Felipe S. Dantas Silva, M. Lima, Charles H. F. Santos, Augusto Neto
{"title":"6G网络中QoE预测的机器学习算法评估","authors":"Felipe S. Dantas Silva, M. Lima, Charles H. F. Santos, Augusto Neto","doi":"10.5753/w6g.2023.823","DOIUrl":null,"url":null,"abstract":"This paper evaluates the performance of Machine Learning (ML) algorithms to provide Quality of Experience (QoE) user decisions in multimedia services. A specialized dataset was built for mapping network Quality of Service (QoS) Key Performance Indicators (KPIs) with video quality metrics to perform QoE predictions. An evaluation is then carried out considering the main regression models to support the development of QoE-aware systems and meet the critical requirements of applications characteristic of 6G scenarios.","PeriodicalId":397027,"journal":{"name":"Anais do III Workshop de Redes 6G (W6G 2023)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Avaliação de Algoritmos de Aprendizado de Máquina para Predição de QoE em Redes 6G\",\"authors\":\"Felipe S. Dantas Silva, M. Lima, Charles H. F. Santos, Augusto Neto\",\"doi\":\"10.5753/w6g.2023.823\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper evaluates the performance of Machine Learning (ML) algorithms to provide Quality of Experience (QoE) user decisions in multimedia services. A specialized dataset was built for mapping network Quality of Service (QoS) Key Performance Indicators (KPIs) with video quality metrics to perform QoE predictions. An evaluation is then carried out considering the main regression models to support the development of QoE-aware systems and meet the critical requirements of applications characteristic of 6G scenarios.\",\"PeriodicalId\":397027,\"journal\":{\"name\":\"Anais do III Workshop de Redes 6G (W6G 2023)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anais do III Workshop de Redes 6G (W6G 2023)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5753/w6g.2023.823\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do III Workshop de Redes 6G (W6G 2023)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/w6g.2023.823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Avaliação de Algoritmos de Aprendizado de Máquina para Predição de QoE em Redes 6G
This paper evaluates the performance of Machine Learning (ML) algorithms to provide Quality of Experience (QoE) user decisions in multimedia services. A specialized dataset was built for mapping network Quality of Service (QoS) Key Performance Indicators (KPIs) with video quality metrics to perform QoE predictions. An evaluation is then carried out considering the main regression models to support the development of QoE-aware systems and meet the critical requirements of applications characteristic of 6G scenarios.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
