  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.
