Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC

2026-09-08

AI Summary

该研究针对近场多用户定位中的散射问题，提出了一种结合两阶段MUSIC的深度学习框架MUSIC-Net，并引入分裂共形预测来提高定位准确性和不确定性量化。

Abstract

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.

Problem

• near-field localization
• coherent propagation
• parameter estimation
• path/source association
• reliability

Innovation

• MUSIC-Net
• end-to-end near-field positioning
• two-stage MUSIC
• split conformal prediction (SCP)
• uncertainty quantification (UQ)