Random Matrix Theory

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

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B1 L4 R4214

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches

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B1 L3 R3119

Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.

Measurement Selection: A Random Matrix Theory Approach

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Khalil Elkhalil , Student Member, IEEE, Abla Kammoun, Member, IEEE, Tareq Y. Al-Naffouri, Member, IEEE, and Mohamed-Slim Alouini , Fellow, IEEE Abstract This paper considers the problem of selecting a set of k measurements from n available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain m dimensional parameter vector. The exhaustive search inspecting each of the (n) possible choices would require very high computational k complexity and as such is not practical for large n and k. Alternative methods with low

Xiaoke Yang

Visiting Scholar, Information Science Lab
PhD candidate at King Abdullah University of Science and Technology. Visiting Scholar, Information Science Lab

Qurrat-Ul-Ain Nadeem

Qurrat-Ul-Ain Nadeem selected while at KAUST as one of the 2018 Marconi Society Paul Baran Young Scholars and she currently is a Postdoctoral Fellow at the School of Engineering, the University of British Columbia, Okanagan Campus, working with Professor Anas Chaaban in the Communication Theory Lab. Education and Early Career Qurrat obtained her Master of Science and Ph.D. degrees in Electrical Engineering from KAUST in June 2015 and December 2018 respectively. Before joining KAUST, she completed her Bachelor of Science degree in Electrical Engineering from Lahore University of Management

Muath Mahdi

Postdoctoral Fellow with the Electrical Engineering Department, King Abdullah University of Science and Technology (KAUST)

Mohamed A Suliman

Visiting Scholar (former), Information Science Lab
MS Degree at King Abdullah University of Science and Technology, Ph.D. candidate, Imperial College London..

Lama Burhan Niyazi

Ph.D. degree in the Communication Theory Lab (CTL) under the joint supervision of Professor Mohamed-Slim Alouini and Professor Tareq Al-Naffouri , King Abdullah University of Science and Technology (KAUST)

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering
PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Ismail Ben Atitallah

Ismail Ben Atitallah was a ​MS Thesis Student at King Abdullah University of Science and Technology (KAUST).

Ikram Boukhedimi

Ikram Boukhedimi was born in Blida, Algeria. She received an Electrical Engineering degree from the Ecole Polytechnique d'Alger, Algeria, in 2013 and an M.S. in Mathematics, Control and Electrical Engineering from Universite de Poitiers, France in 2014. and she earned a Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology in June 2019, Saudi Arabia in Professor Mohamed Slim Alouini’s Communication Theory Lab in Electrical Engineering research group. Her research interests include random matrix theory approaches applied to wireless communication systems

Houssem Sifaou

Houssem Sifaou is a Ph.D. candidate in Electrical Engineering, King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Laboratory (CTL). Education and Early Career Houssem Sifaou received the Engineering Degree (Hons.) in Signal and Systems from Tunisia Polytechnic School, La Marsa, Tunisia, in 2014 and the M.S. degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2016. Scientific Interest Houssem Sifaou is interested in Random Matrix Theory