用Transformer改进元宇宙流量预测，峰值准确率提升99%
ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction
- 基于Transformer设计渐进误差学习框架，提升时序预测精度
- 在真实VR/AR/MR数据集上，峰值时段预测误差降低99%
- 兼顾隐私保护与实时管理，适合网络服务商部署
本文提出一种面向元宇宙网络流量预测的综合性解决方案，应对扩展现实（XR）服务日益增长的智能资源管理需求。首先构建了一个先进的测试平台，采集并公开了虚拟现实（VR）、增强现实（AR）和混合现实（MR）的真实世界流量数据集。为提升预测精度，提出一种新型视图帧（VF）算法，可精准识别流量中的视频帧，同时保障隐私合规；并开发基于Transformer的渐进误差学习算法，命名为ResLearn。该方法利用全连接神经网络有效降低预测误差，尤其在流量高峰时段表现突出，相比以往方法性能提升99%。研究成果为互联网服务提供商（ISPs）提供了可靠的实时网络管理工具，有助于满足服务质量（QoS）要求，优化元宇宙用户体验。
原文摘要 · Abstract (English)
Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。