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

arXiv: https://arxiv.org/abs/1911.06257 · HTML: https://mlsanity.com/paper/1911.06257/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 21 of 21)

- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework](https://mlsanity.com/paper/2504.05187/) (2025)
- [ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks](https://mlsanity.com/paper/2407.15023/) (2024)
- [Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6G](https://mlsanity.com/paper/2507.08716/) (2025)
- [Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs](https://mlsanity.com/paper/2203.16419/) (2022)
- [Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction](https://mlsanity.com/paper/2211.07569/) (2022)
- [Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence](https://mlsanity.com/paper/2103.10419/) (2021)
- [Simulation of machine learning-based 6G systems in virtual worlds](https://mlsanity.com/paper/2204.09518/) (2022)
- [HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning](https://mlsanity.com/paper/2401.01822/) (2024)
- [CONVERGE: A Multi-Agent Vision-Radio Architecture for xApps](https://mlsanity.com/paper/2508.04556/) (2025)
- [Vision Transformer Based User Equipment Positioning](https://mlsanity.com/paper/2511.08549/) (2025)
- [RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks](https://mlsanity.com/paper/2606.26885/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach](https://mlsanity.com/paper/2609.23372/) (2026)

## Abstract (arXiv)

The growing role that 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 textitvision-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 and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.