Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring

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.

arXiv:2607.18285v1 Announce Type: new Abstract: 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.