基于经验重播和蛇优化器的GRU无线频道预测
Qingli Liu1,2, Peiling Wang1,2, Jiaxu Sun1,2
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种先进的封闭循环网络,用于在快速变化的无线环境中精确预测通道状态信息 (CSI). 这种新的方法显著提高了非静止通道的预测准确性和融合速度.
科学领域:
- 无线通信系统无线通信系统
- 机器学习用于信号处理.
背景情况:
- 无线系统中的快速时间变化的频道会降低频道状态信息 (CSI) 预测的准确性.
- 现实世界的非静止通道对传统的预测模型构成重大挑战.
研究的目的:
- 为动态非静止无线通道开发一个强大的实时CSI预测模型.
- 为了提高预测准确度和模型优化能力.
主要方法:
- 一个双通道预测模型,使用封闭的反复单位 (GRU) 来处理CSI的真实和虚构部分.
- 使用Snake Optimizer优化模型参数 (学习速度,隐藏层大小).
- 实施经验重复机制,以从历史CSI数据中有效学习.
主要成果:
- 与LSTM,BiLSTM和BiGRU相比,拟建的封闭循环网络展示了优越的优化能力和融合速度.
- 在动态非静止环境中观察到预测准确度的显著改善.
- 该模型有效地适应了CSI的真实和虚构组件.
结论:
- 拟议的带有经验重播和Snake Optimizer的封闭循环网络为在具有挑战性的无线条件下准确的CSI预测提供了一个强大的解决方案.
- 这种方法提高了在动态环境中运行的无线通信系统的性能和效率.
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