AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909688394678272 |
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| author | Moon, Hyung-Joo Chae, Chan-Byoung Wong, Kai-Kit Heath Jr, Robert W. |
| author_facet | Moon, Hyung-Joo Chae, Chan-Byoung Wong, Kai-Kit Heath Jr, Robert W. |
| contents | Massive Aerial Processing for X MAP-X is an innovative framework for reconstructing spatially correlated ground data, such as environmental or industrial measurements distributed across a wide area, into data maps using a single high altitude pseudo-satellite (HAPS) and a large number of distributed sensors. With subframe-level data reconstruction, MAP-X provides a transformative solution for latency-sensitive IoT applications. This article explores two distinct approaches for AI integration in the post-processing stage of MAP-X. The DNN-based pointwise estimation approach enables real-time, adaptive reconstruction through online training, while the CNN-based image reconstruction approach improves reconstruction accuracy through offline training with non-real-time data. Simulation results show that both approaches significantly outperform the conventional inverse discrete Fourier transform (IDFT)-based linear post-processing method. Furthermore, to enable AI-enhanced MAP-X, we propose a ground-HAPS cooperation framework, where terrestrial stations collect, process, and relay training data to the HAPS. With its enhanced capability in reconstructing field data, AI-enhanced MAP-X is applicable to various real-world use cases, including disaster response and network management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09895 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission Moon, Hyung-Joo Chae, Chan-Byoung Wong, Kai-Kit Heath Jr, Robert W. Signal Processing Massive Aerial Processing for X MAP-X is an innovative framework for reconstructing spatially correlated ground data, such as environmental or industrial measurements distributed across a wide area, into data maps using a single high altitude pseudo-satellite (HAPS) and a large number of distributed sensors. With subframe-level data reconstruction, MAP-X provides a transformative solution for latency-sensitive IoT applications. This article explores two distinct approaches for AI integration in the post-processing stage of MAP-X. The DNN-based pointwise estimation approach enables real-time, adaptive reconstruction through online training, while the CNN-based image reconstruction approach improves reconstruction accuracy through offline training with non-real-time data. Simulation results show that both approaches significantly outperform the conventional inverse discrete Fourier transform (IDFT)-based linear post-processing method. Furthermore, to enable AI-enhanced MAP-X, we propose a ground-HAPS cooperation framework, where terrestrial stations collect, process, and relay training data to the HAPS. With its enhanced capability in reconstructing field data, AI-enhanced MAP-X is applicable to various real-world use cases, including disaster response and network management. |
| title | AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.09895 |