基于多Agent强化学习的突发下行链路传输公平用户调度

IF 3.2 Q1 Computer Science
Mingqi Yuan, Qi Cao, Man-On Pun, Yi Chen
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引用次数: 2

摘要

在这项工作中,我们开发了针对下行链路突发业务的实用用户调度算法,重点是用户公平性。与传统的调度算法(在用户之间平均分配传输时隙或最大化一些没有物理意义的比率)不同,我们建议使用5%的用户数据率(5TUDR)作为评估用户公平性的指标。由于很难直接优化5TUDR,我们首先将问题放入随机博弈框架中,然后提出了一种基于多Agent强化学习(MARL)的算法来对资源块组(RBG)的分配进行分布式优化。此外,每个MARL代理被设计为将来自多个网络层的网络计数器测量的信息(例如,信道质量指示符、缓冲区大小)作为输入状态,而RBG分配作为具有被设计为最大化5TUDR的所提出的奖励函数的动作。仿真结果表明,与传统调度器相比,所提出的基于MARL的调度器可以实现公平调度,同时保持良好的平均网络吞吐量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
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.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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