The paper presents HEAR, a dual‑task Transformer that learns to assess heartbeat observability from mmWave radar phase spectra and simultaneously estimates heart rate. Using a controllable FMCW simulator, the authors generate labeled data where observability is defined by the agreement between the dominant heartbeat‑band peak and the known heart rate. Trained only on simulated data, HEAR transfers zero‑shot to real‑world datasets at 60 and 120 GHz, enabling selective heart‑rate estimation that dramatically reduces error and achieves sub‑50 ms latency on edge hardware.
By Yuxuan Hu, Shilin Shan, Jianfei Yang, Feng Xu
arXiv:2609.23945v1 Announce Type: new
Abstract: In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, imp...
By Conrad Prisby, Siyao Li, Chengtao Xu, Thomas Yang
The paper surveys wireless foundation models (WFMs), highlighting their role in learning reusable representations from large-scale wireless data for physical-layer tasks. It systematically reviews WFM design components—pretraining, backbone architectures, and downstream adaptation—and categorizes the literature into five task families: signal recognition and demodulation, channel representation learning, RF sensing and localization, beam management, and spectrum sensing and monitoring, including multi-task models. The analysis reveals that while WFMs show promise, evidence of transferability varies across tasks and evaluation settings, and differences in datasets, modalities, architectures, and distribution shifts hinder clear conclusions about effective design choices.
By Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh, Nelson L. S. da Fonseca, Carlos A. Astudillo, Hatem Abou-Zeid
arXiv:2504. 03772v3 Announce Type: replace-cross Abstract: Respiratory diseases account for a significant portion of global mortality.
By Anton Lambrecht, Stijn Luchie, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter
arXiv:2605.00968v2 Announce Type: replace-cross
Abstract: Wireless foundation models (WFMs) have emerged as a promising paradigm for unified channel state information (CSI) acquisition across diverse...
By Chenyu Zhang, Xinchen Lyu, Chenshan Ren, Yanzhao Hou, Xuefei Zhang, Shuhan Liu, Qimei Cui
WiNeRF is a neural field framework that learns a spatially continuous, complex-valued wireless channel representation from sparse channel state information collected by commodity WiFi devices. It incorporates system constraints such as antenna geometry, limited spatial resolution, and phase uncertainty through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework. In diverse indoor environments with non‑line‑of‑sight regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average, and produces a task‑agnostic channel representation that can be reused in standard signal‑processing pipelines without hardware or protocol changes.
By Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev, Cagri Tanriover, Rahul C. Shah, Elah\'e Soltanaghai
arXiv:2505. 22783v2 Announce Type: replace-cross Abstract: Reliable altitude estimation with frequency-modulated continuous wave (FMCW) radar altimeters is increasingly a challenge due to in-band interference from modern communication systems.
By Charles E. Thornton, Jamie Sloop, Samuel Brown, Aaron Orndorff, William C. Headley, Stephen Young
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:2610.07499v1 Announce Type: new
Abstract: Multimodal wearable systems must remain reliable when sensor streams become noisy or unavailable. Existing multimodal test-time adaptation (TTA) method...
By Payal Mohapatra, Yueyuan Sui, Haodong Yang, Benjamin Lundell, Stephen Xia, Qi Zhu
arXiv:2609.07726v1 Announce Type: cross
Abstract: Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question...
By Nurettin Safak, Muhammet Sefa Demirel, Alperen Marasli, Taha Eren Atmaca, Durdu Can Yerdeyatar, Ozgun Ersoy
The paper introduces NOSTRAdAMUS, a predictive link‑adaptation framework for 5G NR that forecasts retransmissions in the next radio frame using recent HARQ history and adjusts the Modulation and Coding Scheme accordingly. Gradient Boosting models achieve 82.9% overall accuracy, with high‑confidence predictions correct 94.2% of the time and a 5.5 µs inference latency. Evaluated OTA on the X5G testbed and various channel emulators, the approach boosts goodput by up to 71.5% and cuts retransmissions by up to 71.8% without retraining across diverse scenarios.
By Tamerlan Aghayev, Maxime Elkael, Michele Polese, Reshma Prasad, Salvatore D'Oro, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
The study demonstrates that time‑domain‑gated microwave signals can be reconstructed from magnitude‑only measurements using a spectrum analyzer, eliminating the need for phase data typically obtained with a vector network analyzer. By selecting appropriate gate centers, applying per‑bin reference corrections, and training a fully connected neural network on paired sweeps, the authors achieve reconstruction errors comparable to VNA‑based methods for both reflection ($S_{11}$) and transmission ($S_{21}$) measurements on a thoracic phantom with multiple dielectric states. The approach shows that when magnitude sufficiently encodes phase information—particularly in reflection—scalar instruments can provide gated responses without phase measurement, and it outperforms linear mapping on an open breast phantom dataset.
By Maciej Cicho\'n, Patrycja Go\l{}a\'s, Maksymilian Bartosik, Angelika Walczak, Agata Bielecka-D\k{a}browa, Maciej \'Slot