MACHINE LEARNING FOR INTELLIGENT BEAM DIVERGENCE COMPENSATION IN ORBITAL ANGULAR MOMENTUM-BASED WIRELESS COMMUNICATION SYSTEMS

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. Trained on diverse channel data, the model achieves 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. Hardware-aware simulations confirm less than 5% performance degradation under realistic impairments, establishing a clear path to experimental validation. This work demonstrates a viable path toward adaptive, low-latency OAM systems for future 6G networks.