Automatic Modulation Classification Research Articles

Development of Enriched ROA With Dilated Hybrid Network for Automatic Modulation Classification Framework in CRNs

ABSTRACTAutomatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

MCLHN: Toward Automatic Modulation Classification via Masked Contrastive Learning With Hard Negatives

MCLHN: Toward Automatic Modulation Classification via Masked Contrastive Learning With Hard Negatives

SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification

SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification

Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge

Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge

A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification

Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a, RML2016.10b, and RML2016.04c, with average recognition accuracy of 62.86%, 65.08%, and 71.12%. It also performs excellently in low SNR environments below 0dB, significantly outperforming other networks.

An ultra lightweight neural network for automatic modulation classification in drone communications

Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios. To address this challenge, we propose an ultra-lightweight neural network (ULNN). This network incorporates a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion technique. Additionally, we introduce data augmentation (DA) based on signal phase offsets during the model training process, aimed at improving the model’s generalization ability and preventing overfitting. Through experimental validation on the public dataset RML2016.10 A, the ULNN we proposed achieves an average precision of 62.83% with only 8815 parameters and reaches a peak classification accuracy of 92.11% at SNR = 10 dB. The experimental results show that ULNN can achieve high recognition accuracy while keeping the model lightweight, and is suitable for UAV platform with limited resources.

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

AbstractThe 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.

Novel filtering and regeneration technique with statistical feature extraction and machine learning for automatic modulation classification

Automatic modulation classification (AMC) plays a crucial role in the stages of processing signals from unknown sources and monitoring the airwaves. This paper presents an AMC method based on machine learning (ML) using constellation diagrams, distribution test function and high-order cumulants (HOCs) and novel filtering and regeneration technique. A data clustering approach based on the signal phase characteristics is utilized to perform filtering, regeneration, and testing distribution functions. NI PXIe-1065 was used as a source of modulated signals, which is a multifunctional experimental complex for generating signals with various types of modulation. In this work, 5 types of modulation schemes were employed, specifically BPSK, QPSK, PSK-8, PSK-16 and PSK-32. The proposed classification method consists of three main stages. At the first stage, the constellation diagram is partitioned into clusters. At the second stage, the phase distribution function of signals within each cluster is analyzed, HOCs were calculated and novel filtering and regeneration techniques were employed. The main concept of the proposed approach is compressing angular ranges depicted on the constellation diagram. This involves filtering points and subsequently regenerating, which means relocating points that fall within adjacent phase ranges. Also, a statistical analysis of the phase transition distribution function in clusters is conducted at this step. At the last stage the obtained data set was trained using XGBOOST ML method. The proposed approach shows excellent results and can be a strong contender to existing NN based AMC methods, achieving 95 % accuracy at an SNR value of 9 dB.

Multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks

Abstract Nowadays, underwater acoustic communication reconnaissance technology is of great importance in military, marine science, and resource exploration fields. The fields involve military reconnaissance and intelligence collection, ocean monitoring and security, marine scientific research, and resource exploration. This article mainly focuses on the identification of modulation types and emission angles contained in underwater acoustic signals. Convolutional Neural Networks (CNNs) are used for deep learning to train, validate, and obtain final results on signal data. This network achieves the independent tasks of automatic modulation classification and Direction of Arrival (DOA) estimation of radio signals simultaneously. The model achieves efficient identification and filtering by designing different filter sizes and sizes, as well as different Y-shaped connection structures. It includes one input block and two output blocks, and the two output blocks correspond to the outputs of modulation classification and DOA estimation. At the same time, based on this, the parameters of the network layer are modified to achieve the generalization ability of the neural network. The training and validation data of the network consists of simulation data from underwater acoustic communication emissions and real data from the Songhua Lake experiment in Jilin Province, China, to test the network recognition efficiency.

A Novel Lightweight Grouped Gated Recurrent Unit for Automatic Modulation Classification

A Novel Lightweight Grouped Gated Recurrent Unit for Automatic Modulation Classification

A few-shot learning-based automatic modulation classification method for Internet of Things

A few-shot learning-based automatic modulation classification method for Internet of Things

Clarity in Wireless Quantum Communication: Exploring Explainable AI for Signal Modulation Classification

The advent of artificial intelligence (AI) and edge computing technologies has ushered in a new era for automatic modulation classification (AMC), with a particular focus on deep learning (DL)-based approaches. These AI-based AMC systems, deployed at edge devices, have shown remarkable advancements in recognizing and classifying a wide range of wireless signals. However, a significant challenge in this domain lies in the lack of adequate explanations for the models employed, which, in turn, hampers model accuracy and training efficiency. Consequently, the practical applications and further enhancements of these models are constrained. To address this limitation, researchers have started to explore interpretable methods for the technical analysis of DL-based AMC systems. In this paper, we delve into the subject, leveraging recent developments in interpretable methods, to provide a comprehensive review of applicable techniques and to outline the existing research challenges in the field of interpretable AMC. Moreover, we introduce a novel interpretable AI-based AMC framework designed to augment the interpretability of AMC outcomes. This framework achieves this by elucidating the contribution of wireless signal features to the training process of DL networks. In addition, we explore the potential impact of quantum computing on AMC systems. Quantum computing, with its ability to process complex computations at unprecedented speeds, offers a promising avenue for enhancing the efficiency and accuracy of AMC. By integrating quantum algorithms with AI-based AMC frameworks, it is possible to significantly improve the performance of wireless signal classification, especially in environments with high signal variability and noise. Our experimental results demonstrate that the proposed approach possesses the capacity to unravel the classification mechanisms hidden within opaque auto-modulated classification networks. Furthermore, it exhibits the potential to facilitate the training of networks on edge devices with reduced energy consumption and enhanced classification accuracy, thereby addressing critical challenges in the field of wireless signal classification. Additionally, the integration of quantum computing techniques holds the promise of further advancing the capabilities of AMC systems, paving the way for more robust and efficient wireless communication technologies.

MobileAmcT: A Lightweight Mobile Automatic Modulation Classification Transformer in Drone Communication Systems

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Modified receiver architecture in software-defined radio for real-time modulation classification

Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications.

Adversarial training for signal modulation classification based on Ulam stability theory

The modulation classification of radar, communication, and other radiation source signals under complex electromagnetic conditions is an important task in the field of military electronic countermeasures. Deep learning is an important tool for automatic modulation classification (AMC) tasks. However, AMC models based on deep neural networks are easily attacked by adversarial methods such as gradient attacks, and even under small perturbations, the model may exhibit significant differences in prediction. To address adversarial attacks, we proposed a Ulam stability adversarial training method, which could improve the robust accuracy and the natural accuracy of the adversarial training model. We introduced a class of Jensen type Ulam stability theorem, which transforms the Jensen condition into a special class of stability preserving regularization terms for adversarial training models, enhancing the model's adversarial robustness. More importantly, our method provided a framework for improving the stability of adversarial training, which can be combined with existing adversarial training methods to effectively enhance their adversarial defense capabilities. In the experimental section, we compared four types of adversarial training methods on three types of modulation recognition datasets, and the results showed that Ulam adversarial training improved robust accuracy by an average of 4.8% and natural accuracy by an average of 2.2% on three datasets. The difference between the best and final accuracies of the models decreased by an average of 4.5%.

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

AbstractAutomatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios.

A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence

Introduction/Background This research introduces the EO-optimized Lightweight Automatic Modulation Classification Network (EO-LWAMCNet) model, employing AI and sensor data for forecasting chronic illnesses within the Internet of Things framework. A transformative tool in remote healthcare monitoring, it exemplifies AI's potential to revolutionize patient experiences and outcomes. This study unveils a novel Healthcare System integrating a Lightweight Convolutional Neural Network (CNN) for swift disease prediction through Artificial Intelligence. Leveraging the efficiency of lightweight CNN, the model holds promise for revolutionizing early diagnosis and enhancing overall patient care. By merging advanced AI techniques, this healthcare model holds the potential for revolutionizing early diagnosis and improving overall patient care. Materials and Methods The Lightweight Convolutional Neural Network (CNN) is implemented to analyze sensor data in real-time within an Internet of Things (IoT) framework. The methodology also involves the integration of the EO-LWAMCNet model into a cloud-based IoT ecosystem, demonstrating its potential for reshaping remote healthcare monitoring and expanding access to high-quality care beyond conventional medical settings. Results Utilizing the Chronic Liver Disease (CLD) and Brain Disease (BD) datasets, the algorithm achieved remarkable accuracy rates of 94.8% and 95%, respectively, showcasing the robustness of the model as a reliable clinical tool. Discussion These outcomes affirm the model's reliability as a robust clinical tool, particularly crucial for diseases benefiting from early detection. The potential transformative impact on healthcare is emphasized through the model's integration into a cloud-based IoT ecosystem, suggesting a paradigm shift in remote healthcare monitoring beyond traditional medical confines. Conclusion Our proposed model presents a cutting-edge solution with remarkable accuracy in forecasting chronic illnesses. The potential revolutionization of remote healthcare through the model's integration into a cloud-based IoT ecosystem underscores its innovative impact on enhancing patient experiences and healthcare outcomes.

An Analysis of Radio Frequency Transfer Learning Behavior

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.

Transferring Learned Behaviors between Similar and Different Radios.

Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC.