在具有完美传感的CR网络中以DRL为基础的调度以尽量减少AOI
Juan Sun1, Shubin Zhang2, Xinjie Yu1
1School of Computer and Data Engineering, NingboTech University, Ningbo 315104, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究利用先进的人工智能优化了能源有限的网络中的数据新鲜度 (信息时代). 结果显示对时间敏感的物联网应用程序的基本方法的性能有所改善.
科学领域:
- 无线通信网络
- 信息理论
- 人工智能
背景情况:
- 信息时代 (AoI) 对时间敏感的应用至关重要.
- 在能源有限的物联网 (IoT) 网络中优化AoI是一个挑战.
- 认知无线电网络 (CRN) 提供了高效频谱利用的框架.
研究的目的:
- 在单个和多用户认知无线电网络 (CRN) 中尽量减少信息时代 (AoI).
- 为能源采集和数据传输制定最佳的调度政策.
- 根据现有方法评估拟议的算法的性能.
主要方法:
- 单个用户CRN问题作为部分可观察的马尔科夫决策过程 (POMDP) 的制定.
- 应用动态编程以在单个用户的情况下进行最佳决策.
- 为多用户CRN方案开发一个增强的深度Q网络 (DQN) 算法.
- 模拟分析以比较拟议的政策与近视政策.
主要成果:
- 拟议的POMDP和增强的DQN算法在最小化AOI方面显著优于近视政策.
- 对能源采集,频谱传感和数据传输确定了有效的调度策略.
- 在各种参数设置下分析系统性能, 提供最佳配置的见解.
结论:
- 先进的AI技术,特别是增强的DQN,对于优化复杂CRN中的AoI是有效的.
- 开发的方法为在能源有限的物联网环境中管理数据新鲜度提供了强大的解决方案.
- 这项研究强调了复杂的时间表对于实时数据应用的重要性.
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