D3PicoNet: Enabling Fast and Accurate Indoor D-Band Millimeter-Wave Picocell Deployment.

We propose D3PicoNet, which allows network deployers to quickly complete realistic indoor site surveys at Dband (mmWave) frequency. D3PicoNet models the mmWave reflection profile of a given environment, considering the primary reflecting objects. It then utilizes this model to identify places that optimize the efficiency of the reflectors. D3PicoNet understands an environment and deploys D-band picocells at such locations that picocells provide coverage with Non-Line-of-Sight (NLoS) paths when Line-of-Sight (LoS) is obstructed. The core module of D3PicoNet is a deep learning network that learns the relationship between the visual depth images to the mmWave signal reflection profiles and can accurately predict signal reflection profiles at any unobserved location, which allows D3PicoNet to find the best deployment locations maximizing the coverage and data rate with a minimum number of picocells in an environment. We implement and evaluate D3PicoNet on two buildings with multiple indoor environments. D3PicoNet can adapt to new environments, allowing it to be used in other indoor environments with minimal adjustments.