ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications

@article{Alrabeiah2020ViWiAD,
  title={ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications},
  author={Muhammad Alrabeiah and Andrew Hredzak and Z. Liu and A. Alkhateeb},
  journal={2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)},
  year={2020},
  pages={1-5}
}
The growing role artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of vision-aided wireless communications, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first… Expand
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References

SHOWING 1-10 OF 21 REFERENCES
DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
  • 74
  • PDF
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning
  • 179
  • PDF
Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
  • X. Li, A. Alkhateeb
  • Computer Science, Engineering
  • 2019 53rd Asilomar Conference on Signals, Systems, and Computers
  • 2019
  • 41
  • PDF
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems
  • 155
  • PDF
MACHINE LEARNING FOR RELIABLE MMWAVE SYSTEMS: BLOCKAGE PREDICTION AND PROACTIVE HANDOFF
  • A. Alkhateeb, Iz Beltagy
  • Computer Science, Mathematics
  • 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
  • 2018
  • 43
  • PDF
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
  • 73
  • PDF
LIDAR Data for Deep Learning-Based mmWave Beam-Selection
  • 25
  • PDF
Over-the-Air Deep Learning Based Radio Signal Classification
  • 278
  • PDF
Are we ready for autonomous driving? The KITTI vision benchmark suite
  • 5,508
  • PDF
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
  • 952
  • PDF
...
1
2
3
...