  Reconfigurable intelligent surface (RIS)-aided localization systems have
attracted extensive research attention due to their accuracy enhancement
capabilities. However, most studies primarily utilized the base stations (BS)
received signal, i.e., BS information, for localization algorithm design,
neglecting the potential of RIS received signal, i.e., RIS information.
Compared with BS information, RIS information offers higher dimension and
richer feature set, thereby significantly improving the ability to extract
positions of the mobile users (MUs). Addressing this oversight, this paper
explores the algorithm design based on the high-dimensional RIS information.
Specifically, we first propose a RIS information reconstruction (RIS-IR)
algorithm to reconstruct the high-dimensional RIS information from the
low-dimensional BS information. The proposed RIS-IR algorithm comprises a data
processing module for preprocessing BS information, a convolution neural
network (CNN) module for feature extraction, and an output module for
outputting the reconstructed RIS information. Then, we propose a transfer
learning based fingerprint (TFBF) algorithm that employs the reconstructed
high-dimensional RIS information for MU localization. This involves adapting a
pre-trained DenseNet-121 model to map the reconstructed RIS signal to the MU's
three-dimensional (3D) position. Empirical results affirm that the localization
performance is significantly influenced by the high-dimensional RIS information
and maintains robustness against unoptimized phase shifts.
