基于 RS-DRL 的 F-MEC 系统卸载策略和无人机轨迹设计

IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS
Yulu Yang , Han Xu , Zhu Jin , Tiecheng Song , Jing Hu , Xiaoqin Song
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引用次数: 0

摘要

为了获得更好的灵活性和更大的覆盖范围,无人机(uav)已应用于飞行移动边缘计算(F-MEC)系统,为用户设备(ue)提供卸载服务。本文考虑了一个受灾害影响的场景,其中无人机承担MEC服务器的角色,为救灾设备(DRDs)提供计算资源。考虑DRDs的公平性,在无人机能量容量约束下,通过联合设计无人机的飞行轨迹、卸载策略和服务时间,建立了最大最小问题,以优化节省的时间。为了解决上述非凸问题,我们首先利用奖励塑造(Reward Shaping, RS)技术将服务过程建模为马尔可夫决策过程(Markov Decision process, MDP),然后提出一种基于深度强化学习(Deep Reinforcement Learning, DRL)的算法来寻找马尔可夫决策过程的最优解。仿真结果表明,所提出的RS-DRL算法是有效的,具有比基准算法更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems
For better flexibility and greater coverage areas, Unmanned Aerial Vehicles (UAVs) have been applied in Flying Mobile Edge Computing (F-MEC) systems to offer offloading services for the User Equipment (UEs). This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices (DRDs). Considering the fairness of DRDs, a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs, the offloading policy and serving time under the constraint of the UAVs' energy capacity. To solve the above non-convex problem, we first model the service process as a Markov Decision Process (MDP) with the Reward Shaping (RS) technique, and then propose a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution for the MDP. Simulations show that the proposed RS-DRL algorithm is valid and effective, and has better performance than the baseline algorithms.
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来源期刊
Digital Communications and Networks
Digital Communications and Networks Computer Science-Hardware and Architecture
CiteScore
12.80
自引率
5.10%
发文量
915
审稿时长
30 weeks
期刊介绍: Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.
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