The ever-increasing demand for higher data rates in wireless communications in the face of
limited or underutilized spectral resources has motivated the introduction of cognitive radio …

It has long been a significant but difficult problem to identify propagation sources based on
limited knowledge of network structures and the varying states of network nodes. In practice …

This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …

Cognitive radio (CR) has been considered as a potential candidate for addressing the
spectrum scarcity problem of future wireless networks. Since its conception, several …

When the data are high dimensional, widely used multivariate statistical methods such as
principal component analysis can behave in unexpected ways. In settings where the …

We study the fluctuations of the largest eigenvalue λ max of N× N random matrices in the
limit of large N. The main focus is on Gaussian β ensembles, including in particular the …

We give an overview of random matrix theory (RMT) with the objective of highlighting the
results and concepts that have a growing impact in the formulation and inference of …

Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in
communication systems has changed from being a theoretical scaled-up version of MIMO …

Identifying the infection sources in a network, including the index cases that introduce a
contagious disease into a population network, the servers that inject a computer virus into a …

This paper addresses the statistical performance of subspace DoA estimation using a
sensor array, in the asymptotic regime where the number of samples and sensors both …