POPT：面向分布式MIMO相位定位的物理信息深度学习
POPT: Physics-Informed Deep Learning for Phase-Only Positioning in Distributed MIMO
- Tampere University（坦佩雷大学）
- Nokia Bell Labs（诺基亚贝尔实验室）
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出物理信息深度学习框架POPT，利用高斯过程学习地面反射相位畸变，以仅编码器Transformer实现分布式MIMO单快照相位定位，达到接近PEB精度并显著降低复杂度。
AI中文摘要：
本文针对窄带相位相干分布式多输入多输出（D-MIMO）网络中两径传播条件下的单快照3D相位定位问题，提出了一种物理信息深度学习框架。与先前主要假设仅视距（LoS）信道的相位相干D-MIMO定位方法不同，我们考虑了LoS路径和镜面地面反射。由于窄带单快照观测无法在时延上分辨这些分量，它们的相干叠加会在载波相位测量中引入结构扰动。为解决这一问题，我们开发了一种基于高斯过程（GP）的模型，该模型从少量训练位置收集的载波相位测量中学习地面反射引起的准周期相位畸变，并利用它生成高质量的合成样本。基于这些样本，我们提出了相位定位Transformer（POPT），这是一种仅编码器的Transformer，无需解决基于模型的估计器所隐含的高度非凸最大似然（ML）问题，即可捕获天线间接入点（AP）的相位关系。我们还推导了所考虑的多径相位D-MIMO定位问题的基本位置误差界（PEB），并开发了最大似然估计（MLE）。数值结果表明，仅用50个GP训练位置，所提方法即可达到接近PEB的精度。解析MLE对相对介电常数失配高度敏感，而所提方法通过直接从测量数据学习相位扰动来减轻这种依赖性。与MLE相比，所提方法还将浮点运算（FLOP）复杂度和推理时间分别降低了约1.7和3.6个数量级。
英文摘要：
This article develops a physics-informed deep learning framework for single-snapshot 3D phase-only positioning in narrowband phase-coherent distributed multiple-input multiple-output (D-MIMO) networks under two-ray propagation. Unlike prior phase-coherent D-MIMO localization methods that largely assume line-of-sight (LoS)-only channels, we consider a LoS path and a specular ground reflection. Since the narrowband single-snapshot observation cannot resolve these components in delay, their coherent superposition induces structured perturbations in the carrier phase measurements. To address this, we develop a Gaussian process (GP)-based model that learns the quasi-periodic phase distortion caused by ground reflection from carrier phase measurements collected at a small number of training locations and uses it to generate high-quality synthetic samples. Building on these samples, we propose the Phase-Only Positioning Transformer (POPT), an encoder-only transformer that captures inter-antenna point (AP) phase relationships without solving the highly non-convex maximum-likelihood (ML) problem underlying model-based estimators. We also derive the fundamental position error bound (PEB) and develop maximum-likelihood estimation (MLE) for the considered multipath phase-only D-MIMO positioning problem. Numerical results show that, with only 50 GP training locations, the proposed method achieves near-PEB accuracy. The analytical MLE is highly sensitive to relative permittivity mismatch, whereas the proposed method mitigates this dependence by learning the phase perturbation directly from measurement data. Compared with MLE, the proposed approach also reduces floating-point operation (FLOP) complexity and inference time by about 1.7 and 3.6 orders of magnitude, respectively.
