在MIMO中使用序列级联多级自编码器和注意力LSTM与混合启发式算法的新频道估计框架
B M R Manasa1, Venugopal Pakala1, Ravikumar Chinthaginjala1
1School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种新的启发式优化技术,用于增强多输入多输出 (MIMO) 系统中的通道估计. 拟议的混合串行级联网络 (HSCN) 与注意力LSTM显著提高预测准确性并降低计算成本.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 优化技术 优化技术
背景情况:
- 多输入多输出 (MIMO) 系统使用多个天线来增强无线通信,但在系统复杂性和功耗方面面临挑战,特别是在模拟到数字转换器 (ADC) 中.
- 准确的通道估计对于MIMO系统性能至关重要,但传统方法与ADC相关的复杂性和数据丢失问题作斗争.
研究的目的:
- 提出一种高效的基于启发式的优化技术,以提高MIMO系统中的通道估计.
- 开发一种新的通道预测框架,根据接收器的反,准确估计发射器的通道系数.
- 在通道估计中最大限度地减少根平均平方误差 (RMSE),比特误差率 (BER) 和平均平方误差 (MSE).
主要方法:
- 开发了一种混合串行级联网络 (HSCN),集成了一个多级级联自动编码器与长短期内存 (LSTM) 和注意力机制.
- 通过反获得的接收器的误差比率,在发射器上预测通道系数.
- 使用基于混合修订位置的野生马和能源谷优化器 (RP-WHEVO) 算法优化了HSCN和注意力LSTM的参数.
主要成果:
- 开发的MIMO模型显示了增强的融合率和预测性能.
- 与现有方法相比,计算成本大幅降低.
- 拟议的RP-WHEVO算法有效地将RMSE,BER和MSE最小化用于通道估计.
结论:
- 提出的基于启发式的优化技术为改善MIMO系统中通道估计提供了有效的解决方案.
- 集成HSCN,注意力LSTM和RP-WHEVO为准确和计算高效的无线通信提供了一个强大的框架.
- 这项研究有助于克服ADC带来的挑战,并增强MIMO系统的整体功能.
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