  This work describes the orchestration of a fleet of rotary-wing Unmanned
Aerial Vehicles (UAVs) for harvesting prioritized traffic from random
distributions of heterogeneous users with Multiple Input Multiple Output (MIMO)
capabilities. In a finite-horizon offline setting, the goal is to optimize the
beam-forming design, the 3D UAV positioning and trajectory solution, and the
user association/scheduling policy, to maximize the cumulative fleet-wide
reward obtained by satisfying the quality-of-service mandates imposed on each
user uplink request, subject to an average per-UAV mobility power constraint.
With a probabilistic air-to-ground channel model, a multi-user MIMO uplink
communication model with prioritized traffic, and a novel 3D mobility model for
rotary-wing UAVs, the fleet-wide reward maximization problem is solved via a
cross-layer optimization framework: first, K-means clustering is employed to
obtain user clusters; then, equipped with a zero-forcing beam-forming design,
the positions of the UAVs are optimized via two-stage grid search; next,
treating these optimal positions as the graph vertices of a fully-connected
mesh, the 3D UAV trajectories (i.e., graph edges) are designed via a learning
based competitive swarm optimization algorithm, under an average UAV power
consumption constraint, coupled with projected subgradient ascent for dual
optimization; consequently, the user association/scheduling strategy is solved
via a graphical branch-and-bound method on the underlying multiple traveling
salesman problem. Numerical evaluations demonstrate that the proposed solution
outperforms static UAV deployments, adaptive Voronoi decomposition techniques,
and state-of-the-art iterative fleet control algorithms, with respect to user
quality-of-service and per-UAV average power consumption.
