发表机构
The Hong Kong University of Science and Technology (Guangzhou); Southern University of Science and Technology; The University of Hong Kong; The Hong Kong University of Science and Technology（香港科技大学（广州）; 南方科技大学; 香港大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MUSIC-Net，一种嵌入两级MUSIC的端到端深度学习框架，结合分裂共形预测，在相干多径场景中直接恢复多用户位置，降低定位误差并提供统计保证的置信集估计。
AI 中文摘要
近场定位是未来无线系统中实现高分辨率多用户定位的一项有前景的技术，但其性能常因散射引起的相干传播而下降。现有的近场定位方法需要分别进行参数估计和路径/信源关联，存在计算开销大、误差累积的问题，且通常无法提供可靠性保证。本文提出MUSIC-Net，一种由混合视距（LoS）和非视距（NLoS）多径场景中的两级多信号分类（MUSIC）算法指导的端到端近场定位深度学习（DL）框架，该框架将两级MUSIC对象嵌入训练中，以分离与LoS相关的信号子空间并识别替代距离。所提框架直接恢复多用户位置，无需复杂的NLoS参数估计或路径/信源关联。此外，我们引入分裂共形预测（SCP），将基于点估计的定位推进到对所有用户具有统计保证的（置信）集合估计。数值结果表明，所提出的MUSIC-Net在平均定位误差（MPER）上低于现有基准，并产生更紧凑的SCP校准预测区域，展示了在相干多径环境中精确的LoS定位和高效的不确定性量化（UQ）。
英文摘要
Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.
Comments6 pages, 5 figures, and it was accepted by IEEE GLOBECOM 2026
