适应性DCS-SOMP用于5G网络中的本地化参数估计
Paulo Francisco da Conceição1, Flávio Geraldo Coelho Rocha1
1Department of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种适应性算法,用于使用5G毫米波和大规模MIMO技术精确地定位3D设备. 增强的分布式压缩传感-子空间直角匹配追求方法提高了工业4.0应用程序的准确性和效率.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器人和自动化机器人与自动化
背景情况:
- 5G毫米波 (mmWave) 和大规模多输入多输出 (mMIMO) 是先进无线系统的关键技术.
- 在3D环境中精确定位设备对于工业4.0应用,如智能仓储至关重要.
- 现有的本地化方法可能缺乏高精度任务所需的准确性和效率.
研究的目的:
- 开发和评估一个适应算法,用于精确的3D设备定位.
- 为了估计关键的定位参数:到达时间 (TOA),2D出发角度 (2D-AoD) 和2D到达角度 (2D-AoA).
- 为了提高本地化在工业4.0场景中的性能.
主要方法:
- 建模一个5G下行链路通道,结合mmWave和mMIMO.
- 使用一个自适应的分布式压缩传感子空间正角匹配追求 (DCS-SOMP) 算法.
- 在每个代中动态调整传感矩阵以限制搜索空间.
主要成果:
- 适应性DCS-SOMP方法显著提高了参数估计的准确性.
- 与传统技术相比,拟议的方法显示了更快的融合速度.
- 观察到内存使用量减少,这表明计算效率更高.
结论:
- 适应性DCS-SOMP算法为5G毫米波/毫米IMO系统中的3D设备定位提供了卓越的性能.
- 这种增强的本地化能力对于精密驱动的工业4.0应用程序至关重要.
- 该方法为复杂环境中准确高效的设备跟踪提供了一个有前途的解决方案.
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