Machine Learning-Driven Meta Surfaces for Adaptive 6G Beamforming in Dynamic Terahertz Channels

Abstract

The accelerated development of 6G networks necessitates innovative solutions to overcome the limitations of conventional beamforming techniques, particularly in highly mobile and densely obstructed environments. This paper presents a machine learning (ML)-based framework that synergizes intelligent meta surfaces (IMS) with reconfigurable antenna arrays to dynamically optimize beamforming in real time. The core challenge involves adapting to rapidly fluctuating terahertz (THz) channels while ensuring high performance and ultra-low latency. To address this, we propose a hybrid architecture leveraging deep reinforcement learning (DRL), for adaptive beamforming policy optimization and convolutional neural networks (CNNs) for real-time spatial feature extraction. The DRL agent maximizes spectral efficiency by learning optimal beamforming weights, while the CNN maps angle-of-arrival (AoA) and angle-of-departure (AoD) profiles to IMS configurations. Simulations conducted on a dataset of 1,000 channel realizations demonstrate a 93.6% beam alignment accuracy and a 41% reduction in latency compared to genetic algorithms. The framework achieves an impressive spectral efficiency of 14.2 bps/Hz at 140 GHz, with inference times under 5ms on a high-end GPU (e.g., NVIDIA A100) for 64×64 IMS arrays. These results highlight the potential of ML-driven meta surfaces to enable scalable, adaptive, and energy-efficient 6G systems. The study concludes by advocating standardized IMS interfaces and large-scale prototyping to accelerate commercial adoption. By bridging metamaterial advancements with practical network optimization, this work lays the foundation for next-generation wireless systems capable of supporting immersive and mission-critical applications.