基于强化学习的802.11ax上行链路调度算法  

802.11ax Uplink Scheduling Algorithm Based on Reinforcement Learning

作　　者：黄新林 郑人华 HUANG Xinlin;ZHENG Renhua(College of Electronic and Information Engineering,Tongji University,Shanghai 201800,China)

机构地区：[1]同济大学电子与信息工程学院,上海201800

出　　处：《电子与信息学报》2022年第5期1800-1808,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62071332);上海市青年科技启明星计划(19QA1409100);中央高校基本科研业务费专项资金。

摘　　要：随着物联网(IoT)时代的到来,无线网络饱和的问题已经越来越严重。为了克服终端密集接入问题,IEEE标准协会(IEEE-SA)制定了无线局域网的最新标准—IEEE 802.11ax。该标准使用正交频分多址(OFDMA)技术对无线信道资源进行了更细致的划分,划分出的子信道被称为资源单元(RU)。为解决密集用户环境下802.11ax上行链路的信道资源调度问题,该文提出一种基于强化学习的RU调度算法。该算法使用演员-评论家(Actor-Critic)算法训练指针网络,解决了自适应RU调度问题,最终合理分配RU资源给各用户,兼具优先级和公平性的保障。仿真结果表明,该调度算法在IEEE 802.11ax上行链路中比传统的调度方式更有效,具有较强的泛化能力,适合应用在密集用户环境下的物联网场景中。With the arrival of the Internet of Things(IoT)era,the problem of wireless network saturation has become more and more serious.In order to overcome this problem,the IEEE Standards Association(IEEE-SA)has formulated the latest standard for wireless local area networks—IEEE 802.11ax.In this standard,the Orthogonal Frequency Division Multiple Access(OFDMA)technology is utilized to divide wireless channel into several groups of tones,and the divided sub-channels are called Resource Units(RUs).In order to solve the channel resource scheduling problem of 802.11ax uplink in dense user environments,an RU scheduling algorithm based on reinforcement learning is proposed in this paper.The Actor-Critic algorithm is used to train the pointer network and solve the adaptive allocation problem of RU.Finally,RUs are allocated to each user reasonably with the guarantee of priority and fairness.The simulation results show that the scheduling algorithm is more effective than traditional scheduling methods in the IEEE 802.11ax uplink and has a strong generalization ability,which is suitable for the IoT scenario in dense user environments.

关 键 词：物联网 IEEE 802.11ax 强化学习 上行链路 演员-评论家 

分 类 号：TN915[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]