arXiv Machine Learning August 4, 2026 By Nurettin Safak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Alperen Marasli, Taha Eren Atmaca, Ozgun Ersoy

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 10

DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification

arXiv:2607. 08031v1 Announce Type: cross Abstract: The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models.

By Shuang Wang, Chenxu Wang, Hantong Xing, Hanlin Mo, Lirong Han, Licheng Jiao

safety

arXiv AI

1d ago

Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data

arXiv:2510. 04927v2 Announce Type: replace-cross Abstract: Automatic modulation classification (AMC) is a core enabler of cognitive wireless systems, providing spectrum awareness and supporting adaptive communication at the network edge.

By Usman Akram, Yiyue Chen, Haris Vikalo

safety

arXiv Machine Learning

Jul 21

Compact convolutional neural networks for AI-based drone detection system

arXiv:2607. 16455v1 Announce Type: new Abstract: The increasing use of first-person-view drones in modern conflicts has created a demand for compact and reliable detection systems capable of operating in complex electromagnetic environments.

By G\'abor Farkas, G\'abor Fazekas, Karakai Patrik, Andr\'as N\'emeth, G\'abor Farkas

robotics

arXiv Machine Learning

Jul 2

Computer vision-based neural networks for radioisotope identification in urban environments

arXiv:2607. 00270v1 Announce Type: cross Abstract: Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements.

By Masen Bachleda, Peter Lalor

llms computer-vision benchmarks

arXiv Machine Learning

1d ago

Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation

arXiv:2608. 14676v1 Announce Type: cross Abstract: In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions.

By Javad Zolfaghari-Bengar, Rakibul Rony, Elisa Gomez-de-Lope, Alejandro Villena-Rodriguez, Abhinav Mahadevan, Nicolas Kourtellis

arXiv AI

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.

By Jing Wang, Zhu Wang, Changlong Cheng, Yifan Guo, Yin Zhang

Related stories

DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification

Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data

Compact convolutional neural networks for AI-based drone detection system

Computer vision-based neural networks for radioisotope identification in urban environments

Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation

FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing