Machine learning for intelligent beam divergence compensation in orbital angular momentum-based wireless communication systems
- 1Department of Electrical and Electronic Engineering, Southeast University
Abstract
Orbital Angular Momentum (OAM) multiplexing enables high spectral efficiency but suffers from severe signal degradation due to beam divergence. Traditional compensation methods are computationally intensive and slow, making them unsuitable for real-time use. This paper proposes a machine learning-based solution using a Deep Neural Network (DNN) that learns to predict optimal beamforming weights directly from environmental factors like distance and turbulence. A key innovation of this work is the use of a low-complexity learning framework that directly predicts hybrid beamforming weights for OAM beam divergence compensation, thereby avoiding repeated iterative optimization and enabling substantial latency reduction. Trained on diverse channel data, the model achieves over 96.2% of the optimal received power while reducing latency from 100 ms to just 0.5 ms—a 200× improvement. It generalizes well to unseen scenarios, including mobile users and hardware impairments, outperforming conventional and adaptive baselines. This work demonstrates a viable path toward adaptive, low-latency OAM systems for future 6G networks.
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BibTeX
@article{hossain2026machine,
title = {{Machine learning for intelligent beam divergence compensation in orbital angular momentum-based wireless communication systems}},
author = {Md. Asif Hossain},
journal = {Scientific Reports},
year = {2026},
month = aug,
doi = {10.1038/s41598-026-65185-1},
url = {https://doi.org/10.1038/s41598-026-65185-1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning for intelligent beam divergence compensation in orbital angular momentum-based wireless communication systems
AU - Md. Asif Hossain
PY - 2026
DA - 2026/08/01
JO - Scientific Reports
DO - 10.1038/s41598-026-65185-1
UR - https://doi.org/10.1038/s41598-026-65185-1
AB - Orbital Angular Momentum (OAM) multiplexing enables high spectral efficiency but suffers from severe signal degradation due to beam divergence. Traditional compensation methods are computationally intensive and slow, making them unsuitable for real-time use. This paper proposes a machine learning-based solution using a Deep Neural Network (DNN) that learns to predict optimal beamforming weights directly from environmental factors like distance and turbulence. A key innovation of this work is the use of a low-complexity learning framework that directly predicts hybrid beamforming weights for OAM beam divergence compensation, thereby avoiding repeated iterative optimization and enabling substantial latency reduction. Trained on diverse channel data, the model achieves over 96.2% of the optimal received power while reducing latency from 100 ms to just 0.5 ms—a 200× improvement. It generalizes well to unseen scenarios, including mobile users and hardware impairments, outperforming conventional and adaptive baselines. This work demonstrates a viable path toward adaptive, low-latency OAM systems for future 6G networks.
SN - 2045-2322
ER -