Digital Modulation Types Research Articles

LPI Radar Waveform Recognition Based on Hierarchical Classification Approach and Maximum Likelihood Estimation

The importance of information gathering is emphasized to minimize casualties and economic losses in warfare. Through electronic warfare, which utilizes electromagnetic waves, it is possible to discern the enemy’s intentions and respond accordingly, thereby leading the battle advantageously. Consequently, related research is actively underway. The development of various radar signal modulation techniques has revealed limitations in the existing modulation recognition methods, necessitating the development of distinguishing features to overcome these limitations. This paper proposes and analyzes distinguishing features that can differentiate various modulation schemes. Eleven distinguishing features were employed, and twenty-two types of modulated signals, including analog, digital, and composite modulation, were classified using hierarchical classification approach and maximum likelihood estimation (MLE). The proposed method achieves a recognition performance of 99.76% at an SNR of 20 dB and 98.45% at an SNR of 8 dB.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.

Using cumulant analysis to recognize digital types of radio signal

Problem statement. Recognition of types of modulation of radio signals is an important task solved in radio engineering systems for various purposes. In radio monitoring tools, information about the type of modulation is used to identify unauthorized sources of radio emission and measure their parameters, in electronic warfare systems - when choosing a more effective type of interference, etc. The article substantiates the relevance of improving methods for recognizing digital types of modulation. It is shown that mixed cumulants of higher orders can be used as modulation features for recognizing types of modulation. Analytical expressions are given for calculating mixed cumulants up to the tenth order inclusive through the values of mixed moments and the value of mixed cumulants for radio signals with the most common types of digital modulation. Recommendations have been developed for the synthesis of algorithms for recognizing the type of modulation based on cumulant analysis. Goal. Obtain mathematical expressions for calculating mixed cumulants up to the tenth order inclusive and determine the values of mixed cumulants to recognize the most common digital types of modulation. Results. A general methodology has been developed for the synthesis of algorithms for recognizing the type of modulation based on cumulant analysis. Practical significance. Application of the developed approach in the design of radio-electronic systems makes it possible to obtain a high probability of correct recognition of digital types of modulation with a relatively low design complexity.

Enhancement Cascaded Least Square of RLS and NLMS for Wireless Communication System

Orthogonal Frequency Division Multiplexing or known as OFDM has been a prominent digital communication scheme and was introduced in the 4G network. This technique can be described as an exceptional multicarrier instance where the signal data is transmitted using separate subcarriers through a total of the subcarriers at a lower rate. Although OFDM seems perfect for the development of 5G networks and upcoming 6G, this type of digital modulation still suffers from intersymbol interference (ISI). Enhancement Cascaded Least Square (ECLS) which is a sequence of adaptive algorithms has been proposed in this research to overcome ISI that occurs due to multipath propagation. ECLS is a combination of updated Recursive Least Square (URLS) and Normalized Least Mean Square (NLMS). URLS will reduce complexity while NLMS will overcome problem of convergence before being combined to become ECLS. The research outcome will be illustrated in Bit error rate (BER) to analyse lower interference and Mean Square Error (MSE) to analyse convergence. This research justified that ECLS can minimize the existence of interference in wireless communication systems.

Experiments on Digital Filtering of Analog Signals: Pulse Amplitude Modulation and Delta Modulation

To boost students' active learning and understanding of abstract scientific concepts, hands-on learning experiences such as experiments are necessary. Computer simulations also allow students to carry out experiments virtually. Analog signals are processed using digital signal processing because of the advantages of digital signal processing over analog signal processing. Numerical experiments, results, and observations on digital band pass filtering of analog signals are presented. Two types of digital modulation are employed: Pulse Amplitude Modulation (PAM) and Delta Modulation (DM). Results demonstrated higher suppression of frequencies in the stopband compared with the transition band. Frequencies in the passband suffer minimum or no attenuation. The phase difference between input and output signals, transient response, and steady state response are observed vividly. PAM is found to add less noise to the system compared with DM. These experiments are recommended as virtual laboratory exercises for undergraduate and postgraduate studies for active learning purposes.

METHOD OF DETERMINING PARAMETERS OF MODULATION OF UAV SIGNALS USING ARTIFICIAL NEURAL NETWORKS

The article is devoted to the consideration of the issue of determining the parameters of the modulation of signals of unmanned aerial vehicles using artificial neural networks by recognizing the types of digital modulation and is performed by a system that automatically classifies the type of digital modulation of the received signal. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.

Multimodal attention-based deep learning for automatic modulation classification

Wireless Internet of Things (IoT) is widely accepted in data collection and transmission of power system, with the prerequisite that the base station of wireless IoT be compatible with a variety of digital modulation types to meet data transmission requirements of terminals with different modulation modes. As a key technology in wireless IoT communication, Automatic Modulation Classification (AMC) manages resource shortage and improves spectrum utilization efficiency. And for better accuracy and efficiency in the classification of wireless signal modulation, Deep learning (DL) is frequently exploited. It is found in real cases that the signal-to-noise ratio (SNR) of wireless signals received by base station remains low due to complex electromagnetic interference from power equipment, increasing difficulties for accurate AMC. Therefore, inspired by attention mechanism of multi-layer perceptron (MLP), AMC-MLP is introduced herein as a novel AMC method for low SNR signals. Firstly, the sampled I/Q data is converted to constellation diagram, smoothed pseudo Wigner-Ville distribution (SPWVD), and contour diagram of the spectral correlation function (SCF). Secondly, convolution auto-encoder (Conv-AE) is used to denoise and extract image feature vectors. Finally, MLP is employed to fuse multimodal features to classify signals. AMC-MLP model utilizes the characterization advantages of feature images in different modulation modes and boosts the classification accuracy of low SNR signals. Results of simulations on RadioML 2016.10A public dataset prove as well that AMC-MLP provides significantly better classification accuracy of signals in low SNR range than that of other latest deep-learning AMC methods.

The discrimination algorithms for overlapped MPSK and MQAM modulations using higher-order cumulants

Automatic Modulation Classification (AMC) is responsible for detecting the correct modulation types in the intelligent receivers. AMC performance degrades when the signal-to-noise ratio (SNR) decreases because of the overlapping among the digital modulation types’ features, and this performance worsens under fading channel conditions. This paper proposes two new algorithms that improve the AMC performance accuracy of the overlapped digital modulations in feature space by improving their discrimination. These algorithms are named temporal Fisher discriminant analysis (TFDA) and supervised Fisher discriminant analysis (SFDA). The simulation results show that TFDA improves AMC performance accuracy up to 19.01% compared with the reference paper (Ge et al., 2021) and up to 38.15% compared with the reference paper (Teng et al., 2018). In contrast, SFDA improves AMC performance accuracy up to 23.12 % compared with the reference paper (Ge et al., 2021) and up to 49.025% compared with the reference paper (Teng et al., 2018).

Method of 128-position quadrature amplitude-polarization manipulation

The functioning of modern digital communication systems tends to occur in a complex interference environment. The communication system is affected by various types of jamming: both natural noises associated with the conditions of radio waves propagation, and artificial interference concerned with electromagnetic compatibility of radio equipment, characteristics of communication channels, etc. Applied issues of enhancing digital communication systems interference insusceptibility are quite relevant at the present time. Concurrently, it is advisable to achieve an increase in interference insusceptibility by rational methods that do not require a significant increase in the emitted signals energy. The techniques based on the use of algorithms for processing special types of signals are the most promising to this date. Radio specialists address the methods for digital processing of polarization-manipulated signals which, while maintaining the communication system carrying capacity, allow us to obtain an essential gain in the parameters of the communication system interference insusceptibility. At the same time, a successful combination of these methods with the already known techniques of digital signal processing is noted. This article considers the multi-position method of quadrature amplitude-polarization manipulation (M-QAPM), where the number of positions M = 27 = 128 (128-QAPM). The modulation symbol at 128-QAPM consists of a 7-bit digital code. The purpose of this article is to describe analytically the method of 128-position quadrature amplitude-polarization manipulation when transmitting 7-bit binary modulation symbols over a communication channel. During mathematical modeling, a signal constellation was formed and visualized. The structural diagram of the modulator was given. The practical significance of the given paper lies in the fact that quadrature amplitude-polarization manipulation can be considered as a universal type of digital modulation, which can be applied instead of the currently known quadrature types of manipulation.

Модель спектральной корреляционной функции для сигналов с OFDM модуляцией и циклическим префиксом

The paper is aimed at building analytical models of signals possessing complex types of digital modulation within the framework of the cyclostationary approach. The proposed analytical approach includes procedures leading to closed-form analytical expressions of spectral correlation functions (SCF), which are functions depending on two arguments: frequency and cyclic frequency. They describe the probabilistic properties of the analyzed signals assuming the signals can be modeled as realizations of second-order cyclostationary random processes (CSRP). The proposed approach of obtaining both normal and conjugate SCFs is based on the modified shaping operator technique, which turns out to be an effective tool applied to CSRP analysis. The technique handles the constructing the variety of the typically used in practice CSRPs in the form of chained transforms applied to one or several independent CSRPs of known characteristics by means of relatively simple operations, widely expressed in signal and systems theory. These operations performed over signals, which are assumed to be realizations of the CSRP, correspond to the transformations of their SCF, whose formulae are presented in the paper. The paper also reveals the exact analytical expressions obtained for the normal and conjugate SCF of OFDM signals with cyclic prefix (CP) whose subcarriers are modulated using BPSK and QPSK methods. By example of OFDM signal with a CP and QPSK subcarrier modulation, a quantitative comparison was carried out between the analytical SCF assembled according to the model formula and its estimate obtained by a numerical simulation using the estimation method based on the mixed two-dimensional fast Fourier transform. It is shown that the SCF estimate converges in root-mean sense to the constructed analytical model with an increase in the duration of the analyzed signal data sample.

A New Generation Communication System Based on Deep Learning Methods for the Process of Modulation and Demodulation from the Modulated Images

Demodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.

Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

Recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.

STUDY NEUR NETWORKS FOR SOFTWARE DEFINED RADIO CONTROL

The scientific article is devoted to the issues of SDR system control. Software Defined Radio is a system designed for software control of information transmission processes in a radio communication channel. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.

Deep Learning for Recognition of Digital Modulations: A Detailed Study

The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In recent years, deep learning techniques have received much attention since their excellent performance in signal, audio, image and video processing. This paper examines the feasibility of using deep learning algorithms on automatic recognition of the received radio signals' modulation schemes. Modulation recognition has been performed on eight digital modulation types with a signal-to-noise ratio (SNR) from -20dB to 20dB. First, a vanilla neural network is used to classify the type of modulation. Then, convolutional neural networks (CNN) and recurrent neural networks are applied for modulation recognition. These neural networks are widely used in image and signal processing applications. This is followed by designing the other architectures, including densely connected neural network (DenseNet), inception network, recurrent neural network (RNN), long-short term memory network (LSTM), and convolutional long short-term memory deep neural network (CLDNN) for modulation recognition problem, and their results are presented. During this investigation, a basic model is initially considered for each architecture, and then the network performance is studied by adjusting its parameters. The simulation results show that the proposed modified CLDNN model can provide an accuracy of 98% in high SNRs.

Модулятор для здійснення амплітудної модуляції багатьох складових

The authors consider various anti-noise techniques in systems using digital signals, in particular QAM (quadrature amplitude modulation) signals. To increase the noise immunity of a system, it is proposed to use amplitude modulation of many components (AMMС). The advantages of this approach are demonstrated. The principles of AMMС signal formation are explained. The frequency efficiency, energy efficiency, and information efficiency of such signals are theoretically calculated. Different types of digital modulation are compared, the data being presented as a table, and a conclusion is made about a possible practical implementation of the modulator for AMMC signal formation. Structural and functional diagrams of the modulator for AMMC implementation are proposed. The study formulates basic operating principles of such a device, which are used to propose a design option of the modulator. The paper describes particular aspects of creating the main units of the modulator. The authors investigate the characteristics of such a device, particularly the spectrum of the output signal. The obtained research results allow drawing a conclusion that the device complies with the formulated implementation principles of the declared modulation type. A general conclusion is made that the manufactured modulator is suitable for the AMMC. The device is proposed to be used in modern digital communication systems, such as mobile communication systems.

Automatic recognition of the digital modulation types using the artificial neural networks

As digital communication technologies continue to grow and evolve, applications for this steady development are also growing. This growth has generated a growing need to look for automated methods for recognizing and classifying the digital modulation type used in the communication system, which has an important effect on many civil and military applications. This paper suggests a recognizing system capable of classifying multiple and different types of digital modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). This paper focuses on trying to recognize the type of digital modulation using the artificial neural network (ANN) with its complex algorithm to boost the performance and increase the noise immunity of the system.
 This system succeeded in recognizing all the digital modulation types under the current study without any prior information. The proposed system used 8 signal features that were used to classify these 8 modulation methods. The system succeeded in achieving a recognition ratio of at least 68% for experimental signals on a signal to noise ratio (SNR = 5dB) and 89.1% for experimental signals at (SNR = 10dB) and 91% for experimental signals at (SNR = 15dB) for a channel with Additive White Gaussian Noise (AWGN).

Voltage mode ASK/BPSK modulator, its extended applications in precision rectifier and data communication using DXCCII

In this paper, two new modulator circuits are presented to realize two types of digital modulations, namely amplitude shift keying (ASK) and Binary phase shift keying (BPSK). The modulator circuits use one dual-X second-generation current conveyor (DXCCII), three MOS transistors, and one or two resistors. Since in data communication, there is a need for a demodulator and a modulator, a basic demodulator as an application of ASK modulator is presented using a rectifier, envelope detector, and comparator. Moreover, in an integrated circuit (IC), the modularity of sub-circuit is preferred, especially for the ease of fabrication. In maintaining the modularity, an attempt is made to design a precision rectifier, needed for demodulator, as an extension of the proposed modulator with little modifications. The proposed modulators possess the following important features: use all grounded passive components, ASK and BPSK obtain simultaneously, no need for matching passive components, circuits are fully cascadable and easily extendable to a precision rectifier. A non-ideal analysis is carried out. The proposed circuits are verified by PSPICE simulation with a supply voltage of ±0.9 V in 0.13 µm CMOS TSMC technology. Simulation results corroborate the theoretical proposition. Furthermore, the proposed ASK and BPSK modulator circuit is experimentally verified.

Automatic Modulation Classification Based on Deep Residual Networks With Multimodal Information

Automatic modulation classification (AMC) is becoming increasingly important for its fundamental role in dynamic spectrum access, which can support 5G wireless communications to refarm the spectrum resource with low utilization. In order to achieve a better classification performance, several AMC methods based on prototype and variant of convolutional neural networks (CNNs) have been proposed. However, most existing AMC methods based on CNNs only use monomodal information from either time domain or frequency domain. The complementary processing gain, which can be obatianed by fusing multimodal information from multiple transformation domain together, is neglected. To address the issue, we exploit a waveform-spectrum multimodal fusion (WSMF) method to realize AMC based on deep residual networks (Resnet). After extracting features from multimodal information using Resnet, we adopt a feature fusion strategy to merge multimodal features of signals to obtain more discriminating features. Simulation results demonstrate the superior performance of our proposed WSMF method compared with traditional CNNs based AMC method using single modality information. Our proposed method can distinguish among sixteen modulation signals, and it works well even for higher-order digital modulation types like 256QAM and 1024QAM.

Comparative Study and Performance Analysis of Different Modulation Techniques Relevant to Bangabandhu Satellite Communication System

Satellite communication assumes a crucial job in the innovative advancement of a nation. Being the nation"s first communications satellite, Bangabandhu Satellite-1 (BS-1) is considered as an achievement for Bangladesh in the advanced Era. With the effective dispatch and situated in an orbit, this is a proper journey towards the space age for this developing nation that became freedom just 47 years ago. The main objective of this research is to study and compare to analyze the performance of different modulation techniques relevant to the Bangabandhu Satellite-1 communications system. The working principle and modulation techniques of Bangabandhu Satellite-1 as well as the advantages of this satellite in Bangladesh are illustrated here. The analysis and design of the elements of modern satellite communications systems are considered here. Firstly, we have introduced the concept of satellite communication along with its background and motivation. A discussion of the history of BS-1 and its coverage area, technical aspects, frequency bandwidth, services, advantages, and key features are included. Next, we have described different types of analog and digital modulation, highlighting the ones that are being used in BS-1. The performance of different modulation techniques is implemented and compared using MATLAB based simulation. Finally, we can come to the conclusion that modulation techniques used in BS-1 are well serving the purposes considering its performance and beneficial criteria as compared with others.

Usage of production-based expert system and neural network for signal recognition

At the current paper the system of signal type automatic recognition for the problem of automated testing of radar stations based on collaborative usage of production system and neural network set is presented. The main approaches for solving the problem of automated recognition digital and analog modulation types are shortly outlined. Characteristics calculated based on time-domain and frequency-domain signal representation, typical for the radar impulses are considered. The structural schema of the software for automated classification of radar impulses is presented.