{"slug": "edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring", "title": "Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring", "summary": "Researchers introduced E-SpecFormer (Edge Spectrum monitoring Transformer), an edge-efficient model for end-to-end automatic modulation and covert channel recognition. The Nano variant achieves 86.5% average accuracy on RadioML2018 for modulation recognition and 94.2% accuracy on hardware Trojan-based covert channel datasets, with fewer than 10k parameters and up to 92 μs per frame on FPGA/CPU co-execution, surpassing state-of-the-art edge models. The model uses LiTAN (Linear Tanh Attention Network), a Softmax- and LayerNorm-free attention mechanism, and is available in four scalable variants (Nano, Small, Medium, Large) for diverse hardware constraints.", "body_md": "arXiv:2607.18285v1 Announce Type: new\nAbstract: We present E-SpecFormer (Edge Spectrum monitoring Transformer) for end-to-end automatic modulation and covert channel (CC) recognition. We introduce LiTAN (Linear Tanh Attention Network), a Softmax- and LayerNorm-free attention mechanism that reduces complexity while increasing accuracy in RF tasks. E-SpecFormer is parameterized in four scalable variants (Nano, Small, Medium, Large) to accommodate diverse hardware constraints. Using the RadioML2018 dataset for modulation recognition, the Nano variant achieves 86.5% average accuracy for Signal-to-Noise Ratios (SNRs)>0 dB, and on the hardware Trojan (HT)-based CC dataset it reaches 94.2% accuracy, both with fewer than 10k parameters and up to speed of 92 {\\mu}s per frame on FPGA/CPU co-execution, surpassing state-of-the-art edge models at a fraction of their cost. These results establish E-SpecFormer as an edge-efficient solution for real-time spectrum intelligence on Internet of Things (IoT) devices. GitHub link to the repository: https://github.com/zsniko/E-SpecFormer.", "url": "https://wpnews.pro/news/edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring", "canonical_source": "https://arxiv.org/abs/2607.18285", "published_at": "2026-07-22 04:00:00+00:00", "updated_at": "2026-07-22 04:18:09.084882+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning"], "entities": ["E-SpecFormer", "LiTAN", "RadioML2018", "FPGA", "CPU", "Internet of Things", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring", "markdown": "https://wpnews.pro/news/edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring.md", "text": "https://wpnews.pro/news/edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring.txt", "jsonld": "https://wpnews.pro/news/edge-efficient-transformer-for-end-to-end-rf-spectrum-monitoring.jsonld"}}