ORCHID: Fairness-Aware Orchestration in Mission-Critical Air-Ground Integrated Networks
Chuan-Chi Lai, Chi Jai Choy
Uncrewed Aerial Vehicles (UAVs) provide on-demand wireless coverage for mission-critical Air-Ground Integrated Networks (AGINs). However, DRL-based orchestration faces challenges from multi-agent non-stationarity and the trade-off between energy efficiency (EE) and service fairness. To address these issues, we propose ORCHID (Orchestration of Resilient Coverage via Hybrid Intelligent Deployment), a stability-enhanced two-stage learning framework. First, GBS-aware topology partitioning provides a topology-informed warm start for UAV deployment. Second, a Reset-and-Finetune (R\&F) mechanism is integrated into Multi-Agent Proximal Policy Optimization (MAPPO) to support stable late-stage policy refinement through event-triggered rollback and learning-rate adaptation. To study the efficiency-fairness trade-off, ORCHID employs a Max-Min Fairness (MMF)-oriented bandwidth allocation based on a closed-form Egalitarian Bargaining Game (EBG) solution and is compared with Proportional Fairness (PF). Experiments show stronger served-user bottleneck-rate protection under MMF, together with competitive coverage and user-rate fairness and favorable energy-efficiency and UAV-load-balancing performance under zero-shot scalability and mobility tests. Overall, ORCHID provides an effective balance among user-level service protection, energy efficiency, and policy stability for resilient mission-critical UAV networks.
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