Communications and Signal Processing MSc
This course equips you with the theory and practice necessary to begin a career as a design or development engineer in this exciting field.
You are currently viewing course information for entry year:
Start date(s):
- September 2026
Overview
This unique MSc draws on our research strengths in communications and digital signal processing. You’ll gain a deep understanding of theory and real-world application. Course highlights:
- coverage of all major areas in communications engineering and signal analysis
- a balanced mix of theory and hands-on learning
- innovative teaching that builds practical, industry-relevant knowledge
- training in design, problem-solving, and research methods
- transferable skills in analysis, research, and technical communication
This course is designed for both practising professionals and graduates in:
- electrical and electronic engineering
- computer engineering
- mechanical engineering
- physics
- communications, information and control engineering
- electronics
- information and science technology
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Important information
We've highlighted important information about your course. Please take note of any deadlines.
Please rest assured we make all reasonable efforts to provide you with the programmes, services and facilities described. However, it may be necessary to make changes due to significant disruption, for example in response to Covid-19.
View our Academic experience page, which gives information about your Newcastle University study experience for the academic year 2025-26.
See our terms and conditions and student complaints information, which gives details of circumstances that may lead to changes to programmes, modules or University services.
What you'll learn
Through compulsory modules you'll learn:
- mathematical foundations and fundamental concepts of machine learning
- formulating designing, testing, and validating different types of machine learning algorithms
- ethical considerations, as well as issues regarding privacy and security
- issues relating to modern telecommunications systems, protocols, flow and error control
- security and encryption and their importance in modern communication systems
- the principles of wireless and broadband communication networks
- recent developments in cellular mobile systems and access technology
- fundamental concepts of multimodality sensing, data acquisition and their applications including data fusion and visualisation
- detailed knowledge of the simulation techniques for communications systems and other signal processing problems
- in-depth knowledge of the architecture of DSP devices and communications hardware
- emerging methods for the manipulation and analysis of single, multi-dimensional and random signals
- how to conduct case studies in biomedical and healthcare applications
- discrete-time signal processing algorithms and approaches to measure deterministic and random signals in frequency domain
Modules
You will study modules on this course. A module is a unit of a course with its own approved aims and outcomes and assessment methods.
Module information is intended to provide an example of what you will study.
Our teaching is informed by research. Course content changes periodically to reflect developments in the discipline, the requirements of external bodies and partners, and student feedback.
Full details of the modules on offer will be published through the Programme Regulations and Specifications ahead of each academic year. This usually happens in May.
To find out more please see our terms and conditions.
Optional modules availability
Some courses have optional modules. Student demand for optional modules may affect availability.
How you'll learn
The course is delivered by the School of Engineering. The Communications and Signal Processing MSc programme uses a blend of teaching methods, including:
- lectures and tutorials
- visits to local and regional industry
- computer workshops
- laboratory work
- seminars
- research projects
- case studies
Our industry leading academics are at the cutting edge of the subject. You'll learn from experts that produce high quality research.
Depending on your modules, you'll be assessed through a combination of:
- Computer assessment
- Oral examination
- Oral presentation
- Practical lab report
- Report
- Written examination
- Written exercise
Throughout your studies, you’ll have access to support from:
- academic staff
- personal tutors and research supervisors
- our University Student Services Team
- student representatives
- peers
You'll also be assigned an academic member of staff, who will be your personal tutor throughout your time with us. They can help with academic and personal issues.
Your teaching and learning is also supported by Canvas. Canvas is a Virtual Learning Environment. You'll use Canvas to submit your assignments and access your:
- module handbooks
- course materials
- groups
- course announcements and notifications
- written feedback
Jeffrey Neasham
Professor of Acoustic Signal Processing
Areas of expertise:
- biomedical instrumentation
- underwater acoustics
- ultrasound, analog and digital circuit design, and embedded software
Dr Mohsen Naqvi
Senior Lecturer (Associate Professor) in Signal and Information Processing
Areas of Expertise:
- multimodal (multi-sensor) information processing based artificial intelligence for applications in homeland security and smart healthcare systems. (Video: Multimodal signal and information processing)
- multi-target tracking for security and surveillance (Video: Multiple human tracking, Video: Online multiple human tracking)
- multimodal processing for human behaviour analysis (Video: Pose-driven human behaviour analysis, Video: Multimodal behaviour analysis, Video: AnomalyXPose demo)
- multimodal speech source separation (Video: Multimodal speech separation)
Senior Lecturer in Signal Processing and Machine Learning
Areas of expertise:
- machine learning and AI for predicting disease prevalence, recurrence and survival
- machine learning and intelligent signal processing for complex medical data modelling
- AI-based models for visualization, non-destructive evaluation, and structural health monitoring
- machine learning for financial risk modelling
Lecturer in Sensor Systems
Areas of expertise:
- Internet of Things (IoT); LoRaWAN; wireless sensor networks; sensor technologies; edge computing
- energy harvesting; wireless power transfer; energy neutrality; net zero; energy efficiency; batteryless IoT
- wireless communications; wireless/RF network design and optimisation; network efficiency
- drone networking; Unmanned Aerial Vehicles (UAVs); swarm intelligence
- smart cities; smart buildings; smart infrastructures; smart grids; smart homes; connected places
- wearable devices/electronics; body area networks; smart healthcare; molecular communications
- embedded systems; transient computing; intermittent networking
- cybersecurity; privacy; security by design
Lecturer in Data Engineering & AI
Areas of expertise:
- computer vision
- artificial intelligence
- deep learning
Lecturer in Signal and Image Processing/AI
Areas of expertise:
- machine/deep learning-based radar imaging
- enhanced automotive radar imaging for autonomous driving
- compressive sampling for wireless communications
Your development
Research skills
The course is delivered by the School of Engineering. The School has an established programme of research seminars. These are delivered by guest speakers from academia and industry. They provide excellent insights into a wide variety of engineering research. This course will enable you to:
- access world leading research in signal processing and communications
- gain more experience of industrial context with applications
- have more flexibility and choice in your future career
- receive an enhanced educational experience
Professional skills
Strong communication is an essential for modern engineers. You'll build these skills through:
- guided sessions on report writing and documentation
- presenting your project work through posters and oral presentations
Practical skills
By the end of the course, you’ll be equipped with the technical and practical abilities industry demands. You’ll be able to:
- apply image processing and machine vision techniques
- analyse and plan advanced telecommunications systems
- design and simulate encryption and communication protocols
- evaluate wireless systems using MATLAB and Simulink
- design mobile phone systems from a full-system perspective
- calculate Bit Error Rates for various modulation schemes
- implement efficient signal processing algorithms in software
- analyse both deterministic and random signals in time and frequency domains
- assess the computational cost of transform methods
- classify information and noise from discrete signals
Your future
Graduate destinations
On completing the course, many students progress into employment as design and development engineers in telecommunications and digital signal processing areas or onto a higher research degree.
Industry links
Our close links with industry and graduates provide you with a network allowing you to enhance your employability.
Our Careers Service
Our Careers Service is one of the largest and best in the country, and we have strong links with employers.
Quality and ranking
All professional accreditations are reviewed regularly by their professional body
From 1 January 2021 there is an update to the way professional qualifications are recognised by countries outside of the UK
Facilities
The School of Engineering has a suite of world-class research and teaching laboratories. These have the latest electronic instruments and computer aided design software for Digital Signal Processing (DSP) and Field-programmable gate array (FPGA) devices.
The facilities are among the most advanced of their type. This enables us to join the global race to develop ambient intelligence systems involving tiny sensors and computing devices embedded in much of what we use.
Stephenson Building
The Stephenson Building is a £110 million investment in world-class education, research and collaboration across Engineering. It’s a place for future engineers, researchers and designers to collaborate and tackle global challenges, together.
Fees and funding
Tuition fees for 2026 entry (per year)
As a general principle, you should expect the tuition fee to increase in each subsequent academic year of your course, subject to government regulations on fee increases and in line with inflation.
Depending on your residency history, if you’re a student from the EU, other EEA or a Swiss national, with settled or pre-settled status under the EU Settlement Scheme, you’ll normally pay the ‘Home’ tuition fee rate and may be eligible for Student Finance England support.
EU students without settled or pre-settled status will normally be charged fees at the ‘International’ rate and will not be eligible for Student Finance England support. You may be eligible for a scholarship worth 25% off the international fee. Search our funding database.
If you are unsure of your fee status, check out the latest guidance here.
Scholarships
We support our EU and international students by providing a generous range of Vice-Chancellor's automatic and merit-based scholarships. See our searchable postgraduate funding page for more information.
What you're paying for
Tuition fees include the costs of:
- matriculation
- registration
- tuition (or supervision)
- library access
- examination
- re-examination
- graduation
Find out more about:
If you are an international student or a student from the EU, EEA or Switzerland and you need a visa to study in the UK, you may have to pay a deposit.
You can check this in the How to apply section.
If you're applying for funding, always check the funding application deadline. This deadline may be earlier than the application deadline for your course.
For some funding schemes, you need to have received an offer of a place on a course before you can apply for the funding.
Search for funding
Find funding available for your course
Entry requirements
The entrance requirements below apply to 2026 entry.
Qualifications from outside the UK
English Language requirements
Admissions policy
This policy applies to all undergraduate and postgraduate admissions at Newcastle University. It is intended to provide information about our admissions policies and procedures to applicants and potential applicants, to their advisors and family members, and to staff of the University.
University Admissions Policy and related policies and procedures
Credit transfer and Recognition of Prior Learning
Recognition of Prior Learning (RPL) can allow you to convert existing relevant university-level knowledge, skills and experience into credits towards a qualification. Find out more about the RPL policy which may apply to this course
How to apply
Using the application portal
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Alternatively you can find out more about applying on our applications and offers pages.
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Questions about this course?
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School of Engineering
School Admissions Team
Email: soe.admissions@ncl.ac.uk
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