Paper Info
Title
3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection.
Abstract
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.
Year
DOI
Venue
2019
10.1109/ICC.2019.8761719
IEEE International Conference on Communications
Field
DocType
Volume
Base station,Wireless,Backhaul (telecommunications),Computer science,Scheduling (computing),Communication channel,Real-time computing,Integer programming,Trajectory,Relay,Distributed computing
Journal
abs/1906.00776
ISSN
Citations 
PageRank 
1550-3607
0
0.34
References 
Authors
14
7
Name
Order
Citations
PageRank
Weisen Shi124216.00
Junling Li2878.60
Nan Cheng397081.34
Feng Lv431328.56
Yanpeng Dai5152.28
Haibo Zhou620314.10
Xuemin Shen715389928.67