地理NR-PSW:快速调整定位利用光线追踪5G通道和LLM推理
Wenbin Shi1,2, Zhongxu Zhan2, Jingsheng Lei2
1School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
使用伪信号词和GPT-2模型,GeoNR-PSW提供了准确的5G定位. 这种新的架构可以在具有挑战性的环境中实现精确的本地化,使用最小的数据,提高5G新无线电 (NR) 网络可靠性.
科学领域:
- 无线通信是一种无线通信.
- 定位和定位技术的定位和定位技术.
- 机器学习用于信号处理.
背景情况:
- 全球导航卫星系统 (GNSS) 的可靠性在密集的城市和车辆环境中经常受到损害,原因是多路径效应和信号阻塞.
- 准确的用户设备定位对于5G新无线电 (NR) 网络的部署和性能至关重要.
研究的目的:
- 提出GeoNR-PSW,这是5G NR网络的新型本地化架构.
- 利用伪信号词 (PSW) 范式,使用5G频道指纹进行3D本地化.
- 为了使5G定位能够在最低限度的监督下进行短暂的学习.
主要方法:
- 开发了GeoNR-PSW架构,将5G频道快照编码为简洁的PSW序列.
- 使用了冷的GPT-2骨干,并增强了轻量级的PSW适配器以进行本地化.
- 将该方法应用于Raymobtime S007数据集的6GHz以下 (FR1) 和毫米波 (FR2) 频率.
主要成果:
- 在FR1 (2.8 GHz) 实现了5.90m的中位位置误差,在FR2 (60 GHz) 达到3.25m.
- 尽管数据集大小有限,但证明了有效的少数拍摄3D定位能力.
- 验证了PSW和5G定位提示调整语言模型的性能.
结论:
- GeoNR-PSW显示了准确和可扩展的5G定位的巨大潜力.
- 快速对齐的语言模型为5G本地化提供了一种有希望的方法,在最小的监督下.
- 在具有挑战性的无线环境中,PSW范式有效地提高了定位准确性.
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