用深度学习选天线，相位误差下仍能厘米级定位
Phase-Only Positioning in Distributed MIMO Under Phase Impairments: AP Selection Using Deep Learning
- 用深度学习选择分布式MIMO天线点，提升定位鲁棒性
- 相位误差下定位精度显著优于传统方法，复杂度降低约19.7%
- 适合做高精度无线定位系统的工程师和研究人员
载波相位定位（CPP）可在下一代无线系统中实现厘米级精度，近期研究显示，在分布式多输入多输出（D-MIMO）系统中仅使用相位测量也能保持高精度。然而，相位同步误差对这类系统的影响尚未充分研究。本文首先证明，当使用包含相位误差数据训练时，所提出的双曲线交点法在存在相位同步误差的情况下仍可实现高精度定位。随后，提出一种基于深度学习的D-MIMO接入点（AP）选择框架，确保在相位同步误差下仍能实现高精度定位。仿真结果表明，该框架相比现有方法提升了定位精度，同时推理复杂度降低了约19.7%。
原文摘要 · Abstract (English)
Carrier phase positioning (CPP) can enable cm-level accuracy in next-generation wireless systems, while recent literature shows that accuracy remains high using phase-only measurements in distributed MIMO (D-MIMO). However, the impact of phase synchronization errors on such systems remains insufficiently explored. To address this gap, we first show that the proposed hyperbola intersection method achieves highly accurate positioning even in the presence of phase synchronization errors, when trained on appropriate data reflecting such impairments. We then introduce a deep learning (DL)-based D-MIMO antenna point (AP) selection framework that ensures high-precision localization under phase synchronization errors. Simulation results show that the proposed framework improves positioning accuracy compared to prior-art methods, while reducing inference complexity by approximately 19.7%.
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