Automatic Modulation Classification Algorithm Research Articles

Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method.

A Super-Resolution Data Processing for Automatic Modulation Classification Based on Tree Compression Networks

Automatic modulation classification (AMC) algorithms play a vital role in modern non-cooperative communication systems and are widely used for malicious attack analysis and reliable communication. Our research confirms that reducing the negative impact of few-shot learning and computational complexity is key to implementing the AMC algorithm on edge intelligence devices. This paper proposes a novel super-resolution (SR) data processing method, which is based on a tree compression network (TCNet), aiming to improve the accuracy and reduce the complexity of the few-shot learning AMC algorithm. Firstly, the SR data processing method is used for data augmentation where the number of examples is insufficient. Additionally, the TCNet is proposed to process SR data based on the compression network and the tree classification strategy. The compression network is employed to reduce model complexity, and the tree classification strategy improves classification accuracy. Lastly, the implementation of lightweight TCNet subnet will facilitate deployment on edge devices with limited computing power. The simulation results show that the proposed TCNet achieved a maximum accuracy of more than 91.98%. It is shown by processing fewer dataset examples on two well-known datasets, that TCNet outperforms previous approaches by offering improved classification accuracy and less complexity.

Modulation Classification of Active Attacks in Internet of Things: Lightweight MCBLDN With Spatial Transformer Network

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.

Multitask-Learning-Based Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is to identify the modulation type of a received signal, which plays a vital role to ensure the physical-layer security for Internet of Things (IoT) networks. Inspired by the great success of deep learning in pattern recognition, the convolutional neural network (CNN) and recurrent neural network (RNN) are introduced into the AMC. In general, there are two popular data formats used by AMC, which are the in-phase/quadrature (I/Q) representation and amplitude/phase (A/P) representation, respectively. However, most of AMC algorithms aim at structure innovations, while the differences and characteristics of I/Q and A/P are ignored to analyze. In this article, lots of popular AMC algorithms are reproduced and evaluated on the same data set, where the I/Q and A/P are used, respectively, for comparison. Based on the experimental results, it is found that: 1) CNN-RNN-like algorithms using A/P as input data are superior to those using I/Q at high signal-to-noise ratio (SNR), while it has an opposite result in low SNR and 2) the features extracted from I/Q and A/P are complementary to each other. Motivated by the aforementioned findings, a multitask learning-based deep neural network (MLDNN) is proposed, which effectively fuses I/Q and A/P. In addition, the MLDNN also has a novel backbone, which is made up of three blocks to extract discriminative features, and they are CNN block, bidirectional gated recurrent unit (BiGRU) block, and a step attention fusion network (SAFN) block. Different from most of CNN-RNN-like algorithms (i.e., they only use the last step outputs of RNN), all step outputs of BiGRU can be effectively utilized by MLDNN with the help of SAFN. Extensive simulations are conducted to verify that the proposed MLDNN achieves superior performance in the public benchmark.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multiple-input multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami- distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the MultiBoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., ), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

Automatic modulation classification over MIMO amplify and forward (AF)-relay fading channels

Automatic Modulation Classification (AMC) plays an important role in many civilian applications such as spectrum monitoring (SM) and cognitive radio (CR). The majority of the AMC algorithms for MIMO systems are developed for rich scattering environments, which offer a full rank channel matrix. A rank deficient channel such as a keyhole channel severely affects the AMC performance. It is reported that cooperative relaying is an attractive solution to overcome the rank deficiency of multiple-input multiple-output (MIMO) channels. This work investigates the effect of cooperative relaying on the AMC performance. A higher-order cumulant (HOC)-based AMC method, namely the relay assisted RA-AMC method, is proposed to classify a pool of modulation types, namely BPSK, OQPSK, QPSK, π4-QPSK, 8-PSK and 16-QAM over an amplify and forward (AF) relay network. Simulation results show that the proposed method performs reasonably well over AF-relay fading channels.

Towards an accurate radar waveform recognition algorithm based on dense CNN

Existing algorithms for radar waveform classification currently exhibit the lower recognition accuracy, especially at the lower signal to noise ratio (SNR) environment. To remedy these flaws, this paper proposes an accurate automatic modulation classification algorithm based on dense convolutional neural networks (AAMC-DCNN). The algorithm owns the competitive advantages of strengthening the feature reuse and extracting the detailed feature, for improving the recognition performance of radar waveform at the lower SNR. First, the dense convolutional neural networks (CNN) are designed, which connects each layer to every other layer in a feed-forward pattern. In the latter, 8 types of signals are converted into time-frequency images by choi-williams distribution (CWD), and the large training and testing datasets are fabricated. Then, the transfer learning and Adam optimization are introduced. Finally, the experimental analyses are carried out to evaluate the recognition performance. It is worth mentioning that the classification accuracy can be up to 93.4% when the SNR is −8 dB, and even reach to 100% at 0 dB, which demonstrates the superior performance over others. The present work provides a sound experimental basis for further studying automatic modulation classification for the sake of future field application in electronic warfare systems.

Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems

Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What’s more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.

Low overhead NOMA receiver with automatic modulation classification techniques

Recently non-orthogonal multiple access (NOMA) technique has been proposed for boosting spectral efficiencies in 5G cellular networks. In NOMA the transmitter maps a predefined number of user symbols on to the same resources. The conventional receiver for NOMA is the well-known serial interference cancellation. To overcome the problem of high overhead signalling, a receiver with an automatic modulation classification (AMC) algorithm for a K-user NOMA which identifies the modulation scheme of users before symbol detection is proposed. First, a two-user NOMA (TU-NOMA) scheme is treated and the receiver is extended to more general cases. For evaluating the performance of the proposed receiver its bit error rate (BER) for TU-NOMA case has been driven and shown that this can be approximated as the sum of the demodulator BER and the AMC algorithm error probability. The simulation results show the BER degradation can be neglected if an enough number of symbols with sufficiently large SNR values are available.

Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

This paper introduces a novel automatic modulation classification (AMC) method using the graph-based fractional lower-order cyclic-spectrum analysis in the $\alpha$ -stable noise environment. The noise in the practical communication scenario usually exhibits impulse characteristics in the statistical sense, which could be modeled as the $\alpha$ -stable distribution. This would make the second- or higher-order statistics of the received signal vanish, and thus the performances of the conventional AMC algorithms designed for Gaussian noise significantly deteriorate when directly employed in the $\alpha$ -stable noise. In our proposed framework, the fractional lower-order cyclic spectrum (FLOCS) analysis is first invoked to acquire the polyspectra of the signal corrupted by the $\alpha$ -stable noise. Then, the graph-based AMC mechanism is systematically established upon the graph representation of the FLOCS to identify the modulation type according to the discrepancies between the graph features derived from the training and test data. The performance of our proposed new algorithm is theoretically analyzed, and the correct classification probability $P_{\text{cc}}$ over the modulation candidate set is formulated analytically. The remarkable scalability and efficiency of our proposed approach for the modulation candidate set variation are also theoretically investigated in detail. Monte Carlo simulation results demonstrate the effectiveness and superiority of the proposed AMC scheme.

Modulation Order Reduction Method for Improving the Performance of AMC Algorithm Based on Sixth–Order Cumulants

For the last two decades a large number of different automatic modulation classification (AMC) algorithms were developed, and many improvements in classification performance are reported. This was commonly achieved by engaging complex structures of neural networks, or other adaptable mechanisms for achieving better precision, when it comes to decision-making. Still, from practical implementation point of view, low algorithm complexity, economical usage of resources and fast execution remain to represent very desirable properties of an AMC algorithm. These properties are recognized in AMC algorithms based on higher - order cumulants as classification features, so their further improvement is of interest. Previous performance analysis of an algorithm based on sixth - order cumulants, in scenarios with complex valued signals' classification, showed that improvements are possible in the context of resources engaged and speed of execution. In this paper a novel approach is presented, for improving the correctness of classification process with sixth-order cumulants and simple two-step feature extraction structure, by engaging a new method for reduction of observed signal's modulation order which directly improves the classification performance. While tested with sixth-order cumulants, proposed method preserves good statistical properties of signal's higher - order cumulants in general, so it can be adopted in other AMC algorithms as well. Proposed modulation order reduction method is described in details, tested through computer simulations within the sixth-order cumulant AMC algorithm, and achieved improvements in performance are presented and explained.

Introducing an Efficient Statistical Model for Automatic Modulation Classification

Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as spectrum management, cognitive radio, intelligent modems, and interference identification. In this paper, a new robust AMC algorithm based on Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is proposed. Primarily, multi-level wavelet transform is applied on the received data samples. To select the efficient statistical model for wavelet coefficient description, the statistical characteristics of these coefficients are surveyed. The proposed analysis precisely illustrates that these coefficients have heteroscedasticity property which has not been mentioned before. Subsequently, to describe the wavelet coefficients, the heteroscedastic GARCH model is employed and its parameters are extracted as the features. Finally, the obtained features are applied to support vector machine (SVM) classifier to simultaneously determine the modulation type and constellation size. Eleven different types and sizes of the digital modulation schemes in various channels such as AWGN, flat fading and multipath fading in presence of common channel impairments are examined. The experimental results reveal the superior performance of the proposed method in comparison with the previously introduced approaches.

Automatic modulation classification based on joint feature map and convolutional neural network

The authors propose an automatic modulation classification algorithm to discriminate the radar emitter signals exploiting deep learning of convolutional neural network (CNN) based on a joint feature map. The joint feature map including time-frequency map and instantaneous autocorrelation (IA) map is generated to achieve superior classification performance on the identification of both phase modulated signals and frequency modulated signals. Afterwards, a CNN is designed to extract the features of the joint feature map. Training and test samples are designed to evaluate the recognition rate of the CNN. Simulation results show that the joint feature map makes up for the weakness of a single feature map for the classification of frequency modulated signals and phase modulated signals simultaneously, and superior classification accuracy is obtained.

An Investigation of LPI Radar Waveforms Classification in RoF Channels

Intensive research has been developed to either design or classify low probability of intercept (LPI) radar signals. These types of signals are used in different sensitive electronic warfare applications such as electronic support, electronic attack, and radar emitter identification. Linear frequency modulation, nonlinear frequency modulation, frequency shift keying, polyphase Barker, polyphase P1, P2, P3, P4 and Frank codes are examples of LPI waveforms. In this paper, we consider the modulation classification problem under the effect of transporting the captured radar signals through radio over fiber channels. Distortions and noise introduced by such channels are likely to affect the performance of LPI classification algorithms. Here, we investigate the accuracy of a recently proposed hierarchical decision-tree automatic modulation classification algorithm for additive white Gaussian noise channels and provide the necessary adjustments when the intercepted radar signals are transmitted over fiber optic channels. The investigation is conducted by simulations and experimental demonstration. The obtained results show that for an 80 km fiber link and noisy intercepted LPI signals, the average identification accuracy reaches more than 98%, at 16 dB optical signal-to-noise ratio.

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified. In this paper, to solve this challenging problem, the likelihood-based automatic modulation classification (AMC) is studied. First, in the scenario with known channel state information (CSI), two classifiers based on average likelihood ratio test (ALRT) and hybrid likelihood ratio test (HLRT), respectively, are derived. Concretely, in HLRT-based classifier, energy-based detector, and log-likelihood ratio-based detector are employed to identify the active subcarriers. Second, a HLRT-based blind AMC is proposed in the scenario with unknown CSI, where the efficient implementation of expectation maximization algorithm is presented to estimate channel fading coefficients and noise variance. Finally, the effectiveness of the proposed AMC algorithms is confirmed by computer simulations.

Cumulant based maximum likelihood classification for overlapped signals

A novel automatic modulation classification algorithm named cumulant-based maximum likelihood classification (CMLC) is proposed for overlapped sources. The sample estimate of cumulant is utilised for classification and classification decision is made by maximising the asymptotic distribution function of the cumulant. Simulation results prove the superior performance of CMLC over existing algorithms.

A Self Training Approach to Automatic Modulation Classification Based on Semi-supervised Online Passive Aggressive Algorithm

Automatic classification of modulation type in detected signals is an intermediate step between channel equalization and signal demodulation. This subsystem is an essential task for an intelligent receiver in various civil and military applications. Most of automatic modulation classification algorithms have been evaluated in SNR aware case which is obviously an unrealistic case. In this paper, we propose a semi-supervised online passive-aggressive classifier that uses a self-training approach for additive white Gaussian noise channels with unknown or variable signal to noise ratios to classify the modulated signals. An appropriate set of selected feature helps the general system work for a set of initial samples of each class. In the adaptation phase, unlabeled high confidence samples are used to adapt to system. An appropriate confidence measure is proposed to collect confident samples. Simulation results shows that adding unlabeled input samples to the training set improve the generalization capacity of the presented classifier in the target SNR. In addition to online characteristics of the algorithm that make the approach suitable for cognitive radio systems, this algorithm converges with a few number of signal samples. Simulation results in SNR unaware conditions show that employing this learning method results in high classification rate close to SNR aware conditions.

Modulation Discovery Over Arbitrary Additive Noise Channels Based on the Richardson-Lucy Algorithm

We address the problem of discovering unknown digital amplitude-phase modulations over block-fading additive noise channels. The proposed method uses the iterative Richardson-Lucy algorithm to determine the distribution of the transmitted symbols, which completely characterizes the underlying signal constellation. The decoding of the received signals can then be carried out based on the estimate of the signal constellation. An important application of the proposed method is to construct a modulation dictionary in an offline manner prior to performing any type of real time classification, thereby improving the performance of the automatic modulation classification algorithms proposed in the literature.

An Efficient FPGA IP Core for Automatic Modulation Classification

This letter presents a new algorithm for automatic modulation classification (AMC) and its implementation and validation as an intellectual property (IP) core. AMC aims at accurately identifying the modulation scheme of a given communication system in a short period of time. The proposed IP core consists of a multiclass classifier composed by linear support vector machines and a new parameter extraction (front end) based on histograms. Based on its VHDL implementation and validation using FPGAs, the performance of the proposed system is compared with respect to accuracy and computational complexity to recently proposed AMC algorithms. The new AMC system does not require multipliers and achieves equivalent accuracy with respect to a baseline, while reducing by 50% and 90% the use of logic elements and memory, respectively.

Automatic Modulation Classification Methods for Wireless OFDM Systems in TDD Mode

This paper presents a novel automatic modulation classification (AMC) algorithm in wireless time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation (AM). Based upon the maximum-a-posteriori (MAP) criterion, a classification method for digital quadrature amplitude modulation (QAM) schemes is developed that exploits the channel reciprocity in TDD systems and the receiver knowledge about the overall transmission data rate. The AMC algorithm enables the application of adaptive modulation in wireless OFDM systems without loss in the effective data rate caused by signaling of the bit allocation table (BAT). Numerical results show almost no performance loss in terms of the packet error ratio (PER) compared to the case with perfect knowledge of the modulation schemes if the proposed AMC technique is used.