AI 中文总结
本文从图视角重新审视信道图表，提出用图神经网络替代孪生网络，以线性成本消息传递实现同等定位精度且参数减少512倍，并揭示GNN的谱压缩与解压缩作用。
AI 中文摘要
信道图表是一种自监督框架，从高维信道状态信息中学习低维空间表示。我们从图论视角重新审视信道图表，并证明位置扩散目标等价于图拉普拉斯平滑泛函。我们提出一种图神经网络（GNN）公式，用图平滑目标替代孪生网络的全局测地距离不相似性目标。我们认为重新表述的目标即为位置扩散目标。在不偏离原始优化目标的情况下，GNN用线性成本的角延迟谱（ADP）相似性图上的消息传递替代二次成本的自相关编码，以少512倍的参数实现了相当的定位精度。利用拉普拉斯谱分析，我们证明障碍物压缩了ADP图谱，而GNN作为谱解压缩器对抗障碍物和其他环境语义，但作为压缩器对抗过度的ADP嵌入空间。此外，学习嵌入的第二特征向量编码了视距/非视距边界，而非空间坐标。
英文摘要
Channel charting is a self-supervised framework that learns low-dimensional spatial representations from high-dimensional channel state information. We revisit channel charting from a graph-theoretic perspective, and show that the position-diffusion objective is equivalent to a graph Laplacian smoothness functional. We propose a Graph Neural Network (GNN) formulation that replaces the Siamese network's global geodesic dissimilarity objective with a graph smoothness objective. We consider the reformulated objective to be the position diffusion objective. Without diverging from the original optimization objective, the GNN replaces the quadratic-cost self-correlation encoding with linear-cost message passing over an Angle-Delay Profile (ADP)-similarity graph, achieving comparable positioning accuracy with 512 times fewer parameters. Using Laplacian spectral analysis, we demonstrate that obstacles compress the ADP graph spectrum, while the GNN acts as a spectral decompressor against obstacles and other environmental semantics, but as a compressor against excessive ADP embedding space. Beyond this, the second eigenvector of the learned embedding encodes the line-of-sight/non-line-of-sight boundary rather than spatial coordinates.
CommentsAccepted in IEEE CSCN 2026
