# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 1 Introduction
[21] investigated the physical layer security to bolster secure transmission in satellite networks, which share their downlink spectra with terrestrial cellular networks. Hybrid zero-forcing and partial zero-forcing beamforming schemes are proposed to maximize the instantaneous rate of terrestrial cellular users while satisfying the interference probability constraint of satellite users. Ref. [22] proposed hybrid beamforming at the BS and digital beamforming at satellites to maximize the achievable secrecy-energy efficiency of a satellite-terrestrial integrated network, which shares the millimeter-wave spectral resource with a cellular network. In [23], the authors exploited the effects of an aerial intelligent-reflecting surface (IRS) to improve the communication security. An IRS is transported by a UAV, and the deployment and phase shift of aerial IRS aimed at maximizing the secrecy rate is accomplished by successive convex approximation (SCA) and Riemannian manifold optimization (RMO) methods, respectively. Satellite communications share similarities with UAV networks, as both operate under constraints on limited power at UAVs and satellites and leverage a strong LOS link. In this sense, security techniques proposed in [21; 22; 23] can be adapted for robust transmission in UAV networks. In addition, the authors in [24] introduced a reconfigurable intelligent surface (RIS) to boost the received signal power, and proposed joint beamforming and optimization for RIS-aided hybrid satellite-terrestrial relay networks to minimize the total transmit power at both the satellite and BS, while guaranteeing the required rate of users. This can serve as a potential technique to improve the signal power in UAV relay networks in the future. As mentioned above, there has been research on the construction of UAV networks, UAV deployment, performance enhancement, and reliable and secure transmission because UAV networks ensure unique features over MANETs and VANETs and have the potential to provide robust solutions for problems that current networks are facing. Furthermore, URN can provide many advantages. Compared to the terrestrial relay network, URNis more cost-effective and much easier to be deployed, and can extend the service range of the ground BS with less transmit power, guaranteeing a reliable relay transmission with a strong air-to-ground LOS link. In addition, the mobility of the UAV can improve the performance of URN via the UAV position adjustment to achieve higher throughput and enhance the signal-to-interference-plus-noise ratio (SINR) of a wireless backhaul link. Such advantages allow the URN to support temporary communication services where tremendous data transmissions are suddenly required or emergency communication links are needed due to infrastructure destruction or natural disasters [2]. However, studies on URN are less frequently conducted compared to the UAV BS case, due to its limited capacity to support users with seamless wireless connectivity [13]. In the URNs, the SINR enhancement of a wireless backhaul link is the key to guaranteeing a reliable relay link to isolated user equipment \((U_{o})\) without throughput degradation of another UE in the URN. Several studies have focused on achieving a stronger backhaul link in the URN by focusing on optimal UAV placement and resource allocation [25; 26; 27; 28; 29; 30; 31]. In [25], the resource allocation of the transmission power, bandwidth and transmission rate, and the position of the UAV relay node are jointly investigated to maximize the system throughput. The formulated problem is transferred to a monotonic optimization problem to achieve the optimal global solution. Ref. [26] considered a cellular-to-UAV and an air-to-ground path loss model for the wireless backhaul link and downlink scenario, respectively, to solve the problem of the efficient placement of an aerial relay node for the overall throughput maximization. Ref.