轻量级模型实现边缘端实时射频信号识别，精度高且资源占用极低。
Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring
- 设计无Softmax/LayerNorm的线性Tanh注意力机制，降低计算复杂度
- 纳米版在信噪比>0dB时准确率达86.5%，硬件木马隐道识别准确率94.2%
- 参数少于1万，单帧处理仅需92微秒，适合物联网设备部署
我们提出E-SpecFormer（边缘射频监测变换器）用于端到端自动调制与隐蔽信道（CC）识别。引入LiTAN（线性Tanh注意力网络）——一种无Softmax和LayerNorm的注意力机制，在降低复杂度的同时提升射频任务精度。E-SpecFormer提供四种可扩展变体（Nano、Small、Medium、Large），以适应不同硬件约束。在RadioML2018数据集上，纳米版在信噪比>0 dB条件下平均准确率达86.5%；在基于硬件木马的隐蔽信道数据集上，准确率高达94.2%，且参数少于10,000个，支持FPGA/CPU协同执行，单帧处理速度达92 μs，显著优于现有边缘模型，成本仅为其一小部分。该成果为物联网设备上的实时频谱智能提供了高效解决方案。
原文摘要 · Abstract (English)
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 μs 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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