Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
📰 ArXiv cs.AI
Dual-Graph Multi-Agent Reinforcement Learning optimizes handover control in cellular networks by tuning Cell Individual Offset parameters
Action Steps
- Model the handover control problem as a multi-agent reinforcement learning task
- Represent the cellular network as a dual-graph structure to capture interactions between neighboring cells
- Train agents to optimize Cell Individual Offset parameters for each pair of neighboring cells
- Evaluate the performance of the optimized handover control parameters in a simulated network environment
Who Needs to Know This
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
Key Insight
💡 Multi-agent reinforcement learning can effectively optimize handover control parameters in complex cellular networks
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📱💻 Dual-Graph Multi-Agent RL for handover optimization in cellular networks
Key Takeaways
Dual-Graph Multi-Agent Reinforcement Learning optimizes handover control in cellular networks by tuning Cell Individual Offset parameters
Full Article
Title: Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
Abstract:
arXiv:2603.24634v1 Announce Type: cross Abstract: HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO optimization is the Cell Individual Offset (CIO), defined for each pair of neighboring cells and used to bias HO triggering decisions. At network scale, tuning CIOs becomes a tightly coupled problem: small changes can redirect mobility flows across multiple neighbors, and
Abstract:
arXiv:2603.24634v1 Announce Type: cross Abstract: HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO optimization is the Cell Individual Offset (CIO), defined for each pair of neighboring cells and used to bias HO triggering decisions. At network scale, tuning CIOs becomes a tightly coupled problem: small changes can redirect mobility flows across multiple neighbors, and
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