  This paper introduces a novel mobile tracking framework leveraging the
high-dimensional signal received from extremely large-scale (XL) reconfigurable
intelligent surfaces (RIS). This received signal, named XL-RIS information, has
a much larger data dimension and therefore offers a richer feature set compared
to the traditional base station (BS) received signal, i.e., BS information,
enabling more accurate tracking of mobile users (MUs). As the first step, we
present an XL-RIS information reconstruction (XL-RIS-IR) algorithm to
reconstruct the high-dimensional XL-RIS information from the low-dimensional BS
information. Building on this, this paper proposes a comprehensive framework
for mobile tracking, consisting of a Feature Extraction Module and a Mobile
Tracking Module. The Feature Extraction Module incorporates a convolutional
neural network (CNN) extractor for spatial features, a time and frequency
(T$\&$F) extractor for domain features, and a near-field angles of arrival
(AoAs) extractor for capturing AoA features within the XL-RIS. These features
are combined into a comprehensive feature vector, forming a time-varying
sequence fed into the Mobile Tracking Module, which employs an Auto-encoder
(AE) with a stacked bidirectional long short-term memory (Bi-LSTM) encoder and
a standard LSTM decoder to predict MUs' positions in the upcoming time slot.
Simulation results confirm that the tracking accuracy of our proposed framework
is significantly enhanced by using reconstructed XL-RIS information and
exhibits substantial robustness to signal-to-noise ratio (SNR) variations.
