# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 1 Introduction
[27] cooperatively optimized the UAV trajectory and relay power allocation to maximize the minimum achievable information rate for the communication link between two ground users. Ref. [28] utilized multiple UAVs and proposed a UAV deployment algorithm to provide seamless relay connectivity to a remote user and maximize the average data rate of the user in the URN. In [29], an orthogonal transmission at the BS with a decode and forward (DF) relay scheme was considered to optimize the throughput of \(U_{o}\), but without considering that of other UE. Ref. [29] introduced non-orthogonal transmission at the BS in the URN, which could effectively extend the service range of the BS, and increase the throughput capacity compared to the orthogonal transmission. Ref. [30] applied non-orthogonal transmission to URN and proposed optimal resource management of transmit power and time at BS and UAV to maximize the throughput of the UE within a BS service range and simultaneously guarantee seamless relay transmission to \(U_{o}\). Ref. [31] proposed UAV deployment and the time allocation algorithm for fast and optimal UAV deployment. It first determines the minimum required transmission time slots between ground BS and UAV and between UAV and a remote user to ensure a robust wireless backhaul and access link. Studies in [25; 26; 27; 28; 29; 30; 31] considered a single transmission from a single BS to set up a wireless backhaul link without considering an adjacent BS. However, due to interference from a nearby BS, an outage of \(U_{o}\) can occur because it cannot have a strong relay link, which results in the throughput degradation of other UE in the URN. Motivated by this issue and the importance of the SINR enhancement to guarantee a reliable relay link in the URN, this paper investigates simultaneous transmissions from multiple BSs to improve the wireless backhaul link, utilizing the swift mobility of the UAV. If there is a small cell BS (sBS) capable of providing a communication link to the UAV, when the UAV is inside the macrocell BS (mBS) coverage, the UAV, due to its fast mobility, can move into the region where it can receive signals from both mBS and sBS. This is a very useful way to utilize UAV in the URN practically; however, the performance enhancement should be analyzed. The main contributions of this paper can be summarized as follows:  
* This paper defines simultaneous transmissions from multiple BSs to UAV relay nodes to improve the SINR of a wireless backhaul link. Most of the research studies consider a single transmission from a single BS, which has a limit on the capacity of the wireless backhaul link, thereby degrading the performance of the URN. The fast mobility of the UAV is the most powerful function to use; hence, the deployment of UAVs within the region where it can receive a signal from multiple BSs is a simple concept but a practical way in the real world. * To demonstrate the improvement of the SINR of a wireless backhaul link and guarantee a reliable relay transmission to \(U_{o}\), an outage probability of \(U_{o}\) is first derived mathematically to prove a seamless transmission to \(U_{o}\). When an outage occurs, the throughput of \(U_{m}\) becomes worse, which decreases the overall performance of URN. This paper proves that \(U_{o}\) does not experience an outage, and the performance improvement of \(U_{m}\) is achieved by the proposed method. * To further enhance the throughput of \(U_{m}\) and extend the UAV operation range, the optimal time allocation algorithm for the adaptive time allocation parameter \(\lambda\) is proposed, utilizing a bisection method, which guarantees the optimality in the worst case when the length of uncertainty interval is smaller than the predetermined tolerance. The rest of this paper is organized as follows. Section 2 explains the different system models of URNs and analyzes the outage probabilities. In Section 5, we formulate the optimization problem and propose an optimal time allocation algorithm.