通过在动态带宽下通过自适应PDP表示进行变压器增强本地化.
Lei Cao1, Tianqi Xiang2, Weiyan Chen1
1China Mobile Research Institute, Beijing 100080, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了一个用于准确无线定位的新框架,提高了跨不同带宽的适应性. 这种新方法可以提高物联网 (IoT) 和自动驾驶飞行器 (AAV) 的定位精度.
科学领域:
- 无线通信是一种无线通信.
- 信号处理 信号处理
- 机器学习是机器学习.
背景情况:
- 准确的无线定位对于物联网 (IoT) 和自动驾驶飞行器 (AAV) 来说至关重要.
- 现有的方法与动态带宽和多路径环境作斗争.
- 目前基于学习的本地化方法由于依赖带宽特定的通道状态信息 (CSI),在不同的信号带宽中缺乏适应性.
研究的目的:
- 为带宽适应性无线定位提出一个统一的,以神经网络为导向的框架.
- 为了提高在不同信号带宽的本地化模型的概括性和稳定性.
- 在动态带宽条件下克服传统方法的局限性.
主要方法:
- 一个新的框架构建带宽适应功率延迟配置 (PDP) 表示.
- 一个PDP预处理方案使用适应式零填充和过量采样异质CSI的IFFT.
- 开发了一个分频段切片的PDP表示,将PDP处理为变压器令牌.
- 一个专门的变压器模型从多个接入点PDP执行位置估计.
主要成果:
- 拟议的框架展示了卓越的跨带宽泛化能力.
- 与现有的基于分析和学习的基线相比,实现了显著更高的本地化准确性.
- 基于PDP预处理和变压器的方法有效地处理动态带宽条件.
结论:
- 预处理-PDP-plus-Transformer框架为动态带宽下的无线定位提供了一个强大的解决方案.
- 这种方法提高了物联网和AAV应用程序的本地化适应性和准确性.
- 该方法在传统的本地化技术上提供了显著的进步.
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