将强化学习集成到6G应用程序的M/M/1/K重试队列模型中
1Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了一种深度Q网络 (DQN) 方法,用于6G网络中的智能队列管理. 强化学习模型增强了移动终端的资源配置和服务,提高了效率和公平性.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 下一代无线网络,特别是以太赫兹频率运行的6G,在可持续,高效和公平的资源分配方面面临挑战.
- 现有的队列管理系统很难适应动态的交通条件和高速需求.
研究的目的:
- 开发和评估使用强化学习的6G网络的智能队列管理系统.
- 为了尽量减少网络延迟,减少能源消耗,并确保公平的资源获取.
主要方法:
- 将强化学习算法深度Q网络 (DQN) 集成到6G复试队列系统 (RQS) 中.
- 进行了广泛的模拟,以分析不同抵达率,队列大小和奖励扩展因子下的表现.
- 单值分解 (SVD) 用于分析代理人的学习过程和适应.
主要成果:
- 拟议的6G-RQS模型与DQN显著提高了队列管理的有效性.
- 该系统通过增加服务的移动终端数量来提高性能,即使在高流量需求的情况下.
- SVD分析证实了强化学习剂的有效学习和适应.
结论:
- 基于强化学习的队列管理是解决6G网络挑战的可行和有前途的解决方案.
- 智能6G-RQS提供了一种动态和自适应的方法来优化高速通信网络的性能.
- 这项研究为更高效,更公平的无线网络资源分配铺平了道路.
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