Publication Date
Fall 2023
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Genya Ishigaki
Third Advisor
Abhishek Roy
Keywords
O-RAN, 5G, QoE, AI/ML
Abstract
Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into opensource elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user’s subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program and control RAN functions using AI/ML-based models. We argue that various QoE parameters can be measured and operated by the RIC in O-RAN. We propose to improve the efficiency of O-RAN’s radio resources by creating a RIC xApp that estimates the QoE measured using Video Mean Opinion of Score (MOS), and accurately optimizes the usage of radio resources across multiple network slices. We use predictive AI/ML-based models to accurately predict the QoE parameters in the network after which we can optimize the usage of network components leading to an enhanced user experience. Simulation results on 2 simulated data sets show that our proposed approach can achieve up to 95% QoE prediction accuracy.
Recommended Citation
Kulkarni, Aditya, "Efficient Video QoE Prediction in Intelligent O-RANs" (2023). Master's Projects. 1307.
https://scholarworks.sjsu.edu/etd_projects/1307
