In this paper, we propose and investigate an aerial reconfigurable intelligent surface (aerial-RIS)-aided wireless communication system. Specifically, considering practical composite fading channels, we characterize the air-to-ground (A2G) links by Namkagami-m small-scale fading and inverse-Gamma large-scale shadowing. To investigate the delay-limited performance of the proposed system, we derive a tight approximate closed-form expression for the end-to-end outage probability (OP). Next, considering a mobile environment, where performance analysis is intractable, we rely on machine learning-based performance prediction to evaluate the performance of the mobile aerial-RIS-aided system. Specifically, taking into account the three-dimensional (3D) spatial movement of the aerial-RIS, we build a deep neural network (DNN) to accurately predict the OP. We show that: (i) fading and shadowing conditions have strong impact on the OP, (ii) as the number of reflecting elements increases, aerial-RIS achieves higher energy efficiency (EE), and (iii) the aerial-RIS-aided system outperforms conventional relaying systems.

中文翻译：

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空中可重构智能水面辅助无线通信系统

在本文中，我们提出并研究了一种空中可重构智能表面（aerial-RIS）辅助无线通信系统。具体来说，考虑到实际的复合衰落信道，我们通过 Namkagami-m 小尺度衰落和逆伽玛大尺度阴影表征空对地 (A2G) 链路。为了研究所提出系统的延迟限制性能，我们推导出端到端中断概率 (OP) 的严格近似封闭形式表达式。接下来，考虑到性能分析难以处理的移动环境，我们依靠基于机器学习的性能预测来评估移动空中 RIS 辅助系统的性能。具体来说，考虑到空中 RIS 的三维（3D）空间运动，我们构建了一个深度神经网络（DNN）来准确预测 OP。