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

• URL: http://arxiv.org/abs/2609.09409v1
• Date: Tue, 08 Sep 2026 20:03:33 GMT
• Title: Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC
• Abstract summary: Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems.<n>Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high overhead and accumulated errors.<n>We propose 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.
• Score: 52.33021019321273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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