Machine Learning Solutions for Quality-Energy Balancing for Rich Media Content Delivery in Heterogeneous Network Environments
Machine Learning (ML) enables scientists to design self-learning solutions which solve important problems which are too complex to solve with classic methods. As the demand for mobile traffic is increasing day by day, 5G networking is intended to govern the infrastructure in the telecommunication industry. This project will design a set of ML solutions to address Quality-Energy balancing when delivering rich media content over heterogeneous 5G networks. The proposed solutions will balance the rich media content, including multi-sensorial video and VR, important time and bitrate requirements, with energy efficiency goals set for devices and networks. Machine Learning solutions are predicted to help in making the 5G solution feasible. The project will involve network simulations and prototyping Bringing Machine Learning Solutions/Algorithms into 5G infrastructure for various applications involves a lot of challenges that need to be addressed before beginning any project or research: 1. Interpretability of results 2. Computational Power required by ML Algorithms 3. The Long training times of some ML Algorithms 4. Maximization of the utilization of the unlicensed spectrum 5. Opportunistic exploitation of white spaces 6. Adaptive Leasing between carriers 7. To run the new applications like VR, multi-sensorial videos above 30 GHz in a mobile phone, the upcoming mobile phones and devices need smaller and adaptive antennas to receive the higher frequency waves. 8. Most important Challenge for the delivery of rich media content is the availability of real data. Any ML algorithm needs high quality data for it’s working and the type of data decides which type of learning to use. Generating datasets from computer simulators (Ns3) is not always a good practice as the ML algorithm will end up learning the rules and the environment with which the simulator was programmed. The main point of using ML is to learn from the real data which will not happen when we generate datasets from computer simulators. The availability of real datasets available for 5G is one of the biggest challenges. 9. Another important challenge is to apply the correct kind of distribution to our specific 5G application and which algorithm works well on the specific data. The end goal of ML algorithms is to optimize and improve the delivery of rich media content over heterogeneous 5G Networks.