3D CNN-Enabled Positioning in 3D Massive MIMO-OFDM Systems

Abstract

In this paper, we investigate the three-dimensional (3D) user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with the base station (BS) equipped 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 angles in the vertical and horizontal directions, which are beneficial to positioning. Taking ADCPM fingerprints as the inputs, we propose a novel 3D convolution neural network (CNN) enabled learning method to localize users' 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 robust to noise contamination.