When Learning Meets Dynamics: Distributed User Connectivity Maximization in UAV-Based Communication Networks
| Autor: | Li, Bowei, Tripathi, Saugat, Hosain, Salman, Zhang, Ran, Jiang, Xie, Wang, Miao |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Distributed management over Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) has attracted increasing research attention. In this work, we study a distributed user connectivity maximization problem in a UCN. The work features a horizontal study over different levels of information exchange during the distributed iteration and a consideration of dynamics in UAV set and user distribution, which are not well addressed in the existing works. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem. A heuristic two-stage UAV-user association policy is proposed to faster determine the user connectivity. To tackle the NP-hard problem in scalable manner, the distributed user connectivity maximization algorithm 1 (DUCM-1) is proposed under the multi-agent deep Q learning (MA-DQL) framework. DUCM-1 emphasizes on designing different information exchange levels and evaluating how they impact the learning convergence with stationary and dynamic user distribution. To comply with the UAV dynamics, DUCM-2 algorithm is developed which is devoted to autonomously handling arbitrary quit's and join-in's of UAVs in a considered time horizon. Extensive simulations are conducted i) to conclude that exchanging state information with a deliberated task-specific reward function design yields the best convergence performance, and ii) to show the efficacy and robustness of DUCM-2 against the dynamics. Comment: 12 pages, 12 figures, journal draft |
| Databáze: | arXiv |
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