Optimized Hybrid Precoding in mm Wave Massive MIMO via Federated Reinforcement Learning and Metaheuristic Swarm Optimization

Abstract

Hybrid precoding is a promising technology for realizing spectral and energy efficiency in mm Wave massive MIMO systems. Nevertheless, the complexity of joint analog and digital precoder optimization and the difficulty of decentralized implementation in large-scale networks call for the design of intelligent and scalable solutions. In this paper, we propose an optimized hybrid precoding solution that combines Federated Reinforcement Learning (FRL) and Metaheuristic Swarm Optimization (MSO) to facilitate improved precoding performance with reduced computational burdens. The FRL framework allows for distributed learning among various base stations (BSs) without explicit data sharing, thus preserving privacy and responding to dynamic channel conditions. Concurrently, MSO, motivated by nature-inspired algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), optimizes the learned precoding policies to realize near-optimal beam forming performance. Extensive simulation results confirm that the proposed FRL-MSO hybrid precoding solution outperforms conventional deep learning-based and heuristic solutions in sum-rate capacity, convergence rate, and robustness against channel variations. Our results show the promising potential of combining federated intelligence with swarm optimization methods for future 5G/6G mm Wave MIMO networks.