This module will provide you with a systematic understanding of wired and wireless telecommunication systems, including current and future generations of mobile networks. You will learn the process of designing a complex model of the communication channel and using simulation methods to overcome challenges in a harsh environment. The module will provide in-depth knowledge of advanced topics such as equalisation, diversity, Smart Antennae and Forward Error Correction. The module will also discuss different machine learning techniques and will enable you to choose the most appropriate technique to solve complex real-world problems. Deep knowledge and understanding of machine learning techniques for physical layer design as well as for the design of mobile networks will be provided in this module.

A 2500-word individual report based on an investigation using simulations, weighted at 50% assessing learning outcomes 1 and 2. Meeting AHEP 4 Outcomes: M3, M4

A 3000-word group portfolio providing solution to a complex problem, weighted at 50% assessing learning outcomes 1, 2 and 3. Meeting AHEP 4 Outcomes: M3, M4, M5, M7, M16

Professional Body requirements mean that a minimum overall score of 50% is required to pass a module, with each element of assessment requiring a minimum mark of 40% unless otherwise stated.

Note: Assessment 2 is a CORE assessment and must be passed with a mark of 50% or above in order to pass the module.

This module will enable students to gain understanding, apply knowledge, analyse and evaluate problems and create solutions through a variety of activities, including
- Problem-based lectures
- Tutorials/laboratories

- Group work
- Independent study: reading, team meetings, information gathering, student centred learning and assignment preparation

1. Demonstrate knowledge and systematic understanding of advanced topics in Wireless Communications and Machine Learning relating to rapidly evolving research areas. (AHEP 4: M4)

Knowledge and Understanding

Learning

2. Apply appropriate analytical, experimental and simulation techniques to solve complex problems in Wireless Communications and Machine Learning. (AHEP 4: M3, M5, M7)

Application

Problem Solving

3. Communicate and work effectively with team members and present findings of your investigation in a report. (AHEP 4: M16)

Analysis

Reflection

Communication

Team Work

Telecommunications lab hardware and software, including:
TIMS equipment, scopes and signal generators
PCs running with software such as Python, MATLAB and LabVIEW.

Alpaydin, E., (2020), Introduction to machine learning. MIT press. ISBN-13: 978-0262043793¿

Choi, K., and Liu, H., (2016), Problem-based learning in communication systems using MATLAB and Simulink.¿

Goodfellow I., et al., (2017), Deep Learning (Adaptive Computation and Machine Learning Series). MIT Press, Cambridge, Massachusetts. ISBN-13: 978-0262035613¿

Hopgood, A.A., (2021), Intelligent systems for engineers and scientists: a practical guide to artificial intelligence. CRC press. ISBN-13: 978-0367336165¿

Leis, J.W., (2018), Communication Systems Principles Using MATLAB. 1st edn. Newark: Wiley. Available at: https://doi.org/10.1002/9781119470663.¿

Maier, M., (2023), 6G and onward to next G¿: the road to the multiverse. Hoboken, New Jersey: John Wiley & Sons, Inc.¿



Mishra, A.R., and India, E., (2018), Fundamentals of network planning and optimisation 2G/3G/4G¿: evolution to 5G. Second edition. Hoboken, NJ, USA: Wiley.¿

Proakis, J.G., and Salehi, M., (2014), 2/E Fundamentals of Communication Systems, Global Edition, Pearson¿

Shalev-Shwartz S., et al., (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, Cambridge, England. ISBN-13: 978-1107057135¿

Sklar, B., (2017), 3/E Digital communications fundamentals and applications. Prentice Hall, USA.¿



Stuart J., et al., (2020), Artificial Intelligence: A Modern Approach, Prentice Hall, New Jersey, 4th edition. ISBN-13: 978-0134610993¿

Tom M., et al., (2013), Machine Learning, McGraw Hill Education; New York. ISBN-13: 978-1259096952¿

Wilamowski, B.M., and Irwin, J.D., (2018), Industrial communication systems. 2nd ed. Boca Raton: CRC Press.¿

Due to the need to transmit a huge amount of data over the wireless interface, increasingly complex methods are required to ensure interference is limited, the communications are secured, and retransmissions are limited. Therefore, adaptive and intelligent methods are required to accommodate a diverse range of traffic profiles in a complex wireless environment. This module will provide in-depth knowledge of telecommunication principles and will enable you to employ machine learning techniques at the physical layer and multiple access level as well as in the design stage to optimise the use of the techniques in harsh environments.