在复杂的通信场景中,基于联合感知和深度增强学习的道估计研究
Xin Liu1,2, Shanghong Zhao2, Yanxia Liang3
1School of Information Engineering, Xi'an Eurasia University, Xi'an, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一个智能通道估计模型,使用CNN-GRU网络和DDPG强化学习来增强无线通信. 该模型在复杂环境中比传统方法显著提高了准确性和稳定性.
科学领域:
- 无线通信工程 无线通信工程
- 人工智能在通信中的应用
- 信号处理 信号处理
背景情况:
- 无线系统面临诸如多路径传播和干扰等挑战,降低了性能.
- 准确的通道估计对于保持通信质量和数据速率至关重要.
- 智能反射表面 (IRS) 提供了优化无线通道的潜力.
研究的目的:
- 为IRS辅助的无线系统开发智能通道估计模型.
- 在动态环境中提高通道估计的准确性和稳定性.
- 为了利用深度学习和强化学习来进行适应性道优化.
主要方法:
- 提出了一个智能通道估计模型,将CNN和GRU功能融合在一起.
- 利用深度决定性政策梯度 (DDPG) 强化学习进行适应性优化.
- 开发了一个通道重建预测和生成网络 (CRPG-Net) 用于特征提取.
主要成果:
- 与LS和LMMSE方法相比,拟议的模型实现了明显较低的通道估计误差.
- 在公共数据集和现实世界的场景中表现出卓越的准确性和稳定性.
- 强化学习组件在动态通道条件下实现了持续优化.
结论:
- 基于CNN-GRU-DDPG的CRPG-Net提供了一种创新和有效的方法,用于IRS辅助的频道估计.
- 这种方法在复杂的无线环境中显著提高了通信质量和系统性能.
- 智能模型提供了一个强大的解决方案,以适应不断变化的通道状态.
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