Postgraduate Course: Array Processing and MIMO Systems (MSc) (PGEE11124)
|School||School of Engineering
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||This course will cover the current topics of interest in Array Processing and MIMO systems.
2) Signal Model (1D and 2D or 3D arrays)
3) Different types of Beamforming
4) Different techniques for source Localisation including subspace methods
5) Detection of number of signals
6) MIMO Systems: Channel Modelling and Spatial Multiplexing
7) Maximum Likelihood Parameter Estimation
Practical examples of the above concepts are presented throughout the course.
Lectures and tutorials
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Formative Assessment Hours 1,
Summative Assessment Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
60% online exams and 40% MATLAB coursework.
|No Exam Information
On completion of this course, the student will be able to:
- Understand the modelling of the array received signals for 1D, 2D and 3D arrays. Understand the fundaments concepts of beamforming techniques such as conventional, optimum and adaptive beamformers, Capon method, MSE based beamforming, LCMV beamformers
- Understand the fundamental concepts of source localisation techniques such as conventional techniques, subspace methods, i.e., MUSIC, Root- MUSIC, ESPRIT. Understand the concept of source detection techniques such as MDL and AIC methods.
- Develop a unified way of modelling the MIMO and mmWave channels using array processing concepts.
- Quantify the MIMO wireless channel capacities and degrees of freedom regions for different channel models, such as multiple access channels, broadcast channels, interference channels, etc.
- Understand the concepts of Maximum Likelihood Parameter Estimation techniques
|1) P. Stoica and R. Moses, Spectral Analysis of Signals, Prentice Hall, 2005. Chapter 6 (http://user.it.uu.se/~ps/SAS-new.pdf)|
2) D. Tse and P. Viswanath, Fundamentals of Wireless Communications, Cambridge University Press, 2005
3) S. U. Pillai, Array Signal Processing, Springer-Verlag, 1989.
|Graduate Attributes and Skills
|Course organiser||Prof Tharmalingam Ratnarajah
Tel: (0131 6)50 5578
|Course secretary||Mrs Megan Inch-Kellingray
Tel: (0131 6)51 7079