Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization

1. 1Model the handover control problem as a multi-agent reinforcement learning task
2. 2Represent the cellular network as a dual-graph structure to capture interactions between neighboring cells
3. 3Train agents to optimize Cell Individual Offset parameters for each pair of neighboring cells
4. 4Evaluate the performance of the optimized handover control parameters in a simulated network environment

Telecom engineers and researchers on a team benefit from this approach as it improves network efficiency and reduces handover failures, while also informing product managers about potential applications of multi-agent reinforcement learning

💡 Multi-agent reinforcement learning can effectively optimize handover control parameters in complex cellular networks

📱💻 Dual-Graph Multi-Agent RL for handover optimization in cellular networks