Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data
Abstract
Accurate communication performance prediction is crucial for wireless applications such as network deployment and resource management. Unlike conventional systems with a single transmit and receive antenna, throughput (Tput) estimation in antenna array-based multiple-output multiple-input (MIMO) systems is computationally intensive, i.e., requiring analysis of channel matrices, rank conditions, and spatial channel quality. These calculations impose significant computational and time burdens. This paper introduces Geo2ComMap, a deep learning-based framework that leverages geographic databases to efficiently estimate multiple communication metrics across an entire area in MIMO systems using only sparse measurements. To mitigate extreme prediction errors, we propose a sparse sampling strategy. Extensive evaluations demonstrate that Geo2ComMap accurately predicts full-area communication metrics, achieving a median absolute error of 27.35 Mbps for Tput values ranging from 0 to 1900 Mbps.
Cite
@article{arxiv.2504.00351,
title = {Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data},
author = {Fan-Hao Lin and Tzu-Hao Huang and Chao-Kai Wen and Trung Q. Duong},
journal= {arXiv preprint arXiv:2504.00351},
year = {2025}
}
Comments
5 pages, 8 figures, 1 table, this work has been submitted to IEEE for possible publication. The source code and datasets used in this study are publicly available at https://github.com/geo2commap/Geo2ComMap