The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.

中文翻译：

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无人机通信网络通过吞吐量最大化的高效部署

本文介绍了无人机辅助地面网络的吞吐量最大化方法。吞吐量最大化包括通过UAV轨迹优化来最大程度地减少延迟和数据包丢失，从而增强拥塞的节点和传输通道。通过通过延迟，损耗，吞吐量和距离来表征节点，链路和整体拓扑，可以实现积极的增强策略。位置感知图神经网络（GNN）用于表征，预测和动态UAV轨迹增强。为了建立正确性，针对优化的链接状态路由（OLSR）驱动的无人机辅助地面网络对提出的方法进行了验证。所提出的方法通过证明吞吐量和分组传送率的显着提高以及延迟和分组丢失的显着减少，大大优于传统方法。针对软件定义的UAV（美国）和作为基站（UB）的UAV的提议方法的性能分析验证了一致性和平均吞吐量的提高，同时最大程度地减少了延迟和数据包丢失。通过改变数据速率和无人机数量来执行所提出方法的可伸缩性测试。