论文标题
3to：通过近端策略优化深入强化学习，在6G网络中对无人机的吞吐量和轨迹优化
3TO: THz-Enabled Throughput and Trajectory Optimization of UAVs in 6G Networks by Proximal Policy Optimization Deep Reinforcement Learning
论文作者
论文摘要
下一代网络需要满足无处不在的高数据率需求。因此，本文考虑了第六代（6G）通信网络中Terahertz（THZ）启用的无人机（UAV）的吞吐量和轨迹优化。在考虑的情况下，多个无人机必须与现有的陆地网络一起为城市地区提供按需Terabits（TB/S）服务。但是，THZ授权的无人机构成了一些新的约束，例如，地面用户（GUS）关联和无人机轨迹优化的动态THZ通道条件，以满足GU的吞吐量需求。因此，提出了一个框架来应对这些挑战，在研究联合无人机联合，传输功率和轨迹优化问题的情况下。配方的问题是混合企业非线性编程（MINLP），它是NP固定的解决方案。因此，提出了一种迭代算法，以迭代三个子问题迭代，即无人机-GUS关联，传输功率和轨迹优化。模拟结果表明，与基线算法相比，所提出的算法分别增加了10％，68.9％和69.1％的吞吐量。
Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (TB/s) services to an urban area along with existing terrestrial networks. However, THz-empowered UAVs pose some new constraints, e.g., dynamic THz-channel conditions for ground users (GUs) association and UAV trajectory optimization to fulfill GU's throughput demands. Thus, a framework is proposed to address these challenges, where a joint UAVs-GUs association, transmit power, and the trajectory optimization problem is studied. The formulated problem is mixed-integer non-linear programming (MINLP), which is NP-hard to solve. Consequently, an iterative algorithm is proposed to solve three sub-problems iteratively, i.e., UAVs-GUs association, transmit power, and trajectory optimization. Simulation results demonstrate that the proposed algorithm increased the throughput by up to 10%, 68.9%, and 69.1% respectively compared to baseline algorithms.
