140 citations · 199 across the 20 of their papers we have counts for
6 papers · 1 filter
Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction
Muhammad Alrabeiah, Andrew Hredzak, Ahmed Alkhateeb
This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millim…
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels
Muhammad Alrabeiah, Ahmed Alkhateeb
Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains a…
ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications
Muhammad Alrabeiah, Andrew Hredzak, Zhenhao Liu +1
The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing re…
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
This paper considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework,…
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
Muhammad Alrabeiah, Ahmed Alkhateeb
Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennas---possibly at a different location and a different frequency band?…
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of near…