In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that either equally divides the transmission time slots among users or maximizing some ratios without physcial meanings, we propose to use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness. Since it is difficult to directly optimize 5TUDR, we first cast the problem into the stochastic game framework and subsequently propose a Multi-Agent Reinforcement Learning (MARL)-based algorithm to perform distributed optimization on the resource block group (RBG) allocation. Furthermore, each MARL agent is designed to take information measured by network counters from multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the input states while the RBG allocation as action with a proposed reward function designed to maximize 5TUDR. Extensive simulation is performed to show that the proposed MARL-based scheduler can achieve fair scheduling while maintaining good average network throughput as compared to conventional schedulers.

翻译：在这项工作中,我们为下链路断流流量制定了实用的用户排程算法,重点是用户公平性。与传统的排程算法相比,这些算法或者在用户之间平均分配传输时间档,或者在没有生理意义的情况下实现某种比例最大化,我们提议使用5%平线用户数据率(5TUDR)作为衡量用户公平性的标准。由于很难直接优化5TUDR,我们首先将问题扔入杂乱的游戏框架,然后提出基于多动力强化学习(MARL)的算法,以便对资源块组的分配进行分配优化。此外,每个MARL代理商的设计是将网络对多个网络层(例如频道质量指标、Buffer 大小)测量的信息作为计算结果,同时将RBG分配作为旨在最大限度地增加5TUDR的奖励功能的行动,进行广泛的模拟,以显示拟议的以MARL为基础的调度器可以实现公平的排程,同时保持与常规排程相比,通过良好的平均网络进行输送。