Machine Learning for Wireless Communications

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

Knowledge and Understanding

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

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

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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

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

Resources & Reading list

Journal Articles

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

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

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

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

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

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.

Christopher M. Bishop. Pattern Recognition and Machine Learning.

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

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