Multi-Objective Signal Optimization to Balance Multiple Conflicting Metrics in RIS-Assisted MIMO Systems

The sixth generation (6G) networks promise transformative advancements in wireless communication, significantly enhancing spectral efficiency and supporting a variety of novel applications. This study investigates the configuration of Reconfigurable Intelligent Surfaces to assist communication efficiency within communication systems serving multiple single-antenna users. We address the multi-objective optimization challenges of balancing different metrics to achieve comprehensive system performance. A novel algorithm is then proposed, which integrates Pareto front grid decomposition with the reproduction operators of differential evolution. Experiments are conducted across various scenarios, particularly under different user density conditions. Numerical results demonstrate and validate the superior performance of the proposed algorithm compared to state-of-the-art multi-objective benchmarks across key metrics comprising Hypervolume and Inverted Generational Distance for practical applications.