Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning
This addresses a critical bottleneck in future wireless communication systems by reducing training overhead, though it is incremental as it applies existing deep learning techniques to a specific domain problem.
The paper tackles the high beam training overhead in near-field Extremely Large-scale MIMO systems by proposing a deep learning-based method that uses a CNN to learn channel characteristics and optimize beamforming, achieving more stable beamforming gain and significantly improved performance compared to traditional methods.
Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems' rate and spectral efficiency. 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. The near-field beam training in ELAA requires both angle and distance information, which inevitably leads to a significant increase in the beam training overhead. To address this problem, we propose a near-field beam training method based on deep learning. We use a convolutional neural network (CNN) to efficiently learn channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer. This method maximizes multi-user networks' achievable rate without predefined beam codebooks. Upon deployment, the model requires solely the pre-estimated channel state information (CSI) to derive the optimal beamforming vector. The simulation results demonstrate that the proposed scheme achieves a more stable beamforming gain and significantly improves performance compared to the traditional beam training method. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.