Enhancing reflective elements of intelligent reflective surfaces in 6G communications using artificial intelligence

Abstract

The dynamic landscape of 6G communication networks necessitates innovative strategies to address energy inefficiency and signal degradation in densely populated regions with limited line-of-sight (LOS) coverage. A novel technology known as an intelligent reflecting surface (IRS) has emerged; it can dynamically modify the characteristics of electromagnetic waves to enhance signal propagation. Unfortunately, current IRS models frequently neglect the balance between energy efficiency (EE) and the quantity of reflective elements (N) in Rayleigh fading scenarios. This study introduces an algorithm called dynamic-static particle swarm optimization (DS-PSO) aimed at improving EE and decreasing the quantity of reflective components in the performance optimization of IRS. The research assesses the proposed model in comparison to single-input single-output (SISO) systems, conventional IRS models, and IRS models from prior studies within a realistic urban framework. The optimized IRS, which only uses seven reflective elements, has a peak EE of 366 Mbit/Joule. This is a big improvement over IRS models from earlier research, as shown by the numbers. The findings indicate that artificial intelligence (AI)-driven optimization can enhance IRS technology for sustainable and efficient 6G networks.