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Anglia Ruskin University ARU Featured PhD Programmes
Anglia Ruskin University ARU Featured PhD Programmes

Modulation recognition/classification


Electronic and Electrical Engineering

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Prof AK Nandi Applications accepted all year round Self-Funded PhD Students Only

About the Project

All radio communication signals (radio, television, mobile phones, etc.) are modulated before transmission. Modulation recognition/classification is fundamental for correct demodulation. This has many applications in military, intelligence, and civil communities In recent years much emphasis are put on the development of signal processing and artificial intelligence techniques to address the relevant issues.
Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is an essential process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification. The project will review existing techniques and develop new and improved ones with either better accuracy or lower complexity or both.
This project will involve programming, signal processing, machine learning, mathematical analysis, and good writing ability for presentation of technical work. An ideal candidate will have a very good Master degree or a First Class Bachelor degree.

Funding Notes

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU students and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.)

References

Below are some publications from my group. These will give you good indications of the work we have done already and the developments of our ideas, techniques, and implementations.
1. Z Zhu and A K Nandi, "Automatic Modulation Classification: Principles, Algorithms and Applications", Published by John Wiley & Sons, Chichester, West Sussex, UK, 2015 (ISBN 978-1-118-90649-1).

2. E E Azzouz and A K Nandi, "Automatic modulation recognition of communication signals", Published by Kluwer Academic Publishers, Amsterdam, Netherlands, 1996 (ISBN 0-7923-9796-7).

3. Z Zhu and A K Nandi, "Blind digital modulation classification using minimum distance centroid estimator and non-parametric likelihood function", IEEE Transactions on Wireless Communications, DOI: 10.1109/TWC.2014.2320724, vol. 13, no. 8, pp. 4483-4494, 2014.

4. Z Zhu, M W Aslam, and A K Nandi, "Genetic algorithm optimized distribution sampling test for M-QAM modulation classification", Signal Processing, vol. 94, no. 1, pp. 264-277, 2014.

5. M W Aslam, Z Zhu, and A K Nandi, "Automatic modulation classification using combination of genetic programming and KNN", IEEE Transactions on Wireless Communications, DOI: 10.1109/TWC.2012.060412.110460, vol. 11, no. 8, pp. 2742-2750, 2012.

6. A K Nandi and E E Azzouz, "Algorithms for automatic modulation recognition of
communication signals", IEEE Transactions on Communications, vol. 46, no. 4, 1998, pp. 431-436.

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