  In this paper, we consider the user positioning problem in the massive
multiple-input multiple-output (MIMO) orthogonal frequency-division
multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking
advantage of the UPA array geometry and wide bandwidth, we advocate the use of
the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to
replace the traditional ones. The ADCPM embeds the stable and stationary
multipath characteristics, e.g. delay, power, and angle in the vertical and
horizontal directions, which are beneficial to positioning. Taking ADCPM
fingerprints as the inputs, we propose a novel three-dimensional (3D)
convolution neural network (CNN) enabled learning method to localize users' 3D
positions. In particular, such a 3D CNN model consists of a convolution
refinement module to refine the elementary feature maps from the ADCPM
fingerprints, three extended Inception modules to extract the advanced feature
maps, and a regression module to estimate the 3D positions. By intensive
simulations, the proposed 3D CNN-enabled positioning method is demonstrated to
achieve higher positioning accuracy than the traditional searching-based ones,
with reduced computational complexity and storage overhead, and the ADCPM
fingerprints are more robust to noise contamination.
