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Found 8 papers, 2 papers with code
ISAC with Backscattering RFID Tags: Joint Beamforming Design
Then, we study a joint beamforming design problem with the goal of minimizing the total transmit power while satisfying the tag detection and communication requirements.
Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration
Utilizing radar sensing for assisting communication has attracted increasing interest thanks to its potential in dynamic environments.
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.
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.
Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles
The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.
Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems
The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks.
Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration
This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications.
Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence
To demonstrate the potential of the proposed framework, a wireless network scenario with two coexisting URLL and eMBB services is considered, and two deep learning algorithms are designed to utilize RGB video frames and predict incoming service type and its request time.
