  Extremely large-scale reconfigurable intelligent surface (XL-RIS) has
recently been proposed and is recognized as a promising technology that can
further enhance the capacity of communication systems and compensate for severe
path loss . However, the pilot overhead of beam training in XL-RIS-assisted
wireless communication systems is enormous because the near-field channel model
needs to be taken into account, and the number of candidate codewords in the
codebook increases dramatically accordingly. To tackle this problem, we propose
two deep learning-based near-field beam training schemes in XL-RIS-assisted
communication systems, where deep residual networks are employed to determine
the optimal near-field RIS codeword. Specifically, we first propose a far-field
beam-based beam training (FBT) scheme in which the received signals of all
far-field RIS codewords are fed into the neural network to estimate the optimal
near-field RIS codeword. In order to further reduce the pilot overhead, a
partial near-field beam-based beam training (PNBT) scheme is proposed, where
only the received signals corresponding to the partial near-field XL-RIS
codewords are served as input to the neural network. Moreover, we further
propose an improved PNBT scheme to enhance the performance of beam training by
fully exploring the neural network's output. Finally, simulation results show
that the proposed schemes outperform the existing beam training schemes and can
reduce the beam sweeping overhead by approximately 95%.
