International Research Journal of Education and Technology

 The evolution of wireless communication has led to the emergence of Reconfigurable Intelligent Surfaces (RIS) as a transformative technology in 6G networks. RIS can intelligently manipulate the wireless propagation environment to enhance signal quality, improve spectral efficiency, and optimize beamforming strategies. However, efficient channel estimation and beamforming remain significant challenges due to the complex interactions between RIS elements and the dynamic nature of wireless channels. This paper presents a comprehensive study on joint channel estimation and beamforming techniques in RIS-aided 6G networks. We propose an optimized framework that leverages advanced signal processing techniques and machine learning-based estimation methods to enhance system performance. Simulation results, conducted in MATLAB, validate the efficacy of the proposed approach by demonstrating improved spectral efficiency, reduced latency, and enhanced signal-to-noise ratio (SNR). The findings suggest that integrating intelligent RIS configurations with optimized beamforming can significantly boost 6G network capabilities, making it a promising solution for next-generation wireless communication.