The rapid advancement of sixth-generation (6G) wireless networks is driving the exploration of terahertz (THz) communication as a key enabler for ultra-high data rate transmission and massive connectivity. However, THz signals suffer from severe path loss, molecular absorption, and high susceptibility to blockage, which significantly limit their reliability and coverage. To address these challenges, this paper proposes a Reconfigurable Intelligent Surface (RIS)-assisted intelligent beamforming framework designed to enhance the reliability and efficiency of THz communication in 6G networks. The proposed approach leverages programmable metasurfaces to dynamically manipulate electromagnetic wave propagation, enabling precise control over signal reflection, direction, and phase. By integrating artificial intelligence techniques, particularly deep learning and reinforcement learning, the system adaptively optimizes beamforming strategies in real time based on channel conditions, user mobility, and environmental dynamics. The framework also incorporates joint optimization of RIS phase shifts and transmitter beamforming to maximize signal strength and minimize interference. Furthermore, the proposed model supports energy-efficient operation and scalable deployment in dense network scenarios. Simulation results demonstrate significant improvements in signal-to-noise ratio, coverage extension, and communication reliability compared to conventional beamforming techniques. The findings highlight the potential of RIS-assisted intelligent beamforming as a transformative solution for overcoming the inherent limitations of THz communication and enabling robust, high-performance wireless systems. This work provides a comprehensive foundation for the integration of RIS and AI technologies in future 6G communication architectures.
Gulista Khan (Thu,) studied this question.