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

arXiv: https://arxiv.org/abs/2406.03249 · HTML: https://mlsanity.com/paper/2406.03249/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [Integrated Sensing and Communications: Recent Advances and Ten Open Challenges](https://mlsanity.com/paper/2305.00179/) (2023)
- [Beam Focusing for Near-Field Multi-User MIMO Communications](https://mlsanity.com/paper/2105.13087/) (2021)
- [Near-Field Communications: A Tutorial Review](https://mlsanity.com/paper/2305.17751/) (2023)
- [Codebook Design and Beam Training for Extremely Large-Scale RIS: Far-Field or Near-Field?](https://mlsanity.com/paper/2109.10143/) (2021)
- [Seventy Years of Radar and Communications: The Road from Separation to Integration](https://mlsanity.com/paper/2210.00446/) (2022)
- [6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing](https://mlsanity.com/paper/2203.13035/) (2022)
- [Low-overhead Beam Training Scheme for Extremely Large-Scale RIS in Near-field](https://mlsanity.com/paper/2211.15910/) (2022)
- [Two-Stage Hierarchical Beam Training for Near-Field Communications](https://mlsanity.com/paper/2302.12511/) (2023)
- [Near-Field Integrated Sensing and Communications](https://mlsanity.com/paper/2302.01153/) (2023)
- [Physical Layer Security in Near-Field Communications](https://mlsanity.com/paper/2302.04189/) (2023)
- [Near-Field Communications: What Will Be Different?](https://mlsanity.com/paper/2303.04003/) (2023)
- [InFocus: A spatial coding technique to mitigate misfocus in near field LoS beamforming](https://mlsanity.com/paper/2006.04964/) (2020)
- [Communication Modes with Large Intelligent Surfaces in the Near Field](https://mlsanity.com/paper/2108.10569/) (2021)
- [Near-Field Rainbow: Wideband Beam Training for XL-MIMO](https://mlsanity.com/paper/2205.03543/) (2022)
- [Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain](https://mlsanity.com/paper/2209.13138/) (2022)
- [Fast Near-Field Beam Training for Extremely Large-Scale Array](https://mlsanity.com/paper/2209.14798/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO](https://mlsanity.com/paper/2605.12578/) (2026)

## Abstract (arXiv)

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.