Name
Abstract | ||
|---|---|---|
Driven by the increasing demand of compute-intensive mobile applications and the battery-constrained of unmanned aerial vehicles (UAVs). This paper investigates the cellular-connected UAV mobile edge computing systems where the UAV is served by terrestrial base station (TBS) for computation offloading. For tackling the large number of bits of UAV, we propose a resource partitioning strategy where one portion of tasks is migrated to TBS for computing and the other portion of tasks is locally computed at UAV. Our goal is to minimize the UAV energy consumption by jointly optimizing resource partitioning, UAV trajectory and bit allocation under the constraints of UAV mobility and TBS energy budget. The formulated problem is shown to be a non-convex optimization problem, which is hard to tackle. To this end, we derive a sub-optimal solution by leveraging successive convex approximation (SCA) technique. The numerical results show that there exists a tradeoff between propulsion energy consumption of UAV and computation energy consumption of UAV. In addition, it also shows that our proposed scheme saves a large amount of energy compared with the benchmark scheme. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICCS.2018.8689226 | 2018 IEEE International Conference on Communication Systems (ICCS) |
Keywords | Field | DocType |
unmanned aerial vehicle,mobile edge computing,bit allocation,resource partitioning | Base station,Mathematical optimization,Computer science,Real-time computing,Computation offloading,Mobile edge computing,Energy consumption,Optimization problem,Trajectory,Energy minimization,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5386-7864-0 | 3 | 0.41 |
References | Authors | |
0 | 5 | |