Project TitleDeep Reinforcement Learning for Joint Video Adaption and Resource Allocation in Cloud Radio Access Networks
LocationSg Long Campus
Stipend RM2,500.00 
Entry Requirement1. Master's Degree OR Bachelor's Degree (First Class Honours) in a field related to Electrical, Electronic, Telecommunications Engineering, and Computer Science.
2. Able to work with minimal supervision, self-motivated and independent
3. Good track records in journal publications
Specific Skills / Knowledge RequiredNext generation networks are envisioned to support dynamic video content delivery and agile network management, in order to maximize the user quality of experience (QoE). Cloud radio access network (CRAN) emerges as a promising candidate since the limited network resources can be virtualized and shared among distributed remote radio heads (RRHs). To reconcile with the fluctuation in RRH wireless links, video clients can select a video chunk/segment of appropriate bitrate by adopting the popular dynamic adaptive streaming over HTTP (DASH) technique. Inspired by the recent success of deep reinforcement learning (DRL), this project aims to maximize the long-term QoE by jointly considering DASH video adaption and resource allocation in a CRAN environment.
Field of Study Related to the Research Project Doctor of Philosophy (Engineering)
How to ApplyIf you are interested, please contact Ir. Dr. Ts. Tham Mau Luen via email at