强化学习辅助道估计器在时间变化的MIMO系统中
1Department of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了动态MIMO系统的强化学习通道估计器. 它有效地选择数据符号,以提高频道估计在时间变化的环境中的准确性.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 准确的通道估计对于多输入多输出 (MIMO) 系统至关重要,特别是在动态环境中.
- 传统的数据辅助通道估计方法与现代无线通道的复杂性和时间变化的性质作斗争.
研究的目的:
- 为时间变化的MIMO系统开发一种新的通道估计器,克服现有方法的局限性.
- 通过智能选择检测到的数据符号来提高通道估计的准确性和效率.
主要方法:
- 制定一个优化问题,以尽量减少数据辅助通道估计错误.
- 使用马尔科夫决策过程开发一个顺序符号选择策略.
- 关于强化学习算法的建议,用于优化政策计算的状态元素精细化.
主要成果:
- 拟议的强化学习辅助道估计器显著优于传统方法.
- 该估计器有效地捕获并适应动态MIMO系统中的通道变化.
- 在道估计准确度和系统性能方面有明显的改进.
结论:
- 强化学习为时间变化的MIMO系统中的自适应通道估计提供了一种强大的方法.
- 拟议的方法为复杂的通道条件提供了计算高效和有效的解决方案.
- 这项工作提升了在动态环境中运行的无线通信系统的能力.
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