Enhancing Beamformed Fingerprint Outdoor Positioning with Hierarchical Convolutional Neural Networks

With 5G millimeter wave communications, the resulting radiation reflects on most visible objects, creating rich multipath environments. The radiation is thus significantly shaped by the obstacles it interacts with, carrying latent information regarding the relative positions of the transmitter, the obstacles, and the mobile receiver. Through a pre-estabilhed codebook of beamforming patterns transmitted by a base station, the concept of beamformed fingerprints for mobile devices' outdoor positioning has been previously proposed. In this paper, a tailored hierarchical convolutional neural network is proposed to further leverage the structure in the aforementioned hidden information. Average errors of down to 3.3 meters are obtained on a simulation environment based on realistic outdoor scenarios, containing mostly non-line-of-sight positions, making it a very competitive and promising alternative for outdoor positioning.