基于深度强化学习的计算卸载与资源分配策略

TP301; 为了扩大车载边缘网络的覆盖范围及其计算能力,提出了一种适用于空天地融合车载网的计算卸载架构.考虑计算任务的时延和能耗约束,以及空天地融合车载网的频谱、计算和存储约束,将计算卸载决策和资源分配的联合优化问题建模为一个混合整数非线性规划问题.基于强化学习方法,将原问题转换成一个马尔可夫过程,提出了一种深度强化学习算法以求解该问题,所提算法具有较好的收敛性.仿真结果表明,所提算法在任务时延和成功率方面优于其他算法....

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Published in通信学报 Vol. 44; no. 7; pp. 124 - 135
Main Authors 曾锋, 张政, 陈志刚
Format Journal Article
LanguageChinese
Published 中南大学计算机学院,湖南 长沙 410083 25.07.2023
Subjects
资源分配
resource allocation
空天地融合车载网
深度强化学习
计算卸载
space-air-ground integrated vehicle network
deep reinforcement learning
computation offloading
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Abstract TP301; 为了扩大车载边缘网络的覆盖范围及其计算能力,提出了一种适用于空天地融合车载网的计算卸载架构.考虑计算任务的时延和能耗约束,以及空天地融合车载网的频谱、计算和存储约束,将计算卸载决策和资源分配的联合优化问题建模为一个混合整数非线性规划问题.基于强化学习方法,将原问题转换成一个马尔可夫过程,提出了一种深度强化学习算法以求解该问题,所提算法具有较好的收敛性.仿真结果表明,所提算法在任务时延和成功率方面优于其他算法.
AbstractList TP301; 为了扩大车载边缘网络的覆盖范围及其计算能力,提出了一种适用于空天地融合车载网的计算卸载架构.考虑计算任务的时延和能耗约束,以及空天地融合车载网的频谱、计算和存储约束,将计算卸载决策和资源分配的联合优化问题建模为一个混合整数非线性规划问题.基于强化学习方法,将原问题转换成一个马尔可夫过程,提出了一种深度强化学习算法以求解该问题,所提算法具有较好的收敛性.仿真结果表明,所提算法在任务时延和成功率方面优于其他算法.
Author 曾锋
张政
陈志刚
AuthorAffiliation 中南大学计算机学院,湖南 长沙 410083
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ZHANG Zheng
ZENG Feng
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Keywords 资源分配
resource allocation
空天地融合车载网
深度强化学习
计算卸载
space-air-ground integrated vehicle network
deep reinforcement learning
computation offloading
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Title 基于深度强化学习的计算卸载与资源分配策略
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