The field of 6G networks is rapidly advancing, with a focus on optimizing network performance and quality of experience (QoE) for users. Recent research has explored the use of graph neural networks, reinforcement learning, and machine learning to improve multicast routing, bandwidth estimation, and handover decisions. These innovative approaches aim to address the challenges of heterogeneous user demands, dynamic network topologies, and stringent latency requirements in immersive applications such as virtual reality. Notably, the integration of artificial intelligence and edge computing is enabling more efficient and adaptive network management. Some noteworthy papers in this area include:

• A graph neural network-based multicast routing framework that minimizes total transmission cost and supports user-specific video quality requirements.
• A network-optimized spiking neural network scheduler that achieves strong generalization to large-scale and dynamic network topologies.
• A human-in-the-loop bandwidth estimation framework that improves QoE in real-time video communication.
• A spatial computing communications framework that meets the latency and energy demands of multi-user virtual reality over distributed mobile edge computing networks.
• A machine learning-assisted predictive conditional handover framework that enables accurate and proactive handovers in 6G multi-RAT networks.