用图神经网络和QMIX优化无人机网络的时效性，提升未知环境下的数据更新效率。
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
- 用GNN融合无人机与用户间的局部信息，实现分布式轨迹规划。
- 在未知场景下使用户平均时效性降低37.2%，收敛速度提升45%。
- 适合做多无人机协同感知、实时监控等低延迟系统设计者。
无人飞行器（UAV）因成本低、灵活性高，被广泛用于提升网络性能。然而，在缺乏先验信息的未知区域中，仅依赖自身观测及通信范围内其他无人机信息进行轨迹优化时，仍存在规划性能差、分布式执行效率低的问题。为此，本文提出Qedgix框架，结合图神经网络（GNN）与QMIX算法，实现对未知场景下用户时效性（AoI）的分布式优化。该框架利用GNN提取可观测范围内的无人机、用户及其他无人机的信息，支持高效轨迹规划。针对AoI指标的离散性和时序特性，基于集中训练、分布式执行（CTDE）策略，采用QMIX优化部分可观测马尔可夫决策过程（Dec-POMDP），以最小化用户平均AoI。通过引入科莫戈罗夫-阿诺德表示定理，构建基于排列不变性的参数共享机制，显著提升神经网络训练效率。仿真结果表明，所提算法在收敛速度上提升45%，用户平均AoI降低37.2%。代码已开源：https://github.com/UNIC-Lab/Qedgix。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior information, still faces challenges related to poor planning performance and low distributed execution. These challenges arise when UAVs rely solely on their own observation information and the information from other UAVs within their communicable range, without access to global information. To address these challenges, this paper proposes the Qedgix framework, which combines graph neural networks (GNNs) and the QMIX algorithm to achieve distributed optimization of the Age of Information (AoI) for users in unknown scenarios. The framework utilizes GNNs to extract information from UAVs, users within the observable range, and other UAVs within the communicable range, thereby enabling effective UAV trajectory planning. Due to the discretization and temporal features of AoI indicators, the Qedgix framework employs QMIX to optimize distributed partially observable Markov decision processes (Dec-POMDP) based on centralized training and distributed execution (CTDE) with respect to mean AoI values of users. By modeling the UAV network optimization problem in terms of AoI and applying the Kolmogorov-Arnold representation theorem, the Qedgix framework achieves efficient neural network training through parameter sharing based on permutation invariance. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed while reducing the mean AoI values of users. The code is available at https://github.com/UNIC-Lab/Qedgix.
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