Non-cooperative Communication Research Articles

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

Blind Recognition Method for Non-Cooperative Communication Signals Based on Aerial Computing

Blind Recognition Method for Non-Cooperative Communication Signals Based on Aerial Computing

Abandon Locality: Frame-Wise Embedding Aided Transformer for Automatic Modulation Recognition

Automatic modulation recognition (AMR) has been considered as an efficient technique for non-cooperative communication and intelligent communication. In this work, we propose a modified transformer-based method for AMR, called frame-wise embedding aided transformer (FEA-T), aiming to extract the global correlation feature of the signal to obtain higher classification accuracy as well as lower time cost. To enhance the global modeling capability of the transformer, we design a frame-wise embedding module (FEM) to aggregate more samples into a token in the embedding stage to generate a more efficient token sequence. We also present the optimal frame length by analyzing the representation ability of each transformer layer for a better trade-off between the speed and the performance. Moreover, we design a novel dual-branch gate linear unit (DB-GLU) scheme for the feed-forward network of the transformer to reduce the model size and enhance the performance. Experimental results on RadioML2018.01A datasets demonstrate that the proposed method outperforms state-of-the-art works in terms of recognition accuracy and running speed.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance ofthe DVAE model, the Tunicate Swarm Algorithm (TSA) is used to fine-tune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

Classifying wireless signal modulation sorting using convolutional neural network

Deep learning has recently been used for this issue with superior results in automatic modulation classification. Previous studies state that it is challenging to categorize a variety of modulation formats using traditional approaches; however, modulation classification is a crucial component of non-cooperative communication in wireless communication. The deep learning network was applied to solve the issue and get decent outcomes. This work uses a deep learning convolutional neural network (DLCNN) to classify three analog and eight digital modulation techniques by generating channel-impaired and synthetic waveforms as training data. The obtained DLCNN is tested by over-the-air indicators and a Software Define Radio(SDR) platform. The trained DLCNN estimates the modulation kind of each frame by taking 1024 samples of channel-impaired signals. The method includes generating several frames of 4-arry pulse amplitude modulation (PAM4) that are impaired with sampling time drift, Additive white Gaussian noise (AWGN), center frequency, and Rician multipath fading. The DLCNN predicts real inputs when receiving a signal with complex samples of baseband. Before updating the network coefficients and on all iterations, the data store transforms data from files and records it. This network takes about 50 minutes to train using in-memory data and 110 minutes to train using disk data. The evaluation of the trained DLCNN is carried out by obtaining the classification accuracy for the test frames. The obtained outcome demonstrates that the developed network can achieve an accuracy of about 94.3 % in roughly 12 epochs for such types of waveforms, which elapsed about 26 minutes for training. This will increase the efficiency of spectrum usage and detect the modulation type of the wireless communication receivers

Communicative and Linguistic Characteristics of the Comic Discourse (on the Material of English-Language Belles-Lettres Works)

The article deals with the investigation of the communicative and linguistic features of the comic discourse on the basis of the English-language fictional works by M. Spark, R. Dahl, K. Vonnegut Jr, I. Shaw, R. Carver and K. Barry. The author represents the description of a comic discourse in terms of Linguistic Pragmatics. Extra-linguistic components of the literary text creating and actualizing, which include pragmatic characteristics, social and cultural factors, psychological peculiarities, cognitive component and paralinguistic supplement, are described. The article also provides a detailed classification of comic discourse linguistic components on the material of belles-letters communication in the works of aforementioned writers. All linguistic characteristics within five groups, according to the violation/lack/incompleteness of the discourse certain communicative qualities disclosure, are distinguished. Besides, there is a thorough analysis of the presupposition as a constituent part in the comic discourse definition. The article dwells on the interlocutors’ micro and macro presuppositions introducing, the absence of which leads to non-cooperative communication with, predominantly, a comic effect. The results of the experiment show that the participants’ (readers’ particularly) background knowledge helps them interpret a literary discourse. The article continues the study of special special means and techniques of comic effect producing that provides a further understanding of the authors’ individual styles.

Blind recognition of sparse parity‐check matrices of low‐density parity‐check codes in the presence of noise

This paper studies the blind recognition method of the sparse parity-check matrices of low-density parity-check codes in noncooperative communication, which is critical to the reverse analysis of communication protocols using LDPC codes. In this paper, two improvements are made to the algorithm of Liu Qian et al. (2021) for this problem. Firstly, a Gaussian elimination method based on random column exchange and soft information is proposed to enhance the fault tolerance of the elimination process. Secondly, according to the sparse property of the parity-check matrices of LDPC codes, a random extraction method is proposed to further improve the fault tolerance of the algorithm, and it is verified theoretically. Finally, simulations verify the superior performance of the algorithm proposed in this paper.

WIR-Transformer: Using Transformers for Wireless Interference Recognition

Wireless interference recognition (WIR) is one of the most indispensable technologies in non-cooperative communication systems. Recently, compared with convolutional networks, Transformers enjoy the ability of extracting global features and have achieved striking performance for WIR. However, the self-attention module in Transformers brings huge computational overhead that hinders deployment on resource-constrained devices. In this letter, we aim to alleviate the problem and propose WIR-Transformer. Specifically, WIR-Transformer introduces the division of regions and independently calculates self-attention in each region, which effectively reduces the complexity. To overcome the bottleneck of information blocking between regions, we propose an information exchange module (IEM). To further reduce the complexity, we introduce a novel patch aggregation module (PAM) to reduce the number of patches and fuse the local information. Experiments demonstrate that the proposed WIR-Transformer achieves higher accuracy as compared to conventional methods for WIR.

A Soft-GJETP Based Blind Identification Algorithm for Convolutional Encoder Parameters

In an adaptive or non-cooperative communication system, the blind identification of channel coding is an indispensable procedure for recovering the message from the intercepted coding data. Due to the wide applications of convolutional codes, the blind identification problem of convolutional codes has also received extensive studies. In this paper, we consider the blind identification problem of convolution encoder parameters in the general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k/n$ </tex-math></inline-formula> rate case. To improve the blind identification accuracy of existing hard decision based solutions, a novel soft information based blind identification algorithm is designed in this paper. Specifically, a Soft Gaussian-Jordan Elimination Through Pivoting (Soft-GJETP) algorithm is firstly proposed to calculate the rank of the received data matrix. In contrast to the existing hard decision based GJETP algorithm, this Soft-GJETP algorithm formulates the soft information of the received data, then defines three reliability metrics and derives updating rules for this soft information. In this way, the diagonals of the received data matrix have fewer errors in Soft-GJETP algorithm. Furthermore, a soft decision strategy is proposed with a weight-dependent threshold. Employing this strategy, the dependent columns can be distinguished with higher accuracy. Finally, simulation results and comparisons are given to illustrate the performances of our proposed methods.

Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means

Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

Performance evaluation and energy optimization of CM3A cooperative WBAN

This work presents performance evaluation and energy optimization of a cooperative Wireless body area network (WBAN). Link metrics like Bit Error Rate (BER), Signal-to-Noise ratio (SNR), spectral efficiency (SE), and outage probability have been investigated to evaluate the performance of non-cooperative and cooperative WBAN at different frequency bands supporting body-centric communication (Ultra-wideband (UWB), Worldwide Industrial, Scientific and Medical (W-ISM) band, US-ISM (U-ISM) band and Wireless Mobile Telemetry Services (WMTS)). Evaluation reveals that cooperative communication shows better performance by exhibiting improved SNR and SE and lower BER and outage probability compared to non-cooperative communication. A critical comparison of Amplify and forward (A&F) cooperation has been performed with decode and forward (D&F) cooperation. Results reveal that the D&F scheme performs better with increased SNR and SE and lower levels of BER and outage probability in comparison to the A&F scheme by 43.74%, 1.64%, 0.15%, and 99%, respectively. W-MTS with the highest SNR and SE and lowest BER and outage probability outperforms the other frequency bands under both non-cooperative and cooperative scenarios. Further, the threshold distance has been evaluated using the Eigenvalues to optimize the transmission energy for the D&F scenario to enhance the performance of on-body WBAN resulting in an average decrease of 3.8% in the transmitted energy, with UWB showing a maximum reduction of 5.25% thus making the network more energy efficient.

An Advancing Temporal Convolutional Network for 5G Latency Services via Automatic Modulation Recognition

Automatic modulation recognition (AMR) has received significant attention since its decisive factor for modern non-cooperative communication systems. Meanwhile, the existing works on deep learning technique achieve exceptional accuracy; however, these works dissatisfy real-time requirements for 5G low-latency services. To remedy this flaw, this brief proposes a low-latency AMR method by applying temporal convolutional network (TCN). Furthermore, the principal component analysis (PCA)-based TCN and uniform subsampling-based TCN methods are leveraged to further alleviate the computation complexity and render real-time TCN viable. Experimental results demonstrate that the proposed method can achieve lower complexity and superior recognition accuracy than existing works and pave the way for 5G low-latency services.

Low-Bitwidth Convolutional Neural Networks for Wireless Interference Identification

Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into hardware units. This paper addresses the problem of training quantized convolutional neural networks (CNNs), with low-precision weights as well as activations for WII. Optimizing a low-bit width network is very challenging due to the non-differentiable quantization function. To mitigate the difficulty of training, we propose three effective approaches. Firstly, we propose to train the quantized network with the guidance of the full-precision counterpart. The quantized network can learn from the full-precision counterpart. Unfortunately, training an extra full-precision network to assist a quantized model is cumbersome and computationally expensive. To this end, we further propose a training mechanism which makes use of the manually designed probability distributions to provide a virtual full-precision counterpart for guiding the training of the quantization network without extra computational cost. Thirdly, to make the gradient back-propagates more easily, we propose novel auxiliary output modules, which can be seamlessly incorporated into the proposed quantization networks. Experimental results validate the effectiveness of the proposed methods. Furthermore, it is shown that training 3-bit and 4-bit precision networks with the proposed methods leads to performance improvement as compared to their full precision counterparts with standard network architectures.

Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment

The development of 5G technology has brought about a new era of Internet of Thing (IoT), and at the same time, electromagnetic spectrum monitoring and sensing have also ushered in huge challenges. Digital modulation recognition technology is an important content of electromagnetic spectrum sensing. In the increasingly complex wireless communication transmission environment, especially in noncooperative communication, it becomes more and more difficult to receive target signals and accurately extract effective semantic information from diverse modulation signals of the electromagnetic spectrum. At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by many sensors has attracted wide attention from people in related fields. This paper proposes a distributed collaborative sensing spectrum semantic recognition architecture for communication signals based on feature fusion. Perform wireless communication and transmission between multiple sensors to form a self-organizing network to cooperatively sense signal semantic information, and extract the signal features of each sensor in the distributed network structure. Finally, the extracted sensor features are semantically analyzed and modeled, and the effective features are fused to complete the entire perception and recognition process. Even if the channel environment of a small number of receiving nodes deteriorates in a complex transmission environment, the signal quality features can still be accurately extracted, the classification and recognition effect like or higher than the best channel state performance can be achieved, and the fault tolerance of the system can be effectively improved. It can also enhance the performance of spectral semantic information sensing and recognition in the IoT environment.

A novel approach for unlabeled samples in radiation source identification

Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification. However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.

An automatic modulation classification network for IoT terminal spectrum monitoring under zero-sample situations

Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment we live. However, the proliferation of IoT devices has led to the misuse of spectrum resources, and many IoT devices occupy the frequency band without permission. As a consequence, the spectrum regulation has become an essential part of the development of IoT. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. Generally, deep learning (DL)-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. The accuracy of one unseen class and two unseen classes exceeds 90% and 76%, respectively. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.

Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification

Deep learning is a new direction of research for specific emitter identification (SEI). Radio frequency (RF) fingerprints of the emitter signal are small and sensitive to noise. It is difficult to assign labels containing category information in noncooperative communication scenarios. This makes network models obtained by conventional supervised learning methods perform unsatisfactorily, leading to poor identification performance. To address this limitation, this paper proposes a semisupervised SEI algorithm based on bispectrum analysis and virtual adversarial training (VAT). Bispectrum analysis is performed on RF signals to enhance individual discriminability. A convolutional neural network (CNN) is used for RF fingerprint extraction. We used a small amount of labelled data to train the CNN in an adversarial manner to improve the antinoise performance of the network in a supervised model. Virtual adversarial samples were calculated for VAT, which made full use of labelled and large unlabelled training data to further improve the generalization capability of the network. Results of numerical experiments on a set of six universal software radio peripheral (USRP; model B210) devices demonstrated the stable and fast convergence performance of the proposed method, which exhibited approximately 90% classification accuracy at 10 dB. Finally, the classification performance of our method was verified using other evaluation metrics including receiver operating characteristic and precision-recall.

Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System Using Multiscale Deep CNN

Automatic modulation categorization (AMC) is used in many applications such as cognitive radio, adaptive communication, electronic reconnaissance, and non-cooperative communications. Predicting the modulation class of an unknown radio signal without having any prior information of the signal parameters is challenging. This paper proposes a novel multiscale deep-learning-based approach for the automatic modulation classification using radio signals. The approach considered the fixed boundary range-based Empirical wavelet transform (FBREWT) based multiscale analysis technique to decompose the radio signal into sub-band signals or modes. The sub-band signals computed from the radio signal combined with the deep convolutional neural network (CNN) are used to classify modulation types. The approach is tested using the radio signals of different signal-to-noise ratio (SNR) values and four different channel types such as additive white Gaussian noise (AWGN) combination of Rayleigh fading and AWGN channels, the combination of Rician flat fading and AWGN channels, and combination of Nakagami-m fading and AWGN channels for AMC. The results show that the proposed FBREWT based deep-learning approach achieves an overall classification accuracy of 97% for AMC using the radio signals with 10dB SNR for the AWGN channel. Moreover, the proposed approach has obtained the accuracy’s as 94.56%, 95%, and 97.33% using radio signals with 10 dB SNR values for Rayleigh fading, Rician flat fading, and Nakagami-m fading channels combined through AWGN link. The comparison of the proposed multiscale deep learning-based approach with existing methods is shown for AMC.

Recovering the Parameters of an LDPC Code From Noisy Intercepted Sequences

Blind identification of channel codes is a key technology in non-cooperative communication as well as intelligent communication. In this letter, we are interested in recovering the parameters of low-density parity-check (LDPC) codes without any a priori information. We propose to find dual codewords from noisy intercepted sequences and prove that the problem is NP-complete. Afterward, we propose an improved collision-based algorithm that can avoid most of the invalid searches and thus allowing a significant reduction in complexity. The algorithm produces almost no errors when the noise is small. While in the case of non-negligible noise, we propose to use soft information to detect the outputs of the algorithm, and errors in which can therefore be extensively eliminated. After a sufficient number of dual codewords have been found, one can easily recover the code lengths and code rates of LDPC codes, as well as the sizes of sub-matrices for Quasi-Cyclic LDPC codes. Experimental results show that the proposed method has an improved noise tolerance and can be more efficient than existing methods.

Joint Demodulation and Error Correcting Codes Recognition Using Convolutional Neural Network

Demodulation of the communication signals and blind identification of error-correcting codes (ECC) are two essential purposes in adaptive modulation and coding (AMC) techniques and non-cooperative communication fields. The existing approaches treat them as two separate problems, namely the demodulation of signals with known a <i>priori</i> knowledge (such as channel state information (CSI) or channel noise) and the ECC type identification that depends on the demodulation results. In this paper, a novel one-stage ECC identification approach based on a multi-task deep convolutional neural network (MT-DCNN) is presented. With this architecture, the proposed method automatically recognizes the ECC types of the baseband in-phase and quadrature-phase (IQ) data without relying on any conventional demodulators. Precisely, the proposed MT-DCNN consists of three modules: the feature extraction module, the demodulation module, and the ECC types recognition module. Experiment results show that the proposed architecture can accurately identify the ECC types of the baseband IQ signals and is superior to those of the existing two-stage recognition approaches.