无人机辅助可见光通信中，优化飞行高度与奖励机制，提升数据采集效率。
DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design
- 基于信息素驱动的奖励机制，结合强化学习算法规划无人机轨迹。
- 最优飞行高度使飞行距离减少35%，显著提升数据收集效率。
- 适合研究无人机通信系统、智能交通与低功耗网络部署的科研人员。
近年来，无人机（UAV）与可见光通信（VLC）技术的融合为灵活通信与高效照明提供了新方案。本文研究无人机辅助可见光通信系统中的三维轨迹规划问题，旨在通过调度无人机从地面用户（GUs）收集数据，最小化飞行距离以最大化数据采集效率。该问题被建模为复杂的混合整数非凸优化问题。为此，我们首先推导出在特定VLC信道增益阈值下的闭式最优飞行高度；随后，通过将新型信息素驱动奖励机制与双延迟深度确定性策略梯度（Twin Delayed DDPG）算法结合，优化无人机水平轨迹，使其在复杂环境中具备自适应运动能力。仿真结果表明，所推导的最优飞行高度相较基线方法可减少35%飞行距离；同时，提出的奖励机制使收敛步数减少约50%，显著提升了无人机辅助VLC数据采集的效率。
原文摘要 · Abstract (English)
Recently, the integration of unmanned aerial vehicle (UAV) and visible light communication (VLC) technologies has emerged as a promising solution to offer flexible communication and efficient lighting. This letter investigates the three-dimensional trajectory planning in a UAV-assisted VLC system, where a UAV is dispatched to collect data from ground users (GUs). The core objective is to develop a trajectory planning framework that minimizes UAV flight distance, which is equivalent to maximizing the data collection efficiency. This issue is formulated as a challenging mixed-integer non-convex optimization problem. To tackle it, we first derive a closed-form optimal flight altitude under specific VLC channel gain threshold. Subsequently, we optimize the UAV horizontal trajectory by integrating a novel pheromone-driven reward mechanism with the twin delayed deep deterministic policy gradient algorithm, which enables adaptive UAV motion strategy in complex environments. Simulation results validate that the derived optimal altitude effectively reduces the flight distance by up to 35% compared to baseline methods. Additionally, the proposed reward mechanism significantly shortens the convergence steps by approximately 50%, demonstrating notable efficiency gains in the context of UAV-assisted VLC data collection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。