Open AccessCART- A Statistical Model for Predicting QoE using Machine Learning in Smartphones
Author(s) -
R. Renee Christina,
Greeshma Liz Shajan,
B. Ankayarkanni
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/590/1/012001
Subject(s) - computer science , quality of experience , benchmark (surveying) , gadget , machine learning , phone , cellular network , multimedia , quality (philosophy) , quality of service , artificial intelligence , computer network , linguistics , philosophy , geodesy , epistemology , algorithm , geography
The proliferation of fast, cheap, and ubiquitous network access, in particular on mobile devices, has significantly changed the way user’s access online benefits on an everyday premise. Alongside the expanded accessibility of a dependably on Internet, new advancements, for example, LTE have empowered the utilization of a wide range of administrations, with stringent necessities regarding system execution and limit requests. We consider different QoE appraisal models in view of administered machine learning strategies, which are proficient to foresee the QoE experienced by the end client of well-known cell phone applications (e.g., YouTube and Face book), utilizing as info the inactive in-gadget estimations. In this extraordinary issue, we investigate the connection between the Quality of Service (QoS) that administrators screen and oversee and the Quality of Experience (QoE) that the clients really get. Utilizing a rich QoE dataset got from field trials in operational cell systems, we benchmark the execution of various machine learning based indicators, and build a choice tree based model which is able to foresee the per-client general understanding and administration worthiness with a win rate of 91% and 98% separately. To the best of our knowledge, this is the initial proposal using end-user, in-device passive measurements and machine learning models to predict the QoE of smartphone users in operational cellular networks.