AI-Driven Beamforming Optimization for Reconfigurable Intelligent Surfaces in 6G Terahertz Networks

The advent of Reconfigurable Intelligent Surfaces (RIS) has introduced a transformative approach to enhancing wireless communication efficiency by controlling electromagnetic wave propagation. In the domain of 6G Terahertz (THz) networks, optimizing beamforming for RIS remains a challenging task due to the highly dynamic channel variations and the computational complexity involved in adjusting metasurface elements in real-time. Conventional beamforming techniques, which depend on static channel assumptions and iterative optimization algorithms, often fall short in adapting to the rapid fluctuations characteristic of THz environments. This research presents an AI-driven beamforming optimization model that integrates deep reinforcement learning (DRL) with a physics-guided neural network (PGNN) to enhance the adaptability and efficiency of RIS-assisted 6G THz networks. By leveraging real-time learning mechanisms, the proposed framework dynamically tunes the reflection coefficients of RIS elements to optimize signal-to-noise ratio (SNR) and spectral efficiency. Unlike traditional heuristic and gradient-based optimization approaches, which suffer from computational overhead and slow convergence, the proposed model enables faster decision-making with reduced processing complexity. Through extensive simulations, our approach demonstrates up to 40% improvement in spectral efficiency and a 25% reduction in power consumption compared to conventional RIS beamforming strategies. Moreover, the reinforcement learning agent significantly reduces computation time, making it a practical solution for real-time 6G network deployment. These findings lay the groundwork for intelligent, adaptive beamforming techniques in next-generation wireless systems, contributing to the development of ultra-reliable low-latency communications (URLLC) in THz-band 6G networks.