利用分布式天线网络实现无需设备的高精度定位
Device-Free Localization Using Multi-Link MIMO Channels in Distributed Antenna Networks
- 基于多链路MIMO信道的空间时间多样性进行无设备定位
- 室内实验达亚米级精度，复杂多径下仍稳定有效
- 适合6G融合感知通信场景，无需额外传感硬件
面向未来6G无线接入网中的感知与通信一体化（ISAC），本文提出一种基于分布式天线网络（DANs）的新型无设备定位（DFL）框架。该方法利用射频断层成像（RTI）技术，结合DAN中多链路多输入多输出（MIMO）信道的空间与时间多样性，实现高精度定位。采用软件定义无线电（SDRs）在亚6 GHz频段搭建原型系统，在不同节点密度和目标类型条件下进行室内全面评估。结果表明，该框架在多数场景下实现亚米级定位精度，并在复杂多径环境中保持鲁棒性能。此外，通过贝叶斯优化对稀疏度和路径厚度等关键参数进行调优，显著提升了图像重建质量与目标估计精度。这些结果验证了基于DAN的DFL在可扩展性与基础设施兼容性方面的可行性与有效性，是一种无需专用传感硬件即可实现精准被动定位的ISAC解决方案。
原文摘要 · Abstract (English)
Targeting integrated sensing and communication (ISAC) in future 6G radio access networks (RANs), this paper presents a novel device-free localization (DFL) framework based on distributed antenna networks (DANs). In the proposed approach, radio tomographic imaging (RTI) leverages the spatial and temporal diversity of multi-link multiple-input multiple-output (MIMO) channels in DANs to achieve accurate localization. Furthermore, a prototype system was developed using software-defined radios (SDRs) operating in the sub-6 GHz band, and comprehensive evaluations were conducted under indoor conditions involving varying node densities and target types. The results demonstrate that the framework achieves sub-meter localization accuracy in most scenarios and maintains robust performance under complex multipath environments. In addition, the use of Bayesian optimization to fine-tune key parameters, such as sparsity and path thickness, led to significant improvements in image reconstruction quality and target estimation accuracy. These results demonstrate the feasibility and effectiveness of DAN-based DFL as a scalable and infrastructure-compatible ISAC solution, capable of delivering accurate, passive localization without dedicated sensing hardware.
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