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The MSc (Communications Engineering) is designed for aspiring engineers and information technologists who wish to improve their knowledge and skills in the broad area of communications engineering.
The courses offered cover various important topics in telecommunications, RF engineering and wireless communications.
Have a good relevant bachelor's degree
Relevant working experience is an advantage
For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:
TOEFL Score (Test dates must be within 2 years or less from the date of application):
≥ 563 (paper-based)
≥ 223 (computer-based)
≥ 85 (internet-based)
IELTS Score (Test date must be within 2 years or less from the date of application):
≥ 6.0
Applicants without TOEFL/IELTS would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.
Programme Structure
There are two options of study, one with coursework only, and the other with coursework and dissertation. Each course is of 3 AUs, and consists of 39 hours of lectures. Candidates who undertake a project of 6 AUs must submit a dissertation on it. Students are encouraged to choose the dissertation option only if they have a strong interest in pursuing further research studies.
Option 1Coursework Only | Option 2Coursework + Dissertation |
10 Courses (30 AUs in total) | 8 Courses + Dissertation Project (30 AUs in total) |
4 specialized electives (≥ 12 AUs) | 4 specialized electives ((≥ 12 AUs) |
6 general electives (≤ 18 AUs) | 4 general electives (≤ 12 AUs) |
| Dissertation (6 AUs) |
Note: The programme structure will be subject to change without prior notice.
Duration
Both full-time and part-time programmes are offered (unless stated). Part-time candidates are expected to obtain permission from their employer before admission to the programme. Most classes are conducted in the evenings, while examinations are conducted during office hours.
Type of Coursework Programme | Minimum Candidature | Maximum Candidature |
Master of Science (Full Time) | 1 year | 3 year |
Master of Science (Part Time) | 2 year | 4 year |
Programme Calendar
| Semester 1 | August to December |
| Semester 2 | January to May |
| Week 1 to 14 | Lecture (Inclusive of 1-week recess) |
| Week 15 to 17 | Examinations |
| Other | Vacation |
Graduate courses offered by Master of Science (MSc) Communications Engineering:
Specialized Elective Courses (Students are required to take a min of 4 out of all the 6 specialized elective courses)
| Course Code | Course Title | Course Content | AUs |
| EE6101 | DIGITAL COMMUNICATION SYSTEMS | Communication signals and baseband transmission. Digital modulation/demodulation. Error correction coding. Spread-spectrum techniques. | 3 |
| EE6108 | COMPUTER NETWORKS | Network protocols and services. Transport protocols and services. Local area networks. Wide area networks and internetworking. Broadband and Asynchronous Transfer Mode (ATM) networks. | 3 |
| EE6111 | 5G COMMUNICATION & BEYOND | Evolution of Mobile Standards. Modern Communications Standards (3GPP, IEEE Standards, etc). Fundamental of wireless communications (wireless channel, channel capacity, etc). Multicarrier Communications. MIMO System. Multi-user MIMO System. Cooperative Communications (COMP, Relay, etc). 5G and Supporting Technology. Machine Type Communications (M2M, IoT, LPWAN, etc). IMT 2023 - 6G Standards 11. IMT 2023 - 6G Supporting Technology. Guest Lectures from industry / overseas | 3 |
| EE6122 | OPTICAL FIBRE COMMUNICATIONS | Optical fibre fundamentals. System components. Optical fibre transmission systems. WDM systems and subsystems. Optical networks. Measurement techniques. | 3 |
| EE6128 | RF CIRCUITS FOR WIRELESS COMMUNICATIONS | Microstrip Line and Network Parameters. Microwave Power Dividers and couplers. Microwave Filters. Amplifiers. Oscillators and Synthesizers. Detectors and Mixers. Frequency Multipliers and Control Circuits. RF Receiver Design. | 3 |
| EE6129 | WIRELESS & MOBILE RADIO SYSTEMS | Wireless channel models. Fading and ISI mitigation techniques. Cellular concept and Multiple access techniques. Satellite communications. | 3 |
General Elective Courses
| Course Code | Course Title | Course Content | AUs |
| EE6008 | COLLABORATIVE RESEARCH & DEVELOPMENT PROJECT | Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration | 3 |
| EE6010 | PROJECT MANAGEMENT & TECHNOPRENEURSHIP | Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship. | 3 |
| EE6102 | CYBER SECURITY & BLOCKCHAIN TECHNOLOGY | Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies | 3 |
| EE6223 | COMPUTER CONTROL NETWORKS | Data Networks in Control and Automation. Local Area Network Concepts and Fieldbus. Application Layer of Fieldbus and MAP. Internetworking and Protocols. Real-time Operating Systems and Distributed Control. Network Performance and Planning. Multimedia in Advanced Control and Instrumentation. | 3 |
| EE6285 | COMPUTATIONAL INTELLIGENCE | Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications | 3 |
| EE6301 | SMART BIOSENSORS & SYSTEMS FOR HEALTHCARE | Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging | 3 |
| EE6303 | ELECTROMAGNETIC COMPATIBILITY DESIGN | EMC Regulatory Requirements. Non-Ideal Behaviors of Passive Components. Conducted EMI and Filter Design. Electromagnetic Shielding. Basic Grounding Concept. Crosstalk. Printed Circuit Board Layout and Radiated EMI. Electrostatic Discharge. Radio Frequency Interference. Emission and Susceptibility Measurement Methods. | 3 |
| EE6401 | ADVANCED DIGITAL SIGNAL PROCESSING | Discrete-time signal analysis and filter design. Multi-rate digital signal processing. Linear prediction and optimal linear filters. Power spectrum estimation. | 3 |
| EE6403 | DISTRIBUTED MULTIMEDIA SYSTEMS | Discrete signal analysis and digital filters. Power spectrum estimation. Linear prediction and optimal linear filters. Multi-rate digital signal processing. DSP Architectures and applications. | 3 |
| EE6405 | NATURAL LANGUAGE PROCESSING | Traditional: Bag-of-words, Preprocessing, Term weighting scheme, Feature extraction,. Topic modeling , ML classifiers and clustering methods, Evaluation Metrics, Word Embeddings Deep Neural Networks: Graph convolutional network, Seq2Seq, Attention mechanism, Transformers and self-attention, Pretrained Language Models, Fine-tuning (hyper-parameter tuning), Applications (chatbot, machine translation, sentiment analysis, summarisation, classification, generation, auto-complete) | 3 |
| EE6406 | ANALYTIC AND ENSEMBLE LEARNING | This course aims to equip students with a foundational understanding of machine learning from both analytic and ensemble perspectives. The curriculum is designed to offer an overview of the key technologies underpinning modern machine learning and deep learning. Initially, the course will recap the basic components of machine learning. It will then delve into fundamental learning techniques, including linear methods for regression, linear methods for classification, and polynomial plus kernel methods—all grounded in linear algebra. Subsequently, students will explore applied ensemble methods based on bagging and boosting. The course will cover classical ensemble methods such as Random Forest, AdaBoost, and standard Gradient Boosting, as well as more advanced boosting techniques, including XGBoost and LightGBM, which find practical applications in the industry. In addition, the course will also introduce reinforcement learning and its connection with ensemble learning. To highlight the practical relevance, the course will include examples from industrial contexts. | 3 |
| EE6407 | GENETIC ALGORITHMS & MACHINE LEARNING | Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods. | 3 |
| EE6427 | VIDEO SIGNAL PROCESSING | Image and Video Basics. Image and Video Transform Coding. Filtering and Error Resilience for Image and Video. Image and Video Coding Principles and Standards. Recent and Emerging Topics in Image and Video Processing. | 3 |
| EE6483 | ARTIFICIAL INTELLIGENCE & DATA MINING | Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis. | 3 |
| EE6497 | PATTERN RECOGNITION & DEEP LEARNING | Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning. | 3 |
| EE6509 | RENEWABLE ENERGY SYSTEMS IN SMART GRIDS | Introduction to Power Systems with Distributed Generation. Distributed Generation. Energy Storage. Smart Grids. | 3 |
| EE7401 | PROBABILITY & RANDOM PROCESSES | Probability concepts. Random variables. Multiple random variables. Sum of random variables and multidimensional distributions. Random Sequences. Probability density function estimation. Random variable simulation. Random processes. Correlation functions. Spectral density. Random processes | 3 |
| EE7402 | STATISTICAL SIGNAL PROCESSING | Signal Estimation Theory. Properties of Estimators. Sequential estimation methods. Fundamentals of Detection Theory. Detection of Deterministic and Random Signals. | 3 |
| EE7403 | IMAGE ANALYSIS & PATTERN RECOGNITION | Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction. | 3 |
| EE7207 | NEURAL NETWORKS & DEEP LEARNING | This course is intended to provide PhD students with an in depth understanding of the fundamental theories and learning methods, as well as advanced issues of neural networks and fuzzy logic systems. After the course, the students will be able to apply the learned knowledge to solve problems in their respective research fields. | 3 |
Special Term - General Elective Courses
| EE6131 | SPECIAL TOPICS IN COMMUNICATION NETWORKS: DESIGN & ANALYSIS OF ALGORITHMS | The course provides a foundation in algorithm design and analysis, focusing on problem-solving and computational efficiency. It prepares students to optimize solutions and implement effective algorithms, with rich discussions, examples and hands-on experiments on design and application of optimization algorithms in communication networks, smart grid, and IoT etc. |
| EE6230 | MULTI-ROBOT SYSTEMS | This course aims to equip you with a comprehensive understanding of the models, principles, methodologies, and technologies for multi-robot systems, focusing on their models, dynamics, control, information exchange, coordination, and optimization. This course is designed for graduate students with backgrounds in robotics, automation, control, computer science, electrical engineering, mechanical engineering, or related fields. By exploring real-world applications such as autonomous vehicles, swarm robotics, and collaborative industrial robots, students will gain both theoretical insights and practical skills essential for careers in robotics R&D, AI-driven automation, and emerging fields like human-robot interaction and autonomous systems. Whether preparing for cutting-edge research or tackling complex challenges in robotics-related industries, this course offers you a vital foundation for future innovation and impact. |
| EE6231 | SPECIAL TOPICS ON REINFORCEMENT LEARNING | Reinforcement learning is inherently interdisciplinary, combining principles from machine learning and control theory. The course aims to equip you with expertise in a cutting-edge AI field, empowering students with the skills needed for innovation, research, and impactful applications in academia and industry. After completing this course, you should understand core concepts and theories in reinforcement learning and implement and evaluate key reinforcement learning algorithms. You can apply the algorithms to real-world problems, especially in autonomous systems, robotics, financial technology, and healthcare. |
| EE6317 | CHIP SECURITY WITH MACHINE LEARNING | The objective of this course is to provide graduate students with an understanding of security challenges in chip design and manufacturing, and how machine learning can be used to address them. Students will learn to identify and mitigate hardware vulnerabilities such as hardware trojans, counterfeit ICs, and side-channel attacks. The course covers the chip development cycle, basic and advanced machine learning models, and their application in chip security analysis. Additionally, students will explore design-for-trust practices to ensure security from design to production. By the end, they will be able to analyze threats and apply secure design principles using machine learning. |
| EE6430 | SPECIAL TOPICS IN MACHINE LEARNING | To propose the new course of “Special topics in machine learning” and to make this course general, so that it can be tailored to one of the special topics in machine learning. The special topics include, but not limited to one of the following: - Reinforcement learning, - Explainable AI, - Large language model, - Natural language processing, - Generative modelling |
| EE6431 | SPECIAL TOPICS IN COMPUTER VISION | Computer vision has become an integral part of our daily lives, yet it presents diverse practical challenges across different application scenarios. This course aims to provide you a comprehensive foundation in computer vision principles and their practical applications, focusing on state-of-the-art solutions driven by deep learning. You will explore milestone models and influential research papers, gaining insights into tackling computer vision tasks under varying constraints of data, labels, and deployment. |
| EE6515 | ENERGY STORAGE SYSTEMS & APPLICATIONS IN POWER SYSTEMS | This course aims to equip students with a comprehensive understanding of energy storage systems (ESS) and their critical role in modern power systems. By bridging technical, economic, and sustainability aspects, the course provides in-depth knowledge of ESS technologies, economic evaluation, market participation, and lifecycle management. It prepares students to tackle real-world challenges such as grid stability, renewable energy integration, and sustainable battery use. With a multidisciplinary approach and opportunities for industry collaboration, the course empowers students with advanced skills to innovate, lead, and contribute effectively to the evolving energy landscape and the global energy transition. |
Note: the above curriculum is subject to change.