140 citations · 199 across the 20 of their papers we have counts for
20 papers
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios
Gouranga Charan, Muhammad Alrabeiah, Tawfik Osman +1
Leveraging sensory information to aid the millimeter-wave (mmWave) and sub-terahertz (sub-THz) beam selection process is attracting increasing interest. This sensory data, captured…
Progressive with Purpose: Guiding Progressive Inpainting DNNs through Context and Structure
Kangdi Shi, Muhammad Alrabeiah, Jun Chen
The advent of deep learning in the past decade has significantly helped advance image inpainting. Although achieving promising performance, deep learning-based inpainting algorithm…
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
Shunyao Wu, Muhammad Alrabeiah, Chaitali Chakrabarti +1
Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previ…
Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
Muhammad Alrabeiah, Umut Demirhan, Andrew Hredzak +1
The support of coexisting ultra-reliable and low-latency (URLL) and enhanced Mobile BroadBand (eMBB) services is a key challenge for the current and future wireless communication n…
Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. Being pre-defined, however, these codeb…
Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction
Nof Abuzainab, Muhammad Alrabeiah, Ahmed Alkhateeb +1
We consider the problem of proactive handoff and beam selection in Terahertz (THz) drone communication networks assisted with reconfigurable intelligent surfaces (RIS). Drones have…
Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff
Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. Since these networks mainly rely on line-of-sigh…
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. However, due to the adoption of fully analog or hybrid analo…
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak +2
Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by…
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
Muhammad Alrabeiah, Yu Zhang, Ahmed Alkhateeb
Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data tran…
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. T…
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-sca…
ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions
Muhammad Alrabeiah, Jayden Booth, Andrew Hredzak +1
Vision-aided wireless communication is motivated by the recent advances in deep learning and computer vision as well as the increasing dependence on line-of-sight links in millimet…
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…
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Zheng Liu, Botao Xiao, Muhammad Alrabeiah +2
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. This paper proposes an end-to-end generative method for image…