arXiv Machine Learning
CorteXforge: Scenario-Based RF Dataset Generation on Real Testbeds
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Aug 11
FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing
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 Machine Learning
Sep 22
HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification
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 Machine Learning
Aug 4
An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition
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.
arXiv AI
Sep 7
Wireless Foundation Models: State-of-the-Art and Open Challenges
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 Machine Learning
Oct 8
UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing
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...
arXiv AI
Sep 7
A Deep Generative Model for Synthesizing Labeled Wireless Signals
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.