The received in-phase and quadrature (I/Q) baseband signals inherently encode physical-layer and channel characteristics of wireless links. Learning robust and transferable representations directly from such raw signals, however, remains challenging due to heterogeneous communication systems, diverse propagation environments, and limited labeled data. To address this, we present LWM-Spectro, a transformer-based foundation model pretrained on large-scale I/Q data represented as time-frequency spectrograms. The model leverages self-supervised masked modeling, contrastive learning, and a mixture-of-experts (MoE) architecture to learn general-purpose wireless representations. These representations transfer effectively to downstream tasks such as modulation classification and joint SNR/mobility recognition, even with minimal supervision. Across tasks, LWM-Spectro consistently outperforms state-of-the-art deep learning baselines in both few-shot and data-rich regimes, providing a unified foundation for wireless learning.

翻译：接收到的同相和正交（I/Q）基带信号内在地编码了无线链路的物理层和信道特性。然而，由于通信系统的异构性、传播环境的多样性以及有限的标注数据，直接从这种原始信号中学习鲁棒且可迁移的表征仍然具有挑战性。为此，我们提出了LWM-Spectro，这是一个基于Transformer的基础模型，在表示为时频谱图的大规模I/Q数据上进行了预训练。该模型利用自监督掩码建模、对比学习和专家混合（MoE）架构来学习通用的无线信号表征。这些表征能够有效地迁移到下游任务，如调制分类和联合信噪比/移动性识别，即使在极少量监督下也是如此。在所有任务中，无论是少样本还是数据丰富的场景，LWM-Spectro均持续优于最先进的深度学习基线，为无线信号学习提供了一个统一的基础。