Free space optics (FSO), which uses light as the carrier to transmit data in free space, has been demonstrated as a secure and high-speed solution for long distance and line-of-sight wireless communications. Applying FSO as fronthaul/backhaul communications between base stations (BSs) and the gateway can significantly increase the fronthaul/backhaul link capacity. Traditionally, the gateway has to be equipped with multiple FSO transceivers, each of which is used to communicate with a BS by establishing a dedicated FSO. In this paper, we propose to use a steerable FSO system, where the gateway is equipped with a steerable FSO transceiver to communicate with multiple FSO transceivers at different BSs in a time division multiplexing manner. Applying the steerable FSO system can reduce the number of FSO transceivers at the gateway, and thus reduce the capital cost of implementing an FSO based fronthaul/backhaul network. We formulate the transmission scheduling problem in the steerable FSO system to optimize the active time for each FSO link associated with the steerable FSO transceiver such that the overall delay of transmitting a packet from geo-distributed BSs to the steerable FSO transceiver at the gateway is minimized, while guaranteeing the latency requirements of the BSs. We propose the laTency aWare transmIssionScheduling for sTeerable FSO (TWIST) algorithm, which is designed based on Sequential Quadratic Programming, to efficiently solve the proposed problem. The performance of TWIST is validated via extensive simulations.

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.