几何辅助的信道推断：基于部分信道估计与未校准数字孪生
Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
- Zhejiang University（浙江大学）
- Zhejiang Provincial Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing（浙江省多模态通信网络与智能信息处理实验室）
- Khalifa University（哈利法大学）
- Research Institute for Digital Future（数字未来研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对无线通信中CSI获取开销高的问题，提出利用未校准数字孪生的几何信息辅助信道推断，通过随机提示增强融合少量导频估计，实现低开销、高鲁棒性的信道获取，并支持可变导频模式。
AI中文摘要：
在无线MIMO-OFDM通信中，获取高维信道状态信息（CSI）通常需要较高的导频开销，或者依赖于准确且完整的位置或环境信息。本文提出了一种几何辅助的信道推断（GCD）方法，利用仅具有近似环境几何和位置的未校准数字孪生（DT）来辅助信道获取。其关键原理在于，即使不精确的几何信息（可通过无线电感知技术或现有地理数据库提前轻松获得）也能提供当前信道的某些结构特征；同时，仅使用少量导频获得的粗略瞬时信道估计提供了与信道结构对齐的专用信息，并进一步补偿几何误差和其他信道未知量。为此，我们首先从DT中提取几何特征，这些特征仅包含信道的简单结构信息。然后，我们提出了随机提示增强，一种新颖的方法来生成合适的提示，将几何多径结构转换为类似CSI的表示，同时抑制其他未知信道参数的干扰。随后，该提示通过信道推断网络与基于导频的瞬时信道估计融合。为了进一步增强网络的通用性，我们将导频配置纳入现有学习架构，以支持可变的导频模式。综合实验验证了所提方法的优越性，其展示了高信道获取质量、低导频开销和强鲁棒性。此外，结构提示还充当场景相关上下文，使我们的方法能够在新场景中良好泛化。
英文摘要：
The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise geometric information, which can be easily obtained in advance through radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract geometric features from the DT, which contain only simple structural information of the channel. Then we propose random prompt augmentation, a novel method to generate an appropriate prompt that converts geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.
