AI RESEARCH
Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
arXiv CS.AI
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ArXi:2603.24634v1 Announce Type: cross 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 static rules often degrade under non-stationary traffic and mobility.