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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Huynh-The, Thien Pham, Quoc-Viet Nguyen, Toan-Van da Costa, Daniel Benevides Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi South Korea Pusan Natl Univ Korean Southeast Ctr Ind Revolut Leader Educ 4 Busan South Korea Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Technol Innovat Inst AI & Telecom Res Ctr Abu Dhabi U Arab Emirates Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Taiwan

ISBN: (纸本)9781665442664

In this paper, we propose a deep learning (DL)-based method to automatically identify the modulations of orthogonal frequency-division multiplexing (OFDM) signals in wireless communication systems. In particular, a cost-efficient OFDM modulation classification convolutional neural network (COM-ConvNet) is principally designed with grouped convolutional layers to reduce computing complexity significantly. Remarkably, reconstructing the high-dimensional data array of OFDM signals allows our deep network to learn the underlying sample correlations within every symbol and among different symbols sufficiently. We leverage residual connection and attention connection with element-wise addition and element-wise multiplication layers in specific-designed processing blocks to enhance the pattern learning efficiency. For performance evaluation, we test the proposed method on a synthetic six-modulation OFDM signal dataset under impaired channel conditions and conduct diverse simulations, such as ablation study, parameter investigation, and complexity analysis. COM-ConvNet achieves cost efficiency (i.e., small network size and low computational cost) while maintaining an acceptable accuracy when compared with other DL models.

关键词： automatic modulation classification deep learning orthogonal frequency-division multiplexing (OFDM) signals group convolution residual connection attention connection

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Designing novel deep learning based frameworks for performance improvement of automatic modulation classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Islam, Soyabul Maswood, Mirza Mohd Shahriar Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna 9203 Bangladesh

Efficiently recognizing modulated signals has grown more crucial as communication technologies advances. Unfortunately, obstacles including a large number of data and inaccurate identification provide a barrier to advancement in this field. automatic modulation classification (AMC) based on deep learning (DL) has been suggested as a productive way to get good classification performance. However, under different noise situations, particularly at low signal-to-noise ratios (SNR), the majority of existing DL-AMC algorithms have limited generalization capabilities. In this paper, we developed three AMC models: a Bidirectional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM-GRU), a hybrid Convolutional Neural Network (BiLSTM-GRU-CNN) for spatiotemporal features, and Bidirectional LSTM-GRU-CNN-Attention (AMC-BLGCA) with multihead attention. Our models can be implemented on the receiver end of a transmission system as modulation classifiers, which are capable of differentiating modulated schemes in various scenarios. According to the experiment, the AMC-BLGCA has the best overall accuracy on both datasets at 62.54% and 65.00% on RadioML2016.10a and RadioML2016.10b dataset respectively. The BiLSTM-GRU model takes second place with much fewer parameters and a very close accuracy score to the AMC-BLGCA model. The parallel hybridBiLSTM-GRU-CNN model has the lowest performance among the proposed methods, but still outperforms the recent AMC models. Our proposed methods are very lightweight and easily implementable to edge devices as they use fewer parameters and the need for computation resources is very low. The performance is 3% to 9% better than the current state-of-the-art (SOTA) AMC methods.

关键词： automatic modulation classification Convolutional neural network Bidirectional recurrent neural network Multihead attention Signal classification

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Channel-Robust automatic modulation classification Using Companding Spectral Quotient Cumulants

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第11期73卷 17749-17753页

作者： Huang, Sai Chen, Yuting He, Jiashuo Chang, Shuo Feng, Zhiyong Beijing Univ Posts & T elecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named companding spectral quotient cumulant classification (CSQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the companding spectral quotient (CSQ) sequence by spectral circular shift division operations and modified- sigmoid function mapping. Secondly, we extract the companding spectral quotient cumulants (CSQCs) from the CSQ sequence. At last, the CSQCs serve as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Notably, the classifier is only trained using the data under the Additive White Gaussian Noise (AWGN) channel condition but tested under Rician multipath fading channel. Simulation results show that the proposed CSQCC method performs robust and superior classification performance when subjected to unknown Rician multipath fading channels, in comparison to other methods. Specifically, the proposed CSQCC method achieves nearly 100% classification accuracy at the signal-to-noise ratio (SNR) of 4 dB when testing under multiple channels but only training under AWGN channel.

关键词： modulation OFDM Fading channels Rician channels Feature extraction AWGN channels Symbols automatic modulation classification Rician multipath fading channel companding spectral quotient cumulant

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MAMCOptimal on Accuracy and Efficiency for automatic modulation classification With Extended Signal Length

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2864-2868页

作者： Zhang, Yezhuo Zhou, Zinan Cao, Yichao Li, Guangyu Li, Xuanpeng Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

In automatic modulation classification (AMC), extended signal lengths offer a bounty of information, yet impede the model's adaptability, introduce more noise interference, extend the training and inference time, and increase memory usage. To bridge the gap between these requirements, we propose a novel AMC framework, designated as the Mamba-based automatic modulation classification (MAMC), which addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model (Mamba), which enhances the model's capabilities in long-term memory and information selection, and reduces computational complexity and spatial overhead. We further design a denoising unit to filter out effective semantic information to improve accuracy. Rigorous experimental evaluations on the publicly available dataset RML2016.10 and TorchSig affirm that MAMC delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available on https://***/ZhangYezhuo/MAMC.

关键词： Computational modeling Accuracy Mathematical models Adaptation models modulation Training Noise reduction Memory management Feature extraction Convolution automatic modulation classification mamba state space model cognitive radio long sequence

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Multi-Scale Feature Fusion and Distribution Similarity Network for Few-Shot automatic modulation classification

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2890-2894页

作者： Tan, Haoyue Zhang, Zhenxi Li, Yu Shi, Xiaoran Zhou, Feng Xidian Univ Xian 710126 Peoples R China

automatic modulation classification (AMC), as a key technology of cognitive radio, has become a focal point of research. However, most deep learning-based AMC methods require an extensive number of labeled signals to acquire a comprehensive understanding of modulation types, placing substantial pressure on signal acquisition and labeling. To solve this issue, we propose a few-shot AMC (FSAMC) method to facilitate rapid generalization and recognition with limited data, namely multi-scale feature fusion and distribution similarity network (MS2F-DS). Firstly, we design a multi-scale feature fusion (MS2F) model, which aims to extract features with varying fields of view and boost feature fusion, enabling the derivation of contextual information from the signal. Furthermore, we introduce a distribution similarity (DS) classifier to address the insufficient measurement of current similarity measurement functions by considering both micro and macro perspectives of vectors, further increasing intra-class compactness and inter-class separability. Finally, extensive experiments were conducted on 3-way 1, 3, and 5-shot FSAMC tasks using public datasets RML2016.10a and RML2016.10b, and the results demonstrated the effectiveness of our method.

关键词： Feature extraction Convolution modulation Kernel Vectors Accuracy Data mining Convolutional neural networks Signal to noise ratio Current measurement automatic modulation classification distribution similarity few-shot learning multi-scale feature fusion

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A lightweight deep learning architecture for automatic modulation classification of wireless internet of things

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IET COMMUNICATIONS 2024年第18期18卷 1220-1230页

作者： Han, Jia Yu, Zhiyong Yang, Jian PLA Rocket Force Univ Engn Dept Comp Xian Shaanxi Peoples R China PLA Rocket Force Univ Engn Dept Engn Xian Shaanxi Peoples R China

The wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal-to-noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two-dimensional (2-D) curves input of the spectral correlation function (SCF) is proposed, which uses in-phase and quadrature (I/Q) signals to generate 2-D cross-section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low-cost embedded platforms. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. image

关键词： automatic modulation classification deep learning self-attention spectral correlation function spectrum sensing wireless Internet of Things

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GAF-MAE: A Self-Supervised automatic modulation classification Method Based on Gramian Angular Field and Masked Autoencoder

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第1期10卷 94-106页

作者： Shi, Yunhao Xu, Hua Zhang, Yue Qi, Zisen Wang, Dan Air Force Engn Univ Xian 710038 Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China

With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based automatic modulation classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.

关键词： automatic modulation classification masked autoencoder self-supervised

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Meta-Transformer: A Meta-Learning Framework for Scalable automatic modulation classification

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IEEE ACCESS 2024年 12卷 9267-9276页

作者： Jang, Jungik Pyo, Jisung Yoon, Young-Il Choi, Jaehyuk Gachon Univ Sch Comp Seongnam 13120 South Korea LIG Nex1 Res & Dev Ctr Seongnam 13488 South Korea

Recent advances in deep learning (DL) have led many contemporary automatic modulation classification (AMC) techniques to use deep networks in classifying the modulation type of incoming signals at the receiver. However, current DL-based methods face scalability challenges, particularly when encountering unseen modulations or input signals from environments not present during model training, making them less suitable for real-world applications like software-defined radio devices. In this paper, we introduce a scalable AMC scheme that provides flexibility for new modulations and adaptability to input signals with diverse configurations. We propose the Meta-Transformer, a meta-learning framework based on few-shot learning (FSL) to acquire general knowledge and a learning method for AMC tasks. This approach empowers the model to identify new unseen modulations using only a very small number of samples, eliminating the need for complete model retraining. Furthermore, we enhance the scalability of the classifier by leveraging main-sub transformer-based encoders, enabling efficient processing of input signals with diverse setups. Extensive evaluations demonstrate that the proposed AMC method outperforms existing techniques across all signal-to-noise ratios (SNRs) on RadioML2018.01A. The source code and pre-trained models are released at https://***/cheeseBG/meta-transformer-amc.

关键词： automatic modulation classification few-shot learning meta-learning transformer unseen dataset

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Reparameterization Causal Convolutional Network for automatic modulation classification

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 8576-8583页

作者： Tang, Ning Wang, Xiaoyu Zhou, Fei Tang, Shengyu Lyu, Yaohui Ocean Univ China Coll Informat Sci & Engn Qingdao 266003 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130061 Peoples R China

With the proliferation of wireless technologies in vehicular networks, robust automatic modulation classification (AMC) has become crucial for optimizing spectrum utilization and maintaining reliability. However, AMC in dynamic vehicular channels poses significant challenges for traditional machine learning techniques. This paper proposes a novel CNN-based approach named Reparameterization Causal Convolutional Network (RepCCNet) to achieve highly accurate and noise-robust AMC performance. RepCCNet incorporates causal convolutions and structural reparameterization techniques to extract long-term time-domain features. A bottleneck structure with channel attention dynamically calibrates feature channels, retaining only helpful information. Multi-sample dropout is integrated during training to improve generalization capability. We demonstrate RepCCNet's state-of-the-art classification accuracy on the two widely used datasets, RadioML 2016.10a and RadioML 2018.01a, across varying signal-to-noise ratios. Compared to existing methods, RepCCNet achieves highly competitive results compared to state-of-the-art approaches, utilizing fewer than 40 k parameters. Ablation studies validate the contributions of the proposed architectural innovations. This work represents a significant advancement toward developing deep learning solutions for robust wireless signal classification tasks.

关键词： automatic modulation classification automatic modulation recognition deep learning reparameterization causal convolutional network

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automatic modulation classification in Deep Learning 5

Automatic Modulation Classification in Deep Learning

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5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Alnajjar, Khawla A. Ghunaim, Sara Ansari, Sam Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

Due to the evolution and availability of vast amounts of data for transferring, receiving, and detection, the field of signal recognition and modulation classification has become vital in various fields and applications. Additionally, the classical approaches to machine learning (ML) no more can satisfy the current needs. Hence, this urged researchers to apply deep learning (DL) algorithms that have a very strong ability to train, learn, and automatically classify types of modulation categories. This paper focuses on three vital DL network algorithms, which are deep neural networks (DNN), convolutional neural networks (CNN), and deep belief networks (DBN). The mentioned algorithms are widely used in many applications for automatic modulation classification/recognition (AMC/AMR). Additionally, an empirical study is performed in this paper to compare a large number of different methods for the performance and recognition percentage of each considered technique.

关键词： automatic modulation classification convolutional neural networks deep belief neural networks deep learning deep neural networks machine learning

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