Cooperative Trajectory Design of Multiple UAV Base Stations With Heterogeneous Graph Neural Networks

Abstract

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.