Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910900360839168 |
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| author | Lin, Fan-Hao Huang, Tzu-Hao Wen, Chao-Kai Duong, Trung Q. |
| author_facet | Lin, Fan-Hao Huang, Tzu-Hao Wen, Chao-Kai Duong, Trung Q. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_00351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data Lin, Fan-Hao Huang, Tzu-Hao Wen, Chao-Kai Duong, Trung Q. Information Theory 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. |
| title | Geo2ComMap: Deep Learning-Based MIMO Throughput Prediction Using Geographic Data |
| topic | Information Theory |
| url | https://arxiv.org/abs/2504.00351 |