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Found 15 papers, 1 papers with code
Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration
The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data.
Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios
The solutions developed so far, however, have mainly considered single-candidate scenarios, i. e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets.
Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond
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 distributed infrastructure and user devices, and are used to make communication and sensing decisions.
DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset
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.
Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management.
User Identification: A Key Enabler for Multi-User Vision-Aided Communications
In this paper, we define the \textit{user identification} task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications.
Multi-Modal Beam Prediction Challenge 2022: Towards Generalization
In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices.
Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power.
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed.
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
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 blockages before they happen.
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems.
Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff
This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations.
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements.
Single-Net Continual Learning with Progressive Segmented Training (PST)
Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference.
Towards Efficient Neural Networks On-a-chip: Joint Hardware-Algorithm Approaches
Machine learning algorithms have made significant advances in many applications.
