DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

By Ahmed Alkhateeb, Gouranga Charan, Tawfik Osman, Andrew Hredzak, João Morais, Umut Demirhan, Nikhil Srinivas. IEEE Communications Magazine, 2022

330 citations recorded by the source corpus.

This article presents the DeepSense 6G data-set, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data. The Deep-Sense 6G dataset is built to advance deep learning research in a wide range of applications in the intersection of multi-modal sensing, communication, and positioning. This article provides a detailed overview of the DeepSense dataset structure, adopted testbeds, data collection and processing methodology, deployment scenarios, and example applications, with the objective of facilitating the adoption and reproducibility of multi-modal sensing and communication datasets.

Subfields: Computer Networks and Communications, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7446a7a37cca491169f1956d1cbf0e599388786e

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In Computer Networks and Communications

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Citations in the corpus

• Integrated Sensing and Communications: Recent Advances and Ten Open Challenges (2023, methodology): “Furthermore, deep learning (DL) algorithms can be leveraged to map the features from the sensory data to the optimal beam, thus further reducing the communication overheads [89], [90], [91].”