4.8 Article

Energy-Effective Offloading Scheme in UAV-Assisted C-RAN System

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 13, 页码 10821-10832

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3126290

关键词

Task analysis; Power control; Planning; Unmanned aerial vehicles; Power demand; Internet of Things; Resource management; C-RAN; location planning; power control; resource allocation; unmanned aerial vehicle (UAV)-assisted communication; user association

资金

  1. National Natural Science Foundation of China [62101161, 61801302]
  2. Shenzhen Basic Research Program [20200811192821001, JCYJ20190808122409660]
  3. Guangdong Basic Research Program [2019A1515110358, 2021A1515012097, 2020ZDZX1037, 2020ZDZX1021]
  4. Shenzhen Stabilization Support Grant [20200809153412001]
  5. Open Research Fund of National Mobile Communications Research Laboratory, Southeast University [2021D16, 2022D02]

向作者/读者索取更多资源

This article proposes an algorithm to minimize the total power consumption of Internet of Things Devices (IoTDs) by optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs). By solving four subproblems iteratively and introducing slack variables to handle feasibility-check problems, the algorithm effectively tackles the nonconvex problem. The simulation results confirm that the proposed algorithm can significantly reduce the total power consumption of IoTDs.
In this article, we aim to minimize the total power of all the Internet of Things Devices (IoTDs) by jointly optimizing user association, computational capacity, transmit power, and the location of unmanned aerial vehicles (UAVs) in an UAV-assisted cloud radio access network (C-RAN). In order to solve this nonconvex problem, we propose an effective algorithm by solving four subproblems iteratively. For the user association and the computational capacity subproblems, the nonconvex constraints are relaxed and the optimal solutions are obtained. For the transmit power control and the location planning subproblems, the successive convex approximation (SCA) technique is used to transform the nonconvex constraints into convex ones. Moreover, to obtain the suboptimal solutions, slack variables are also introduced to deal with the feasibility-check problems. The simulation results demonstrate that the proposed algorithm can greatly reduce the total power consumption of IoTDs.

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