Integrated sensing and communications (ISAC) is pivotal for 6G communications
and is boosted by the rapid development of reconfigurable intelligent surfaces
(RISs). Using the channel state information (CSI) across multiple frequency
bands, RIS-aided multi-band ISAC systems can potentially track users' positions
with high precision. Though tracking with CSI is desirable as no communication
overheads are incurred, it faces challenges due to the multi-modalities of CSI
samples, irregular and asynchronous data traffic, and sparse labeled data for
learning the tracking function. This paper proposes the X2Track framework,
where we model the tracking function by a hierarchical architecture, jointly
utilizing multi-modal CSI indicators across multiple bands, and optimize it in
a cross-domain manner, tackling the sparsity of labeled data for the target
deployment environment (namely, target domain) by adapting the knowledge
learned from another environment (namely, source domain). Under X2Track, we
design an efficient deep learning algorithm to minimize tracking errors, based
on transformer neural networks and adversarial learning techniques. Simulation
results verify that X2Track achieves decimeter-level axial tracking errors even
under scarce UL data traffic and strong interference conditions and can adapt
to diverse deployment environments with fewer than 5% training data, or
equivalently, 5 minutes of UE tracks, being labeled.

提出了一种名为 X2Track 的框架，通过多模态 CSI 指标和跨领域优化的方式模型化追踪功能，并设计了基于转换神经网络和对抗学习技术的高效深度学习算法，以实现在稀缺的 UL 数据流量和强干扰条件下的几毫米级轴向追踪误差，并能够适应多种部署环境，只需少于 5% 的训练数据或等效于 5 分钟的 UE 轨迹进行标注。