在毫米波巨型MIMO系统中学习SBL-GAMP的混合预编码器/组合器
Shoukath Ali K1, Arfat Ahmad Khan2, Perarasi T3
1Department of Electronics and Communication Engineering, Presidency University, Itgalpura, Rajanukunte, Yelahanka, Bengaluru, Karnataka, India.
PloS one
|September 8, 2023
概括
我们介绍了L-SBL-GAMP算法,以高效地为毫米波大规模MIMO系统设计混合前编码器. 这种方法显著降低了计算复杂性,并提高了与现有算法相比的光谱效率.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习是机器学习.
背景情况:
- 毫米波 (mmWave) 大规模的多输入多输出 (MIMO) 系统需要高效的混合前编码器/组合器设计来提高天线增益并最大限度地降低硬件复杂性.
- 通过预期最大化 (SBL-EM) 进行传统的稀疏贝叶斯学习面临着高计算挑战,信号尺寸不断增加,限制了其实际应用.
- 现有的方法难以满足为大型毫米波MIMO系统设计最佳混合前编码器的计算需求.
研究的目的:
- 提出一种新的算法,即学习-分散贝叶斯式学习与通用近似消息传递 (L-SBL-GAMP),用于设计毫米波大规模MIMO系统中的最佳混合预编码器/组合器.
- 为解决与传统SBL-EM算法相关的大型数据集的高计算复杂性和适用性的局限性.
- 通过先进的机器学习方法提高毫米波大规模MIMO系统的光谱效率和性能.
主要方法:
- 拟议的L-SBL-GAMP算法通过集成深度神经网络 (DNN) 来扩展SBL-GAMP框架.
- 训练DNN组件以基于训练数据的特征来优化混合预编码器/组合器的设计.
- 该算法利用了通用的近似消息传递原则,以高效地估计信号和预编码器设计.
主要成果:
- 与SBL-EM算法相比,L-SBL-GAMP算法显示了较低的计算复杂性,较少的代和较低的训练开销.
- 模拟结果显示,L-SBL-GAMP实现了更高的可实现率和更高的精度.
- 拟议的方法在毫米波大规模MIMO架构中优于现有的算法,如OMP,SOMP,SBL-EM和SBL-GAMP.
结论:
- L-SBL-GAMP算法为设计毫米波大规模MIMO系统中的混合预编码器/组合器提供了一个计算高效和有效的解决方案.
- 在SBL-GAMP框架内集成DNN显著提高了系统性能,包括频谱效率和可实现率.
- 对于下一代无线通信系统的实际部署,L-SBL-GAMP是一个有希望的进步.
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