LWM-Spectro：一种用于无线基带信号频谱图的基础模型
LWM-Spectro: A Foundation Model for Wireless Baseband Signal Spectrograms
AI总结：
LWM-Spectro是一种基于变换器的基础模型，通过预训练大规模I/Q数据生成时频频谱图，用于无线信号的通用表示学习，能够有效提升调制分类和SNR/移动性识别等下游任务的性能。
摘要：
接收到的同相和正交（I/Q）基带信号本质上编码了无线链路的物理层和信道特性。然而，从此类原始信号中学习鲁棒且可迁移的表示仍然具有挑战性，因为存在异构的通信系统、多样的传播环境以及有限的标记数据。为此，我们提出了LWM-Spectro，一种基于变换器的基础模型，该模型在大规模I/Q数据上进行预训练，这些数据以时频频谱图的形式表示。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表示。这些表示能够有效地转移到下游任务，如调制分类和联合SNR/移动性识别，即使在最小监督的情况下。在各种任务中，LWM-Spectro在少样本和数据丰富的环境中均优于最先进的深度学习基线，为无线学习提供了统一的基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
