Millimeter-wave communication for internet of vehicles: status, challenges, and perspectives

KZ Ghafoor, L Kong, S Zeadally… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
The Internet of Vehicles has attracted a lot of attention in the automotive industry and
academia recently. We are witnessing rapid advances in vehicular technologies that …

LEO mega-constellations for 6G global coverage: Challenges and opportunities

H Xie, Y Zhan, G Zeng, X Pan - IEEE Access, 2021 - ieeexplore.ieee.org
Mega-constellations have the potential for providing 6G Internet owing to the unique
advantage of global coverage. However, current satellite technologies are not omnipotent …

Enabling large intelligent surfaces with compressive sensing and deep learning

A Taha, M Alrabeiah, A Alkhateeb - IEEE access, 2021 - ieeexplore.ieee.org
Employing large intelligent surfaces (LISs) is a promising solution for improving the
coverage and rate of future wireless systems. These surfaces comprise massive numbers of …

DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications

A Alkhateeb - arXiv preprint arXiv:1902.06435, 2019 - arxiv.org
Machine learning tools are finding interesting applications in millimeter wave (mmWave)
and massive MIMO systems. This is mainly thanks to their powerful capabilities in learning …

Deep learning for mmWave beam and blockage prediction using sub-6 GHz channels

M Alrabeiah, A Alkhateeb - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels
has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work …

Vision-aided 6G wireless communications: Blockage prediction and proactive handoff

G Charan, M Alrabeiah… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in
5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link …

Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction

M Alrabeiah, A Hredzak… - 2020 IEEE 91st vehicular …, 2020 - ieeexplore.ieee.org
This paper investigates a novel research direction that leverages vision to help overcome
the critical wireless communication challenges. In particular, this paper considers millimeter …

Deep reinforcement learning for intelligent reflecting surfaces: Towards standalone operation

A Taha, Y Zhang, FB Mismar… - 2020 IEEE 21st …, 2020 - ieeexplore.ieee.org
The promising coverage and spectral efficiency gains of intelligent reflecting surfaces (IRSs)
are attracting increasing interest. To adopt these surfaces in practice, however, several …

Deep learning for TDD and FDD massive MIMO: Mapping channels in space and frequency

M Alrabeiah, A Alkhateeb - 2019 53rd asilomar conference on …, 2019 - ieeexplore.ieee.org
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? If this …

A survey of machine learning applications to handover management in 5G and beyond

MS Mollel, AI Abubakar, M Ozturk, SF Kaijage… - IEEE …, 2021 - ieeexplore.ieee.org
Handover (HO) is one of the key aspects of next-generation (NG) cellular communication
networks that need to be properly managed since it poses multiple threats to quality-of …