Near-Field Beam Training for Extremely Large-Scale MIMO Based on Deep Learning

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, playing a crucial role in enhancing the rate and spectral efficiency of wireless networks. 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. Near-field beam training requires information on both angle and distance, which inevitably leads to a significant increase in the beam training overhead. To address this challenge, we propose a near-field beam training method based on deep learning. Specifically, we employ a convolutional neural network (CNN) to efficiently extract 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, maximizing the achievable rate in multi-user networks without relying on predefined beam codebooks. Once deployed, the model requires only pre-estimated channel state information (CSI) to compute the optimal beamforming vector. Simulation results demonstrate that the proposed scheme achieves more stable beamforming gains and substantially outperforms traditional beam training approaches. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

I. Introduction

The sixth-generation (6G) wireless communication networks are anticipated to deliver heightened spectral efficiency, reduced power consumption, and increased antenna numbers and densities to support faster and more reliable data transmission for future intelligent systems, Internet of Things, and other emerging applications [1], [2], [3]. The deployment of extremely large-scale antenna arrays (ELAA) is crucial to achieving the key performance indicators (KPIs) of 6G networks [4], [5]. Particularly in the transition high-frequency bands such as millimeter wave (mmWave) and terahertz (THz) bands, ELAA is envisioned to be widely deployed in base stations (BS) to mitigate the significant path loss inherent in highly focused beamforming [6], [7], [31].