论文标题
通过深度加强学习对无线网络的延迟公平优化
Latency Fairness Optimization on Wireless Networks through Deep Reinforcement Learning
论文作者
论文摘要
在本文中，我们提出了一个新颖的深入增强学习框架，以最大程度地提高用户的公平性。为此，我们设计了一个新版本的修改后的最大加权延迟（M-LWDF）算法，该算法称为$β$ -M-LWDF，旨在实现用户公平和平均延迟之间的适当平衡。此余额定义为用户延迟的累积分布函数（CDF）的可行区域，该区域允许识别不公平状态，可行的状态和过高的状态。模拟结果表明，我们提出的框架在潜伏公平和平均延迟方面优于传统资源分配技术
In this paper, we propose a novel deep reinforcement learning framework to maximize user fairness in terms of delay. To this end, we devise a new version of the modified largest weighted delay first (M-LWDF) algorithm, which is called $β$-M-LWDF, aiming to fulfill an appropriate balance between user fairness and average delay. This balance is defined as a feasible region on the cumulative distribution function (CDF) of the user delay that allows identifying unfair states, feasible-fair states, and over-fair states. Simulation results reveal that our proposed framework outperforms traditional resource allocation techniques in terms of latency fairness and average delay
