用于O-RAN SLA风险预测的物理约束联邦加法模型
Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction
AI总结：
研究O-RAN中每切片SLA违规预测问题，提出单调联邦NAM模型，将明确物理方向KPI表示为单调样条，经联邦平均聚合保留约束，该模型消除单调性违规，提升形状一致性，推广调度策略并减少流量，支持可审核SLA风险推断。
AI中文摘要：
O-RAN中的主动服务保障需要在每切片SLA违规发生之前进行预测。预测模型必须可由运营商审核，并且必须在不汇总每切片KPI的情况下跨基站进行训练，因为切片租给了各个租户，所以这些KPI具有商业敏感性。神经加法模型（NAM）具有可审核性，因为每个KPI通过可见的形状函数起作用。然而，仅可见性并不能保证物理有效性。在ColO-RAN测试平台数据集上，无约束的NAM学习到与无线物理相矛盾的效应。我们提出了单调联邦NAM，它将具有明确物理方向的KPI表示为单调样条，其约束在联邦平均聚合中得以保留。该模型作为非实时RIC rApp进行训练和运行，并且足够紧凑可部署为近实时RIC xApp。单调联邦NAM消除了所有单调性违规，将约束形状一致性从0.71提高到1.00，推广到了一种未见的调度策略，并将上行链路流量减少了65%，代价是AUC降低了0.0至0.07。这些结果表明，物理约束联邦加法模型可以支持多租户O-RAN服务保障的可审核SLA风险推断。
英文摘要：
Proactive service assurance in O-RAN requires predicting per-slice SLA violations before they occur. The prediction model must be auditable by operators and must train across base stations without pooling per-slice KPIs, which are commercially sensitive because slices are leased to individual tenants. Neural additive models (NAMs) offer auditability because each KPI contributes through a visible shape function. However, visibility alone does not guarantee physical validity. On the ColO-RAN testbed dataset, unconstrained NAMs learn effects that contradict wireless physics, for example predicting higher risk when channel quality improves. This failure appears under both local and centralized training, and non-IID federated averaging worsens it. We present Monotone FedNAM, a federated additive model in which KPIs with unambiguous physical direction are represented as monotone splines whose constraints survive FedAvg aggregation by construction, while contestable KPIs remain unconstrained. The model trains and operates as a Non-RT RIC rApp and is compact enough for deployment as a Near-RT RIC xApp. Monotone FedNAM eliminates all monotonicity violations, raises constrained shape consistency from 0.71 to 1.00, generalizes to an unseen scheduling policy, and reduces uplink traffic by 65%, at a cost of 0.04 to 0.07 AUC. These results show that physically constrained federated additive models can support auditable SLA risk inference for multi-tenant O-RAN service assurance
