论文标题
移动边缘网络中的联合缓存和传输：一种多代理学习方法
Joint Caching and Transmission in the Mobile Edge Network: A Multi-Agent Learning Approach
论文作者
论文摘要
由于决策之间的深层耦合，关节缓存和传输优化问题是具有挑战性的。本文提出了一种迭代分布的多代理学习方法，以共同优化缓存和传输。这种方法的目的是最大程度地减少所有用户的总传输延迟。在这种迭代方法中，每次迭代都包括缓存优化和传输优化。开发了一个基于多代理的加固学习（MARL）的缓存网络，以缓存流行的任务，例如回答哪些文件从缓存中驱逐的文件以及要存储哪些文件。基于缓存网络的缓存文件，传输网络通过单个传输（ST）或联合传输（JT）传输用户使用多代理贝叶斯学习自动机（MABLA）方法的文件。然后用户使用最小传输延迟访问边缘服务器。实验结果证明了所提出的多项式学习方法的性能。
Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.
