Machine Learning for Wireless Communications

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Modules

Courses / Modules / ELEC6253 Machine Learning for Wireless Communications

When you'll study it

Semester 2

CATS points

ECTS points

7.5

Level

Level 7

Module lead

Mohammed El-Hajjar

Academic year

2026-27

Module overview

The aim of the module is to introduce students to the fundamentals of machine learning and then to apply the advanced machine learning principles for the design and optimisation of wireless communications systems and mobile networks. Recently, the research and development in wireless communications have been focused on the techniques for the fifth generation (5G) wireless systems and the potential to make these networks intelligent by adding machine learning. Therefore, this course motivates to deliver a general introduction and fundamentals of machine learning followed by the application of machine learning in the design of physical layer techniques in wireless communications and in the optimisation of mobile networks. Exclusions: Cannot be taken with COMP3222 or COMP3223 or COMP6245 or COMP6246 or COMP6208.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

apply machine learning principles in the design of some physical layer techniques in wireless communications
design and optimise intelligent mobile networks by applying the principles of machine learning
apply the mathematical principles of probability, linear algebra and optimisation
understand the principles of machine learning and apply the fundamental principles for regression and classification

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

The application of machine learning in network design
The fundamentals of Machine learning
The application of machine learning in the design of physical layer techniques for wireless communications

Learning and Teaching

Teaching and learning methods

The teaching and learning methods of the module include lectures, tutorials, coursework, coursework feedback, etc.

Study time
Type	Hours
Lecture	36
Revision	16
Wider reading or practice	14
Tutorial	12
Preparation for scheduled sessions	18
Follow-up work	24
Assessment tasks	30
Total study time	150

Resources & Reading list

Journal Articles

T. J. OShea, T. Erpek, and T. C. Clancy. Deep learning based MIMO communications.

T. J. OShea and J. Hoydis. An introduction to machine learning communications systems.

M. A. Alsheikh, S. Lin, D. Niyato and H. P. Tan. Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications.

S. Bi, R. Zhang, Z. Ding, and S. Cui. Wireless communications in the era of big data.

T. J. OShea, K. Karra, and T. C. Clancy. Learning approximate neural estimators for wireless channel state information.

C. Jiang and H. Zhang and Y. Ren and Z. Han and K. C. Chen and L. Hanzo. Machine Learning Paradigms for Next-Generation Wireless Networks.

Textbooks

David J.C. Mackay. Information Theory, Inference and Learning Algorithms.

Jeremy Watt and Reza Borhani. Machine Learning Refined: Foundations, Algorithms, and Applications.

Christopher M. Bishop. Pattern Recognition and Machine Learning.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Examination	70%
Coursework	15%
Coursework	15%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Examination	100%

Repeat Information

Repeat type: Internal & External

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