**There are a number of fully funded full-time and part-time places available for students resident in or with settled status in Northern Ireland. Funding provided by AICC. All eligible applicants will be considered for funding. Entry requirements and ranking criteria applies. For more information, search 'AI Collaboration Centre QUB'.
This programme is designed as a specialised extension to the study of Electronics at undergraduate level. The programme provides students with the opportunity to deepen their understanding and develop specialist expertise in a range of advanced electronics subjects including microelectronics, sensors, signal processing, hardware and software design, communications, and digital systems.
PLEASE NOTE:
Applications for this course received after 30th June 2025 may not be accepted. In addition, a deposit will be required to secure a place.
Electronics highlights
World Class Facilities
Our facilities have recently undergone a £10m refurbishment and include laboratories for Microengineering, Electronics, Communications, Circuits, Instrumentation, Virtual Reality, Software Engineering, Renewable Energy, Power and Machines. The Queen's Advanced Micro-engineering Centre (QAMEC) is a Centre of Excellence for research and development employing silicon technology and MEMS technology.
Internationally Renowned Experts
An example of our research includes our work in the area of space technologies, where we are involved in a number of projects with the European Space Agency, the new UK Space Applications Catapult Centre, the European High Power Radio-Frequency Space Laboratory and companies such as Astrium, Thales and QinetiQ.
Professional Accreditations
MSc in Electronics is seeking to update its accreditation by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer. Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements.
Students may enrol on a full-time (1 year) or part-time (2 or 3 years) basis. Taught modules are delivered over two semesters.
Full-time students follow taught modules in Semester 1 (Autumn) and Semester 2 (Spring) and they carry out an independent research project and write their MSc thesis (dissertation) in the summer.
Part-time students may take the course over a two or three year period and are required to take at least two modules per year.
An MSc is awarded to candidates who pass six taught modules (120 CATS points) and the individual research project (60 CATS points).
An “MSc with Professional Placement” is awarded to those students who meet these requirements and are able to secure a (minimum) 9-month placement in an appropriate industrial sector (to be approved by the school).
Two other exit qualifications exist: (i) A Postgraduate Diploma is awarded to students who will pass six modules (120 CATS points) and (ii) a Postgraduate Certificate is awarded to students who pass three modules (60 CATS points).
Course Details
The MSc consists of a practical project of a research nature (60 CATS) plus six modules (120 CATS). The PGDip consists of six modules (120 CATS). Modules normally run from September until June, with the project commencing in November and running until the following September.
In any given year further specialist topics may be available or some topics may not be offered.
Programme Modules
This MSc programme in Electronics is designed to train the next generation of Electrical and Electronic Engineers who will have the necessary skills to occupy prestigious positions in the industry or research institutes and the academia. The curriculum involves the following taught modules (the first two modules are compulsory and you need to choose another four):
Note, we constantly review the syllabus to ensure we are up to date and industry relevant, therefore modules are subject to change and not all modules are guaranteed to be offered each year.
MEMS Devices and Technology: Microelectromechanical devices (MEMS devices) are increasingly common in a wide range of applications, e.g. environmental and biomedical sensing, automotive systems and portable electronics. This module will examine the structure and operation of a range of common MEMS devices including pressure sensors and accelerometers. The design of such devices will be explored, as will a range of sensing and actuation methods. The module will include fabrication technology for silicon-based MEMS including details of processes such as material deposition, etching, and wafer bonding.
Microelectronics Devices and Technology: This module offers a detailed discussion of the fabrication and internal electronics of modern silicon devices. Bipolar and MOS technologies are covered from first principles, such that students should be able to apply their learning to all silicon-based devices. You will be introduced to the realities of present-day scaling and the key parameters which control device performance.
Intelligent Systems and Control: Intelligent Systems and Control develops a robust understanding of the major academic topics which define control methods and intelligent algorithms in dynamic systems. Special focus is given on analysis and design methodologies for control systems alongside an introduction to artificial intelligence.
Wireless Communications: This module provides the concepts and techniques required for the generic design of modern wireless communication systems. Wireless communication systems are emerging as a primary enabling technology in the realisation of smarter connected devices in the future digital society. The module will focus on the fundamentals of wireless system design and include a study of progressive trends in communications and challenges posed by the next generation wireless systems.
Wireless Sensor Systems: This module gives an introduction to Wireless Sensor Networks, their capabilities, applications, the Internet of Things (IoT) concept, enabling technologies and standards. It includes collaborative sensing, aggregation of data, data analysis, communication protocols, MAC-layer, routing protocols, energy-aware operation, power management, time synchronization and synchronization protocols.
Control and Estimation Theory: You will learn how to design a stabilising model predictive controller (MPC), which is an advanced control methodology used in modern control applications (robotics, process industry, aerospace, automotive, etc). We will also give an introduction to probability and statistics and learn the basics of estimation theory, with special focus on the Kalman filter and nonlinear state estimation methodologies. The module includes lab sessions where the students acquire hands-on experience in the applications of control and estimation theory.
Digital Signal Processing: This module covers a number of key topics on digital signal processing and its applications. Particular topics include, but are not limited to, Fourier series, Fourier and Laplace transforms, Sampling, Analog and Digital filter design, Adaptive Filters and the celebrated Discrete and Fast Fourier Transforms.
Each student needs to carry out an individual MSc project under the supervision of an academic. We offer a very large number of project proposals every year. Some of our top students publish the results of their work in esteemed international peer-reviewed journals and conferences.
Note that the above taught modules will be offered conditional on having an adequate number of enrolled students. If you would like further information, do not hesitate to contact the course director, Dr Pantelis Sopasakis at p.sopasakis@qub.ac.uk.
You will be taught by a team of experts in their subject areas and are active researchers in those subjects. Often our students conduct their individual research project within an ongoing research project so they get exposed to the state of the art in electrical and electronics engineering.
English Language Support
The school is offering support on the use of English in academic writing. This will help you not only during your studies at Queen’s, but also in your future career.
Mathematics Support
The school is offering additional support on mathematics. Queen’s University is also home to MASH: the Mathematics and Statistics Helpdesk.
Modules
Each module normally involves two hours of lectures per week. Some modules include additional tutorial sessions (where you solve exercises or address practical problems with the help of your lecturer) or laboratory exercises
Transferable Skills
We give a lot of emphasis on the development of transferable skills, such as the communication skills, time management and prioritisation, research and critical thinking, effective CV writing and interview skills, collaboration and more.
Virtual Learning Environment
All modules have a virtual learning environment (using Canvas) where the students can find all relevant material (lecture notes, handouts, video lectures) as well as online quizzes and assignments. Without a doubt, having all learning resources in one place is very useful.
Assessment
Assessments associated with the course are outlined below:
Coursework
Written examination
Project dissertation
What our academics say
The MSc Electronics programme offers an advanced study pathway to develop graduate engineers with the relevant knowledge, skills and professional competencies necessary for employment in technical development, operational analysis, managerial and senior technical positions such as Chief Engineer, or as preparation for further research - particularly at doctoral level.
Dr Pantelis Sopasakis
Course Director, Senior Lecturer, MSc Electronics
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
Devices and technology for microelectromechanical systems (MEMS).
Design of MEMS devices including pressure sensors, accelerometers, and environmental sensors.
Piezoresistive, capacitive, optical and piezoelectric sensing methods.
MEMs actuators.
Fabrication technology for silicon based MEMS including material deposition, etching, and wafer bonding.
Reliability issues.
Coursework:
1. Analysis and design of a sensor structure, involving Finite Element Modelling, including attention to its associated fabrication processes.
Labs:
1. Practical exercise using COMSOL as part of coursework 1.
Learning Outcomes
• Engineering principles and mathematics are applied throughout the course in the design and analysis of device structures and the fabrication processes. Coursework includes comparison of calculated parameters with those obtained from finite element modelling
• Analysis and design of microelectromechanical structures using engineering and mathematical principles is a major part of the course. In the coursework, students are required to use engineering knowledge judgement to select an appropriate combination of structural parameters and fabrication methods for a particular device.
• Analysis of several structures such as actuators, includes identifying the key parameters for analytical expressions and the understanding the potential impact of overlooking certain effects. In the coursework, the results of finite element modelling are compared to those from calculations.
• Design of a range of device structures throughout the course and included in the coursework. Design of fabrication processes is also covered throughout the course.
• Coursework assignments require reports detailing the analysis of sensor structures (CW1) and design of structures and fabrication processes (CW2).
• The coursework includes the creation of a finite element model for a MEMS sensor using commercial software package.
• Selection of appropriate materials and fabrication technology for a range of MEMS structures. Design considerations of process equipment, e.g. for plasma based processing.
Skills
Ability to design MEMS devices and suitable fabrication processes.
Calculation of key process and device performance parameters.
The project involves the application of engineering design techniques to a topic of electrical and electronic engineering which is typically related to the MSc course and modules. In the project specification, the project originator typically endeavours to ensure an element of theoretical research, theoretical analysis, simulation and performance assessment, design development, manufacture, and testing. Note that all projects involve significant hardware and/or software development components.
Learning Outcomes
- All projects require a solid understanding of the associated engineering context, underlying scientific principles and methodology; this is expected at an early stage of the project.
- Most projects require a solid understanding of mathematical methods, including, but not limited to statistics and probability.
- Some projects require a systems approach to solve engineering problems including following a multi/inter-disciplinary approach
- Projects are of research nature involving developing new technologies, or discovering/testing the properties or limitation of existing ones.
- In all projects the students are expected to formulate and analyse the problems of the projects.
- In all projects the students are expected to analyse the simulation data and/or experimental data using first principles of mathematics, statistics, natural science, and engineering principles.
- In many projects, the students are expected to analyse the effects of incomplete and uncertain data.
- Understanding of project context in which engineering knowledge is applied.
- Systems approach in hardware-based projects and projects that take a holistic approach to an engineering problem.
- Use of equipment, processes, products, materials and components in projects.
- All projects involve either a strong hardware element (i.e., the use of lab equipment to analyse complex systems and solve engineering problems) or the development of appropriate software.
- In all projects the students are expected to use technical literature, textbooks, open-source codes, and online resources to solve complex problems.
- In all projects the students are expected to design novel engineering systems, proofs of concept and demonstrate them using prototypes (hardware and/or software)
- In all projects there are some aspects of problem solving and application of technical knowledge.
- In all projects, the students are expected to consider the health and safety, diversity, inclusion, industry standards, etc to design the methodologies of the projects.
- In many projects, the students are expected to consider the environmental impacts and societal impacts of the project’s outcomes (i.e., projects related to environmental pollution or marine debris) and analyse and minimise the adverse impacts of the solutions if they are not designed appropriately.
-Communicate complex ideas in a technically sound and concise manner to a non-expert audience (there is an expectation that the student’s final report is written in such a way that it can be understood by the moderator, who is not familiar with the student’s work).
- Apply an integrated or systems approach to the solve the complex problems in projects.
- Identify and analyse ethical concerns in some projects (i.e., human participation).
- Identifying risk issues and risk mitigation in first report.
-The students are expected to develop practical and laboratory skills and/or software development or programming skills.
-Use of equipment, processes, products, materials and components in projects.
- Proof of concept / prototype projects involve commercial and economic aspects.
- Project management to target specified project objectives.
- Time management throughout project.
- Awareness of health and safety.
- Understanding of risk issues: risk mitigation, health and safety.
- Understanding of innovative aspects.
- Reflection on progress.
-Evaluate the environmental and societal impact of solutions to complex problems and minimise adverse impacts.
Skills
Ability to apply general principles and design or analytical techniques to the solution of engineering problems. This may require theoretical, practical or design skills or a combination of the three.
MOS transistors. Background MOS capacitor theory, including derivation of threshold voltage. Influence of metal work function difference and oxide charge. MOS transistor structure. Linear and saturation regions of operation. Derivation of MOS transistor equations. Numerical modelling, including transconductance. Self-aligned structures. Threshold voltage control. MOS transistor scaling. Introduction to short-channel effects. Threshold voltage control for short channels. Mid-gap metals and high-κ dielectrics.
Overview of bipolar and MOS transistor fabrication. (a) Thermal oxidation of silicon. Deal-Grove model, including linear and parabolic growth approximations. Influence of process parameters on oxidation rates. Oxidation equipment and procedures. Oxide thickness measurement techniques. Selective oxidation. (b) Dopant diffusion in silicon. Theory of constant-source and limited-source diffusion. Diffusion coefficients and numerical modelling of dopant profiles. Diffusion equipment and techniques. Dopant profile determination, including sheet resistance measurements. (c) Ion implantation of dopants in silicon. Numerical modelling of dopant profiles, including junction depth. Selective ion implantation. Implantation damage and channelling. Modelling of dopant profiles after thermal annealing. Buried insulators by ion implantation.
Bipolar transistors. Origin of internal current components, including recombination currents. Definition of current gain, emitter efficiency and base transport factor. Background diode theory, including minority carrier concentration profiles. Calculation of internal current components and current gain. Non-uniform base profiles. Calculation of base transit time and cut-off frequency. Modelling of base spreading resistance. Current crowding. Bipolar transistor scaling. Self-aligned structures. Heterojunction bipolar transistor theory and Si-based HBT fabrication techniques.
• Comprehensive teaching of natural science and engineering aspects of microelectronics and application to the solution of complex problems.
• Analysis of complex problems using first principles of natural science and engineering aspects of microelectronics
• Application of appropriate analytical techniques to model complex problems
• Selection and application of appropriate semiconductor and associated materials and microfabrication processes
Skills
Assimilation of lecture material. Application to microelectronics device design and fabrication process design
Course Contents This module focuses on multiple-input multiple-output (MIMO) discrete-time, linear and nonlinear systems with imperfect state information. The module aims at equipping you with the necessary theoretical understanding and practical skills, involving the use of software, to enable you to design control and estimation systems for next-generation applications, such as robots, autonomous ground/aerial vehicles, and uncertain cyber-physical systems.
During the first semester, we will have a fresh look at well-known results in linear control and estimation theory. Dynamic programming, Lyapunov's theorem and Bayesian estimation will be our main theoretical tools. We will also look at simple numerical optimisation methods that will allow us to run control and estimation systems on embedded devices.
In the second semester, we will focus on nonlinear dynamical systems subject to state and input constraints. Our approach will largely build up on the theoretical tools we will introduce in the first semester. Our objective will be the design, analysis and implementation of model predictive controller and moving horizon estimators: the bee's knees of modern control and estimation theory.
Part 1 (Introduction to systems theory). Linear time-invariant (LTI) systems in continuous and discrete time; discretisation and numerical integration (Euler/RK methods); controllability and observability; stability and attractivity; Lyapunov's direct and indirect methods.
Part 2 (Optimal control and LQR). The linear quadratic regulator (LQR): finite horizon formulation, dynamic programming, infinite horizon LQR; stability of LQR; general finite and infinite horizon optimal control problems and the Bellman equation; dynamic programming, control Lyapunov functions and positive invariance; the value iteration approach; stability properties of infinite-horizon optimal control formulations.
Part 3 (Model predictive control). Introduction to model predictive control (MPC); terminal costs, terminal sets, and positive invariance; design of stable MPC with different terminal ingredients; MPC for reference tracking and offset-free MPC.
Part 4 (Estimation: Bayesian Estimation, the Kalman Filter and MHE). Probability theory background and introduction to estimation theory: the discrete-time Gauss-Markov model and propagation of means and variances; estimates and estimators; minimum variance estimation; maximum a posteriori estimation; the Kalman filter for discrete-time LTI systems with additive disturbances; the extended Kalman filter for nonlinear systems and its limitations; forward dynamic programming and moving horizon estimation (MHE).
Coursework:
1. Group coursework assignment on control and estimation theory
Labs:
1. Lab 1: Linear quadratic optimal control in Python and MPC design and implementation using CVXPy.
2. Lab 2: Design of a flight control system for a quadcopter by combining LQR with a Kalman filter.
Learning Outcomes
• Demonstrate a good understanding of the notions of invariance, controllability, observability, attractivity and stability
• Demonstrate a thorough understanding of the principles of dynamic programming and use dynamic programming to derive the gain and optimal cost associated with the linear-quadratic regulator (LQR)
• Understand the principles underlying model predictive control (MPC) and design and implement stabilising MPC for discrete-time constrained linear and nonlinear systems
• Understand Bayes theorem and the principle of maximum a posteriori estimation and apply it to derive the Kalman filter for linear discrete-time systems by applying the principle of forward dynamic programming
• Understand the principles of moving horizon estimation (MHE) and the extended Kalman filter (EKF) for nonlinear constrained systems
• Apply Lyapunov's stability theorems to tell whether a given dynamical system is stable and to design stabilising feedback control systems
• Follow a Bayesian estimation approach to estimate unknown parameters, with emphasis on estimation of states/parameters of dynamical systems
• Study the stability properties of control systems, especially for safety-critical system where it is essential that certain constraints be (provably) satisfied
• Ability to understand technical papers on control and estimation theory
• Design and implement controllers and estimators for discrete-time dynamical systems using software. Special emphasis is given on safety.
• Demonstrate the ability to work as a member of a team, organise the work in tasks and use tools such technologies as git and issue trackers to collaborate with the other team members
• Communicate complex ideas in a technically sound and concise manner.
Skills
In this module, the students will develop the following skills:
1. Reasoning using high-level abstract concepts of systems and control theory to address today's engineering challenges
2. Development of an analytical and statistical/Bayesian thinking
3. Problem solving, troubleshooting, debugging (through labs and coursework)
4. Collaboration and project management (group coursework)
5. Programming skills (Python programming)
Intelligent Systems and Control (ELE8066) develops a robust understanding of the major academic topics which define control methods and intelligent algorithms in dynamic systems. The course contents includes:
Semester 1 (control):
• Software simulation of dynamical systems
• State-space modelling and analysis of dynamical systems
•Control design methods in state-space
• State observer design
• Stability analysis
• Introduction to advanced control methods
Semester 2 (Intelligent Systems):
• Introduction to artificial intelligence
• Neural networks and training algorithms
• Metaheuristic Methods (Genetic Algorithms, Particle Swarm Optimisation)
• Fuzzy logic and fuzzy control systems
The module has a final written examination and two individual coursework elements (one in each semester), that are a combination of design calculations, theoretical derivations, algorithm development and practical work in Matlab. Each coursework accounts for 20% of the final mark, while the final exam contributes 60%.
Semester 1 is focused on understanding control problem specifications and objectives, being able to model, simulate and formally analyse the dynamic behaviour of a system using state space methods and being able to design state feedback controllers such that the closed-loop system meets desired performance objectives. In semester 2 the focus is on gaining an understanding of the basics of a range of intelligent systems techniques, and how to apply them to solve practical engineering problems.
Learning Outcomes
• Apply analytical methods to derive dynamic models
• Analyse system properties in state space (e.g., controllability, observability, stability etc)
• Convert from state-space (SS) to linear transfer function (LTF) representation and vice versa
• Develop stabilising state feedback controllers for linear dynamical systems
• Develop state observers for linear dynamical systems
• Apply Lyapunov methods to assess the stability of linear and nonlinear dynamical systems
• Understand and apply simple instances of advanced control methods
• Understand the basic principles of a range of intelligent systems techniques methods
• Design neural network models for classification and modelling tasks
• Describe the training methodology and the design choices that apply when training neural networks
• Design metaheuristic algorithms for practical optimisation problems
• Develop fuzzy control laws for practical applications
• Use software for simulation, control and estimation.
• Use software to apply computational techniques (e.g. Neural Networks, GAs) to solve a practical problem.
Skills
The module will give you experience of how to model, simulate and analyse linear and non-linear systems using software tools (e.g., Matlab), knowledge of linear state-space methods and how to apply them to model, analyse and design control laws for dynamical systems, and knowledge of a range of intelligent systems techniques and how to apply them to solve practical engineering problems.
The module covers key building blocks and essentials in wireless sensor systems. The lecture notes cover mainly protocols at layer 2 (MAC layer) and layer 3 (routing) as well as key technologies for enabling IoT (ZigBee, 6LoWPAN, LoRaWAN, 802.11ah). Power management in WSN, synchronization and synchronization protocols are also covered.
The coursework covers another two key aspects: sensor technology (CW1) and analysing and forecasting sensor data (CW2 and CW3).
• Apply knowledge of mathematics for: Throughput and delay calculations. Time synchronization. Power consumption calculations.
• Apply knowledge of statistics to a broadly define problem (sensor data sets and time series from pollutant data in Coursework 2)
• Analysing the suitability of existing wireless technologies (which may include ZigBee, WiFi, IEEE 802.11ah, LoRaWAN) to support Internet of Things. Limitations of each technology. Appreciation of new developments in IoT (systems and platforms).
• Analyse broadly defined problems reaching conclusions. Conclusions on data patterns, data trends and forecasting in Coursework 3.
• Selecting and applying techniques such as regression for forecasting in Coursework 3 using appropriate software. Recognising limitations on forecasting and measuring performance.
• Select and evaluate technical literature for assessing, comparing, and choosing a specific sensor in the marketplace as part of Coursework 1.
• Understanding sensors to monitor air pollution and how data analysis and forecasting can aid in predicting pollutant concentrations.
• Understanding of different roles in a collaborative project in coursework 3. Initiative and personal responsibility for their individual role.
• Apply an integrated system approach in wireless sensor systems throughout the lectures. Understanding different sub-systems and interfaces in a sensor systems and being able to understand how these are put together for different applications.
• Understanding of telecommunications protocols used for communication in the system. Understanding enabling technologies for the Internet of Things. Recent standardization activity on these new technologies.
• Data Analytics Skills as part of Coursework 3. Use of statistics, forecasting and appropriate software.
• Select and apply appropriate sensors in Coursework 1 and engineering technologies for enabling Sensor networks and IoT throughout the lectures.
• Project management in teams for coursework 3 (data analytics project), commercial context in coursework 1 (researching and choosing a commercial sensor to measure air pollution).
Skills
The ability to critically assess and design modern wireless communications systems and in particular wireless sensor networks and systems.
The ability to understand existing sensors, system architectures, communication protocols and standards in such a context.
Use software, statistics and mathematical techniques for sensor data analysis and forecasting.
Signals and spectral representation, linear systems, Fourier and Laplace transform, convolution, impulse response, transfer function, sampled data, sampling theorem, design of analogue filters, infinite impulse response (IIR) filters, finite impulse response (FIR) filters, truncation and windowing. Decimation, interpolation, multi-rate processing. Discrete Fourier transform (DFT), fast Fourier transform (FFT), spectral analysis, FFT applications. Estimation theory, the Wiener filter, adaptive algorithms, recursive least squares, stochastic gradient algorithms.
Coursework:
1. Assignment 1: problem set (theory and practical Matlab elements)
2. Assignment 2: Digital filter design for noise removal from ECG signal (design in Matlab, submission via report)
Learning Outcomes
• Application of elementary algebra, complex number theory, linear algebra, statistics, and calculus in the derivation and analysis of signal processing systems and algorithms.
• Digital IIR and FIR ideal filter design requires derivation (applying various maths techniques) of coefficients from given filter requirement data, considering application-related constraints on the global filter characteristics; the principles underlying the advantages and limitations of different approaches (e.g., Butterworth, Chebyshev, FIR linear-phase etc) need to be understood and applied. The derivation, choice (e.g., LMS vs RLS) and application of optimal and adaptive filtering techniques requires analysis and characterisation of the signal statistics as determined by the application scenario.
• Selecting appropriate techniques for calculation of convolution output; choice of appropriate window in DFT analysis; selection of filter type / mapping on the basis of given application criteria & requirements; selection of adaptive algorithm in adaptive filtering applications.
• CW2 involves communicating via a technical report the specific features, effectiveness, and limitations of the taken approach to address the ECG signal noise reduction problem.
• In most weeks, the tutorial questions require to addressing a problem both with theory and by validation in software.
• In CW2, the students study digital filter design independently, from a variety of sources and by a variety of techniques.
• Throughout the course, students design and write code for a wide range of signal processing algorithms
• CW1 and CW2 require students to manage their own learning and development including time management and organisational skills.
• In CW2, students need to articulate and effectively communicate the design and technological rationale for a chosen digital filter design in their technical reports.
Skills
• numeric
• problem solving
• design, implement and test digital filter designs in Matlab
• perform spectral analysis on signals
Wireless communications are now part of everyday life, enabling systems and networks such as 5G, WiFi and the internet of things to name but a few. This module develops the necessary concepts and techniques required to understand the design and operation of present-day wireless communications. It also explores some of the technologies which are likely to underpin future wireless systems such as AI and machine learning. Among the topics that will be covered are:
• Historical development of wireless communications
• Information theory (entropy, probability, and channel capacity)
• Digital communications, channel capacity (noiseless and with noise) and bandwidth
• Signal-to-noise ratio (SNR), bit-error-rate (BER) and Friis Law
• Digital modulation and demodulation
• Radio wave propagation
• Free space propagation
• Reflection, diffraction and scattering
• Large and small-scale fading
• Frequency selective and flat fading channels
• Rayleigh and Rice fading
• Outage probability
• Time series analysis and machine learning concepts for communications
• Time series models (autoregressive, moving average, autoregressive moving average)
• Autocorrelation and partial autocorrelation functions
• Stationarity and unit root
• Data scaling and normalisation techniques
• Multiuser systems (multiple access, multiuser diversity techniques)
• Multiple antennas (e.g., maximal ratio combining, MIMO techniques)
• Multicarrier communications
Coursework:
1. Focuses on time series analysis for wireless communications and introduces machine learning concepts
2. Time series analysis is performed on real wireless communications data set (e.g., device-to-device, mmwave communications data).
3. Autoregressive (AR) and autoregressive moving average (ARMA) models will be used to predict future received signal strength information.
4. Solutions will be obtained using a Python notebook that can be accessed via Google Colaboratory.
Additional Resources and Recommended literature:
Google Colab based Python notebooks containing examples of various time-series concepts and models are discussed in class. Students are also pointed towards time-series forecasting materials readily available on GitHub for more information and additional learning. Other references include:
• W. McKinney, Josef Perktold and Skipper Seabold, Time Series Analysis in Python with statsmodels. SCIPY 2011.
• P.J. Brockwell and R.A. Davis (2002). Introduction to Time Series and Forecasting. Springer.
• P.J. Brockwell and R.A. Davis (1991). Time Series: Theory and methods. Springer. P. Diggle (1990). Time Series. Clarendon Press.
Learning Outcomes
• Develop an understanding of information theory and related problems, covering key concepts in wireless communications, such as entropy, probability, and channel capacity.
• Demonstrate a thorough understanding of the principles of noise sources in wireless systems. Learn the application of Friis Law to wireless channels and gain the ability to analyse noise temperature, noise figure, noise factor in cascaded wireless communication systems.
• Understand the principles digital modulation and demodulation, and their application to baseband data for wireless communications. Gain the ability to analyse error probability in signal demodulation.
• Demonstrate a good understanding of the radio wave propagation, relating received power to electric field strength, and the main propagation mechanisms encountered in wireless systems (including free space propagation, reflection, diffraction, and scattering)
• Demonstrate a good understanding of the concepts of effective aperture, gain and directivity in relation to antennas
• Understand the need for statistical approaches to modelling signal propagation and reception in wireless systems (including large and small-scale fading, frequency selective and frequency non-selective fading, etc.)
• Understand the Rayleigh and Rice fading models (including their underlying physical models, signal envelope and received signal power, in the case of Rayleigh, the level crossing rate, average fade duration and outage probability)
• Understand the importance of time series analysis in wireless communications, and the concepts of time-series analysis (autocorrelation function, partial autocorrelation function, stationarity, and unit root). Apply time series models (autoregressive, moving average and autoregressive moving average) to forecast future values based on previously observed values.
• Understand the concepts of multiple antenna diversity techniques including antenna diversity and diversity order. Understand the difference between MISO and SIMO systems. Examine and compare the performances of multi-antenna combining techniques (selection combining, maximal ratio combining, equal gain combining, maximal radio transmission) over Rayleigh fading channel.
• Understand the main concepts and principles behind MIMO systems, and the differences between SIMO, MISO and MIMO systems. Understand spatial multiplexing. Demonstrate the ability to design linear-complexity MIMO receivers (e.g., minimum mean squared error), to model and represent MIMO channel matrices and to evaluate the performance of MIMO systems.
• Understand the OFDM modulation technology, differences between FDM and OFDM, the main concepts behind analogue and digital OFDM. Demonstrate an understanding of the basic properties of OFDM and their benefits (robustness to multipath fading and reduction of inter symbol interference).
• To understand the need for resource management (scheduling), the concepts of multiuser diversity (another way to deal with fading channels) including random access scheduling and greedy access scheduling.
• Apply noise theory and Friis Law to design, analyse and optimize receiver systems in wireless communications.
• Use learning from the Gaussian statistics as well as the Rayleigh and Rice fading models, to work with more advanced (unseen) fading models. Use this knowledge to determine different performance measures related to wireless communications.
• Learn to implement time-series models such as autoregressive, moving average and autoregressive moving average using appropriate software. Learn to check for stationarity and apply scaling as well as normalisation techniques to a given data set using software. Learn to predict future values based on previously observed values using software.
Skills
Assimilation of lecture material, python skills, system model and problem-solving skills as well the application of probability, statistics, electromagnetic theory, and time-series forecasting to wireless data sets.
Normally a 2.2 Honours degree or above or equivalent qualification acceptable to the University in Electrical and/or Electronic Engineering, or Physics with significant electronics content.
Applicants are advised to apply as early as possible. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.
Please note: a deposit will be required to secure a place.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*Taken within the last 2 years.
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
Our graduates have found that earning a prestigious MSc qualification from the School, one of the UK's top engineering schools, has significantly enhanced their job opportunities and employment prospects. Graduates typically find employment in a wide range of fields including with semiconductor companies, electronic equipment manufacturers, design and service providers, software houses and in other electronic engineering-based industries.
Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally. http://www.qub.ac.uk/directorates/sgc/careers/
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
There are no specific additional course costs associated with this programme.
All Students
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.