arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
By Lei Chu, Yuning Zhang, Omer Gokalp Serbetci, Anushka Katiyar, Bassel Abou Ali Modad, Andreas F. Molisch
arXiv:2607. 15713v1 Announce Type: cross Abstract: Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing.
By Yong Chu, Xun Zhou, Zenglin Xu, Hui Wang, Yue Yu
arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.
By Guangjin Pan, Hui Chen, Hei Victor Cheng, Henk Wymeersch
arXiv:2608. 00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue.
By Haozhe Lei, Roberto Bomfin, Marwa Chafii, Sundeep Rangan
arXiv:2607. 05449v1 Announce Type: cross Abstract: Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for infrastructure-aided reconstruction.
By Weizhe Tang, Jiaxi Liu, Junwei you, Steven T. Parker, Pei Li, Sikai Chen, Meng Ran, Bin Ran
The paper introduces MUSIC-Net, an end-to-end deep learning framework for near-field multi-user positioning that incorporates a two-stage MUSIC algorithm to isolate line-of-sight signal components and estimate surrogate distances. By embedding these MUSIC-derived objects into training, the method bypasses separate parameter estimation and path/source association, directly recovering user positions even in mixed LoS/NLoS multipath scenarios. Additionally, the authors employ split conformal prediction to provide statistically guaranteed confidence sets for each user’s position, achieving lower mean positioning error and tighter prediction regions compared to existing benchmarks.
By Jiaying Li, Haifeng Wen, Changsheng You, Yuanwei Liu, Hong Xing