Outdoor Millimeter-Wave Picocell Placement using Drone-based Surveying and Machine Learning

Abstract

Millimeter-Wave (mmWave) networks rely on carefully placed small base stations called “picocells” for optimal network performance. However, the process of conducting site surveys to identify suitable picocell locations is both expensive and time-consuming. The current low-cost approaches for indoor surveying are often unsuitable for outdoor environments due to the presence of various environmental factors. To address this issue, we present Theia, a drone-based system that predicts outdoor mmWave Signal Reflection Profiles (SRPs) and facilitates picocell placement for optimal network coverage. The drone platform integrates optical systems and a mmWave transceiver to collect depth images and mmWave SRPs of the environment. These datasets are fed into a machine learning model that maps the depth data to SRPs, allowing SRPs to be predicted at previously unseen parts of the environment. Theia then leverages these predictions to identify optimal picocell locations that maximize network coverage and minimize link outages. We evaluate Theia in three large-scale outdoor environments and demonstrate that the proposed design can generalize the deployment method with a little refinement of the model.