The field of wireless networks is moving towards more energy-efficient and resource-managed systems. Recent developments have focused on optimizing load balancing and energy efficiency in Open Radio Access Network (O-RAN) deployments, with machine learning-based approaches showing promising results. Another area of research is the application of reinforcement learning and optimization techniques to improve resource allocation and management in wireless networks. Notably, the use of federated multi-agent reinforcement learning and hybrid xApps has been proposed to enhance energy efficiency and privacy preservation in 6G edge networks. Additionally, research has explored the potential of aerial reconfigurable intelligent surfaces (ARIS) and reconfigurable intelligent surfaces (RIS) to improve anti-jamming communication performance and secure short-packet communications in autonomous aerial vehicle (AAV) networks. Some papers that are particularly noteworthy in this regard include: The paper 'Joint Optimisation of Load Balancing and Energy Efficiency for O-RAN Deployments' which presents a comprehensive ML-based framework for joint optimisation of load balancing and EE for ORAN deployments. The paper 'Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks' which introduces a novel Federated Multi-Agent Reinforcement Learning framework that incorporates cross-layer orchestration of both the MAC layer and application layer for energy-efficient, privacy-preserving, and real-time resource management across heterogeneous edge devices.