Dr Tan Chee Keong
Dr. Tan Chee Keong is a senior lecturer attached to the School of Information Technology, Monash University, Malaysia. He received the degrees of B.Eng (Hons) in Electronics majoring in Telecommunication, M.Eng.Sc (Information and Communication Technology) and Ph.D (Information and Communication Technology) from Multimedia University in 2006, 2009 and 2014, respectively.
Dr. Tan is a networking expert and consultant with over 10 years of experience in networking and telecommunication research, lectures and practices. He is also an experienced speaker and teacher, having taught many highly-rated university level courses, industry short courses, tutorials at conferences, to a wide range of audiences from industry, academia and government. In 2016, his teaching contribution was recognized with the prestigious award i.e. Best Teaching Award in from Multimedia University.
Specializing in network system design, Dr. Tan has carried out projects for telecommunication companies and cellular service providers which led to the development of several patents on network algorithm and protocol design. Dr. Tan’s research activities are mainly funded by government and industrial grants in which he has successfully solicited grants worth more than RM 800K as principal or co-principal investigator. Furthermore, he is also the main contributing authors to more than 10 international journal papers, particularly on the radio resource management for various wireless networks.
In addition, Dr. Tan also play a key role in establishing the collaboration between industries and academia.
- Doctor of Philosophy in Engineering (Information and Communication Technology), Multimedia University, 2014
- Master in Engineering Science (Information and Communication Technology), Multimedia University, 2009
- Degree in Engineering (Hons) Electronics majoring in Telecommunications, Multimedia University, 2006
Member of National Professional Bodies
- Board of Engineering Malaysia (BEM), Member
Member of International Professional Bodies
- IEEE, Member
Software-defined networks, beyond 5G networks, indoor positioning scheme, game theory, machine learning and data science.
Title: Machine learning based Knowledge-Defined Networking (KDN) Orchestration for NFV Resource Management
Knowledge-defined networking (KDN) is a new networking paradigm that mainly relies on software-defined networking (SDN) and machine learning (ML) that may potentially shift the way we operate, optimize and troubleshoot data networks. A knowledge plane (KP) is defined in the context of the SDN paradigm which is able to learn the behaviour of the network and autonomously operate the network accordingly. Based on the network analytics collected by the management plane (i.e., pre-processed data or raw data), KP transforms them into knowledge via ML and uses that knowledge to make decisions automatically or manually (through human intervention).
The resource management problem in a network function virtualization (NFV) is a complex problem due to the complexity of the optimal placement of virtual network functions (VNFs) in NFV deployments. The introduction of KDN paradigm can addresses many challenges posed by NFV resource management problem by characterizing, via ML, the behaviour of a VNF as a function of the collected analytics such as traffic characterization, network element characterization, or network performance modelling. This is useful to optimize the placement of the VNFs and thereby optimize the overall networks.
Title: Energy sustainable paradigms for 5G and Beyond 5G Heterogenous (B5G) Networks
The massive deployment of information and communication technology (ICT) undoubtedly increases the energy consumption by the telecommunication infrastructure and its carbon footprint. The 5G or beyond 5G (B5G) enabling technologies must not only offer high spectral efficiency, but are also required to shift to green energy based networks. It is envisioned globally that the future cellular communications have to achieve 20% of energy needs from green energy by 2020 and 30% in 2035. Therefore, in most of the recent deployment of base stations, they are expected to be equipped with energy harvesters (such as solar panels, wind turbines, etc.) other that the conventional grid power. However, merely relying on renewable energy to operate base station might be a very risky strategy as unreliable energy harvested might degrade the quality of service and reduce spectral efficiency of the systems. Furthermore, each energy harvesting base station of a heterogeneous network has a different harvesting capability and the energy consumption for each cell is inconsistent due to diverse user distribution in each cell. This eventually results in unbalanced usage of the harvested energy. The aim of this work is to intelligently share the harvested energy among all base station to maximize energy efficiency (minimize grid power consumption) and at the same time ensuring all users achieve certain levels of quality of service.
New Energy Sustainable Paradigm for 5G and B5G Networks
- The Realization of Wi-Fi Mesh Networks Using Cross-layer Optimization Technique, Tan Chee Keong (PL), June 2012 – May 2014, TM R&D, RM 87,000
- On the Integration of Geometric Energies in Level Set Image Segmentation, Tan Chee Keong (PL), August 2014– July 2016, ERGS, MOHE Malaysia, RM 89,000
- Optimization of Cell Association and Load Balancing in Heterogeneous LTE-Advanced Cellular Networks, Tan Chee Keong (PL), November 2016 – April 2019, TM R&D, RM 260,400
- Energy-efficient Resource Allocation with Interference Mitigation for Cognitive Heterogeneous Cloud Radio Access Network (CH-CRAN), Tan Chee Keong (PL), 1 June 2017 – 31 May 2020, MMU Graduate Research Assistant Scheme, RM 118,800
- Energy-efficient Interference Management Techniques for Multi-cell Multi-tier Heterogeneous Networks, Tan Chee Keong, 1 December 2017 – 30 November 2020, MMU Graduate Research Assistant Scheme, RM 111,600
Osama M.S. Abu Ajwa
Capacity and Coverage Enhancement in 5G Heterogeneous Networks
2017 - Present
Energy-efficient Resource Allocation with Interference Mitigation for Cognitive Heterogeneous Cloud Radio Access Networks (CH-CRAN)
2017 - Present
MHD Amen Summakieh
Optimisation of Cell Association and Load Balancing in Heterogeneous LTE-Advanced Cellular Networks
2017 - Present
Energy-Efficient Interference Management Techniques For Multi-Cell Multi-Tier Hetnet
2018 - Present
Luah Aik Jin
Power Control Algorithm Based on Non-cooperative Game in Cognitive Radio Networks
Md. Kamal Hossain
The Realization of Wi-Fi Mesh Networks Using Cross-layer Optimization Technique
Fong Kok Leong
Enabling Spectrum-Aware Radio Access for Cognitive Radio Networks Using Machine Learning Approach
Investigation of Corner Reflector on Microstrip Antenna
- Excellent Teaching Award, MMU Staff Award 2016, Telekom Malaysia, 2016