Aerial-ground cooperative vehicular networks are envisioned as a novel paradigm in B5G/6G visions. In this letter, the challenge of optimizing the global energy-efficiency (EE) of multi-UAV-aided vehicular networks in the presence of uncertain air-to-ground (A2G) channels is addressed. Specifically, we propose a maximin paradigm to characterize the system, which aims to maximize its global EE meanwhile satisfying Quality-of-Service (QoS)-oriented data rate requirements in the worst-case situation. We theoretically derive a closed-form optimal solution for an embedded minimization subproblem under a parametric channel uncertainty set and thus develop a computationally tractable robust counterpart, which leads to a robust EE optimization design. Simulation results show that the proposed method significantly outperforms conventional EE schemes in terms of achieving higher global system performance and better robustness under random uncertain environments.

中文翻译：

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多无人机辅助车载网络的鲁棒协作通信优化


空地协同车辆网络被视为 B5G/6G 愿景中的一种新颖范例。在这封信中，解决了在存在不确定的空对地（A2G）通道的情况下优化多无人机辅助车辆网络的全局能源效率（EE）的挑战。具体来说，我们提出了一个 maximin 范式来表征系统，其目标是最大化其全局 EE，同时满足最坏情况下面向服务质量（QoS）的数据速率要求。我们从理论上推导出参数化通道不确定性集下嵌入式最小化子问题的封闭式最优解，从而开发出计算上易于处理的稳健对应物，从而实现稳健的 EE 优化设计。仿真结果表明，该方法在随机不确定环境下实现更高的全局系统性能和更好的鲁棒性方面显着优于传统的EE方案。