无人机协同波束成形降低数据时效延迟，提升物联网系统信息新鲜度。
AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
- 用分布式波束成形扩展通信范围，减少无人机飞行频次。
- 联合优化无人机轨迹与通信调度，使平均信息年龄降低37.2%。
- 基于深度强化学习的算法加速收敛，适合高动态物联网场景。
本文提出一种基于分布式波束成形的无人机辅助数据转发系统，以提升物联网（IoT）中信息的新鲜度（Age of Information, AoI）。无人机在传感器节点（SNs）与远端基站（BS）之间收集并中继数据。然而，飞行延迟会增加AoI并降低网络性能。为此，采用分布式波束成形技术扩展通信范围，减少无人机飞行频率，确保连续数据中继和高效能量利用。进一步构建优化问题，联合优化无人机轨迹与通信调度，以最小化AoI和无人机能耗。该问题为非凸且具有高动态性，因此提出基于深度强化学习（DRL）的算法求解，提升了稳定性并加快收敛速度。仿真结果表明，所提算法能有效解决问题，并优于其他基准算法。
原文摘要 · Abstract (English)
This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.
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