Mr. Hassan Nooh


I am a postgraduate research student at University of Southampton Malaysia, affiliated with the School of Engineering at University of Southampton.

I graduated with a BEng in Electronic Engineering from the University of Southampton in 2019. From December 2019 until June 2021, I was an assistant data scientist at Dhiraagu, Male’, Maldives.

I have been involved with research in the field of wireless communications since the summer of 2018, during which I was an EPSRC research intern at the next-generation wireless group at ECS. I completed my undergraduate thesis under the supervision of Prof. Michael Ng. This project – titled “Machine Learning Assisted Caching and Transmission for Next Generation Wireless Systems” investigated a system with multiple UAVs acting as remote radio head units serving users on the ground. The performance of this system was analysed with respect to theoretical bounds.

These experiences sparked my interest in doing further research in the area. This eventually led me to pursue a PhD under the supervision of Dr. SeungHwan Won, Prof. Michael Ng and Dr. Minkwan Kim. 

Research Interest

  1. System design, analysis, and optimisation in non-terrestrial networks
  2. Combinatorial optimisation and deep reinforcement learning
  3. Signal processing and information theory



Mr. Hassan Nooh

University of Southampton Malaysia

C0301, C0302, C0401, Blok C Eko Galleria, 3, Jalan Eko Botani 3/2, Taman Eko Botani, 79100 Nusajaya, Johor, Malaysia


Current Research Project

Title: Hierarchically Layered Intelligent Non-Terrestrial Networks

For key application scenarios in future wireless systems (e.g., global coverage extension to unserved & underserved areas, and to realise the technical requirements for eMBB, mMTC and URLLC) a multi-layered non-terrestrial network architecture with intelligent decision-making capabilities is essential. While several configurations and application specific scenarios can be conceived within this general scope, many of the challenges that are yet to be addressed in literature tend to have a common theme with combinatorially large search spaces. Most such problems are NP-hard, and we may need to settle for approximate solution methods. These ailments are further exacerbated as requirements become increasingly stringent in time, capacity, and resource usage – among others.

Recent advancements in deep reinforcement learning (DRL) provide a general framework to tackle problems in the combinatorial optimisation landscape with increasingly encouraging results. The inherent data driven nature of these algorithms enables the structure of the problem to be exploited. While we do not need to restrict our attention to DRL alone, this provides us an opportunity to explore and characterise the performance bounds and feasibility of such systems utilising DRL-based architectures against other alternatives such as swarm techniques and evolutionary strategies.