  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.
