基于模型数据的混合驱动宽带通道估计,用于光束空间大规模MIMO系统.
Yang Nie1,2, Zhenghuan Ma1, Lili Jing1,2
1School of Physics and Electronic Information Engineering, Jining Normal University, Ulanqab 012000, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种新的混合深度学习网络,用于在大型MIMO系统中准确的光束空间通道估计. 拟议的方法提高了估计的准确性和稳定性,在复杂的环境中优于现有的技术.
科学领域:
- 电气工程 电气工程
- 无线通信无线通信
- 信号处理 信号处理
背景情况:
- 精确的通道估计对于光束形成和光谱高效传输在光束空间大规模MIMO系统中至关重要.
- 传统方法在复杂的传播环境中与模型不匹配作斗争.
- 深度学习方法需要大量的数据集,并且具有有限的概括性.
研究的目的:
- 为宽带光束空间大规模MIMO系统开发一个强大而准确的通道估计方案.
- 克服传统模型驱动和数据驱动方法的局限性.
- 为了提高各种信号噪声比率 (SNR) 的性能.
主要方法:
- 提出了一个模型-数据混合驱动网络 (MD-HDN) 方案.
- 将矢量近似消息传递 (VAMP) 算法展开为可训练网络.
- 引入了一种新的收缩函数,以提高估计准确度.
主要成果:
- MD-HDN计划在各种SNR中显著优于现有计划.
- 在估计准确性和稳定性方面都取得了实质性的改进.
- 通过广泛的数值结果验证.
结论:
- 拟议的MD-HDN方案为宽带光束空间通道估计提供了卓越的解决方案.
- 混合方法有效地结合了基于模型和数据的技术.
- 这种方法在现实条件下提高了大规模MIMO系统的可靠性.
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