# WAIR-D: Wireless AI Research Dataset

arXiv: https://arxiv.org/abs/2212.02159 · HTML: https://mlsanity.com/paper/2212.02159/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [Deep Reinforcement Learning for Scheduling in Cellular Networks](https://mlsanity.com/paper/1905.05914/) (2019)
- [Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning](https://mlsanity.com/paper/1911.05281/) (2019)
- [Realistic Channel Models Pre-training](https://mlsanity.com/paper/1907.09117/) (2019)
- [Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks](https://mlsanity.com/paper/2103.11542/) (2021)
- [Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models](https://mlsanity.com/paper/1901.04119/) (2019)
- [Distributed Learning for Time-varying Networks: A Scalable Design](https://mlsanity.com/paper/2108.00231/) (2021)

## Built on by (most cited first, 8 of 8)

- [Generative AI for RF Sensing in IoT systems](https://mlsanity.com/paper/2407.07506/) (2024)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model](https://mlsanity.com/paper/2505.10003/) (2025)
- [AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G](https://mlsanity.com/paper/2605.00020/) (2026)
- [Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning](https://mlsanity.com/paper/2303.12653/) (2023)
- [EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes](https://mlsanity.com/paper/2405.17366/) (2024)
- [Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation](https://mlsanity.com/paper/2607.22637/) (2026)

## Abstract (arXiv)

It is a common sense that datasets with high-quality data samples play an important role in artificial intelligence (AI), machine learning (ML) and related studies. However, although AI/ML has been introduced in wireless researches long time ago, few datasets are commonly used in the research community. Without a common dataset, AI-based methods proposed for wireless systems are hard to compare with both the traditional baselines and even each other. The existing wireless AI researches usually rely on datasets generated based on statistical models or ray-tracing simulations with limited environments. The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models. In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which consists of two scenarios. Scenario 1 contains 10,000 environments with sparsely dropped user equipments (UEs), and Scenario 2 contains 100 environments with densely dropped UEs. The environments are randomly picked up from more than 40 cities in the real world map. The large volume of the data guarantees that the trained AI models enjoy good generalization capability, while fine-tuning can be easily carried out on a specific chosen environment. Moreover, both the wireless channels and the corresponding environmental information are provided in WAIR-D, so that extra-information-aided communication mechanism can be designed and evaluated. WAIR-D provides the researchers benchmarks to compare their different designs or reproduce results of others. In this paper, we show the detailed construction of this dataset and examples of using it.