用隐空间预测构建无线基础模型，提升跨任务迁移能力。
LatentWave: JEPA Pretraining for Wireless Foundation Models
- 在隐空间中预测掩码区域，避免低层信号偏差。
- 四种下游任务表现优于基线模型，频域掩码利于定位与波束预测。
- 支持不同天线配置，适合异构无线系统应用。
无线基础模型为解决各类无线任务建模问题提供了新思路。然而，现有方法依赖掩码输入重建，易使表征偏向低层信号细节。本文提出LatentWave，一种基于联合嵌入预测架构（JEPA）的无线基础模型，利用多样化的无线频谱图和信道状态信息（CSI）进行预训练。通过在隐空间中预测掩码区域，该模型学习到更具可迁移性的表征，能更好地直接应用于多种下游任务。其架构采用通道级补丁嵌入并结合随机通道采样，支持不同天线数量，提升了对异构无线配置的适应性。我们在四个下游任务上评估：射频信号分类、5G NR定位、波束预测和视距/非视距（LoS/NLoS）分类，与同数据集预训练的掩码建模范式（WavesFM）对比。结果表明，掩码几何结构引入了任务相关的归纳偏置：频率掩码显著提升定位与波束预测性能，区域掩码则更有利于信号分类的判别性保持。
原文摘要 · Abstract (English)
Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruction, which can bias representations toward low-level signal details. In this paper, we propose LatentWave, a wireless foundation model pretrained using a Joint-Embedding Predictive Architecture (JEPA) on diverse wireless spectrograms and channel state information (CSI). By predicting masked regions in latent space, LatentWave learns representations that are more transferable out of the box across diverse downstream tasks. The proposed architecture employs per-channel patch embeddings with stochastic channel sampling during pretraining, allowing it to process variable antenna counts and improving usability across heterogeneous wireless configurations. We evaluate LatentWave on four downstream tasks: RF signal classification, 5G NR positioning, beam prediction, and LoS/NLoS classification, comparing against a masked-modeling baseline (WavesFM) pretrained on the same data. Additionally, we show that the masking geometry introduces a task-dependent inductive bias: frequency masking strongly favors channel-related tasks such as positioning and beam prediction, while region masking better preserves discriminability for signal classification.
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