Automatic Modulation Classification

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Automatic modulation classification infers how a radio signal was encoded from its received waveform, helping receivers and spectrum-monitoring systems interpret transmissions without prior knowledge of the sender. Methods range from CNNs and recurrent networks to Transformers and state-space models, often combining in-phase and quadrature samples with frequency-domain features or physical signal priors. Contrastive and few-shot learning, domain adaptation, and uncertainty-aware models address scarce labels, channel mismatch, and low signal-to-noise ratios. Results are commonly tested on RadioML and HisarMod benchmarks, while real over-the-air evaluation remains important: high benchmark accuracy does not always guarantee reliable performance across channels, hardware, and deployment conditions.

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18 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

A JiT-Based Denoising Defense Method Against Adversarial Examples for Radio Signal Automatic Modulation Classification
Oct 26, 2026Shiwei Liu, Linyuan Wang, Nuolin Sun +2Adversarial Robustness Adversarial Purification
Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou 450000, China
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification
Oct 13, 2026Mohamed Rabie, Chinthana J. Panagamuwa, Konstantinos G. KyriakopoulosContinual Learning Catastrophic Forgetting
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, U.K.
Sub-6 GHz Over-the-Air AMC via Curriculum Fine-Tuned CNN-Transformers
Sep 7, 2026Nurettin Safak, Muhammet Sefa Demirel, Alperen Marasli +3Fine-Tuning Automatic Modulation Classification
Department of Electrical and Electronics Engineering, Ankara Yildirim Beyazit University, Ankara, Turkey
Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation
Aug 28, 2026Kosar Nourolahi, Vahid GhasemiAutomatic Modulation Classification Multi-Task Learning
Department of Artificial Intelligence, Kermanshah University of Technology, Kermanshah Province, Iran
HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification
Aug 25, 2026Qamar Ijaz, Nayyer AafaqAutomatic Modulation Classification Convolutional RNNs
A Unified Backbone--Expert Framework with Relation-Token and Residual--Classifier Interfaces for Automatic Modulation Recognition
Aug 15, 2026Zhixiang Deng, Houbiao Li, Zongyong CuiAutomatic Modulation Classification Modular Neural Networks
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China · School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification
Aug 3, 2026Linzhuo Han, Zongyong Cui, Houbiao LiCross-Fitting Automatic Modulation Classification
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China · School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition
Aug 1, 2026Nurettin Safak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel +3Automatic Modulation Classification
Department of Electrical and Electronics Engineering, Ankara Yildirim Beyazit University, Ankara, Turkey
Bit-Accurate FPGA Evaluation of Learned Feature Gating in a Fixed-Point Fourier-Feature Automatic Modulation Classifier
Jul 27, 2026Gawthaman Senthilvelan, Luthira AbeykoonAutomatic Modulation Classification FPGA Acceleration
University of Toronto · Toronto, Canada
DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
Jul 9, 2026Shuang Wang, Chenxu Wang, Hantong Xing +3Unsupervised Domain Adaptation Automatic Modulation Classification
School of Artificial Intelligence, Xidian University, Xi’an 710071, China · School of Electronics And Information, Northwestern Polytechnical University, Xi’an 710072, China · Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis
Jun 30, 2026Yurui Zhao, Xiang Wang, Jingreng Lei +3Transformer Automatic Modulation Classification
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China. · Department of Electrical and Computer Engineering, The University of Hong Kong
End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing
Jun 23, 2026Xiaohu Li, Chongxiao Qu, Caiyong Lin +2Efficient Neural Network Inference Spiking Transformers
China Nanhu Academy of Electronics and Information Technology, Jiaxing, China · University of Electronic Science and Technology of China, Chengdu, China
Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
May 26, 2026Guanqun Zhao, Yitong Liu, Jiaxuan Fang +2Contrastive Learning Self-Supervised Learning
Beijing University of Posts and Telecommunications, Beijing, China
Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
May 12, 2026Chenxu Wang, Shuang Wang, Lirong Han +4Contrastive Learning Self-Supervised Learning
School of Artificial Intelligence, Xidian University, Xi’an 710071, China · Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China · School of Electronics And Information, Northwestern Polytechnical University, Xi’an 710072, China
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
May 5, 2026Md Raihan Uddin, Tolunay Seyfi, Fatemeh AfghahWireless Foundation Models Automatic Modulation Classification
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
ZoomSpec: A Physics-Guided Coarse-to-Fine Framework for Wideband Spectrum Sensing
Apr 15, 2026Zhentao Yang, Yixiang Luomei, Zhuoyang Liu +2Wireless Communications Frequency-Domain Feature Learning
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification
Apr 11, 2026Prakash Suman, Yanzhen QuAutomatic Modulation Classification
Computer Science, Engineering, and Technology, Colorado Technical University, 1575 Garden of the Gods Road, Colorado Springs, 80907, CO, USA.
Joint Interference Detection and Identification via Adversarial Multi-task Learning
Apr 8, 2026H. Xu, L. Hu, B. He +1Automatic Modulation Classification Multi-Task Learning
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410003, P. R. China

Oct 26, 2026J/K move · Enter open · S save

A JiT-Based Denoising Defense Method Against Adversarial Examples for Radio Signal Automatic Modulation Classification

Shiwei Liu, Linyuan Wang, Nuolin Sun, Libin Hou+1

Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou 450000, China

Adversarial Robustness Adversarial Purification Automatic Modulation Classification

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Automatic modulation classification (AMC) of radio signals is a core technology for spectrum monitoring and electromagnetic situational awareness. Deep neural networks (DNNs) play an important role in this field owing to their powerful feature extraction capabilities and end-to-end learning paradigm. However, their vulnerability to adversarial attacks poses serious security challenges to practical deployment. Adversarial training, a common defense, has limitations including poor generalization across attack types and high training costs. Inspired by Just image Transformers (JiT), a diffusion generative model that directly predicts clean images, this paper proposes a JiT-based denoising defense against adversarial examples for radio signal modulation classification. During training, a signal-specific 1D Transformer denoising network is optimized via flow matching to directly predict clean signal samples. It thereby learns a mapping that projects arbitrary examples back onto the low-dimensional manifold of clean signal examples. During inference, arbitrary examples are fed into the trained network and directly projected onto this manifold. This enables denoising defense without prior knowledge of attacks. Experiments on the RML2018.01A dataset were conducted without attack information, using a single empirically selected, fixed denoising start time. Compared with PGD-based adversarial training, the proposed method achieves a relative improvement of at least 20.58% in robust accuracy under PGD attacks. It also achieves a relative improvement of 10.20% in classification accuracy on clean examples. When evaluated against attack types unseen during adversarial training, the proposed method provides stronger defense than adversarial training. In particular, at higher attack strengths, it achieves a relative improvement of at least 39.72% in robust accuracy.