# GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network

arXiv: https://arxiv.org/abs/2409.00036 · HTML: https://mlsanity.com/paper/2409.00036/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 18 of 18)

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- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis](https://mlsanity.com/paper/2209.12274/) (2022)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023)
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- [Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2007.15105/) (2020)
- [On the Role of Age of Information in the Internet of Things](https://mlsanity.com/paper/1812.08286/) (2018)
- [Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach](https://mlsanity.com/paper/2306.08938/) (2023)
- [Quantum-Inspired Analysis of Neural Network Vulnerabilities: The Role of Conjugate Variables in System Attacks](https://mlsanity.com/paper/2402.10983/) (2024)
- [Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization](https://mlsanity.com/paper/2407.03668/) (2024)

## Abstract (arXiv)

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.