Artificial Intelligence for Power Consumption Optimization in Reconfigurable Intelligent Surface-Assisted Communication Systems

Abstract

Intelligent and flexible resource management systems will be required as Sixth-Generation (6G) wireless networks advance in order to minimize energy consumption and safeguard against security risks. To reduce power consumption in active symbiotic secure communication networks, this study uses Reconfigurable Intelligent Surfaces (RIS) to improve the performance of Successive Interference Cancellation (SIC). Because it offers reliable, scalable, and nearly perfect solutions to this complex, non-convex, multi-variable optimization problem, artificial intelligence is essential. We use the Educational Competition Optimizer (ECO), a unique metaheuristic algorithm based on human cognition, to determine the optimal values for the beamforming vector and the active RIS reflection coefficient matrix. The algorithm finds a good balance between exploration and exploitation by simulating how competition changes over time in elementary, middle, and high school. The simulation results show that the proposed AI-driven approach uses 89.0% less power than traditional passive RIS, 52.5% less power than active RIS with random phases, 12.8% less power than Alternating Optimization with Successive Convex Approximation (AO SCA), 10.1% less power than hybrid Particle Swarm Optimization Grey Wolf Optimizer (PSO GWO), and 4.7% less power than hybrid Deep Neural Network Genetic Algorithm Deep Reinforcement Learning (DNN GA DRL). It also meets all security and reliability requirements with 100% feasibility. These results show that AI has the power to change how we communicate in the future by developing systems that are safe, energy-efficient, and long-lasting.