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.
Authors
- Zhiyong Du (ORCID: https://orcid.org/0000-0001-8925-4960)
- Jiaqi Chen (ORCID: https://orcid.org/0000-0003-4801-9256)
- Shiyu Wang (ORCID: https://orcid.org/0000-0003-1243-7018)
- Ximing Wang (ORCID: https://orcid.org/0000-0003-2216-9352)
Institutions
- National University of Defense Technology (CN)
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
- National Natural Science Foundation of China