In sixth-generation (6G) Open Radio Access Networks (O-RAN), proactive control is preferable. A key open challenge is delivering control-grade predictions within Near-Real-Time (Near-RT) latency and computational constraints under multi-timescale dynamics. We therefore cast RAN Intelligent Controller (RIC) analytics as an agentic perceive-predict xApp that turns noisy, multivariate RAN telemetry into short-horizon per-User Equipment (UE) key performance indicator (KPI) forecasts to drive anticipatory control. In this regard, Transformers are powerful for sequence learning and time-series forecasting, but they are memory-intensive, which limits Near-RT RIC use. Therefore, we need models that maintain accuracy while reducing latency and data movement. To this end, we propose a lightweight Multi-Scale Structured State-Space Mixtures (MS3M) forecaster that mixes HiPPO-LegS kernels to capture multi-timescale radio dynamics. We develop stable discrete state-space models (SSMs) via bilinear (Tustin) discretization and apply their causal impulse responses as per-feature depthwise convolutions. Squeeze-and-Excitation gating dynamically reweights KPI channels as conditions change, and a compact gated channel-mixing layer models cross-feature nonlinearities without Transformer-level cost. The model is KPI-agnostic -- Reference Signal Received Power (RSRP) serves as a canonical use case -- and is trained on sliding windows to predict the immediate next step. Empirical evaluations conducted using our bespoke O-RAN testbed KPI time-series dataset (59,441 windows across 13 KPIs). Crucially for O-RAN constraints, MS3M achieves a 0.057 s per-inference latency with 0.70M parameters, yielding 3-10x lower latency than the Transformer baselines evaluated on the same hardware, while maintaining competitive accuracy.

翻译：在第六代（6G）开放无线接入网络（O-RAN）中，主动控制是更优选择。一个关键开放挑战在于：在多时间尺度动态条件下，于近实时（Near-RT）时延与计算约束内提供控制级预测。因此，我们将RAN智能控制器（RIC）分析建模为一种智能感知-预测型xApp，该应用将含噪声的多变量RAN遥测数据转化为面向单个用户设备（UE）的短时关键性能指标（KPI）预测，以驱动前瞻性控制。在此背景下，Transformer虽在序列学习与时间序列预测方面性能强大，但其高内存需求限制了其在近实时RIC中的应用。因此，我们需要在保持精度的同时降低时延与数据移动量的模型。为此，我们提出一种轻量级多尺度结构化状态空间混合（MS3M）预测器，通过混合HiPPO-LegS核函数来捕捉多时间尺度无线动态。我们采用双线性（Tustin）离散化方法建立稳定的离散状态空间模型（SSM），并将其因果冲激响应作为逐特征深度可分离卷积。挤压激励（Squeeze-and-Excitation）门控机制根据条件变化动态重加权KPI通道，紧凑的门控通道混合层则在不产生Transformer级计算成本的前提下建模跨特征非线性关系。该模型具有KPI无关性——以参考信号接收功率（RSRP）作为典型用例——并通过滑动窗口训练进行单步预测。实验评估基于我们定制的O-RAN测试平台KPI时间序列数据集（涵盖13项KPI的59,441个窗口）。关键的是，在O-RAN约束下，MS3M以0.70M参数实现单次推理0.057秒时延，相较于同硬件上评估的Transformer基线模型获得3-10倍时延降低，同时保持具有竞争力的精度。