利用压缩感知思想提升无线信道表征，降低定位误差28%。
Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels
- 基于压缩感知构建信道模型，约束优化过程符合物理规律。
- 定位误差降低28%，波束成形代码本选择准确率提升26个百分点。
- 适合关注无线定位与网络优化的工程师和研究人员。
无线信道状态信息（CSI）的无监督表示学习可减少对标注数据的依赖，从而降低标注成本，并通常提升下游任务性能。然而，现有先进方法极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。本文提出稀疏预训练无线变压器（SpaRTran），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围绕无线信道模型构建，约束优化过程以获得物理上合理的解，并引入强归纳偏置。相比现有最佳方法，SpaRTran将定位误差降低高达28%，在波束成形的top-1代码本选择准确率上提升26个百分点。结果表明，将无线电传播的稀疏性作为无监督学习目标，能有效提升网络优化与无线定位任务的性能。
原文摘要 · Abstract (English)
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.
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