一个创新的游戏优化器用于扩展适应性RNN的增强通道估计和大规模MIMO检测
G Navabharat Reddy1, C V Ravikumar2, Oliver Takacs3
1Physical Design Engineer, Wafersemiconductors Technologies Pvt Ltd, Bangalore, India.
Scientific reports
|August 29, 2025
概括
这项研究引入了一个深度学习网络,用于高效的大规模多输入多输出 (MIMO) 检测和通道估计. 这种新的方法显著降低了先进无线通信系统的计算复杂性.
科学领域:
- 无线通信
- 信号处理
- 深度学习
背景情况:
- 在多输入多输出 (MIMO) 系统中,传统的信号识别面临着越来越多的天线和调制订单的计算挑战.
- 深度学习由于其多功能性,非线性建模和用于大规模MIMO检测的并行计算效率而提供了一个有前途的解决方案.
研究的目的:
- 开发一个深度学习网络用于通道估计和大规模的MIMO检测,减少计算复杂性.
- 增强MIMO系统中的通道容量和优化检测性能.
主要方法:
- 使用混矩阵和修改的游戏优化器 (MSGO) 进行道估计.
- 通过注意力机制扩展的自适应循环神经网络 (DARNN-AM) 进行大规模MIMO检测.
- 使用MSGO优化DARNN-AM用于微调网络属性.
主要成果:
- 拟议的深度学习网络有效地减少了大规模MIMO检测的计算复杂性.
- 该系统的性能优于现有技术,通过比较分析进行了验证.
- 该网络实现了各种调制方案的多段映射.
结论:
- 开发的深度学习方法为通道估计和大规模MIMO检测提供了有效的解决方案.
- 整合MSGO和DARNN-AM可优化性能并降低计算负载.
- 这种方法为未来的高性能无线通信系统提供了重大进步.
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