Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning

Jiali Nie, Yuanhao Cui, Zhaohui Yang, Weijie Yuan, Xiaojun Jing

TL;DR

This work proposes a near-field beam training method based on deep learning that achieves more stable beamforming gains and substantially outperforms traditional beam training approaches, and substantially diminishes the near-field beam training overhead.

Abstract

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.

Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning

TL;DR

This work proposes a near-field beam training method based on deep learning that achieves more stable beamforming gains and substantially outperforms traditional beam training approaches, and substantially diminishes the near-field beam training overhead.

Abstract

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.
Paper Structure (17 sections, 14 equations, 12 figures, 3 tables, 1 algorithm)

This paper contains 17 sections, 14 equations, 12 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related works
  3. Related works
  4. Near-field Communications
  5. Traditional Far-field Beam Training
  6. Near-field Beam Training
  7. Near-field communications system model
  8. Near-field Channel Model
  9. Near-field Beamforming
  10. Deep learning based near-field beam training Method
  11. Problem Formulation
  12. Problem Formulation
  13. Deep Learning Scheme Design
  14. Experimental Setup and Simulation Analysis
  15. Experimental Setup
  16. ...and 2 more sections

Figures (12)

  • Figure 1: Near-field wavefront and far-field wavefront comparison
  • Figure 2: Near-field and far-field channel model between BS and UE
  • Figure 3: (a) represents the standardized signal energy obtained by the receiver through far-field beam focusing, and (b) represents the standardized signal energy obtained by the receiver through near-field beam focusing. The far-field beam travels in different directions, while the near-field beam splitter focuses the beam at different locations.
  • Figure 4: Overall architecture diagram of the proposed model. This shows how to obtain the beamforming vector by using neural network when the channel state information is available.
  • Figure 5: Illustrate the structure of the convolutional neural network design. The red dashed box depict the details of the feature extraction block. The orange rectangle represents the data transformations in the convolutional neural network.
  • ...and 7 more figures