  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%.
