The Experts below are selected from a list of 120315 Experts worldwide ranked by ideXlab platform
Menguc Oner - One of the best experts on this subject based on the ideXlab platform.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2017Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative Identification of unknown communication Signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to Signal Identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO Signal Identification by considering the modulation type and the STBC classification tasks as a joint classification problem.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2016Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative Identification of unknown communication Signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to Signal Identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO Signal Identification by considering the modulation type and the STBC classification tasks as a joint classification problem.Publisher's Versio
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Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges
IEEE Communications Surveys & Tutorials, 2016Co-Authors: Yahia A. Eldemerdash, Octavia A. Dobre, Menguc OnerAbstract:Signal Identification is an umbrella term for Signal processing techniques designed for the Identification of the transmission parameters of unknown or partially known communication Signals. Initially, a key technology for military applications such as Signal interception, radio surveillance and electronic warfare, Signal Identification techniques recently found applications in commercial wireless communications as an enabling technology for cognitive receivers. With the advance and rapid adoption of multiple-input multiple-output (MIMO) communication systems in the last decade, extension of Signal Identification methods to include this transmission paradigm has become a priority and focus of intensive research efforts. The aim of this work is to provide a comprehensive state-of-the-art survey on algorithms proposed for the new and challenging Signal Identification problems specific to MIMO systems, including space-time block code (STBC) Identification, MIMO modulation Identification, and detection of the number of transmit antennas. Finally, concluding remarks on MIMO Signal Identification are provided along with an outline of the open problems and future research directions.
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cyclostationarity based robust algorithms for qam Signal Identification
IEEE Communications Letters, 2012Co-Authors: Octavia A. Dobre, Menguc Oner, Sreeraman Rajan, Robert InkolAbstract:This letter proposes two novel algorithms for the Identification of quadrature amplitude modulation (QAM) Signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the Identification performance of the proposed algorithms compares favorably with that of alternative approaches.
Ali Gorcin - One of the best experts on this subject based on the ideXlab platform.
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Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function
arXiv: Signal Processing, 2020Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Khalid A. QaraqeAbstract:Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and Signal Identification. The proposed method classifies wireless Signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, Signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of Signals beforehand. Implementation of the method on the measured overthe-air real-world Signals in cellular bands indicates important performance gains when compared to the Signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular Signals, the proposed approach can be extended to the detection and classification of any Signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.
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WCNC - On the Investigation of Wireless Signal Identification Using Spectral Correlation Function and SVMs
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Kursat Tekbiyik, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Ali GorcinAbstract:Signal Identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based Identification has also an important place in Signal Identification domain. In this paper, a generalized Identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless Signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different Signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless Signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.
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Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines
IEEE Access, 2019Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut KurtAbstract:Radio air interface Identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) Signal Identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto–correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over–the–air real–world measurements are taken to show that wireless Signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in–phase/quadrature (I/Q) samples, training set size, or Signal–to–noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well–known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/ $F_{1}$ –scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing.
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An OFDM Signal Identification Method for Wireless Communications Systems
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Ali Gorcin, Huseyin ArslanAbstract:Distinction of orthogonal frequency-division multiplexing (OFDM) Signals from single-carrier Signals is highly important for adaptive receiver algorithms and Signal Identification applications. OFDM Signals exhibit Gaussian characteristics in the time domain, and fourth-order cumulants of Gaussian distributed Signals vanish, in contrast to the cumulants of other Signals. Thus, fourth-order cumulants can be utilized for OFDM Signal Identification. In this paper, first, formulations of the estimates of the fourth-order cumulants for OFDM Signals are provided. Then, it is shown that these estimates are significantly affected by wireless channel impairments, frequency offset, phase offset, and sampling mismatch. To overcome these problems, a general chi-square constant false-alarm rate Gaussianity test, which employs estimates of cumulants and their covariances, is adapted to the specific case of wireless OFDM Signals. Estimation of the covariance matrix of the fourth-order cumulants is greatly simplified, peculiar to the OFDM Signals. A measurement setup is developed to analyze the performance of the Identification method and for comparison purposes. A parametric measurement analysis is provided, depending on modulation order, Signal-to-noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests that are based on fixed thresholds or empirical values, whereas the a priori information requirement and complexity of the proposed method are lower than the coherent Identification techniques.
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An OFDM Signal Identification Method for Wireless Communications Systems
arXiv: Information Theory, 2014Co-Authors: Ali Gorcin, Huseyin ArslanAbstract:Distinction of OFDM Signals from single carrier Signals is highly important for adaptive receiver algorithms and Signal Identification applications. OFDM Signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed Signals vanish in contrary to the cumulants of other Signals. Thus fourth order cumulants can be utilized for OFDM Signal Identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM Signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM Signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM Signals. A measurement setup is developed to analyze the performance of the Identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, Signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent Identification techniques.
Ozgur Bayer - One of the best experts on this subject based on the ideXlab platform.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2017Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative Identification of unknown communication Signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to Signal Identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO Signal Identification by considering the modulation type and the STBC classification tasks as a joint classification problem.
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Joint Space Time Block Code and Modulation Classification for MIMO Systems
IEEE Wireless Communications Letters, 2016Co-Authors: Ozgur Bayer, Menguc OnerAbstract:Non-cooperative Identification of unknown communication Signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to Signal Identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO Signal Identification by considering the modulation type and the STBC classification tasks as a joint classification problem.Publisher's Versio
Kursat Tekbiyik - One of the best experts on this subject based on the ideXlab platform.
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Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function
arXiv: Signal Processing, 2020Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Khalid A. QaraqeAbstract:Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and Signal Identification. The proposed method classifies wireless Signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, Signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of Signals beforehand. Implementation of the method on the measured overthe-air real-world Signals in cellular bands indicates important performance gains when compared to the Signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular Signals, the proposed approach can be extended to the detection and classification of any Signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.
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WCNC - On the Investigation of Wireless Signal Identification Using Spectral Correlation Function and SVMs
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Kursat Tekbiyik, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Ali GorcinAbstract:Signal Identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based Identification has also an important place in Signal Identification domain. In this paper, a generalized Identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless Signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different Signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless Signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.
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Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines
IEEE Access, 2019Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut KurtAbstract:Radio air interface Identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) Signal Identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto–correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over–the–air real–world measurements are taken to show that wireless Signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in–phase/quadrature (I/Q) samples, training set size, or Signal–to–noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well–known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/ $F_{1}$ –scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing.
Gunes Karabulut Kurt - One of the best experts on this subject based on the ideXlab platform.
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Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function
arXiv: Signal Processing, 2020Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Khalid A. QaraqeAbstract:Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and Signal Identification. The proposed method classifies wireless Signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, Signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of Signals beforehand. Implementation of the method on the measured overthe-air real-world Signals in cellular bands indicates important performance gains when compared to the Signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular Signals, the proposed approach can be extended to the detection and classification of any Signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development.
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WCNC - On the Investigation of Wireless Signal Identification Using Spectral Correlation Function and SVMs
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Kursat Tekbiyik, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut Kurt, Ali GorcinAbstract:Signal Identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based Identification has also an important place in Signal Identification domain. In this paper, a generalized Identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless Signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different Signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless Signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.
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Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines
IEEE Access, 2019Co-Authors: Kursat Tekbiyik, Ali Gorcin, Ozkan Akbunar, Ali Riza Ekti, Gunes Karabulut KurtAbstract:Radio air interface Identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) Signal Identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto–correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over–the–air real–world measurements are taken to show that wireless Signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in–phase/quadrature (I/Q) samples, training set size, or Signal–to–noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well–known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/ $F_{1}$ –scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing.
