2026-07-22 04:00 UTCOriginal source2 min readUpdated: 2026-07-22 08:23 UTC

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

E-SpecFormer is an edge-efficient Transformer for end-to-end automatic modulation and covert channel recognition. It introduces LiTAN, a Softmax- and LayerNorm-free attention mechanism that reduces complexity while increasing accuracy. With four scalable variants, the Nano variant achieves 86.5% accuracy on RadioML2018 (SNR>0 dB) and 94.2% on hardware Trojan-based CC datasets, with fewer than 10k parameters and 92 μs per frame on FPGA/CPU co-execution, surpassing state-of-the-art edge models at a fraction of the cost. This establishes E-SpecFormer as an edge-efficient solution for real-time spectrum intelligence on IoT devices.

SourcearXiv Machine LearningAuthor: Zhifan Song, Haralampos-G. Stratigopoulos, Hassan Aboushady

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[Submitted on 30 Jun 2026]

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

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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: this https URL.

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.18285 [cs.LG]

(or arXiv:2607.18285v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.18285

arXiv-issued DOI via DataCite

Submission history

From: Haralampos Stratigopoulos [view email] [v1] Tue, 30 Jun 2026 09:19:22 UTC (8,459 KB)

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