Modulation Classification Research Articles

DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization

Automatic modulation classification (AMC) is an essential and indispensable topic in the development of cognitive radios. It is the cornerstone of adaptive modulation and demodulation capabilities to perceive and understand surrounding environments and make corresponding decisions. In this paper, we propose a priori regularization method in deep learning (DL-PR) for guiding loss optimization during model training process. The regularization factor designed by the combination of inter-class confrontation factor, global and dimensional divergence can help increase the inter-class distance and reduce the intra-class distance of samples. While preserving the original information of received signals as much as possible, it makes full use of the prior knowledge in the signal transmission process and ultimately helps deep learning models to be well generalized on signals with various signal-to-noise ratios (SNRs). As far as we know, this is the first attempt to regularize deep learning models based on SNR distribution of samples to improve AMC accuracy. Moreover, it can be proved that priori regularization can be interpreted as implicit data augmentation and model ensemble methods. By comparing with a series of state-of-the-art AMC methods and different regularization techniques on the public dataset RadioML 2016.10a, experimental results of multiple deep learning models illustrate the superiority of DL-PR, including CNN with accuracy of 62.6% and inference time of 0.82 ms per signal, LSTM with 61.8% and 0.87 ms, and hybrid CNN–LSTM with 64.2% and 0.94 ms. In practical applications, DL-PR can be also easily applied to complex environments due to its robustness to hyper-parameters and SNR estimation.

Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts

Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts

Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems

Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of Things (IoT) systems to achieve high data rates with reasonable complexity, where noncooperative protocols encode signals with different modulations for multiple users. However, conventional modulation recognition is limited to single-carrier communication systems, and there is an urgent need to design an effective method to blindly identify the unknown modulation format of received signals. In this article, we propose an automatic modulation classification method for the OFDM systems with the presence of channel deterioration. Our method first leverages a data reconstruction mechanism to arrange signals into high-dimensional data arrays and then exploits an efficient convolutional network, namely OFDMsym-Net, to learn underlying radio characteristics. OFDMsym-Net is designed by two kinds of processing modules, which manipulate one-dimensional asymmetric convolution filters to extract the intracorrelation within an OFDM symbol and the intercorrelation between different symbols. Moreover, a sophisticated structure with addition and concatenation layers is developed inside every module to improve learning efficiency. Based on simulation results achieved on a synthetic dataset of OFDM signals, our proposed method shows the classification robustness under various channel impairments. It reveals to have an overall accuracy of 95.41% at 10-dB signal-to-noise ratio, being superior to several state-of-the-art approaches.

Probability density function based data augmentation for deep neural network automatic modulation classification with limited training data

AbstractDeep neural networks (DNN) based automatic modulation classification (AMC) has achieved high accuracy performance. However, DNNs are data‐hungry models, and training such a model requires a large volume of data. Insufficient training data will cause DNN models to experience overfitting and severe performance degradation. In practical AMC tasks, training the deep model with sufficient data is challenging due to the costly data collection. To this end, a novel probability density function (PDF) based data augmentation scheme and a method to determine the required minimum sampling size for data enlargement is proposed. Compared with the known image‐based augmentation scheme, the proposed waveform‐based PDF technique has low complexity and is easy to implement. Experimental results show that the required size of the training dataset is one order of magnitude smaller than the sufficient dataset in the additive white Gaussian noise channel, and effective recognition can be achieved using around 60% of the total examples under the Rayleigh channel. Moreover, the presented scheme can expand training data under frequency and phase offsets.

AMSCN: A Novel Dual-Task Model for Automatic Modulation Classification and Specific Emitter Identification

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification

Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment.

Fan-beam projection based modulation classification for optical systems with phase noise effect

Fan-beam projection based modulation classification for optical systems with phase noise effect

TERFDA: Tensor Embedding RF Domain Adaptation for varying noise interference

Radio frequency machine learning (RFML) can be loosely termed as a field that machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications. However, traditional RFML basically assume that the data of training set and test set are independent and identically distributed and only a large number of labeled data can train a classification model which can effectively classify test set data. In other words, without enough training samples, it is impossible to learn an automatic modulation classifier that performs well in varying noise interference environment. Feature-based transfer learning minimizes the distribution difference between historical modulated signal data and new data by learning similarity-maximizing feature spaces. Therefore, in this paper, Dynamic Distribution Adaptation (DDA) is adopted to address the above challenges. We propose a Tensor Embedding RF Domain Adaptation (TERFDA) approach, which learns the latent subspace of the tensors formed by the time–frequency maps of the signals, so that use the multi-dimensional domain information of the signals to jointly learn the shared feature subspace of the source domain and the target domain, then perform DDA in the shared subspace. The experimental results show that under the modulated signal data, compared with the state-of-the-art DA algorithm, TERFDA has less requirements on the number of samples and categories, and has superior performance for confrontation the varying noise interference between source domain and target domain.

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.

An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system

In recent years, chronic disease (heart or kidney disease) is a leading cause of death worldwide. Chronic disease prediction is a very complicated task. Doctors who are experienced and familiar with this disease can only be able to predict chronic disease. In a healthcare system, the Internet of Things (IoT) is the key technology. In this paper, an EO optimized Lightweight Automatic modulation classification Network named the EO-LWAMCNet model is proposed to accurately predict a patient's chronic health condition (kidney or heart disease). A sensor implanted in the patient's body can able to collect every data and it uses a gateway to transmit the data toward the cloud. Based on the achieved sensor data, the EO-LWAMCNet model initiates the classification process to predict chronic disease. The model undergoes a testing and training stage. The disease is predicted using CKD and HD datasets. Here, the preprocessed data is used for classification in the training stage. Once the training process is completed, the Cloud server's (CS) sensor data is tested and categorized into abnormal (heart or kidney disease) and normal. The awareness message is sent to the doctor to treat a patient in case of an abnormal result. The performance of the model is evaluated using accuracy, MCC, F1-score, and miss rate. This model can accurately predict the presence or absence of heart or kidney disease with an accuracy of 93.5% using the CKD dataset and an accuracy of 94% using the HD dataset. Also, the miss rate of the model is less in classification.

Correction to: The modulation classification methods in PPM–VLC systems

Correction to: The modulation classification methods in PPM–VLC systems

Attention-Based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Due to great success of transformers in many applications, such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in an environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios, including the fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

External Attention Mechanism-Based Modulation Classification

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy.

Intelligent identification technology for high‐order digital modulation signals under low signal‐to‐noise ratio conditions

Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation-type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high-order digital signals. This model uses the Morlet wavelet transform to analyse time-frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise-contaminated time-frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high-order digital modulated signals under low signal-to-noise ratio conditions and can achieve 90% recognition accuracy at a signal-to-noise ratio of 1 dB.

Modulation Classifier: A Few-Shot Learning Semi-Supervised Method Based on Multimodal Information and Domain Adversarial Network

This letter addresses the issue of underfitting or failure of deep learning models caused by insufficient training samples. Unlike previous supervised methods, a new few-shot learning semi-supervised automatic modulation recognition method based on multimodal information and domain adversarial network is proposed herein. The fusion input of multimodal information realizes the joint utilization of modulated signal modal features in the time and frequency domains. Domain adversarial training mines the potential knowledge information of a large number of unlabeled target domain data and introduces the convolutional block attention module (CBAM) to enhance the ability of the network to represent the key features of data. Numerical results validate the high-average classification accuracy of the proposed scheme compared to that of state-of-the-art schemes using fewer samples, particularly in high-order modulation classification. Alternative network structures are compared to confirm the applicability of multimodal information, domain adversarial training, and CBAM.

Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.

Blind modulation classification in multiple input and output‐orthogonal frequency division multiplexing using time‐frequency analysis and customized convolutional neural network architecture

AbstractMIMO with OFDM is a powerful communication technology in both army and civilian environments. Because there is no prior knowledge of carrier state information or signal overlapping in MIMO‐OFDM systems, outdated probability and feature‐based approaches can be applied. However, these methods cannot easily be used to provide the channel characteristics. Blind modulation categorization is a critical stage in deploying 5G networks. The issue of blind modulation classification for a multiple input and output (MIMO) antenna system using orthogonal frequency division multiplexing (OFDM) is investigated in this research. The independent component analysis (ICA) method has been used to separate the MIMO modulated signal, and the smoothed pseudo Wigner‐Ville distribution (SPWVD) with a Hamming window of 0.2 s has been used to analyze the time‐frequency characteristics of time‐domain modulated signals to get around the challenge of blind attenuation categorization in MIMO‐OFDM arrangements. The recovered time‐frequency characteristics are then transformed into red‐green‐blue (RGB) spectrogram pictures and a perfect‐tweaked customized CNN in which CNN (convolutional neural network) is integrated with LSTM (long short term memory) is used to categorize modulation kinds based on the illustrations of RGB spectrograms. Finally, a module for merging decisions based on majority voting was employed to combine the categorization findings of all receiving antennas. As a result, a customized CNN‐based blind modulation technique outperforms existing works to find the exact modulation type in the MIMO‐OFDM communication.

Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication

Deep neural networks have been recently applied in automatic modulation classification task and achieved remarkable success. However, Existing neural networks mainly focus on the purely data-driven architecture design, and fail to explore the hand-crafted feature mechanisms which are particularly significant for radio signal presentation in wireless communication. Inspired by digital signal processing theories, we propose frequency learning attention networks (FLANs) to analyze the radio spectral bias from frequency perspective, based on a multi-spectral attention mechanism for learning-based frequency components selection. FLANs are the general case of classical global average pooling and leverage identical structures of the popular neural networks. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset.

Dependable modulation classifier explainer with measurable explainability.

The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication. These new-age networks use artificial intelligence (AI) that plays a crucial role in reducing network roll-out and operation costs, improving entire system performance, enhancing customer services, and generating possibilities to embed a wide range of telecom services and applications. For IoT devices, it is essential to have a robust and trustable network for reliable communication among devices and service points. The signals sent between the devices or service points use modulation to send a password over a bandpass frequency range. Our study focuses on modulation classification performed using deep learning method(s), adaptive modulation classification (AMC), which has now become an integral part of a communication system. We propose a dependable modulation classifier explainer (DMCE) that focuses on the explainability of modulation classification. Our study demonstrates how we can visualize and understand a particular prediction made by seeing highlighted data points crucial for modulation class prediction. We also demonstrate a numeric explainability measurable metric (EMM) to interpret the prediction. In the end, we present a comparative analysis with existing state-of-the-art methods.

The modulation classification methods in PPM–VLC systems

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of L-PPM schemes in VLC systems by using Decision Tree, K-nearest neighbor (KNN), Support Vector Machine, and a Direct Decision-based Linear Model technique. A novel feature extraction model is derived to be able to classify the type of L-PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The 2-PPM scheme has the highest success rate against other schemes in terms of performance metrics at the overall distance. Additionally, the KNN method gives the best accuracy performance at the distance of 2.32 m and more, while it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20 m.