Automatic Modulation Classification

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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
Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification
Aug 3, 2026Linzhuo Han, Zongyong Cui, Houbiao LiAutomatic Modulation Classification 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
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

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Sub-6 GHz Over-the-Air AMC via Curriculum Fine-Tuned CNN-Transformers

Nurettin Safak, Muhammet Sefa Demirel, Alperen Marasli, Taha Eren Atmaca+2

Department of Electrical and Electronics Engineering, Ankara Yildirim Beyazit University, Ankara, Turkey

Fine-Tuning Automatic Modulation Classification Wireless Communications

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Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question of how they behave once path loss and antenna pointing error are introduced by a genuine free-space link. We report a curriculum fine-tuning study of a hybrid CNN-Transformer AMC model. The general-purpose, all-32-class dataset underlying the model was built entirely at 915 MHz, on a controlled, clock/PPS-synchronized MIMO-expansion-cable link (not spatial-multiplexing transmission); all subsequent free-space, real-hardware experimentation - the sequential fine-tuning curriculum, matched-distance evaluation, and every reported over-the-air accuracy figure - was carried out at 4 GHz, across five directional-antenna distances (25, 35, 50, 70, 75 cm) under fixed TX/RX gain. Three distances used near-ideal antenna alignment (~99%) and two used a deliberately introduced partial misalignment (~85%), fine-tuned last in the curriculum. We report matched-distance test accuracy (91.8-93.7% across all five 4 GHz conditions) and confusion-matrix analysis grounded in RF theory, and report honestly where our sequential fine-tuning order confounds cumulative link adaptation with antenna alignment, rather than overstating what the data can support.

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