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arXiv · 2607.18285

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

Zhifan Song·Haralampos-G. Stratigopoulos·Hassan Aboushady
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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.

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Zhifan Song, Haralampos-G. Stratigopoulos, Hassan Aboushady. 2026-06-30. Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring. https://arxiv.org/abs/2607.18285

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