标题： 通过大规模非正交直接设备到HAPS传输的AI增强广域数据成像 显示英文标题

标题： AI-Enhanced Wide-Area Data Imaging via Massive Non-Orthogonal Direct Device-to-HAPS Transmission

摘要： 基于X MAP-X的大规模空中处理是一种创新框架，用于将空间相关地面数据（如分布在广阔区域的环境或工业测量数据）重建为数据地图，使用一个高空伪卫星（HAPS）和大量分布式传感器。 通过子帧级数据重建，MAP-X为对延迟敏感的物联网应用提供了变革性的解决方案。 本文探讨了两种不同的方法，用于在MAP-X的后处理阶段集成人工智能。 基于深度神经网络的逐点估计方法通过在线训练实现实时、自适应重建，而基于卷积神经网络的图像重建方法则通过非实时数据的离线训练提高重建准确性。 仿真结果表明，这两种方法均显著优于传统的基于逆离散傅里叶变换（IDFT）的线性后处理方法。 此外，为了实现增强人工智能的MAP-X，我们提出了一种地面-HAPS协作框架，其中地面站收集、处理并中继训练数据到HAPS。 凭借其在重建现场数据方面的增强能力，增强人工智能的MAP-X适用于各种实际应用场景，包括灾害响应和网络管理。 显示英文摘要

摘要： 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.