面向无人机辅助通信的可解释且安全的轨迹优化
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication
AI总结：
本文针对无人机辅助通信中的碰撞避免问题，提出结合D3QN与MCTS的可解释轨迹优化方案，并构建可扩展XAI框架，在提升系统可靠性、效率和安全性方面优于现有方法。
摘要：
无人机（UAV）因其灵活的机动性、按需部署能力以及建立高概率视距无线通信的能力而受到广泛关注。因此，无人机已被广泛用作空中基站（ABS），以补充地面蜂窝网络，服务于各种应用。然而，现有的无人机辅助通信方案主要关注轨迹优化和功率分配，而忽略了无人机飞行过程中的碰撞避免问题。为解决这一问题，本文提出了一种可解释的无人机辅助通信方案，将可靠的无人机服务分解为两个子问题。第一个是带约束的无人机坐标与功率分配问题，采用Dueling Double DQN（D3QN）方法求解。第二个是带约束的无人机碰撞避免与轨迹优化问题，通过蒙特卡洛树搜索（MCTS）方法解决。该方法同时确保了无人机运行的可靠性和高效性。此外，我们提出了一个可扩展的可解释人工智能（XAI）框架，使系统决策更加透明和可靠。所提方案的可解释性生成了可解释且可信的结果，使无人机辅助通信解决方案更易于理解、验证和控制。通过大量实验，我们证明所提算法在性能和泛化能力方面优于现有技术。所提模型提高了无人机辅助通信系统的可靠性、效率和安全性，为未来无人机辅助通信应用提供了一种有前景的解决方案。
英文摘要：
Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication. As a result, UAVs have been extensively used as aerial base stations (ABSs) to supplement ground-based cellular networks for various applications. However, existing UAV-assisted communication schemes mainly focus on trajectory optimization and power allocation, while ignoring the issue of collision avoidance during UAV flight. To address this issue, this paper proposes an interpretable UAV-assisted communication scheme that decomposes reliable UAV services into two sub-problems. The first is the constrained UAV coordinates and power allocation problem, which is solved using the Dueling Double DQN (D3QN) method. The second is the constrained UAV collision avoidance and trajectory optimization problem, which is addressed through the Monte Carlo tree search (MCTS) method. This approach ensures both reliable and efficient operation of UAVs. Moreover, we propose a scalable interpretable artificial intelligence (XAI) framework that enables more transparent and reliable system decisions. The proposed scheme's interpretability generates explainable and trustworthy results, making it easier to comprehend, validate, and control UAV-assisted communication solutions. Through extensive experiments, we demonstrate that our proposed algorithm outperforms existing techniques in terms of performance and generalization. The proposed model improves the reliability, efficiency, and safety of UAV-assisted communication systems, making it a promising solution for future UAV-assisted communication applications
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
