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arXiv · 2504.00351

Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data

Fan-Hao Lin·Tzu-Hao Huang·Chao-Kai Wen·Trung Q. Duong
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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.

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Fan-Hao Lin, Tzu-Hao Huang, Chao-Kai Wen, Trung Q. Duong. 2025-04-01. Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data. https://arxiv.org/abs/2504.00351

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