Wireless Network Optimization

Wireless network optimization aims to enhance network performance metrics like throughput, spectrum efficiency, and energy consumption through intelligent resource allocation and control. Current research heavily utilizes reinforcement learning (RL), including variations like Q-learning and inverse RL, often combined with deep learning models to handle the complexity of large-scale networks. These approaches are being refined through techniques such as federated learning for distributed optimization and knowledge distillation to leverage existing optimization algorithms. The resulting improvements in network efficiency and resource management have significant implications for both the design of future 6G networks and the operational efficiency of existing wireless infrastructure.