Moon, Hyung-Joo;
Chae, Chan-Byoung;
Wong, Kai-Kit;
Heath Jr, Robert W;
(2025)
AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission.
IEEE Communications Magazine
10.1109/MCOM.001.2500183.
(In press).
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Abstract
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 model-driven pointwise estimation approach enables real-time, adaptive reconstruction through online training, while the end-to-end 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.
| Type: | Article |
|---|---|
| Title: | AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MCOM.001.2500183 |
| Publisher version: | https://doi.org/10.1109/mcom.001.2500183 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Sensors, Wireless sensor networks, Time-frequency analysis, Image reconstruction, Channel estimation, Symbols,Antenna arrays, Uplink, Real-time systems, Internet of Things |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10214864 |
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