arXiv:2608. 08439v1 Announce Type: cross Abstract: Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse across devices, environments, and RF modalities.
arXiv:2609.22139v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring,...
arXiv:2608. 00796v1 Announce Type: cross Abstract: Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods.
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.
arXiv:2610.09559v1 Announce Type: new Abstract: Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental...
The paper introduces Inter-Instance Generative Adversarial Networks (IIns‑GAN), a deep learning approach for synthesizing realistic labeled wireless signals. Unlike traditional environmental‑model based methods, IIns‑GAN adapts to various scenarios and produces signals that closely match the physical characteristics of real measurements. Experiments on public Ultra‑Wideband datasets show that the generated signals improve model training for tasks such as distance estimation and environment identification.
TrustAgent‑RFF is a framework that enhances the reliability of radio frequency fingerprinting (RFF) models when faced with domain shifts such as changes in receiver, distance, or environment. It operates on a frozen I/Q classifier and gathers diagnostic evidence—counterfactual stability, prototype consistency, acquisition domain similarity, and signal quality—to estimate prediction reliability. A large language model planner selects diagnostic actions based on evidence, trust state, costs, and budget, while a calibrated decision layer classifies predictions as Accept, Reject, or Defer. Experiments on five RFF datasets show a 38.7% drop in source‑to‑target accuracy and highlight that many incorrect target‑domain predictions remain highly confident.
arXiv:2610.19015v1 Announce Type: new Abstract: Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pre...
arXiv:2606. 01862v1 Announce Type: cross Abstract: Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges.
arXiv:2606. 06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task.
The paper introduces Radio‑Frequency Convolutional Neural Networks (RF‑CNNs), which repurpose the frequency mixer in wireless radios to perform convolutional neural network inference directly on edge devices. By mapping multi‑channel convolutions onto frequency tones, the passive mixer can execute the entire operation in a single pass, enabling deep CNNs with up to 26.4 million parameters and nine layers to run on smartphones, wearables, and drones. Experimental results show near full‑precision performance while reducing energy consumption to 0.72 fJ per multiply‑accumulate—two orders of magnitude lower than adding a digital processor. "whyItMatters":"The approach leverages existing radio hardware to deliver efficient, state‑of‑the‑art AI inference on billions of devices without increasing size, weight, power, or cost."
arXiv:2603.16497v3 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,...