期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 5, 页码 4955-4967出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3072065
关键词
Unmanned aerial vehicles; Task analysis; Delays; Approximation algorithms; Resource management; Trajectory; Edge computing; UAV; multi-access edge computing; mmWave; generalized benders decomposition
资金
- National Key R&D Program of China [2018YFC0603204]
- National Natural Science Foundation of China [61771054, 61501028]
- NSF [EARS-1839818, CNS1717454, CNS-1731424, CNS1702850]
The paper proposes a UAV-aided MEC network with mmWave backhaul to address the performance improvement needs of communication networks and solve the network latency issue. Through an iterative algorithm framework, routing and joint trajectory design resource allocation problems were successfully modeled and solved.
As an emerging technique, unmanned aerial vehicle (UAV) aided multi-access edge computing (MEC) network has been improving the performance of the communication network. The novel architecture is beneficial for coverage, flexibility, and reliability. However, reducing network latency is a critical issue. In this paper, we design a UAV-aided network with a millimeter wave (mmWave) backhaul to achieve the multi-access edge computing. The routing problem is formulated and solved first to obtain the optimal routes through the ad hoc link for all users. Then, we formulate the joint trajectory design and resource allocation problem, which is a mixed-integer nonconvex programming, to minimize the network latency. Furthermore, we design a novel iterative algorithm framework to handle this challenging problem. In the outer loop, the proposed problem is separated into the primal problems and master problems by adopting generalized benders decomposition (GBD). In the inner loop, we design the algorithm to solve the continuous nonconvex primal problem by combining the alternating direction method of multipliers (ADMM), Dinkelbach algorithm, and successive convex approximation (SCA) algorithm. The simulation results demonstrate that our proposed algorithm framework is effective and feasible.
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