  Extremely Large-scale Array (ELAA) is considered a frontier technology for
future communication systems, pivotal in improving wireless systems' rate and
spectral efficiency. However, as ELAA employs a multitude of antennas operating
at higher frequencies, users are typically situated in the near-field region
where the spherical wavefront propagates. This inevitably leads to a
significant increase in the overhead of beam training, requiring complex
two-dimensional beam searching in both the angle domain and the distance
domain. To address this problem, we propose a near-field beamforming method
based on unsupervised deep learning. Our convolutional neural network
efficiently extracts complex channel state information features by
strategically selecting padding and kernel size. We optimize the beamformers to
maximize achievable rates in a multi-user network without relying on predefined
custom codebooks. Upon deployment, the model requires solely the input of
pre-estimated channel state information to derive the optimal beamforming
vector. Simulation results show that our proposed scheme can obtain stable
beamforming gain compared with the baseline scheme. Furthermore, owing to the
inherent traits of deep learning methodologies, this approach substantially
diminishes the beam training costs in near-field regions.
