ResLearn-XR：面向扩展现实的网络流量与体验质量感知建模的残差学习框架

中文摘要 AI 辅助

我们提出ResLearn-XR，一种用于预测扩展现实（XR）网络流量和评估体验质量（QoE）风险的残差学习框架。ResLearn-XR采用两阶段时序学习结构，包含基础序列预测模型及针对特定任务的残差学习组件，以提升对突发、非平稳XR流量动态的适应性。残差学习阶段在值空间执行连续XR流量预测，在对数几率空间执行概率QoE风险评估。对于QoE风险分支，我们引入数据描述符算法（DDA），这是一种因果特征构建模块，可将数据包级应用层观测值转换为适用于加密流量分析的帧时序感知描述符。我们还构建了XR流量-QoE数据集，将连续XR流量轨迹与会话级用户报告的QoE标签配对。ResLearn-XR在帧数、帧大小和帧间到达时间预测中，使对称平均绝对百分比误差（SMAPE）降低最多17.84%，在QoE风险估计中，使SMAPE较单阶段基准降低最多87.8%。

英文摘要

We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) risk. ResLearn-XR adopts a two-stage temporal learning structure comprising a base sequence prediction model augmented with task-specific residual learning components to improve adaptability to bursty, non-stationary XR traffic dynamics. The residual learning stages operate in the value space for continuous XR traffic forecasting and in the logit space for probabilistic QoE risk estimation. \rev{For the QoE-risk branch, we introduce a Data Descriptor Algorithm (DDA), a causal feature-construction module that converts packet-level application-layer observables into frame-timing-aware descriptors suitable for encrypted traffic analysis. We also construct an XR Traffic-QoE dataset that pairs continuous XR traffic traces with session-level user-reported QoE labels. ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction, while reducing QoE-risk estimation SMAPE by up to 87.8% over single-stage baselines.

发表机构

• Carleton University（卡尔顿大学）
• Toronto Metropolitan University（多伦多都会大学）

机构由 AI 辅助整理，请以论文原文为准。