{"ID":2894649,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09895","arxiv_id":"2507.09895","title":"AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission","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 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.","short_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 subfr...","url_abs":"https://arxiv.org/abs/2507.09895","url_pdf":"https://arxiv.org/pdf/2507.09895v1","authors":"[\"Hyung-Joo Moon\",\"Chan-Byoung Chae\",\"Kai-Kit Wong\",\"Robert W. Heath\"]","published":"2025-07-14T04:03:54Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}