The paper presents a deep learning‑enhanced Wi‑Fi sensing system that uses only a single transceiver pair to achieve real‑time human pose estimation and localization. By leveraging prior information and temporal correlation as side information, the system reduces estimation error under hardware constraints. Experimental results show an average pose error of 0.2189 m and a localization error of 0.6124 m while running at 42 fps on commodity hardware.
By Yuxuan Liu, Chiya Zhang, Yifeng Yuan, Chunlong He, Weizheng Zhang, Gaojie Chen
arXiv:2609.06131v1 Announce Type: new
Abstract: Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situationa...
By Yuxiao Li, Keke Hu, Bobai Zhao, Santiago Mazuelas, Yuan Shen
arXiv:2405. 17366v3 Announce Type: replace Abstract: We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments.
By Ruichen Wang, Dinesh Manocha
EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.
By MaTais Caldwell, Yixin Chen, Xin Dang, Charles Walter
arXiv:2507. 21799v2 Announce Type: replace-cross Abstract: The empirical success of deep learning has spurred its application to the radio-frequency (RF) domain, leading to significant advances in Deep Wireless Sensing (DWS).
By Xie Zhang, Yina Wang, Chenshu Wu
The paper introduces Physics-Unrolled Hybrid Neural Operator (PU‑HNO), a three‑stage cascade that transforms low‑fidelity ray‑tracing outputs and scene priors into high‑fidelity indoor radio maps by sequentially modeling reflection, diffraction, and scattering. It demonstrates that, under conditionally unbiased label noise, the model can learn stable propagation structures and surpass its own training labels. Experiments on varied floorplans show PU‑HNO outperforming image‑to‑image baselines, wireless learning models, and monolithic neural operators in both image quality and wireless deployment metrics.
By Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang, Elahe Soltanaghai
The paper introduces a physics‑informed autoencoder that separates reflector speed from sensing geometry in WiFi spectrograms, enabling joint recovery of speed, geometry factor, relative amplitude, and ridge width for each Doppler ridge. It builds a compact parametric representation of spectrograms validated on a large human‑activity dataset and employs a synthetic‑to‑real training framework to avoid real‑data collection. Experiments on synthetic and 31 real WiFi scenarios show the method outperforms existing baselines in accurately extracting speed and geometry information.
By Mert Torun, Darius Cuenca, Yasamin Mostofi
arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.
By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott
arXiv:2607. 01907v1 Announce Type: cross Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable.
By Francisco Sede\~no, Francisco Chicano, Jamal Toutouh
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 presents a calibrated radio‑frequency fingerprinting approach that handles co‑channel interference from multiple transmitters. By framing the task as a multi‑label classification problem, the authors use a 1D CNN and calibrate confidence thresholds to bound the average number of false negatives, ensuring reliable detection of spectrum violations. Experiments on the POWDER 5G testbed with Wi‑Fi, LTE, and 5G NR signals achieve up to 97% accuracy, with calibrated recall closely matching the specified false‑negative bounds even under out‑of‑distribution interference.
By Tariq Abdul-Quddoos, Xiangfang Li, Lijun Qian
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad