9 citations · 29 across the 9 of their papers we have counts for
9 papers
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
Gouranga Charan, Andrew Hredzak, Ahmed Alkhateeb
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disas…
DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset
Ahmed Alkhateeb, Gouranga Charan, Tawfik Osman +4
This article presents the DeepSense 6G dataset, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data. The DeepS…
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
Gouranga Charan, Andrew Hredzak, Christian Stoddard +4
Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead as…
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
Gouranga Charan, Tawfik Osman, Andrew Hredzak +2
Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenn…
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…
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…
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…
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…