  Drone cell (DC) is an emerging technique to offer flexible and cost-effective
wireless connections to collect Internet-of-things (IoT) data in uncovered
areas of terrestrial networks. The flying trajectory of DC significantly
impacts the data collection performance. However, designing the trajectory is a
challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground
(D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In
this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data
collection where multiple DCs periodically fly over IoT devices and relay the
IoT data to the base stations (BSs). The trajectory design is formulated as a
mixed integer non-linear programming (MINLP) problem to minimize the average
user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G
channel model. We decouple the MINLP problem into multiple quasi-convex or
integer linear programming (ILP) sub-problems, which optimizes the user
association, user scheduling, horizontal trajectories and DC flying altitudes
of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is
developed to solve the MINLP problem, in which the sub-problems are optimized
iteratively through the block coordinate descent (BCD) method. Compared with
the static DC deployment, the proposed trajectory design can lower the average
U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by
56%, which indicates the improvements in both link quality and user fairness.
