11 citations · 46 across the 21 of their papers we have counts for
13 papers · 1 filter
Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration
Gouranga Charan, Ahmed Alkhateeb
In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their…
Pixel-Level GPS Localization and Denoising using Computer Vision and 6G Communication Beams
Gouranga Charan, Tawfik Osman, Ahmed Alkhateeb
Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Nav…
DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset
Joao Morais, Gouranga Charan, Nikhil Srinivas +1
High data rate and low-latency vehicle-to-vehicle (V2V) communication are essential for future intelligent transport systems to enable coordination, enhance safety, and support dis…
Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond
Ahmed Alkhateeb, Shuaifeng Jiang, Gouranga Charan
This article presents a vision where \textit{real-time} digital twins of the physical wireless environments are continuously updated using multi-modal sensing data from the distrib…
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…
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
Gouranga Charan, Umut Demirhan, João Morais +3
Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external se…
User Identification: A Key Enabler for Multi-User Vision-Aided Communications
Gouranga Charan, Ahmed Alkhateeb
Vision-aided wireless communication is attracting increasing interest and finding new use cases in various wireless communication applications. These vision-aided communication fra…
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…
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
Shuaifeng Jiang, Gouranga Charan, Ahmed Alkhateeb
This paper presents the first large-scale real-world evaluation for using LiDAR data to guide the mmWave beam prediction task. A machine learning (ML) model that leverages the LiDA…
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
Gouranga Charan, Ahmed Alkhateeb
This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockage…
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…
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…