On Optimizing the Divergence Angle of an FSO-Based Fronthaul Link in Drone-Assisted Mobile Networks

In a free space optics (FSO)-based drone-assisted mobile network, a drone-mounted base station (DBS) can be rapidly deployed over a place of interest to relay traffic between Internet of Things (IoT) devices and a macro base station (MBS), and FSO is applied as the fronthaul solution between the DBS and the MBS to provide a high link capacity at a long distance. However, due to inevitable optical beam misalignment, having a smaller divergence angle of the optical beam may increase the outage probability of the FSO-based fronthaul link. On the other hand, having a larger divergence angle may reduce the capacity of the FSO-based fronthaul link. So, it is critical but challenging to determine the divergence angle in order to optimize the tradeoff between minimizing the outage probability and maximizing the capacity for the FSO-based fronthaul link in the context of the FSO-based drone-assisted mobile network. In this article, we formulate an optimization problem to determine the optimal divergence angle that can minimize the link outage probability, while guaranteeing the capacity of the FSO-based fronthaul link no less than the threshold. The link outage and capacity aware divergence angle (LEARN) algorithm is designed to efficiently solve the problem. The performance of LEARN is demonstrated via extensive simulations.

Automated summary

This article discusses the issue of determining the divergence angle in a drone-assisted mobile network to optimize the tradeoff between capacity and outage probability of the fronthaul link. An algorithm called LEARN is proposed to solve this problem, and its performance is demonstrated through extensive simulations.