English(EN) Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC

新的MUSIC-Net框架通过深度学习和共形预测增强近场多用户定位

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了MUSIC-Net，一个用于无线系统中精确近场多用户定位的深度学习框架。该框架集成了两阶段MUltiple SIgnal Classification (MUSIC) 方法，以有效处理具有视距（LoS）和非视距（NLoS）信号的复杂多径环境。通过嵌入MUSIC对象，MUSIC-Net可以直接估计用户位置，无需单独的参数估计或路径关联，并结合了分割共形预测（SCP）为这些估计提供统计保证的置信区域。 AI

影响该框架通过利用深度学习和先进的信号处理技术，有望提高未来无线通信中定位系统的准确性和可靠性。

排序理由该条目是一篇在arXiv上发表的研究论文，详细介绍了一种新的信号处理和定位技术框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiaying Li, Haifeng Wen, Changsheng You, Yuanwei Liu, Hong Xing · 2026-09-11 04:00

基于两阶段MUSIC的可靠近场多用户定位

arXiv:2609.09409v1 Announce Type: cross 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 localiza…

本文如何被排名

报道来源 [1]

基于两阶段MUSIC的可靠近场多用户定位

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