Cross-Region Cooperation of UAV Swarms Towards Air-Ground ISAC via Hierarchical Multi-Agent Reinforcement Learning

Abstract

The limited communication resources and sensing perspectives of a single uncrewed aerial vehicle (UAV) motivate UAV swarms for integrated sensing and communication (ISAC) in air-ground networks. However, in multi-region scenarios, regional UAV allocations may not match vehicle demand, while local observations may lack sufficient diversity for accurate sensing. To address these issues, this paper proposes a cross-region cooperation framework for UAV swarms. For communication, cross-region UAV reallocation reduces service disparities by matching resources to vehicle demand. For sensing, combining UAV observations from different regions increases observation diversity, while adaptive handshaking guided by marginal Cramér--Rao bound (CRB) utilities balances phase synchronization gains against handshaking energy consumption. Joint optimization of UAV positions and transmit power within each region then further improves communication service and sensing performance. These coupled decisions are coordinated by a hierarchical multi-agent reinforcement learning (H-MARL) approach through slow-timescale inter-region UAV reallocation and fast-timescale intra-region UAV control. Under the considered simulation settings, reallocation can improve quality of service (QoS) by over 20 percentage points compared with retaining the initial allocation under severe resource-demand mismatches. Cooperative sensing with adaptive handshaking incurs only about a 5.0% increase in mean CRB relative to perfect synchronization.