Abstract
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.
Highlights
Unmanned aerial vehicles (UAVs) 1 are increasingly becoming part of our lives by enhancing how we work, e.g., package delivery, how we entertain ourselves and how we extend the safety and security of our society
In addition to the optimization framework, we extend RIS to compensate flight effects (RiFe) to account for practical considerations such as the need to update the Reconfigurable intelligent surfaces (RISs) parameters due to rapidly-changing channel statistics, the mobility of the unmanned aerial vehicles (UAVs) as well as complexity issues and rename it as Fair-RiFe
In order to cope with any given general probability density function of the distribution of the receivers fw(w), we propose to apply a Monte Carlo sampling approach whereby we drop a number of sample points Nw within the target area A and according to a-priori statistics fw(w)
Summary
Unmanned aerial vehicles (UAVs) 1 are increasingly becoming part of our lives by enhancing how we work, e.g., package delivery, how we entertain ourselves and how we extend the safety and security of our society. UAVs are proposed to bring back-up connectivity in such areas and/or leverage on advanced sensing and localization techniques exploiting the cellular protocol stack to find missing people [7] Since they are envisioned as flying mobile base stations (BSs) carrying one or more active antennas, a significant increase of the total power consumption and, in turn, battery drain issues are expected due to i) the weight of active elements, ii) the power irradiated to reach ground targets and iii) the additional. To overcome the above-mentioned issues, lightweight and low-energy equipment is needed on board In this context, reconfigurable intelligent surfaces (RISs) are currently gaining much attention owing to their ability to control the propagation environment by altering reflection, absorption and amplitude properties of the material where the signal bounces off [8]– [11]. To the best of our knowledge, RiFe is the first of its kind tackling such undesired oscillations while steering signal reflections to build a robust and reliable air-to-ground network solution
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