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



Wu, Chi, Yi, Xinping ORCID: 0000-0001-5163-2364, Wang, Wenjin, Huang, Qing and Gao, Xiqi
(2020) 3D CNN-Enabled Positioning in 3D Massive MIMO-OFDM Systems. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020-6-7 - 2020-6-11.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Massive MIMO, positioning, deep learning, 3D convolution neural network
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 19 May 2021 11:22
Last Modified: 17 Mar 2024 09:31
DOI: 10.1109/icc40277.2020.9149427
Open Access URL: https://arxiv.org/pdf/1910.12378.pdf
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3123302