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Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems
AU - Qin, Xintong
AU - Song, Zhengyu
AU - Hou, Tianwei
AU - Yu, Wenjuan
AU - Wang, Jun
AU - Sun, Xin
N1 - ©2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach's method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.
AB - The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach's method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.
KW - Autonomous aerial vehicles
KW - Energy efficiency
KW - Internet of Things
KW - NOMA
KW - Performance evaluation
KW - Servers
KW - Task analysis
KW - Trajectory
KW - mobile edge computing (MEC)
KW - phase shift
KW - reconfigurable intelligence surface (RIS)
KW - resource allocation
KW - trajectory design
KW - unmanned aerial vehicles (UAV)
U2 - 10.1109/TGCN.2023.3287604
DO - 10.1109/TGCN.2023.3287604
M3 - Journal article
VL - 7
SP - 1778
EP - 1792
JO - IEEE Transactions on Green Communications and Networking
JF - IEEE Transactions on Green Communications and Networking
SN - 2473-2400
IS - 4
ER -