Swarm and Evolutionary Intelligence Algorithms for 6G Network Optimization

This paper investigates the application of artificial intelligence (AI) and sixth generation (6G) wireless technology, highlighting their ability to revolutionize next generation cellular telecommunications. Specifically, this research addresses the critical challenge of radio resource allocation in 6G networks, focusing on maximizing spectral efficiency while ensuring ultra-low latency and high data rate requirements. The integration of AI-based solutions, particularly swarm and evolutionary intelligence algorithms including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GA), offers promising strategies to solve the complex mixed-integer non-linear programming problem of joint power allocation and subcarrier assignment in 6G networks. Through comprehensive simulation using realistic 6G network conditions with proper statistical validation, our results demonstrate that PSO achieves superior performance across all evaluated metrics: spectral efficiency exceeding 12 bits/s/Hz, energy efficiency above 150 Mbits/Joule, and QoS satisfaction rate of 94%. Statistical analysis (ANOVA, p<0.001) confirms significant performance differences, with PSO outperforming GA by 4.7% and ACO by 16.0% in spectral efficiency. Comparative analysis with established baseline methods shows PSO achieving 10.9% better spectral efficiency than water-filling algorithms and 28.4% improvement over recent 6G optimization studies in literature.