Radio-FM：用于无线电信号表示学习的基础模型及其应用
Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications
中文总结 AI 辅助
本文提出Radio-FM基础模型，采用双通道处理等技术，预训练于15个数据集，在15个下游基准中13个达最优，少样本迁移能力强，可作为无线电信号理解通用骨干。
中文摘要 AI 辅助
将基础模型应用于射频（RF）领域面临独特挑战，原因在于原始I/Q信号固有的物理复杂性以及频谱数据的极端异质性。本文提出Radio-FM，这是一个可扩展的基础模型系列，旨在实现通用无线电信号表示学习。与标准架构不同，Radio-FM采用双通道处理，专门针对I/Q独立性进行优化，同时通过轻量注意力机制捕获通道间交互。为了在具有高度可变序列长度的异质多源语料库上扩展预训练，我们提出了一种令牌预算动态批处理策略，结合通道独立的掩码重构。我们在涵盖调制、雷达和通信领域的15个多样化数据集上对Radio-FM进行预训练，并在15个下游基准上对其进行严格评估。实验结果表明，Radio-FM在15个基准中的13个上达到了最先进的性能，在调制、雷达、发射机识别、无线技术识别和无线干扰识别任务中均表现出一致的提升。值得注意的是，它展现出卓越的少样本迁移能力，在数据稀缺场景中显著优于现有基线，验证了其作为无线电信号理解通用骨干的潜力。
英文摘要
Applying foundation models to the radio frequency (RF) domain presents unique challenges due to the intrinsic physical complexity of raw I/Q signals and the extreme heterogeneity of spectral data. In this paper, we present Radio-FM, a scalable family of foundation models designed for universal radio signal representation learning. Unlike standard architectures, Radio-FM employs dual-channel processing specifically optimized for I/Q independence while capturing cross-channel interactions through a lightweight attention mechanism. To scale pretraining across heterogeneous multi-source corpora with highly variable sequence lengths, we propose a token-budgeted dynamic batching strategy coupled with channel-independent masked reconstruction. We pretrain Radio-FM on a diverse collection of 15 datasets spanning modulation, radar, and communication domains, and rigorously evaluate it on 15 downstream benchmarks. Experimental results show Radio-FM achieves state-of-the-art performance on 13 of 15 benchmarks, consistently improving across modulation, radar, emitter identification, wireless technology recognition, and wireless interference identification. Notably, it exhibits superior few-shot transferability, significantly outperforming existing baselines in data-scarce regimes, validating its potential as a general-purpose backbone for radio signal understanding.
