Scalable Multi-UAV Distributed Coordination for Aerial RIS Platforms

Achieve scalable, resilient, and energy-efficient coordination among multiple cooperative UAV-mounted RIS platforms, jointly accounting for trajectory planning, RIS phase configuration, inter-UAV interference, synchronization, signaling overhead, limited backhaul capacity, and dynamic mobility conditions.

Background

The paper considers future 6G deployments in which multiple UAV-mounted RIS platforms cooperate to extend coverage, improve reliability, and increase spectral efficiency. Coordinating these platforms is difficult because the UAV trajectories, RIS phase configurations, and inter-UAV interference are coupled in a multi-agent optimization problem.

Additional practical difficulties include synchronization, signaling overhead, intermittent connectivity, limited backhaul capacity, and stability under dynamic mobility. The paper identifies federated learning, sparse graph neural networks, and distributed multi-armed bandit algorithms as promising approaches, but states that achieving scalable and resilient energy-efficient coordination remains unresolved.

References

Achieving scalable, resilient, and EE coordination among multiple aerial RIS platforms remains a fundamental open problem for practical large-scale deployment .

— Lightweight AI for UAV-Mounted RIS: An Overview  (2608.25402 - Hashima et al., 26 Aug 2026) in Section 5, subsection “Multi-UAV and Distributed Coordination”

We additionally evaluate both policies in an extreme 40-robot scenario, where slight throughput improvements are observed; however, achieving highly efficient coordination remains an open challenge.

— Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations  (2610.05677 - Matsumoto et al., 5 Oct 2026) in Section 4.4, Sensitivity to Training Robot Population