Mohamed-Slim Alouini

TL;DR: This paper exploits the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases.

TL;DR: A deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead and demonstrate the superiority of the proposed solution over state-of-the-art solutions.

TL;DR: It is posited that the breakthrough the THz band will introduce will not be solely driven by achievable high data rates, but more profoundly by the interaction between THz sensing, imaging, and localization applications.

TL;DR: This is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient, and it is shown to provide a perfect fit with the measured data under all channel conditions for both types of water.

TL;DR: It is shown that the spectral efficiency is limited due to the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large, which is in contrast with the conventional case with ideal hardware.