We describe the orchestration of a decentralized swarm of rotary-wing
UAV-relays, augmenting the coverage and service capabilities of a terrestrial
base station. Our goal is to minimize the time-average service latencies
involved in handling transmission requests from ground users under Poisson
arrivals, subject to an average UAV power constraint. Equipped with rate
adaptation to efficiently leverage air-to-ground channel stochastics, we first
derive the optimal control policy for a single relay via a semi-Markov decision
process formulation, with competitive swarm optimization for UAV trajectory
design. Accordingly, we detail a multiscale decomposition of this construction:
outer decisions on radial wait velocities and end positions optimize the
expected long-term delay-power trade-off; consequently, inner decisions on
angular wait velocities, service schedules, and UAV trajectories greedily
minimize the instantaneous delay-power costs. Next, generalizing to UAV swarms
via replication and consensus-driven command-and-control, this policy is
embedded with spread maximization and conflict resolution heuristics. We
demonstrate that our framework offers superior performance with respect to
average service latencies and average per-UAV power consumption: 11x faster
data payload delivery relative to static UAV-relay deployments and 2x faster
than a deep-Q network solution; remarkably, one relay with our scheme
outclasses three relays under a joint successive convex approximation policy by
62%.

本文描述了一种使用无人机中继的去中心化群体编队协同技术，以增强地面基站的覆盖范围和服务能力，通过使用半马尔可夫决策过程和竞争群体优化对单个中继器进行了优化控制策略的导出，并通过复制和共识驱动的命令和控制泛化到 UAV 群体。我们演示了我们的框架相对于静态 UAV 中继部署的平均服务延迟和每个 UAV 功耗的优越性。