Near-field Beamforming for Extremely Large-scale MIMO Based on Unsupervised Deep Learning

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.