在6G移动复试队列系统中基于高阶马尔科夫模型的强化学习分析
1Faculty of Informatics, University of Debrecen, Egyetem ter 1, 4032 Debrecen, Hungary.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
深度Q网络增强学习 (DQN-RL) 优化了6G移动网络. 马尔科夫链分析表明,5-10个培训环节足以实现有效的政策融合,提高绩效和减少能源消耗.
科学领域:
- 电信工程 电信工程 电信工程
- 人工智能的人工智能
- 排队理论 排队理论
背景情况:
- 6G移动通信服务在排队系统中面临着动态挑战.
- 深度Q网络增强学习 (DQN-RL) 为优化网络行为提供了一个潜在的解决方案.
- 了解代理学习融合对于有效实施至关重要.
研究的目的:
- 在6G重审队列系统中分析DQN-RL代理人的学习趋同.
- 使用马尔科夫链方法和混合时间分析量化趋同特征.
- 在不确定性下为优化6G队列策略提供基础.
主要方法:
- 使用一级和二级马尔科夫链方法来分析DQN-RL代理融合.
- 奖励序列的模拟时间演变作为马尔科夫链.
- 采用马尔科夫模型的混合时间分析和光谱差距特性来评估趋同.
主要成果:
- 马尔科夫链分析表明,在DQN-RL中,10个培训环节足以实现政策趋同.
- 在某些场景中,只要5集就能提高移动网络性能,同时能耗低.
- 混合时间计算评估了120个参数组合的学习稳定性和系统响应性.
结论:
- 在6G队列中,DQN-RL融合对系统参数和重试动态敏感.
- 马尔科夫链分析为评估学习趋同提供了一种严格的方法.
- 这些发现支持优化6G队列策略,以提高效率和性能.
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