Automatic Modulation Classification Using PDF Approximation

Cognitive Radio (CR) systems allow optimizing the use of the shared radio spectrum and enhancing the coexistence among different technologies by efficiently changing certain operating parameters of the radios such as transmit-power, carrier frequency, and modulation and coding scheme in real-time. Dynamic Spectrum Access (DSA), which allows radios to dynamically access and use the unused spectrum, is one of the tasks that are fundamental for a better use of the spectrum. In this paper, we extend the previous work on Automatic Modulation Classification (AMC) by using Deep Neural Network (DNNs) and evaluate the performance of these architectures on signals that are not only modulated but are also encoded. We call this the Automatic Modulation and Coding Scheme Classification problem, or \(AMC^2\). In this problem, radio signals are classified according to the Modulation and Coding Scheme (MCS) used during their transmission. Evaluations on a data set of 802.11 radio signals, transmitted with different MCS and Signal to Noise Ratio (SNR), provide important results on the impact of some DNN hyperparameters, e.g. number of layers, batch size, etc., in the classification accuracy.

Automatic modulation classification (AMC) is a typical technology for identifying different modulation types, which has been widely applied into various scenarios. Recently, deep learning (DL), one of the most advanced classification algorithms, has been applied into AMC. However, these previously proposed AMC methods are centralized in nature, i.e., all training data must be collected together to train the same neural network. In addition, they are generally based on powerful computing devices and may not be suitable for edge devices. Thus, a distributed learning-based AMC (DistAMC) method is proposed, which relies on the cooperation of multiple edge devices and model averaging (MA) algorithm. When compared with the centralized AMC (CentAMC), there are two advantages of the DistAMC: the higher training efficiency and the lower computing overhead, which are very consistent with the characteristics of edge devices. Simulation results show that there are slight performance gap between the DistAMC and the CentAMC, and they also have similar convergence speed, but the consumed training time per epoch in the former method will be shorter than that on the latter method, if the low latency and the high bandwidth are considered in model transmission process of the DistAMC. Moreover, the DistAMC can combine the computing power of multiple edge devices to reduce the computing overhead of a single edge device in the CentAMC.

Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.

Automatic modulation classification (AMC), which aims to identify the modulation types of unknown signals without any prior knowledge, plays a key role in intelligent wireless communication. In recent years, the outstanding achievements of deep learning in the fields of computer vision and nature language processing have promoted the continuous researches of deep learning in AMC. In view of the fact that the existing deep learning-based AMC ignores the frequency attribute of the modulated signals, this paper proposed an AMC approach based on deep learning which comprehensively considers the contribution of frequency and spatiotemporal characteristics of the modulated signals. Specifically, we utilize the sliding window to divide the raw signals into short-time signal slices. In order to obtain the representation of frequency domain of the signals, each short-time signal slice is decomposed into several modes by variational mode decomposition (VMD), which is an adaptive signal decomposition technique. Finally, all the modes are used as input of the convolutional neural network (CNN) as a tensor to learn high-level features from the frequency and spatiotemporal domain to identify the modulation type. In this paper, we verify the effectiveness of the proposed approach with 10 kinds of modulation types generated by SMW200A Vector Signal Generator. More importantly, the proposed approach achieves 99.2% of the overall classification accuracy at 8dB signal to noise ratio (SNR) and shows better robustness to noise than the existing AMC approaches.

Automatic modulation classification (AMC) plays a vital role in advancing future wireless communication networks. Although deep learning (DL)-based AMC frameworks have demonstrated remarkable classification capabilities, they typically require large-scale training datasets and assume consistent class distributions between training and testing data-prerequisites that prove challenging in few-shot and open-set scenarios. To address these limitations, we propose a novel few-shot open-set automatic modulation classification (FSOS-AMC) framework that integrates a multi-sequence multi-scale attention network (MS-MSANet), meta-prototype training, and a modular open-set classifier. The MS-MSANet extracts features from multi-sequence input signals, while meta-prototype training optimizes both the feature extractor and the modular open-set classifier, which can effectively categorize testing data into known modulation types or identify potential unknown modulations. Extensive simulation results demonstrate that our FSOS-AMC framework achieves superior performance in few-shot open-set scenarios compared to state-of-the-art methods. Specifically, the framework exhibits higher classification accuracy for both known and unknown modulations, as validated by improved accuracy and area under the receiver operating characteristic curve (AUROC) metrics. Moreover, the proposed framework demonstrates remarkable robustness under challenging low signal-to-noise ratio (SNR) conditions, significantly outperforming existing approaches.

Internet of Things (IoT) networks are often subject to many malicious attacks in untrusted environments, and automatic modulation classification (AMC) is an effective way to combat IoT physical-layer threats. However, most existing AMC methods assume sufficient labeled signals and invariant signal distribution, which is often impossible in untrusted environments. In this article, a new meta-learning method is proposed for a few-shot AMC with distribution bias. First, a multi-frequency octave ResNet (MFOR) is constructed to learn coarse (low-frequency) and fine (high-frequency) features, which can efficiently identify the modulation type of the signal while saving computational resources. Second, a large number of classification-related meta-tasks are established for training MFOR to explore general knowledge in signal classification, and then transfer it to the AMC. Different with deep neural networks (DNNs) that learn a mapping by multiple instances, the MFOR with meta-learning (denoted as M-MFOR) can improve the generalization ability of new AMC tasks with very few instances and distribution bias. Furthermore, we find that the distribution bias between data can be reduced by adjusting the normalized distribution and propose a class-related mixup. Extensive experiments are taken on several datasets to investigate the effectiveness of M-MFOR. The results show its feasibility and superiority over existing methods.

Automatic modulation classification (AMC) is of interest for spectrum sensing and network coexistence applications, and it is desirable to find AMC techniques that work with limited computational complexity and memory. This work is an initial exploration of how to apply existing dimensionality reduction symbolic representation approaches for real-valued time series, that claim computational and memory efficiency in data mining and other applications, to feature-based automatic modulation classification of complex-valued time series data. This initial investigation shows that it is feasible to apply these symbolic representation techniques to automatic modulation classification problems with promising performance. Future work needs to be done to refine the approach and to compare the computational complexity and storage efficiency to other techniques.

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 modulation classification (AMC) is deployed, as the intermediate step between signal detection and demodulation, to identify modulation schemes automatically. Modulation classification is a challenging task, especially in a non-cooperative environment, owing to the lack of prior information on the transmitted signal at the receiver; the problem will be more challenging in the multipath fading channel. The proposed AMC method based on optimal decision fusion by using wireless sensor networks provides a more accurate classification result than any one of the individual signal alone. Wireless sensor networks offer increased reliability and optimal decision fusion provides huge gains in overall classification performance as compared to that of the single sensor. Thus, optimal decision fusion based AMC by using wireless sensor networks greatly enhances classification performance of weak signals in non-cooperative communication environment. Classification performances of optimal decision fusion based AMC by using wireless sensor networks in the multipath fading channel are investigated and evaluated in terms of correct classification probability. Through Monte Carlo simulations, we demonstrate that the proposed AMC algorithm can greatly outperform that of single sensor in multipath fading channel.

Automatic modulation classification (AMC) for single signals has been widely studied in the past two decades. Meanwhile, the demand for multi-signal AMC is also increasing; however, single-signal AMC performs poorly and few methods were proposed for multi-signal AMC. To address this problem, a blind AMC method for overlapped co-channel signals applying capsule networks is proposed. Multiple transmitters transmit signals simultaneously and signals are received by a single-antenna receiver. The method handle signals at intermediate frequency directly, and is able to detect the number of signals automatically. Numerical results demonstrate the effectiveness of our proposed method, and excellent generalization ability provided by capsule 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.

In this research, a classifier is proposed for automatic modulation classification of some common modulation schemes, i.e., BPSK, QPSK, 8-PSK and 16-QAM. Our proposed classifier considers multipath fading effects on the received signal in a non-Gaussian noise environment. Automatic modulation classification is very challenging in real-world scenarios due to fading effects and additive Gaussian mixture noise on modulation schemes. Most of the available modulation classifiers do not consider the fading effects which results in degradation of classification in a blind channel environment. In our work, the channel is supposed to be suffering from excessive additive Gaussian mixture noise and frequency selective fading resulting in low signal SNR. The estimation of the unknown channel along with noise parameters is performed using ECM algorithm and then used in maximum-likelihood classifier for the classification of modulation schemes. Simulation results are presented that show 2 dB improvement in performance than classifier which considers only Gaussian noise in the received signal.

Amidst the evolving landscape of non-cooperative communication, automatic modulation classification (AMC) stands as an essential pillar, enabling adaptive and reliable signal processing. Due to the advancement of deep learning (DL) technology, neural networks have found application in AMC. However, the previous DL models face the inter-class confusion problem in high-order modulations. To address this issue, we propose a multitask-learning-empowered hybrid neural network, named CrossTLNet. Specifically, after the signal enters the model, it is first transformed into two task components: in-phase/quadrature (I/Q) form and amplitude/phase (A/P) form. For each task, we design a method that combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network to effectively capture long-term dependency features in high-order modulations. To enable interaction between these two different dimensional features, we innovatively introduce a cross-attention method, thereby further enhancing the model’s ability to distinguish signal features. Moreover, we also design a simple and efficient knowledge distillation method to reduce the size of CrossTLNet, making it easier to deploy in real-time or resource-limited scenarios. The experimental results indicate that the suggested method exhibits exceptional performance in AMC on public benchmarks, especially in high-order modulations.

As the Internet of Things (IoT) expands and spectrum resources become increasingly scarce, Automatic Modulation Classification (AMC) has become critical for enabling dynamic spectrum access, interference mitigation, and spectrum monitoring without coordination or prior signaling. Most deep learning-based AMC methods (e.g., CNNs, LSTMs, Transformers) operate in Euclidean spaces and therefore overlook the non-Euclidean relationships inherent in modulated signals. We propose KGNN, a graph-based AMC architecture that couples a KNN-driven graph representation with GraphSAGE convolutions for neighborhood aggregation. In the KNN stage, each feature vector is connected to its nearest neighbors, transforming temporal signals into structured graphs, while GraphSAGE extracts relational information across nodes and edges for classification. On the RML2016.10b dataset, KGNN attains an overall accuracy of 64.72%, outperforming strong baselines (including MCLDNN) while using only one-eighth the number of parameters used by MCLDNN and preserving fast inference. These results highlight the effectiveness of graph convolutional modeling for AMC under practical resource constraints and motivate further exploration of graph-centric designs for robust wireless intelligence.

Automatic modulation classification (AMC) is an important component in cognitive radio and many efforts have been made to improve the AMC's successful classification rate, especially when the environment is noisy. The cyclic feature has excellent resiliency to noise, so it has been frequently adopted as the feature for AMC. In this paper, in order to enhance the reliability of the system, we propose a distributed AMC scheme based on compressive sensing by taking advantage of the sparse property of cyclic feature map. A novel method based on compressive sensing principle for capturing the prominent peaks of the feature map is introduced, which is able to achieve good modulation recognition performance at sub-Nyquist rates. And a novel neural network fusion strategy is proposed for better cooperation. It is shown that the proposed distributed approach improves the classification rate compared with the single radio and can reduce the required samples compared with traditional sampling scheme.