Modulation Classification Research Articles

Optimizing Automatic Modulation Classification through Gaussian-Regularized Hybrid CNN-LSTM Architecture

Optimizing Automatic Modulation Classification through Gaussian-Regularized Hybrid CNN-LSTM Architecture

Data and Knowledge Dual-Driven Automatic Modulation Classification for 6G Wireless Communications

Data and Knowledge Dual-Driven Automatic Modulation Classification for 6G Wireless Communications

Deep multilevel architecture for automatic modulation classification

In the realm of wireless communication, deep learning has exhibited promising outcomes across various tasks, including automatic modulation classification (AMC), channel estimation, and signal detection, yielding substantial enhancements in performance and efficiency. AMC plays a pivotal role in identifying the modulation scheme of a received signal without prior knowledge of the transmitter. However, existing AMC techniques attempt to classify both analog and digital modulations together, disregarding their distinct characteristics. In this work, we propose a multilevel approach for AMC, wherein the received signals are first classified into their parent class, i.e., analog or digital modulation, and subsequently further classified into their respective modulation type. For implementing multilevel approach, we propose three distinct deep learning models: analog digital modulation classifier (AD-MC), analog modulation classifier (An-MC), and digital modulation classifier (Dig-MC). Performance analysis is conducted for the individual models as well as for the multilevel model that integrates all three models. The multilevel model demonstrates accurate signal classification at Signal-to-Noise Ratios (SNRs) of 0 dB or higher, achieving a remarkable classification accuracy of over 90%. Furthermore, the proposed multilevel model surpasses state-of-the-art AMC methods in accuracy while maintaining lower complexity.

Edge-Enabled Modulation Classification in Internet of Underwater Things Based on Network Pruning and Ensemble Learning

Edge-Enabled Modulation Classification in Internet of Underwater Things Based on Network Pruning and Ensemble Learning

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Automatic modulation classification (AMC) aims to blindly recognize the modulation type of a received signal in wireless systems. It is also a critical component of non-cooperative communication systems after the detection of the presence of a signal. In this paper, we introduce a robust approach, termed DET-AMC (joint Detection and Automatic Modulation Classification), employing Convolutional Neural Networks (CNNs) trained via transfer learning methodology. The main advantage of our approach is its ability to handle a wide range of modulation types, including 10 different schemes generated in Gnuradio and their detection using the same model. Through extensive experimentation, we evaluate the performance of our light CNN-based DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well as in the presence of phase noise and frequency offset. We find that the CNN’s learned features, obtained through transfer learning, exhibit robustness, particularly in low SNR and various challenging conditions, leading to accurate modulation classification. In general, our approach outperforms existing methods by using the effectiveness of deep learning in capturing relevant discriminative features. Additionally, our model offers a robust solution for join detection and AMC by achieving an accurate probability of detection and modulation classification without the need for manual feature engineering or the consideration of frequency offset, phase noise or noise estimation. Our model achieves 100% accuracy for synthetic and real data at an SNR equal to -10 dB for detection, and 100% and 98% for classification of synthetic and real signals at −4 dB, respectively.

Modeling an Enhanced Modulation Classification Approach using Arithmetic Optimization with Deep Learning for MIMO-OFDM Systems

Abstract In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an Arithmetic Optimization Algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.

Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation

Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation

Federated learning based modulation classification for multipath channels

Deep learning (DL)-based automatic modulation classification (AMC) is a primary research field for identifying modulation types. However, traditional DL-based AMC approaches rely on hand-crafted features, which can be time-consuming and may not capture all relevant information in the signal. Additionally, they are centralized solutions that are trained on large amounts of data acquired from local clients and stored on a server, leading to weak performance in terms of correct classification probability. To address these issues, a federated learning (FL)-based AMC approach is proposed, called FL-MP-CNN-AMC, which takes into account the effects of multipath channels (reflected and scattered paths) and considers the use of a modified loss function for solving the class imbalance problem caused by these channels. In addition, hyperparameter tuning and optimization of the loss function are discussed and analyzed to improve the performance of the proposed approach. The classification performance is investigated by considering the effects of interference level, delay spread, scattered and reflected paths, phase offset, and frequency offset. The simulation results show that the proposed approach provides excellent performance in terms of correct classification probability, confusion matrix, convergence and communication overhead when compared to contemporary methods.

Semisupervised Radar Intrapulse Signal Modulation Classification With Virtual Adversarial Training

Semisupervised Radar Intrapulse Signal Modulation Classification With Virtual Adversarial Training

UniQGAN: Towards Improved Modulation Classification With Adversarial Robustness Using Scalable Generator Design

UniQGAN: Towards Improved Modulation Classification With Adversarial Robustness Using Scalable Generator Design

Wireless Universal Adversarial Attack and Defense for Deep Learning-Based Modulation Classification

Wireless Universal Adversarial Attack and Defense for Deep Learning-Based Modulation Classification

Automatic Modulation Classification for OFDM Systems Using Bi-Stream and Attention-Based CNN-LSTM Model

Automatic Modulation Classification for OFDM Systems Using Bi-Stream and Attention-Based CNN-LSTM Model

Automatic Modulation Classification for low-power IoT applications

Automatic Modulation Classification for low-power IoT applications

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Purpose Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise. Design/methodology/approach This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise. Findings Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36. Originality/value At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

A comprehensive view into MPSK modulation classification

This paper provides a relative comprehensive overview on current modulation classification of MPSK from both the likelihood-based perspective and the feature-based perspective. Traditional methods based on maximum likelihood (ML) method mainly diverges in the way how unresolved parameters from the received signal are viewed. Some recent work adopting feature recognition such as SVM-based and Deep Learning-based classifying algorithms are also introduced. Fundamental equations are also provided for each method. This paper makes comparison among different methods in each section and explained the preferred utilization circumstance of each, aiming to help readers find the best algorithm in each of their specific case. Moreover, advantages and disadvantages of different algorithms are clearly stated for the readers information. Based on the pros and cons, it is also suggested for readers to develop new compound algorithms of better functionality for further research.

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

Imperceptible UAPs for Automatic Modulation Classification Based on Deep Learning

Imperceptible UAPs for Automatic Modulation Classification Based on Deep Learning

GAN-Based Siamese Neuron Network for Modulation Classification Against White-Box Adversarial Attacks

GAN-Based Siamese Neuron Network for Modulation Classification Against White-Box Adversarial Attacks

Ensemble Learning Approach for Digital Communication Modulation’s Classification

This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signals. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and deciphering communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multiscale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.

Refined Semi-Supervised Modulation Classification: Integrating Consistency Regularization and Pseudo-Labeling Techniques

Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processing without prior information. While deep learning has been applied to AMC, its effectiveness largely depends on the availability of labeled samples. To address the scarcity of labeled data, we introduce a novel semi-supervised AMC approach combining consistency regularization and pseudo-labeling. This method capitalizes on the inherent data distribution of unlabeled data to supplement the limited labeled data. Our approach involves a dual-component objective function for model training: one part focuses on the loss from labeled data, while the other addresses the regularized loss for unlabeled data, enhanced through two distinct levels of data augmentation. These combined losses concurrently refine the model parameters. Our method demonstrates superior performance over established benchmark algorithms, such as decision trees (DTs), support vector machines (SVMs), pi-models, and virtual adversarial training (VAT). It exhibits a marked improvement in the recognition accuracy, particularly when the proportion of labeled samples is as low as 1–4%.