We develop a new method for deriving local laws for a large class of random matrices. It is
applicable to many matrix models built from sums and products of deterministic or …

Recently, cell-free (CF) architectures, in which every user can potentially communicate with
every base station, have received a lot of attention. This paper considers the uplink of fully …

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a
number of new opportunities to further improve wireless networks. However, cell-free UAV …

Double descent refers to the phase transition that is exhibited by the generalization error of
unregularized learning models when varying the ratio between the number of parameters …

We investigate the problem of estimating a given real symmetric signal matrix C from a noisy
observation matrix M in the limit of large dimension. We consider the case where the noisy …

We study the effect of mini-batching on the loss landscape of deep neural networks using
spiked, field-dependent random matrix theory. We demonstrate that the magnitude of the …

We consider the problem of subspace estimation in situations where the number of available
snapshots and the observation dimension are comparable in magnitude. In this context …

The author presents a unified treatment of this highly interdisciplinary topic to help define the
notion of cognitive radio. The book begins with addressing issues such as the fundamental …

Consider a N*n matrix n=1nR_n^1/2X_n, where R_n is a nonnegative definite Hermitian
matrix and X_n is a random matrix with iid real or complex standardized entries. The …

This paper considers a Massive MIMO network with L cells, each comprising a base stations
(BS) with M antennas and K single-antenna user equipments. Within this setting, we are …