  This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor
network, in which one UAV flies in the sky to collect the data transmitted from
a set of ground nodes (GNs) via distributed beamforming. We consider two
scenarios with delay-tolerant and delay-sensitive applications, in which the
GNs send the common/shared messages to the UAV via adaptive- and fixed-rate
transmissions, respectively. For the two scenarios, we aim to maximize the
average data-rate throughput and minimize the transmission outage probability,
respectively, by jointly optimizing the UAV's trajectory design and the GNs'
transmit power allocation over time, subject to the UAV's flight speed
constraints and the GNs' individual average power constraints. However, the two
formulated problems are both non-convex and thus generally difficult to be
optimally solved. To tackle this issue, we first consider the relaxed problems
in the ideal case with the UAV's flight speed constraints ignored, for which
the well-structured optimal solutions are obtained to reveal the fundamental
performance upper bounds. It is shown that for the two approximate problems,
the optimal trajectory solutions have the same multi-location-hovering
structure, but with different optimal power allocation strategies. Next, for
the general problems with the UAV's flight speed constraints considered, we
propose efficient algorithms to obtain high-quality solutions by using the
techniques from convex optimization and approximation. Finally, numerical
results show that our proposed designs significantly outperform other benchmark
schemes, in terms of the achieved data-rate throughput and outage probability
under the two scenarios. It is also observed that when the mission period
becomes sufficiently long, our proposed designs approach the performance upper
bounds when the UAV's flight speed constraints are ignored.
