发表机构
School of Electronic and Information Engineering,Beijing Jiaotong University（北京交通大学电子与信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城轨异构业务流量分类难题，提出优先级感知双通道特征融合框架，结合迁移学习ResNet与轻量CNN提取灰度图像特征，并设计优先级敏感损失，在真实数据集上实现98.74%准确率与低延迟边缘部署。
AI 中文摘要
随着5G与物联网在城轨交通中的深度融合，业务流量呈爆炸式增长，对异构业务流的准确分类对于铁路安全高效运营至关重要。城轨通信系统不仅面临设备故障、维护干扰等运行扰动，还面临多业务流量交织的复杂传输模式。在此运行条件下，专有协议的广泛部署和加密流量的高普及进一步削弱了传统基于端口和深度包检测（DPI）分类方法的适用性。为解决这些挑战，我们提出了一种优先级感知的双通道特征融合框架。原始流量字节和统计特征被映射为灰度图像，并由双分支架构处理：迁移学习增强的ResNet提取细粒度字节级纹理，而轻量级CNN捕获宏观统计模式。通道注意力机制动态重新校准跨模态特征，并提出一种新颖的优先级敏感损失（PSL），该损失将业务关键性感知与类别平衡加权相结合，以最大化安全关键业务的召回率。在真实城轨数据集上使用优先级加权指标评估，该方法实现了98.74%的准确率和99.24%的加权召回率，在两个安全关键业务——基于通信的列车控制（CBTC）和紧急无线电调度（ERD）上的召回率分别为99.94%和99.41%，为城轨通信中的优先级感知资源调度提供了可靠的分类基础。该方法仅需0.18M参数和0.4–0.7毫秒端到端延迟，在高精度分类、低推理延迟和边缘部署可行性之间实现了出色平衡。
英文摘要
With the deep integration of 5G and IoT in urban rail transit, service traffic grows explosively and accurate classification of heterogeneous service flows is essential for safe and efficient railway operations. Urban rail communication systems are subject not only to operational disturbances such as equipment failures and maintenance interference, but also to complex transmission patterns characterized by the interleaving of multi-service traffic flows. Under these operating conditions, the widespread deployment of proprietary protocols and the high prevalence of encrypted traffic further diminish the applicability of conventional port-based and Deep Packet Inspection (DPI) classification methods. To address these challenges, we propose a priority-aware dual-channel feature fusion framework. Raw traffic bytes and statistical features are mapped into grayscale images and processed by a dual-branch architecture: a transfer learning-enhanced ResNet extracts fine-grained byte-level textures, while a lightweight CNN captures macroscopic statistical patterns. A channel attention mechanism dynamically recalibrates cross-modal features, and a novel Priority-Sensitive Loss (PSL) that integrates business-criticality awareness with class-balance weighting to maximize recall for safety-critical services. Evaluated on a real-world urban rail dataset using priority-weighted metrics, the method achieves 98.74\% accuracy and 99.24\% weighted recall, with recall of 99.94\% and 99.41\% on the two safety-critical services:Communication-Based Train Control(CBTC) and Emergency Radio Dispatch(ERD), providing a reliable classification foundation for priority-aware resource scheduling in urban rail communications. With only 0.18M parameters and 0.4--0.7 ms end-to-end latency, offering an excellent balance between high-precision classification, low inference latency, and edge-deployment feasibility.
Comments15 pages, 9 figures
