UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells

By Fen Cheng, Shun Zhang, Zan Li, Yunfei Chen, Nan Zhao, F. Yu, Victor C. M. Leung. IEEE Transactions on Vehicular Technology, 2018

308 citations recorded by the source corpus.

In future mobile networks, it is difficult for static base stations (BSs) to support the rapidly increasing data services, especially for cell-edge users. Unmanned aerial vehicle (UAV) is a promising method that can assist BSs to offload the data traffic, due to its high mobility and flexibility. In this correspondence, we focus on the UAV trajectory at the edges of three adjacent cells to offload traffic for BSs. In the proposed scheme, the sum rate of UAV-served edge users is maximized subject to the rate requirements for all the users, by optimizing the UAV trajectory in each flying cycle. The optimization is a mixed-integer nonconvex problem, which is difficult to solve. Thus, it is transformed into two convex problems, and an iterative algorithm is proposed to solve it by optimizing the UAV trajectory and edge user scheduling alternately. Simulation results are presented to show the effectiveness of the proposed scheme.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/16c99c6e2a6e4c4866cd46f2e808467086584bff

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Citations in the corpus

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, background): “The future wireless communication systems are expected to meet unprecedented demands for high quality wireless services, which imposes challenges on the conventional terrestrial communication networks, especially in traffic hotspots such as in a football stadium or rock concert [4]–[6].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “This problem can be alleviated by hybrid cellular networks employing Unmanned Aerial Vehicles (UAVs) as mobile BSs [59].”
Space-Air-Ground Integrated Network: A Survey (2018, methodology): “[88] proposed an iterative algorithm to maximize the user sum rate in the UAV-aided cellular network.”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, unknown): “in [272] studied UAV trajectory at the edges of three adjacent cells to offload traffic of ground base stations.”