分布式多站点信道映射的层理论框架
A Sheaf-Theoretic Framework for Distributed Multi-Site Channel Charting
AI总结：
针对多小区场景中基站独立学习本地信道映射致表示空间不一致问题，提出基于拓扑信号处理的多站点信道映射框架，建模本地映射集合为网络层，开发架构与算法，实验验证方法有效性。
摘要：
信道映射（CC）通过将信道状态信息（CSI）嵌入低维表示来实现无线网络中数据驱动的用户定位。在多小区场景中，每个基站通过神经编码器独立学习局部映射，导致重叠覆盖区域的表示空间不一致。这种缺乏一致性阻碍了诸如用户跟踪、切换预测和资源分配等网络级任务。为了解决这个问题，我们提出了一个基于拓扑信号处理的多站点信道映射的原则框架。我们将局部映射的集合建模为网络层，它对网络中的一致性约束进行编码，并能够将局部学习的表示连贯地集成到共享的全局结构中。这种公式引入了一种可解释的归纳偏差，促进基站之间的对齐，同时保持局部几何保真度。在此模型的基础上，我们开发了一种多站点信道映射架构和一种交替优化算法，该算法联合更新神经编码器和站点间正交传输映射，并具有一致性的理论保证。实验结果验证了所提方法的有效性，证明了在不降低局部嵌入质量的情况下提高了跨站点对齐。
英文摘要：
Channel charting (CC) enables data-driven user localization in wireless networks by embedding channel state information (CSI) into low-dimensional representations. In multi-cell scenarios, each base station independently learns a local chart via neural encoders, leading to misaligned representation spaces across overlapping coverage areas. This lack of consistency hinders network-level tasks such as user tracking, handover prediction, and resource allocation. To address this issue, we propose a principled framework for multi-site channel charting based on topological signal processing. We model the collection of local charts as a network sheaf, which encodes consistency constraints across the network and enables the coherent integration of locally learned representations into a shared global structure. This formulation introduces an interpretable inductive bias that promotes alignment across base stations while preserving local geometric fidelity. Building on this model, we develop a multi-site channel charting architecture and an alternating optimization algorithm that jointly updates neural encoders and inter-site orthogonal transport maps, with theoretical guarantees on consistency. Experimental results validate the effectiveness of the proposed approach, demonstrating improved cross-site alignment without degrading the quality of local embeddings.
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