  Machine learning has been considered a promising approach for indoor
localization. Nevertheless, the sample efficiency, scalability, and
generalization ability remain open issues of implementing learning-based
algorithms in practical systems. In this paper, we establish a zero-shot
learning framework that does not need real-world measurements in a new
communication environment. Specifically, a graph neural network that is
scalable to the number of access points (APs) and mobile devices (MDs) is used
for obtaining coarse locations of MDs. Based on the coarse locations, the
floor-plan image between an MD and an AP is exploited to improve localization
accuracy in a floor-plan-aided deep neural network. To further improve the
generalization ability, we develop a synthetic data generator that provides
synthetic data samples in different scenarios, where real-world samples are not
available. We implement the framework in a prototype that estimates the
locations of MDs. Experimental results show that our zero-shot learning method
can reduce localization errors by around $30$\% to $55$\% compared with three
baselines from the existing literature.
