Next generation mobile networks need to expand towards uncharted territories in order to enable the digital transformation of society. In this context, aerial devices such as unmanned aerial vehicles (UAVs) are expected to address this gap in hard-to-reach locations. However, limited battery-life is an obstacle for the successful spread of such solutions. Reconfigurable intelligent surfaces (RISs) represent a promising solution addressing this challenge since on-board passive and lightweight controllable devices can efficiently reflect the signal propagation from the ground BSs towards specific target areas. In this paper, we focus on air-to-ground networks where UAVs equipped with RIS can fly over selected areas to provide connectivity. In particular, we study how to optimally compensate flight effects and propose RiFe as well as its practical implementation Fair-RiFe that automatically configure RIS parameters accounting for undesired UAV oscillations due to adverse atmospheric conditions. Our results show that both algorithms provide robustness and reliability while outperforming state-of-the-art solutions in the multiple conditions studied.
翻译:下一代移动网络需要向未知地区扩展,以便实现社会的数字转型。在这方面,无人驾驶飞行器等航空装置预计将弥补难以到达地点的这一差距。然而,电池寿命有限是成功推广此类解决方案的障碍。可配置智能表面(RIS)是应对这一挑战的一个大有希望的解决方案,因为机载被动和轻量控制装置能够有效地反映从地面BS向特定目标地区的信号传播。在本文中,我们侧重于空对地网络,配备了RIS的无人驾驶飞行器能够飞越选定地区提供连接。特别是,我们研究如何最佳地补偿飞行效应,并提出RiFe及其实际实施Fair-RiFe,自动配置RIS参数,用于核算由于不利的大气条件造成的不理想的UAV振荡。我们的结果显示,两种算法既可靠又可靠,同时在所研究的多种条件下表现优异的状态解决方案。