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The University of Southampton

ELEC6253 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 the design of physical layer techniques for wireless communications
  • The application of machine learning in network design
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

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


General introduction and fundamentals of machine learning - Introduction to learning and machine learning: supervised/unsupervised/reinforcement learning - Revision of probability and statistics revision - Revision of linear algebra - Fundamentals of numerical optimisation - Introduction to machine learning assisted linear regression and classification Machine learning for wireless communications - Machine learning for physical layer design 1- Adaptive modulation and coding (AMC): classical AMC, using support vector machines, using k-nearest neighbours, using k-means, using reinforcement learning 2- Code Division Multiple Access (CDMA): classical design, using learning such as ACO/GA 3- Precoder design: classical design, channel prediction using deep learning - Machine learning for mobile network design 1- User grouping/clustering in D2D, HetNets for offloading 2- Traffic prediction and interference management in HetNets 3- Clustering of small cells in Hetnet to avoid interference in CoMP

Learning and Teaching

Teaching and learning methods

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

Follow-up work24
Assessment tasks30
Wider reading or practice14
Preparation for scheduled sessions18
Total study time150

Resources & Reading list

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

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. 

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. 

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

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

Christopher M. Bishop. Pattern Recognition and Machine Learning. 

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

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



MethodPercentage contribution
Continuous Assessment 30%
Final Assessment  70%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: ELEC3203 or ELEC3204 or ELEC3218

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