Maximizing Energy Efficiency of 6G Intelligent Reflecting Surfaces Based on Improved Frilled Lizard Optimization Algorithm

With the continuous advancement of wireless communication technologies and the development and maturity of 5 G systems, research into next-generation (6 G) communication technologies has gradually intensified. Among the primary research directions in 6G communication, Reconfigurable Intelligence Surface (RIS) have gained widespread attention. RIS is a key technology that actively controls the wireless signal propagation environment through electromagnetic modulation techniques. By incorporating programmable reflective components into the communication environment, RIS enhances signal quality and coverage, thus improving the efficiency and performance of communication systems. Energy efficiency is a crucial metric for RIS-assisted communication systems. It aims to maximize energy efficiency (EE) by controlling the switching states of each RIS unit and optimizing power consumption between the base station and RIS. In this paper, we enhance the Frilled Lizard Optimization Algorithm by integrating strategies such as Latin Hypercube Sampling. These improvements are designed to enhance the algorithm's performance. Simulation results indicate that the improved Frilled lizard Optimization Algorithm significantly improves energy efficiency maximization in a Multiple-Input, Single-Output (MISO) communication system assisted by RIS-controlled unit states.