Unsupervised representation learning for wireless channel state information (CSI)reduces reliance on labeled data, thereby lowering annotation costs, and often improves performance on downstream tasks. However, state-of-the-art approaches take little or no account of domain-specific knowledge, forcing the model to learn well-known concepts solely from data. We introduce Sparse pretrained Radio Transformer (SpaRTran), a hybrid method based on the concept of compressed sensing for wireless channels. In contrast to existing work, SpaRTran builds around a wireless channel model that constrains the optimization procedure to physically meaningful solutions and induces a strong inductive bias. Compared to the state of the art, SpaRTran cuts positioning error by up to 28% and increases top-1 codebook selection accuracy for beamforming by 26 percentage points. Our results show that capturing the sparse nature of radio propagation as an unsupervised learning objective improves performance for network optimization and radio-localization tasks.

翻译：针对无线信道状态信息（CSI）的无监督表示学习能够减少对标注数据的依赖，从而降低标注成本，并通常能提升下游任务的性能。然而，现有最先进的方法很少或未考虑领域特定知识，迫使模型仅从数据中学习已知概念。我们提出了稀疏预训练无线电Transformer（SpaRTran），这是一种基于无线信道压缩感知概念的混合方法。与现有工作不同，SpaRTran围绕一个无线信道模型构建，该模型将优化过程约束于物理意义明确的解，并引入强归纳偏置。相比现有最佳方法，SpaRTran将定位误差降低了高达28%，并将波束赋形的top-1码本选择准确率提升了26个百分点。我们的结果表明，将无线电传播的稀疏特性作为无监督学习目标进行捕捉，能够提升网络优化与无线电定位任务的性能。