Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems

Abstract

In this paper, we investigate user positioning in massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems where the base station (BS) is equipped with a uniform planar array (UPA). Taking advantage of the UPA 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. We further exploit the sparsity of the ADCPM to reduce the noise contamination in the ADCPM. Taking ADCPM fingerprints as the inputs, we propose a novel three-dimensional (3D) convolution neural network (CNN) enabled learning method to localize the 3D positions of the mobile terminals (MTs). 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 robust to noise contamination.