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（RAN）中，基于规则的切换决策（如A3/A5）仅依赖用户设备（UE）的测量值，导致处于相同位置的UE可能获得不一致的切换结果。基于图神经网络（GNN）的方法通过利用更丰富的上下文信息（而非仅依赖测量值）改善了切换关键绩效指标（KPI）。然而，循环模型或图模型在切换时会丢弃每个UE的循环状态，并在目标下一代节点B（gNB）处重新初始化，从而丢失移动历史记录，迫使目标模型仅能基于切换后的测量值重新构建上下文。本文提出归纳式隐状态持续性（ILCP）来解决这一切换后冷启动问题：通过对源端循环状态进行压缩，以128字节的有效载荷通过3GPP Xn接口传输，并在目标gNB处进行自适应调整。我们将RAN建模为一个动态异构图，包含UE节点、gNB节点、测量边和Xn边。在维也纳4G/5G路测数据集上，ILCP实现了0.0%的乒乓切换率，相比之下，相同架构的无传输基线为6.5%，Transformer基线为22.6%；在切换后50-250毫秒窗口内，切换后准确率平均提升5.1个百分点（峰值提升13.3个百分点）。在单块NVIDIA GTX 1080（8 GB）显卡上，ILCP的端到端每次切换决策延迟p99为7.7毫秒。在扰动场景（阴影衰落、非视距堵塞、SSB突发稀疏性）下，经过鲁棒训练的ILCP将切换失败率保持在10-13%范围内。在相同的固定参考标签设置下，当测量值受到扰动时，A3/A5的切换失败率从1.1%上升至57-65%，暴露出仅依赖测量值的规则所存在的局限性。