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arXiv 2408.02047eess.SYcs.AIcs.SY
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基于深度强化学习的移动边缘生成与计算时延感知资源分配

Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning

Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院）；Shenzhen University of Advanced Technology（深圳理工大学）

Yinyu Wu, Xuhui Zhang, Jinke Ren, Huijun Xing, Yanyan Shen, Shuguang Cui

备注：
5 pages, 6 figures. This paper has been accepted for publication by IEEE Networking Letters
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AI总结：

针对移动边缘生成与计算系统中的通信、计算与AIGC资源联合分配问题，提出一种基于深度强化学习的时延最小化算法，并通过数值实验验证其相比基线算法能显著降低时延。

摘要：

近年来，移动边缘计算（MEC）与生成式人工智能（GAI）技术的融合催生了一个新领域——移动边缘生成与计算（MEGC），为移动用户提供任务计算和内容生成等异构服务。本文研究了MEGC系统中通信、计算与AIGC资源的联合分配问题。首先构建了一个时延最小化问题，以提升移动用户的服务质量。由于优化变量之间存在强耦合，我们提出了一种新的基于深度强化学习的算法来高效求解该问题。数值结果表明，与两种基线算法相比，所提算法能够实现更低的时延。

英文摘要：

Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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