用迁移学习让模拟网络数据更贴近真实，少量实测数据就能大幅提准。
Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning
- 用预训练模型+小量真实数据微调，实现模拟与真实数据的高效融合。
- 仅10组真实场景即降低37%延迟预测误差，50组达48%。
- 适合网络仿真、故障预测等缺乏实测数据的场景。
基于机器学习的网络模型能快速准确预测复杂网络行为，但需大量训练数据。真实网络数据采集成本高且受限，尤其在故障等关键场景下。因此研究者常依赖模拟数据，导致模型部署到真实环境时精度下降。本文提出一种混合方法，利用迁移学习融合模拟与真实数据。以RouteNet-Fermi为例，通过少量真实数据微调预训练模型，显著提升性能。实验使用OMNeT++和自建测试平台，使包延迟预测的平均绝对百分比误差（MAPE）最高降低88%。仅需10个真实场景，MAPE下降37%；50个场景时下降48%。
原文摘要 · Abstract (English)
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real networks is often costly and limited, especially for critical scenarios like failures. As a result, researchers commonly rely on simulated data, which reduces accuracy when models are deployed in real environments. We propose a hybrid approach leveraging transfer learning to combine simulated and real-world data. Using RouteNet-Fermi, we show that fine-tuning a pre-trained model with a small real dataset significantly improves performance. Our experiments with OMNeT++ and a custom testbed reduce the Mean Absolute Percentage Error (MAPE) in packet delay prediction by up to 88%. With just 10 real scenarios, MAPE drops by 37%, and with 50 scenarios, by 48%.
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