  Unmanned aerial vehicle (UAV)-enabled communication is a promising technology
to extend coverage and enhance throughput for traditional terrestrial wireless
communication systems. In this paper, we consider a UAV-enabled wireless sensor
network (WSN), where a multi-antenna UAV is dispatched to collect data from a
group of sensor nodes (SNs). The objective is to maximize the minimum data
collection rate from all SNs via jointly optimizing their transmission
scheduling and power allocations as well as the trajectory of the UAV, subject
to the practical constraints on the maximum transmit power of the SNs and the
maximum speed of the UAV. The formulated optimization problem is challenging to
solve as it involves non-convex constraints and discrete-value variables. To
draw useful insight, we first consider the special case of the formulated
problem by ignoring the UAV speed constraint and optimally solve it based on
the Lagrange duality method. It is shown that for this relaxed problem, the UAV
should hover above a finite number of optimal locations with different
durations in general. Next, we address the general case of the formulated
problem where the UAV speed constraint is considered and propose a traveling
salesman problem (TSP)-based trajectory initialization, where the UAV
sequentially visits the locations obtained in the relaxed problem with minimum
flying time. Given this initial trajectory, we then find the corresponding
transmission scheduling and power allocations of the SNs and further optimize
the UAV trajectory by applying the block coordinate descent (BCD) and
successive convex approximation (SCA) techniques. Finally, numerical results
are provided to illustrate the spectrum and energy efficiency gains of the
proposed scheme for multi-antenna UAV data harvesting, as compared to benchmark
schemes.
