Latency Optimization in UAV-Assisted Mobile Edge Computing Empowered by Caching Mechanisms

被引：0

作者：

Zhang, Heng ^{[1
]}

Sun, Zhemin ^{[1
]}

Yang, Chaoqun ^{[2
]}

Cao, Xianghui ^{[2
]}

机构：

[1] Jiangsu Ocean University, School of Computer Engineering, Lianyungang

[2] Southeast University, School of Automation, Nanjing

来源：

IEEE Journal on Miniaturization for Air and Space Systems | 2024年 / 5卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Deep reinforcement learning (DRL); mobile edge computing (MEC); unmanned-aerial-vehicle (UAV);

D O I：

10.1109/JMASS.2024.3448433

中图分类号：

学科分类号：

摘要：

Mobile edge computing (MEC) revolutionizes data processing by shifting it from the network core to the edge, significantly reducing latency and ensuring Quality of Service. Integrating the agile and flexible unmanned- aerial-vehicle (UAV) technology with MEC offers new opportunities and challenges in decision making for dynamic and complex environments due to the UAVs' mobility and Line of Sight advantages. Motivated by the potential of UAV-assisted MEC systems with caching mechanisms, this study addresses the optimization problem under uncertain conditions and user demand. To tackle the complex nonconvex sequential decision problem, a deep reinforcement learning framework named delay hybrid action actor-critic is proposed, possessing the capability to handle scenarios requiring both continuous and discrete actions. Comprehensive simulations are conducted to validate the capability of the proposed framework, demonstrating its superiority over traditional methods. © 2019 IEEE.

引用

页码：228 / 236

页数：8

共 33 条

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