论文标题
XR服务的ML驱动KQI估计。 360-VIDEO的案例研究
ML-powered KQI estimation for XR services. A case study on 360-Video
论文作者
论文摘要
XR等尖端技术和服务的出现有望改变日常工作的概念。同时，现代和分散的建筑方法的出现已经诞生了新一代的移动网络，例如5G，并概述了B5G和后部的路线图。预计这些网络将成为使元和其他未来派方法栩栩如生的推动者。从这个意义上讲，这项工作提出了一个基于ML的（机器学习）框架，该框架允许估计服务密钥质量指标（KQIS）。为此，仅需要向操作员获取的信息，例如这些网络的统计和配置参数。该策略可防止操作员避免侵入用户数据并保证隐私。为了测试该建议，已选择360-VIDEO作为虚拟现实（VR）的用例，从中估算了特定的KQI，例如视频分辨率，帧速率，初始启动时间，吞吐量和延迟等。为了选择每个KQI的最佳模型，已使用具有交叉验证策略的搜索网格来确定最佳的超参数调整。为了提高每个KQI模型的创建，已经使用了功能工程技术以及交叉验证策略。使用MAE（平均平均误差）和预测时间评估性能。结果指出，KNR（K-Near邻居）和RF（随机森林）是结合特征选择技术的最佳算法。同样，这项工作将有助于基于网络切片，虚拟化和MEC以及其他促进剂技术的E2E质量基于经验的网络管理。
The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.
