Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data

TL;DR

This paper introduces X2Track, a hierarchical deep learning framework that leverages multi-modal CSI data and cross-domain adaptation to accurately track users in RIS-aided multi-band ISAC systems with minimal labeled data.

Contribution

The paper proposes a novel hierarchical architecture and cross-domain learning approach for user tracking in RIS-aided ISAC systems, effectively handling sparse labeled data and multi-modal CSI.

Findings

Achieves decimeter-level tracking accuracy under low data conditions.

Effectively adapts to new environments with less than 5% labeled data.

Utilizes transformer networks and adversarial learning for robust tracking.

Abstract

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.