The paper presents a method for training neural networks on synthetic data to approximate the optimal Bayes estimator for dense emitter localization. By doing so, it demonstrates that neural networks can effectively handle complex localization tasks in high-density scenarios. The study supports future efforts to develop high-throughput, large-field-of-view super‑spatiotemporal resolution single‑molecule localization microscopy (SMLM) systems.
By Yi Sun, Mona Sharifi, Muzna Yumman
arXiv:2606. 01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments.
By H. Nazim Bicer, J. Nick Laneman
arXiv:2609.26214v1 Announce Type: new
Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...
By \'Eloi Sainte-Beuve (Orange Research), Guillaume Larue (Orange Research), Louis-Adrien Dufr\`ene (Orange Research), Quentin Lampin (Orange Research), Ali Al Khansa (Orange Research)
arXiv:2605. 12569v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging.
By M. Shamail J. Khan, Nisha L. Raichur, Lucas Heublein, Christian Wielenberg, Alexander Mattick, Tobias Feigl, Christopher Mutschler, Felix Ott
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
By Chenghong Bian, Chaozheng Wen, Hongze Chen, Jun Zhang
arXiv:2605. 13092v2 Announce Type: replace-cross Abstract: Density estimation in high-dimensional settings is an important and challenging statistical problem.
By Ruitong Zhang, Ke Deng
arXiv:2607. 22704v1 Announce Type: cross Abstract: Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges.
By Xiao Wang, Hao Si, Qiang Chen, Yu-Xiang Zhang, Beihe Zhang, Jianhua Yang, Qingquan Yang, Dengdi Sun, Wanli Lyu, Guosheng Xu, Jin Tang
SymNetPro is a localization framework that extends SymNet by adding a line‑of‑sight aware attention bias and a transmitter‑drop augmentation technique. These components help the model learn obstruction‑aware spatial relations and handle varying numbers of transmitters. Experiments on ray‑traced urban environments demonstrate lower OSPA errors compared to baseline methods, especially under sparse sampling, noise, and higher transmitter counts.
By Lyuzhou Ye, Heng Fan, Yan Huang
arXiv:2607. 01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization.
By Lizhou Liu, Xiaohui Chen, Zihan Tang, Mengyao Ma, Wenyi Zhang
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
arXiv:2608. 19377v1 Announce Type: cross Abstract: Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensitivity to statistical noise.
By Liyun Zhang, Naoya Mamada, Kentaro Sakai, Takeo Hoshi, Toru Aonishi
WiFlow introduces a method for estimating optical flow using WiFi channel state information (CSI) instead of camera frames, addressing privacy and lighting issues. The paper presents a CSI-based flow estimator, a preprocessor evaluation, and three model architectures balancing accuracy and complexity. Additionally, it provides the first dataset for training and evaluating CSI-based optical flow estimators, with experiments offering insights into key design elements.
By Thomas Weigel, Simon Kiefhaber, Fabian Portner, Matthias Hollick, Simone Schaub-Meyer