL;DR
A unified backbone-expert framework for automatic modulation recognition (AMR) enhances performance by using specialized interfaces for short and long sequences, achieving accuracies of 67.28% and 87.19% on benchmark datasets.
✦ Why It Matters
Consider implementing specialized interfaces in your AMR systems to improve accuracy based on sequence length.
Key Takeaways
- Utilize relation tokens for short sequence processing.
- Implement gated multi-scale refinement for long sequences.
- Adopt fixed-averaging classifiers for better evidence integration.
- Test your models with varying observation lengths for optimal performance.
How It Works
The framework employs a common convolutional state-space backbone that adapts to different sequence lengths. For short sequences, it uses relation tokens that capture lag-aware complex-plane descriptors, enhancing the model's ability to retain critical information.
In contrast, for longer sequences, a gated multi-scale residual refinement module corrects feature maps, ensuring that the model can effectively process and integrate information across various scales. This dual approach allows the framework to dynamically adjust its processing strategy based on the input length.