Оценка потенциальной помехоустойчивости алгоритма распознавания вида модуляции узкополосных сигналов методом численного моделирования

Context. The article considers the features of solving non-traditional problems of recognition of specified types modulation signals in automated radio monitoring. The practical features of this problem determine the increased a priori uncertainty, which consists in the absence of a priori information about the distribution densities of the given signals and the presence of unknown signals. Objective. It is proposed to solve the problem using an unconventional method for the recognition of statistically specified random signals in the presence of a class of unknown signals. This method assumes that for the given signals there is a classified training sample of realizations, according to which the unknown parameters of their distributions are estimated, as well as some threshold values that determine the probabilities of correct recognition of the given types of signal modulation in the presence of unknown signals. Method. A general solution to the problem of recognition of given signals in the presence of unknown signals is given, and recognition methods of types modulation based on the description of signals by probabilistic model in the form of a mixture of distributions are given. The method is based on the description of signals by a probabilistic model in the form of a mixture of distributions and construction of a closed area for given signals in the probabilistic space of signals. Results. Studies of the recognition problems of given types of modulation of signals have been carried out. The studies were performed by statistical tests on samples of signals for radio monitoring of communications. In this case, the decisive rule for recognizing the given types of signal modulation is implemented in software on a computer. As a result of the statistical tests carried out on control samples of signals, estimates of the probabilities of correct recognition of the given types of signal modulation in the presence of unknown signals were obtained. Conclusions. Values of indicators of quality of radio emissions recognition acceptable for the practice of radio monitoring are obtained. The dependences of quality indicators on some conditions and recognition parameters are property. As a result of the research, practical recommendations were obtained on the use of the proposed method for recognizing specified types of signal modulation in automated radio monitoring systems.

Представлены этапы создания модели сети. Работа в данном направлении включает в себя накопление необходимых знаний и навыков для реализации инфокоммуникационных сетей для разных целей, а также знакомство со специализированным программным обеспечением симуляции процессов реализуемых OMNeT++. Среда моделирования OMNeT++ имеет ряд неоспоримых преимуществ в сравнении с аналогами, что позволяет не только создавать модели инфокоммуникационных сетей, но и, благодаря графическому интерфейсу, демонстрировать и оценивать процессы, протекающие в системе на всех стадиях его работы, включая установление соединения и отключение, передачу данных, а также возможные разрывы соединения и восстановления связи. Результаты симуляции и их анализ показаны на примере базовой модели APSKRadio, содержащей приемопередатчики, использующие один тип модуляции сигнала - амплитудно-фазовую манипуляцию (АФМ, APSK). Целью настоящих исследований и последующих работ, связанных с подобной тематикой, является изучение общих принципов построения и проектирования инфокоммуникационных сетей, предназначенных как для общегражданского применения (локальные вычислительные сети), так и для сетей ведомственного или двойного назначения. Всё вышесказанное позволит эффективно использовать предоставленные производственные ресурсы для реализации сети с необходимым набором параметров и приемлемой стоимостью.

Spectrum awareness is a prominent characteristic of cognitive radio technologies. Realizing such awareness in cognitive radio requires a capability to recognize the incoming signal's modulation type. In this paper, a novel approach to classify digital modulated signals is proposed for cognitive radio. This method combines high-order spectra with sparse representation. We cast the modulation classification problem as finding a sparse representation of the test bispectrum features w.r.t. the training set. The sparse representation can be accurately obtained by solving L1-minimization. Unlike conventional modulation recognition method, if sparsity in the recognition problem is properly harnessed, high-dimensional data with highly distinctive features can be applied in the signal identification. The classification results for the modulation types 2-FSK, 4-FSK, QPSK and 16-QAM, obtained from computer simulations, show the proposed feature extraction and classification method has high classification correct ratio in strong noise condition.

Noise protection of existing radio lines with noise-shaped signals and digital types of modulation was studied. Analysis has shown that the use of such signals in conditions of the radio-electronic conflict does not permit to provide necessary level of noise immunity and transmission security of radio communication lines. It was explained by presence of cyclo-conditionality of the carrier oscillation in signals with digital modulation types. Such properties simplify detection and search of signals by means of spectral correlation methods of modern hostile means of electronic surveillance. To solve this problem, the use of nonstationary signal structures with variable central frequency and spectral density of power was proposed. A procedure of forming such signal structures by application of the Gram-Schmidt orthogonalization procedure to the ensemble of multicomponent LFM signals with controlled spectral characteristics was developed. It was proposed to estimate various signal structures of multicomponent signal by means of phase portraits of summed signals depending on the scaling factor value. This factor’s boundary values at which complexity of the multicomponent signal structure is ensured and degeneration of the process into classical LFM is prevented were established. Change of probability of a symbol error in a channel with the use of multicomponent orthogonal signal structures was studied depending on the signal/noise ratio. This makes it possible to estimate potential noise immunity of the radio line provided that the signal/noise ratio is determined by energy indicators of the radio channel and the spectral density of the noise of natural origin. Structural security of the developed signal structures was estimated by means of an energy detector and a cyclo-stationarity detector. It was established that in the case of energy detection, nonstationary signals, and signals of any other type of modulation are equivalent. However, probability of detecting nonstationary signal structures decreased 2–2.5 times compared to other types of signal modulation when using the cyclo-stationarity detector

Identification of the type of modulation of an intercepted communication signal out of the vast hierarchy of possible modulation types is fundamental before advising a suitable type of demodulator. This process is usually manual. In this paper, a new methodology is suggested- and validated (via computer simulations) for automatic identification of the modulation type (analog and digital) of intercepted communication signals[1]. The methodology is based on zero-based representation of signals and utilization of new algorithms for such identification. Another contribution presented in this paper is a novel ASK modulation and demodulation scheme utilizing zero-manipulation. Zero-crossing analysis describes any of several techniques which make use of information pertaining to the locations in time of successive zero-crossings of a time waveform. Such techniques have found applications for several signal processing and pattern recognition tasks. Some of these tasks include: speech analysis and recognition [2], communications applications [33,...etc. Each of these applications has its specific features regarding the nature of the zero-crossing data measurements and the features extracted from such data, Someapplications utilize the number of zero-crossings, the other may count the time interval between successive zeros.In this paper we shall try tg extend the prowess of zero-based representations to cover important utilities in the field of identification of unknown modulation of intercepted signals. Further, a novel modulation scheme, based on manipulating the zeros of the base band and carrier waveforms, is illustrated. This scheme has been mathematically and experimentally verified to emulate most analog and digital modulation schemes.

The identification of different types of modulation for any intercepted communication signal out of the vast hierarchy of possible modulation types is a key fundamental before advising a suitable type of demodulator, where this process is usually a manual option. This technique is extremely important for the purposes of communication intelligence. In this paper, a proposed methodology is suggested, validated, and tested (through computer simulations) for the automatic identification of the modulation type (analog and digital) of the intercepted communication signals. The methodology is based on the zero-based representation of signals and utilization of new algorithms for such identification.

This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.

The theory of compressed sensing (CS) has shown that compressible signals can be accurately reconstructed from a very small set of randomly projected measurements. Sparse representation of the signals plays an important role in the signal reconstruction of compressed sensing. In this paper, we propose to use signal modulation information to obtain a better sparse representation for communication signals in compressed sensing. In our approach, a tree-structured modulation classification system is used to classify five types of signal modulations: Amplitude Modulation (AM), Frequency Modulation (FM), Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK) and Phase Shift Keying (PSK). The tree-structured classification system uses four signal features to classify the five modulation types, and all features are computable in the analog domain. To select a sparse transformation for the input signal, we propose a pre-trained Karhunen-Loeve transform (KLT) based CS, in which a set of KLT transformation matrices is obtained by an offline learning process for all modulation types. In an online real-time process, the modulation information of the input signal is classified and then used to select one of the pre-trained KLT matrices for providing a better sparse representation of the signal for CS-based signal reconstruction. Our experimental results show that our modulation classification technique is effective in identifying the five modulation types of noisy input signals, and our KLT based CS reconstruction has much better performances than Fourier and wavelet packet based CS for the communication signals we tested.

The identification of signal modulation type in the perception supervision mechanism in the power Internet of Things environment is the primary content. Because the wireless communication channel environment is complex and difficult to predict, especially in the case of strong noise interference, many parameter estimation and modulation type identification methods are difficult to achieve the desired accuracy. In this paper, based on the obvious differences between the cyclic spectrum of different modulation types of signals, related features are extracted, and some recognition techniques are used to distinguish these features. The identification of signal modulation based on cyclic spectrum has the characteristics of strong anti-interference ability, and can also be applied well in the case of low signal-to-noise ratio.

A mathematical model of an algorithm for determining the type of radar signal modulation in an autocorrelation receiver has been developed. The limits of the applicability of the developed algorithm are determined. The parameters of the filters used in the algorithm are substantiated. The influence of the delay time on the detection efficiency is investigated.

In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones.

Certain widely used state-of-the-art, as well as newly suggested, types of signal modulation are studied. The proposed amplitude modulation of many components (AMMC) is characterized by a large Euclidean distance, higher noise immunity under the same maximum power and informativity of the modulated signal, or requires lower power of the modulated signal under the same noise immunity and informativity in comparison with the well-known amplitude shift keying (ASK), phase shift keying (PSK), and quadrature amplitude modulation (QAM). It is found that the technical efficiency of telecommunication systems and networks with limited bandwidth and signal power that use AMMC is overall higher in comparison with the other considered well-known types of modulation. In particular, 37-AMMC with 3 components is the most efficient type among the studied modulations with the number of symbols being up to 37.

The ability to classify different types of signal modulations in radio transmissions is an important task with applications in defense, networking, and communications. This process has traditionally been done manually by human analysts. Recent advances have shown that applying deep learning methods to this task is feasible. But existing recognition networks are complex, with heavy computational requirements, and poor accuracy on some modulation types and in noisy environmentsWe have built a robust radio frequency signal classifier with a hybrid approach that uses images derived from signal constellation and spectrogram data, combined with an efficient convolutional neural network. Compared to the state-of-the-art deep learning classifier, our system obtains better accuracy, with lower computational requirements.

With the development of communication technology, patterns of communication signals have become more complex and diverse. Therefore, the technology of identifying the modulation mode of the communication signal in transmission, especially the modulation recognition technology based on artificial intelligence, has become an extremely important technology in the communication field. For a variety of modulation methods, the traditional method is extremely complicated to implement, and cannot meet the requirements of accurate identification in a short time. In order to increase the speed and reduce the redundancy, this paper proposes a method based on the convolutional autoencoder and the residual network which can realize the denoising, identification and classification of different modulated signals. This method generates ten different modulation types of signals under each signal-to-noise ratio. After the model is trained, the data set is input to the convolutional autoencoder to denoise, and then the data set denoised by the autoencoder is input to the residual network to obtain the classification and recognition accuracy of each modulation type. And an average recognition rate of 92.86% was achieved at -2dB.

This paper presents a method based on Higher-order cumulants for classifying modulation type of PSK, ASK and QAM signals. The method is robust to the presence of frequency offset. The high performance and robustness of the method are proved by computer simulation.