Formation-Aware UAV Network Self-Organization With Game-Theoretic Distributed Topology Control

One crucial advantage of UAV nodes is their flexible mobility and relocation capabilities, which are essential for adapting the topology of a UAV network in response to link blockages or node dynamics. Current works rarely investigate global networking performance from a multi-hop UAV network perspective and related solutions are neither efficient nor robust enough for distributed self-organization. Therefore, this paper introduces a formation-aware distributed topology control approach. Firstly, a novel networking utility jointly accounting for the load, the coverage link quality of UAV gateways, and the inter-UAV gateway link quality is defined. In order to optimize UAV topology in an autonomous manner, we decouple the networking utility optimization problem into local optimization problems within a game theory framework. The existence and property of the Nash equilibrium (NE) are analyzed. Finally, we propose a distributed and concurrent UAV gateway topology control algorithm that relies solely on local wireless link quality and topology information. This algorithm is theoretically proven to converge to optimal solutions under the conditions of “action reachability”, “reversibility”, and “state reachability”. Simulation results demonstrate that the proposed algorithm achieves superior performance, with the fastest convergence while maintaining a connected topology compared to other distributed algorithms.

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Publication Details

Journal
IEEE Transactions on Cognitive Communications and Networking
Published
2025-01-16
DOI
https://doi.org/10.1109/tccn.2025.3530443
Citations
7
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
10.41

Funders

Controls
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article

Formation-Aware UAV Network Self-Organization With Game-Theoretic Distributed Topology Control

Zhiyong Du, Jiaqi Chen, Shiyu Wang, Ximing Wang
7 citations
IEEE Transactions on Cognitive Communications and Networking
Distributed Control Multi-Agent Systems
10.41
article

Formation-Aware UAV Network Self-Organization With Game-Theoretic Distributed Topology Control

Zhiyong Du, Jiaqi Chen, Shiyu Wang, Ximing Wang
article en
7 citations

Abstract

One crucial advantage of UAV nodes is their flexible mobility and relocation capabilities, which are essential for adapting the topology of a UAV network in response to link blockages or node dynamics. Current works rarely investigate global networking performance from a multi-hop UAV network perspective and related solutions are neither efficient nor robust enough for distributed self-organization. Therefore, this paper introduces a formation-aware distributed topology control approach. Firstly, a novel networking utility jointly accounting for the load, the coverage link quality of UAV gateways, and the inter-UAV gateway link quality is defined. In order to optimize UAV topology in an autonomous manner, we decouple the networking utility optimization problem into local optimization problems within a game theory framework. The existence and property of the Nash equilibrium (NE) are analyzed. Finally, we propose a distributed and concurrent UAV gateway topology control algorithm that relies solely on local wireless link quality and topology information. This algorithm is theoretically proven to converge to optimal solutions under the conditions of “action reachability”, “reversibility”, and “state reachability”. Simulation results demonstrate that the proposed algorithm achieves superior performance, with the fastest convergence while maintaining a connected topology compared to other distributed algorithms.

IEEE Transactions on Cognitive Communications and NetworkingVol. 11(5)
National University of Defense Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Distributed Control Multi-Agent Systems
10.41
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Formation-Aware UAV Network Self-Organization With Game-Theoretic Distributed Topology Control — Zhiyong Du, Jiaqi Chen, et al. · IEEE Transactions on Cognitive Communications and Networking (2025) | TGRS Research Map | TGRS