Weighted Sum Rate Maximization for Smart Reflective Surfaces Based on Improved Lion Swarm Algorithm

With the maturation and widespread adoption of 5G communication network technology, research into next-generation (6G) communication technology has intensified to meet the demands of data transmission in the big data era. Reflective Intelligent Surface (RIS) technology has emerged as a promising approach for advancing 6G communication capabilities. The objective of employing intelligent reflective surfaces in wireless network transmission is to optimize beamforming at the base station transmitter and the reflection coefficient of the RIS. This optimization aims to maximize the weighted sum-rate (WSR) at the user side, while adhering to constraints related to base station transmitter power and RIS unit modes. In this paper, we propose enhancements to the basic lion swarm optimization algorithm by incorporating elements such as a good point set, chaotic search, and a mining mechanism inspired by the honey badger algorithm. These modifications are intended to improve the algorithm's performance in optimizing the aforementioned parameters. Simulation results demonstrate that our enhanced lion swarm optimization algorithm achieves significant improvements in weighting sum rate maximization, thereby enhancing the efficiency of reflective intelligent surface-assisted wireless networks.