  A swarm of cooperating UAVs communicating with a distant multiantenna ground
station can leverage MIMO spatial multiplexing to scale the capacity. Due to
the line-of-sight propagation between the swarm and the ground station, the
MIMO channel is highly correlated, leading to limited multiplexing gains. In
this paper, we optimize the UAV positions to attain the maximum MIMO capacity
given by the single user bound. An infinite set of UAV placements that attains
the capacity bound is first derived. Given an initial swarm placement, we
formulate the problem of minimizing the distance traveled by the UAVs to reach
a placement within the capacity maximizing set of positions. An offline
centralized solution to the problem using block coordinate descent is developed
assuming known initial positions of UAVs. We also propose an online distributed
algorithm, where the UAVs iteratively adjust their positions to maximize the
capacity. Our proposed approaches are shown to significantly increase the
capacity at the expense of a bounded translation from the initial UAV
placements. This capacity increase persists when using a massive MIMO ground
station. Using numerical simulations, we show the robustness of our approaches
in a Rician channel under UAV motion disturbances.

Signal Processing;Networking and Internet Architecture