Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC
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...
| Authors | Jiaying Li, Haifeng Wen, Changsheng You et al. |
|---|---|
| Primary category | eess.SP |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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