针对无线网络时序数据，提出专有预训练模型APEX，实现精准预测与异常检测。
APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations
- 设计网络原生的解码器仅变压器架构，适配突发、零值密集的网络时序数据
- 在4天预测任务中比最强基线低18%误差，异常检测F1达0.93
- 提供轻量版可部署于边缘设备，支持亚秒级隐私保护推理
通用时间序列基础模型在无线网络遥测数据上迁移效果差，因其信号具有突发性、零值密集且跨协议层耦合。本文提出APEX，一种面向企业接入点（AP）遥测的网络原生解码器仅变压器模型，并以DHCP退化作为典型网络任务进行评估。APEX在来自约4,500个生产无线网络的10通道多变量遥测数据上预训练（约10万条AP时间序列，每台AP含34项指标），提供云端版APEX-Large（269M参数）和边缘版APEX-Edge（10.5M参数）。在192步（4天）的DHCP退化基准测试中，APEX-Large相较最强基础模型基线Toto降低18%的平均绝对误差，较SARIMA降低38%，异常检测F1达0.93；APEX-Edge则可在接入点级边缘硬件上实现亚秒级、隐私保护的推理。结果表明，网络原生预训练是实现主动无线运维的可行基础。
原文摘要 · Abstract (English)
Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative network task. APEX is pre-trained on 10-channel multivariate telemetry from ~4,500 production wireless networks (~100K AP time series, 34 metrics per AP), and is available as APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge). On a 192-step (4-day) DHCP degradation benchmark, APEX-Large reduces MAE by 18% over the strongest foundation-model baseline (Toto) and 38% over SARIMA, with anomaly-detection F1 = 0.93, while APEX-Edge enables sub-second, privacy-preserving inference on AP-class edge hardware. These results suggest network-native pre-training is a practical foundation for proactive wireless operations.
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