通过极化自我注意力辅助的神经网络对大规模MIMO系统进行通道估计
Shuo Yang1,2, Yong Li1,2, Lizhe Liu1,2
154th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
本研究介绍了一种新的深度学习 (DL) 算法,用于大规模MIMO系统中的通道估计. 与传统技术相比,新的方法PACE-Net提高了准确性并降低了计算复杂性.
科学领域:
- 无线通信无线通信
- 深度学习 (Deep Learning) 是一种深度学习.
- 信号处理 信号处理
背景情况:
- 大规模的MIMO系统需要准确的通道估计以获得最佳性能.
- 传统的方法经常受到低精度和高计算复杂性的困扰.
- 深度学习 (DL) 为增强道估计提供了一个有希望的途径.
研究的目的:
- 为大型MIMO系统提出一种新的DL辅助通道估计算法.
- 为了解决传统道估计技术的局限性.
- 引入一个高效的神经网络架构,以提高性能.
主要方法:
- 频道估计问题被重新定义为一个图像删除任务.
- 开发了一个新的极化自我注意力辅助通道估计神经网络 (PACE-Net).
- 为培训和测试构建了一个专门的道数据集.
主要成果:
- PACE-Net 显示出优越的正常化平均平方误差 (NMSE) 性能.
- 拟议的算法性能优于传统和其他DL辅助方法.
- 与MMSE估计相比,计算复杂性的显著降低得到了实现.
结论:
- 拟议的DL辅助通道估计算法提供了更高的准确性和效率.
- PACE-Net为大规模MIMO中复杂的通道估计挑战提供了可行的解决方案.
- 图像染方法有效地解决了传统方法的局限性.
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