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Clustering algorithm performances of PSK and QAM modulations. (a) Clustering algorithm performed on signals with SNR = 10 dB. The red dots show the center of each cluster. (i) QPSK, (ii) 8-PSK, (iii) 16-PSK, and (iv) 32-PSK. (b) Clustering algorithm performed on signals with SNR = 10 dB. The red dots show the center of each cluster. (i) 4-QAM, (ii) 16-QAM, (iii) 64-QAM, and (iv) 256-QAM.
Source publication
We report here on the utilization of signal in-phase-quadrature (I-Q) diagrams in a novel modulation classification (MC) technique. This MC technique is able to classify linear digital single-carrier modulations as well as multi-carrier modulations. The method uses the waveforms' I-Q diagrams and, by employing a combination of k-center and k-means...
Contexts in source publication
Context 1
... simulations were produced using MATLAB R2009a x64 on Windows 7. To test the algo- rithm against different types of modulations, we have considered the highly used modulations of M-PSK and M-QAM. Figure 5a, b corresponds to results of our single-carrier MC that uses k-means and k-center algorithms on I-Q http://jwcn.eurasipjournals.com diagrams of modulations to determine the modulation type. ...
Context 2
... the case of 256-QAM modulation in Figure 5b, we see a few missing symbols in the constellation, which decreases when we double the number of samples from 512 to 1,024. This is due to the fact that, for the case of 256-QAM modulation and 512 samples, there are only two samples per symbol on average in the I-Q diagram and, in some cases, there are no samples present for some of the clusters in the I-Q diagram. ...
Citations
... The ability of modulation detection in low SNR conditions using short observation intervals is one of most important criteria of good classifiers [17][18][19][20]. These classifiers must also be robust against processing errors and be able to detect large number of modulations in different propagation conditions. ...
In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
... The ability of modulation detection in low SNR conditions using short observation intervals is one of most important criteria of good classifiers [17]- [20]. These classifiers must also be robust against processing errors and be able to detect large number of modulations in different propagation conditions. ...
In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power through frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. Results show improvement of success rate compared of some published method. Furthermore, the algorithm directly computes SINR after signal is detected by successive interference cancellation (SIC) and before any signal decoding. Moreover, because of the direct sense of physical channel, the presented algorithm can discount occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity through the eNode B connections. Simulation results in compare to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
... Based on this explanation at the first step of the proposed algorithm, the normality test is performed to discriminate OFDM from other modulation schemes, as it is shown in Fig.1 . In other words, the normality test of the received signal is used to discriminate single-carrier modulations from multi-carrier modulations [19,20]. In this paper, we assume that multi-carrier modulation category includes only OFDM, and single-carrier modulation category includes QAM, PSK, FSK, and MSK. ...
An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.
... Os testes de distribuição mais comuns são: Hinich, Kolmogorov-Smirnov, Jarque-Bera, Giannakis-Tsatsanis, Anderson-Darling, D'agostino-Pearson, Shapiro-Wilk, Cramer-von-Mises e Lilliefors. Apesar de haver vários testes, eles apresentam deficiências na presença de ruído e por esse motivo não são adequados para classicação de sinais digitais [34]. ...
A subutilização do espectro de frequência é uma problema recorrente atualmente e, com o aumento da demanda de usuários que utilizam sistemas de comunicação remota, foi necessário buscar uma maneira mais eficiente de alocar usuários no espectro, surgindo assim, as técnicas que aplicam o rádio cognitivo. Nesta dissertação propõe-se, para classificar sinais modulados, utilizar uma gama de classificadores multiclasse supervisionados baseados em aprendizado de máquina e aprendizado profundo, com seus parâmetros pré-estabelecidos. Dentre os classificadores englobados em aprendizado de máquina, abordamos algoritmos baseados em árvore de decisão e algoritmo de classificação probabilística, Naive Bayes. Dentro do aprendizado profundo, aplicou-se redes neurais artificiais através de uma rede perceptron multicamada totalmente conectada com retropropagação utilizando algoritmo de Levenberg-Marquardt para atualização dos pesos da rede. Foram obtidos taxas de acurácia de 95,28% e 93,12% nos classificadores baseados em árvore de decisão, 87,40% na rede neural e 74,78% no Naive Bayes. Na literatura foi encontrado um trabalho com base de dados semelhante qualitativamente a utilizada nesta dissertação e sua acurácia foi de 89,72%, enquanto a melhor acurácia apresentada nesta dissertação foi de 95,28%.
... The model proposed by Liu et al process certain parameters before entering in the neural network in the purpose of modulation recognition. Okhtay et al [6] proposed an algorithm of classification that use a combination of clustering algorithm kmeans and k-center using I-Q diagram of constellation for modulation classification. Zhechen et al used K Nearest Neighbor (KNN) combined with genetic algorithm [15] to achieve classification. ...
... KNN is one of famous learning algorithms which is categorized into learning and classification phases [14]. The principal problem is to classify modulated numerical signal, at the first step many papers [5,6,15,16] used combination of 2 methods to solve the problem. We proposed a new approach based on I-Q diagram of constellation coded in pixel. ...
... In our model for classification, we suppose that Gaussian test for detecting single carrier or multiple carrier signal have been done [6]. In our case we are in face of single carrier signal. ...
Automatic Modulation Classification (AMC) with intelligent system is an attracting area of research due to the development of SDR (Software Defined Radio). This paper proposes a new algorithm based on a combination of k-means clustering and Artificial Neural Network (ANN). We use constellation diagram I-Q (In phase, Quadrature) as basic information. K-means algorithm is used to normalize data transmitted and pollute by the Additive White Gaussian Noise (AWGN), then the new diagram obtained is considered as an image and coded in pixel before entering in MLP (Multi-Layer Perceptron) Neural Network. Simulation results show an improvement of recognition rate under low SNR (Signal Noise Rate) compare to some results obtained in the literature.
... The model proposed by Liu et al process certain parameters before entering in the neural network in the purpose of modulation recognition. Okhtay et al [6] proposed an algorithm of classification that use a combination of clustering algorithm kmeans and k-center using I-Q diagram of constellation for modulation classification. Zhechen et al used K Nearest Neighbor (KNN) combined with genetic algorithm [15] to achieve classification. ...
... KNN is one of famous learning algorithms which is categorized into learning and classification phases [14]. The principal problem is to classify modulated numerical signal, at the first step many papers [5,6,15,16] used combination of 2 methods to solve the problem. We proposed a new approach based on I-Q diagram of constellation coded in pixel. ...
... In our model for classification, we suppose that Gaussian test for detecting single carrier or multiple carrier signal have been done [6]. In our case we are in face of single carrier signal. ...
... In [31], the K-means algorithm has been proposed to extract the features of the input data x. The K-means and the K-center algorithms have been used in [6] for modulation identification. The Fuzzy k-means algorithm has been proposed for AMC in [2]. ...
... In [31], K-means algorithm has been proposed to extract the features of the input data x. K-means and K-center algorithms have been used in [6] for the modulation identification. The Fuzzy k-means algorithm has been proposed for AMC in [2]. ...
Modulation identification of the transmitted signals remain a challenging area in modern intelligent communication systems like cognitive radios. The computation of the distinct features from input data set and applying machine learning algorithms has been a well-known method in the classification of such signals. However, recently, deep neural networks, a branch of machine learning, have gained significant attention in the pattern recognition of complex data due to its superior performance. Here, we test the application of deep neural networks to the automatic modulation classification in AWGN and flat-fading channel. Three training inputs were used; mainly 1) In-phase and quadrature (I-Q) constellation points, 2) the centroids of constellation points employing the fuzzy C-means algorithm to I-Q diagrams, and 3) the high-order cumulants of received samples. The unsupervised learning from these data sets was done using the sparse autoencoders and a supervised softmax classifier was employed for the classification. The designing parameters for training single and 2-layer sparse autoencoders are proposed and their performance compared with each other. The results show that a very good classification rate is achieved at a low SNR of 0 dB. This shows the potential of the deep learning model for the application of modulation classification.
... Nos sistemas AMC-FB, há uma grande diversidade de características utilizadas que utilizam as informações de amplitude, fase e frequência instantâneas presentes nas relações entre as componentes em fase (do inglês, in-phase, I) e em quadratura (do inglês, quadrature, Q) da envoltória complexa, conforme apresentado na equação (4.15).A metodologia proposta abrange sinais com modulações analógicas, digitais, por espalhamento espectral e por multiportadoras. Neste sentido, realizou-se uma ampla pesquisa bibliográca para a determinação das características utilizadas em cada uma dessas categorias de sinais modulados e vericou-se que sobre sinais ana-41 lógicos e digitais, destacam-se as características espectrais e estatísticas[7,38,76], enquanto que para sinais modulados por espalhamento espectral e por multiportadoras, destacam-se características de cicloestacionaridade[54,58,81,82,84], por transformadas[38] e baseados em teste de distribuição[53,79].Assim, a etapa de extração de características é composta de uma miscelânea das principais características disponíveis na literatura. No total são calculadas 29 características sobre cada envoltória complexa proveniente da amostragem dos sinais modulado em banda passante, formando um vetor de característica de 29 posições, onde cada posição do vetor corresponde à um valor de determinada característica. ...
O Rádio Cognitivo é uma nova tecnologia que busca resolver o problema de subutilização do espectro de radiofrequências, por meio do sensoriamento do espectro, cujo objetivo é detectar os buracos espectrais. A classificação automática de modulação desempenha um papel importante neste cenário, pois, provém informação sobre os usuários primários de modo a auxiliar nas tarefas de sensoriamento do espectro. Nesta dissertação, propomos uma metodologia para a classificação multiclasse e hierárquica de sinais modulados utilizando SVM, com um conjunto de parâmetros pré-definidos. Na literatura, outros trabalhos tratam da classificação automática de modulação tanto com SVM como com outros tipos de classificadores, porém, poucos fazem uma análise detalhada do projeto dos classificadores. O SVM é conhecido por sua alta capacidade de discriminação, todavia, seu desempenho é bastante sensível aos parâmetros usados na geração dos classificadores. Com a utilização de um conjunto pré-definido de parâmetros, buscamos analisar o comportamento do classificador de forma ampla e investigar a influência das mudanças de parâmetros na constituição de classificadores. Além disso, utiliza-se as técnicas de decomposição multiclasse um-contra-todos, um-contra-um, códigos de saída corretores de erros e hierárquica. Por m, foram utilizados nove tipos de modulações (AM, FM, BPSK, QPSK, 16QAM, 64QAM, GMSK, OFDM e WCDMA). Tanto os tipos de modulação quanto as técnicas de decomposição abrangem quase a totalidade de técnicas de decomposição e de classes de modulação presentes na literatura.
... Various conventional spectrum sensing techniques have been proposed so far (e.g., [5,6,7,8,9,10,11]) These techniques have various shortcomings that can highly hinder their practical use in cognitive radio environments. To overcome these drawbacks, considerable attention has been recently paid to the use of Modulation Classification (MC) for spectrum sensing in cognitive radio systems [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. This technique enables cognitive radio devices to reliably sense and detect all forms of primary radio signals in the spectrum environment and enhance the overall performance of cognitive radio systems [22]. ...
... Considerable research work has been carried out over the past few years on spectrum sensing for cognitive radio systems using modulation classification. Several methods are found in the literature [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. ...
... In [17,18], spectrum sensing methods using modulation classification are proposed, for different modulation sets under various transmission conditions, by employing the constellation shapes of the received signals as key features for modulation classification and K-means classifiers as classification systems. These methods are computationally efficient, and therefore suitable for real time applications. ...
Spectrum sensing is the most important procedure for the operation of cognitive radio systems. To overcome the shortcomings of the conventional spectrum sensing techniques, considerable attention has been recently focused on the use of Modulation Classification (MC) for spectrum sensing. It relies on the fact that all primary users employ one modulation scheme or another for the transmission over the wireless channel; therefore, detecting any modulation scheme would be enough to confirm the presence of the primary user’s signal in the wireless channel. This paper discusses the spectrum sensing for cognitive radio systems using modulation classification and provides a critical review of the existing methods; where gaps in the knowledge base are highlighted and directions for future research are suggested.
The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.
