Unmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative. This paper presents a novel variable height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as BSs. We propose a multi-tier variable height UAV-based network deployment and compare its performance with the state-of-the-art equal height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. The results show that our proposed method is more energy-efficient in a multicell UAV network than the most widely used height optimization method in the literature. In UAV networks, users at the cell edges can receive very poor signal-to-interference-plus-noise ratio (SINR) levels due to interfering UAVs. To cope with this problem, we adopt a fractional frequency reuse (FFR) scheme to compensate low SINR levels. We optimize the SINR threshold corresponding to each cell to maximize their spectral efficiency (SE), thereby improving the network's area spectral efficiency (ASE). The numerical results show that the proposed deployments provide significant gains in coverage density, SINR coverage probability, rate coverage, and ASE compared to equal height benchmark scheme. As the number of UAVs increases, the number of tiers need to increase to preserve the rate coverage of the network. Moreover, the performance of the proposed variable height model is expected to converge to that of equal height cellular design for a large number of UAVs.

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Free-space optical (FSO) communication is expected to play an indispensable role with high data rates and low system complexity in beyond fifth-generation (B5G) networks. However, infrequent adverse weather conditions can incapacitate its performance. The combination of FSO and radio frequency (RF) has emerged as an effective alternative for meeting the growing need for high data rates in wireless communication networks. Unmanned aerial vehicles (UAVs) are also anticipated to play an instrumental role in B5G networks due to their flexible movement and deployment. In this paper, a UAV-aided hybrid FSO/RF backhauling system using a matching game theory (GT) and reinforcement learning (RL) framework is investigated. We deploy a UAV to provide a user offloading service to an already existing ground base station (GBS), which is facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station (MBS). However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. With the reduced FSO backhaul capacity, the GBS needs an additional backhaul link to support its backhaul data transmission to the destination MBS. As a result, instead of building an expensive permanent parallel RF link for the rare foggy situation, a UAV can be hired to serve a portion of the users, thereby reducing the GBS load. The users perform a matching game-based procedure to select the base station (BS) of their choice to maximize their utility. The UAV is deployed at an optimal altitude, and the bandwidth partition between the GBS and the UAV is optimized to maximize the system throughput using RL. Real weather data from the cities of Edinburgh and London in the U.K. are used to evaluate the performance of the system. The numerical results show the superiority and effectiveness of the proposed scheme compared to conventional methods.