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

arXiv: https://arxiv.org/abs/2609.09409 · HTML: https://mlsanity.com/paper/2609.09409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Near-Field Communications: A Tutorial Review](https://mlsanity.com/paper/2305.17751/) (2023)
- [TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs](https://mlsanity.com/paper/2309.08174/) (2023)
- [Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks](https://mlsanity.com/paper/2407.10147/) (2024)
- [Beam Focusing for Near-Field Multi-User Localization](https://mlsanity.com/paper/2407.20264/) (2024)
- [Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology](https://mlsanity.com/paper/2408.16605/) (2024)
- [Conformal Lyapunov Optimization: Optimal Resource Allocation under Deterministic Reliability Constraints](https://mlsanity.com/paper/2503.00486/) (2025)
- [Unsupervised Learning for AoD Estimation in MISO Downlink LoS Transmissions](https://mlsanity.com/paper/2503.12033/) (2025)
- [SCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees](https://mlsanity.com/paper/2503.13801/) (2025)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)

## Abstract (arXiv)

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 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.