In modern radio access networks (RANs), rule-based handover (HO) decisions (e.g., A3/A5) depend on user equipment (UE) measurements only, so UEs at the same location can receive inconsistent HO outcomes. GNN-based methods improve HO KPIs using richer context than measurements alone. However, recurrent or graph models discard the per-UE recurrent state at HO and reinitialize at the target next-generation Node B (gNB), losing mobility history and forcing the target model to rebuild from post-HO measurements only. We address this post-HO cold start with Inductive Latent Context Persistence (ILCP), compressing the source recurrent state, transporting it on the 3GPP Xn as a 128-byte payload, and adapting it at the target gNB. We model the RAN as a dynamic heterogeneous graph over UE nodes, gNB nodes, measurement edges, and Xn edges. On a Vienna 4G/5G drive-test, ILCP achieves 0.0% ping-pong HOs versus 6.5% for an identical no-transfer baseline and 22.6% for a Transformer baseline; post-HO accuracy improves by +5.1 pp on average (peak +13.3 pp) in the 50-250 ms window. On one NVIDIA GTX 1080 (8 GB), ILCP runs end-to-end at 7.7 ms p99 per handover decision. Under perturbations (shadow fading, NLOS blockage, SSB-burst sparsity), robustly trained ILCP keeps handover failure (HOF) in the 10-13% range. Under the same fixed-reference-label setting, A3/A5 rises from 1.1% to 57-65% HOF when measurements are perturbed, exposing limits of measurement-only rules.

翻译：在现代无线接入网络（RAN）中，基于规则的切换决策（如A3/A5）仅依赖用户设备（UE）的测量结果，导致相同位置的UE可能获得不一致的切换结果。基于图神经网络（GNN）的方法通过利用比单纯测量值更丰富的上下文信息改善了切换关键绩效指标（KPI）。然而，循环模型或图模型会在切换时丢弃每个UE的循环状态，并在目标下一代节点B（gNB）处重新初始化，从而丢失移动历史，迫使目标模型仅从切换后测量值重建状态。我们通过归纳潜在上下文持久化（ILCP）解决这一切换后冷启动问题，该方法压缩源循环状态，通过3GPP Xn接口以128字节载荷传输，并在目标gNB处进行适配。我们将RAN建模为包含UE节点、gNB节点、测量边和Xn边的动态异构图。在维也纳4G/5G路测中，ILCP实现了0.0%的乒乓切换，而相同的无传输基线为6.5%，Transformer基线为22.6%；在切换后50-250毫秒窗口内，平均精度提升+5.1个百分点（峰值+13.3个百分点）。在单块NVIDIA GTX 1080（8 GB）上，ILCP每次切换决策的端到端p99运行时间为7.7毫秒。在扰动场景（阴影衰落、非视距遮挡、SSB突发稀疏性）下，鲁棒训练的ILCP将切换失败率（HOF）维持在10%-13%范围内。在相同固定参考标签设置下，当测量值受到扰动时，A3/A5的HOF从1.1%上升至57%-65%，暴露了纯测量规则的局限性。