The Experts below are selected from a list of 3879 Experts worldwide ranked by ideXlab platform
Antonio Napolitano - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - On Cyclostationarity-Based Signal Detection
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Antonio NapolitanoAbstract:A new Cyclostationarity-based signal detector is proposed. It is based on (conjugate) cyclic autocorrelation measurements at pairs of cycle frequencies and lags for which the signal-of-interest exhibits Cyclostationarity while the disturbance does not. No assumption is made on the noise distribution and/or its stationarity. A comparison is made with a previously proposed statistical test for presence of Cyclostationarity. Monte Carlo simulations are carried out for performance analysis.
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On Cyclostationarity-Based Signal Detection
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Antonio NapolitanoAbstract:A new Cyclostationarity-based signal detector is proposed. It is based on (conjugate) cyclic autocorrelation measurements at pairs of cycle frequencies and lags for which the signal-of-interest exhibits Cyclostationarity while the disturbance does not. No assumption is made on the noise distribution and/or its stationarity. A comparison is made with a previously proposed statistical test for presence of Cyclostationarity. Monte Carlo simulations are carried out for performance analysis.
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algorithms for analysis of signals with time warped Cyclostationarity
Asilomar Conference on Signals Systems and Computers, 2016Co-Authors: Antonio Napolitano, W.a. GardnerAbstract:Two philosophically different approaches to the analysis of signals with imperfect Cyclostationarity or polycy-clostationarity of the autocorrelation function due to warping are presented. The first approach consists of directly estimating the time-warping function (or its inverse) in a manner that transforms data having an empirical autocorrelation with irregular cyclicity into data having regular cyclicity. The second approach consists of modeling the signal as a time-warped poly-Cyclostationarity stochastic process, thereby providing a wide-sense probabilistic characterization-a time-varying probabilistic autocorrelation function-which is used to specify an estimator of the time-warping function that is intended to remove the impact of time-warping. From this estimate, an estimate of the autocorrelation function of the unwarped process is also obtained.
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ACSSC - Algorithms for analysis of signals with time-warped Cyclostationarity
2016 50th Asilomar Conference on Signals Systems and Computers, 2016Co-Authors: Antonio Napolitano, William A. GardnerAbstract:Two philosophically different approaches to the analysis of signals with imperfect Cyclostationarity or polycy-clostationarity of the autocorrelation function due to warping are presented. The first approach consists of directly estimating the time-warping function (or its inverse) in a manner that transforms data having an empirical autocorrelation with irregular cyclicity into data having regular cyclicity. The second approach consists of modeling the signal as a time-warped poly-Cyclostationarity stochastic process, thereby providing a wide-sense probabilistic characterization-a time-varying probabilistic autocorrelation function-which is used to specify an estimator of the time-warping function that is intended to remove the impact of time-warping. From this estimate, an estimate of the autocorrelation function of the unwarped process is also obtained.
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Cyclostationarity new trends and applications
Signal Processing, 2016Co-Authors: Antonio NapolitanoAbstract:Abstract A concise survey of the literature on Cyclostationarity of the last 10 years is presented and an extensive bibliography included. The problems of statistical function estimation, signal detection, and cycle frequency estimation are reviewed. Applications in communications are addressed. In particular, spectrum sensing and signal classification for cognitive radio, source location, MMSE filtering, and compressive sensing are discussed. Limits to the applicability of the cyclostationary signal processing and generalizations of Cyclostationarity to overcome these limits are addressed in the companion paper “Cyclostationarity: Limits and generalizations”.
Peter J. Schreier - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Two-Channel Passive Detection Exploiting Cyclostationarity
2019 27th European Signal Processing Conference (EUSIPCO), 2019Co-Authors: Stefanie Horstmann, David Ramírez, Peter J. SchreierAbstract:This paper addresses a two-channel passive detection problem exploiting Cyclostationarity. Given a reference channel (RC) and a surveillance channel (SC), the goal is to detect a target echo present at the surveillance array transmitted by an illuminator of opportunity equipped with multiple antennas. Since common transmission signals are cyclostationary, we exploit this information at the detector. Specifically, we derive an asymptotic generalized likelihood ratio test (GLRT) to detect the presence of a cyclostationary signal at the SC given observations from RC and SC. This detector tests for different covariance structures. Simulation results show good performance of the proposed detector compared to competing techniques that do not exploit Cyclostationarity.
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two channel passive detection exploiting Cyclostationarity
European Signal Processing Conference, 2019Co-Authors: Stefanie Horstmann, David Ramírez, Peter J. SchreierAbstract:This paper addresses a two-channel passive detection problem exploiting Cyclostationarity. Given a reference channel (RC) and a surveillance channel (SC), the goal is to detect a target echo present at the surveillance array transmitted by an illuminator of opportunity equipped with multiple antennas. Since common transmission signals are cyclostationary, we exploit this information at the detector. Specifically, we derive an asymptotic generalized likelihood ratio test (GLRT) to detect the presence of a cyclostationary signal at the SC given observations from RC and SC. This detector tests for different covariance structures. Simulation results show good performance of the proposed detector compared to competing techniques that do not exploit Cyclostationarity.
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Detection of Cyclostationarity in the presence of temporal or spatial structure with applications to cognitive radio
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Aaron Pries, David Ramírez, Peter J. SchreierAbstract:One approach to spectrum sensing for cognitive radio is the detection of Cyclostationarity. We extend an existing multi-antenna detector for Cyclostationarity proposed by Ramírez et al. [1], which makes no assumptions about the noise beyond being (temporally) wide-sense stationary. In special cases, the noise could be uncorrelated among antennas, or it could be temporally white. The performance of a general detector can be improved by making use of a priori structural information. We do not, however, require knowledge of the exact values of the temporal or spatial noise covariances. We develop an asymptotic generalized likelihood ratio test and evaluate the performance by simulations.
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detection of multivariate Cyclostationarity
Sport Psychologist, 2015Co-Authors: David Ramírez, Peter J. Schreier, Ignacio Santamaria, L L ScharfAbstract:This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loeve spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of Cyclostationarity from ad-hoc functions of these spectra.
G.b. Giannakis - One of the best experts on this subject based on the ideXlab platform.
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Blind channel identification and equalization using periodic modulation precoders: performance analysis
IEEE Transactions on Signal Processing, 2000Co-Authors: A. Chevreuil, E. Serpedin, P. Loubaton, G.b. GiannakisAbstract:The paper deals with blind identification and equalization of communication channels within the so-called modulation induced Cyclostationarity (MIC) framework, where the input symbol stream is modulated by a P periodic precoder with the purpose of inducing Cyclostationarity in the transmit sequence. By exploiting the Cyclostationarity induced by the periodic precoder, a subspace-based channel identification algorithm that is resilient to the location of channel zeros, channel order overestimation errors, and color of additive stationary noise, is developed. The asymptotic performance of the subspace-based identification approach is analyzed and compared with the asymptotic lower bound provided by the nonlinear cyclic correlation matching approach. Criteria for optimally designing the periodic precoder are also presented. The performance of MMSE-FIR and MMSE-DFE equalizers is quantified for the proposed Cyclostationarity-induced framework in terms of the MMSE. Although Cyclostationarity-inducing transmitters present several advantages relative to their stationary counterparts from a channel estimation viewpoint, it is shown that from an equalization viewpoint, MIC-based systems exhibit a slightly increased MMSE/BER when compared with the stationary case.
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Exploiting input Cyclostationarity for blind channel identification in OFDM systems
IEEE Transactions on Signal Processing, 1999Co-Authors: R.w. Heath, G.b. GiannakisAbstract:Transmitter-induced Cyclostationarity has been explored previously as an alternative to fractional sampling and antenna array methods for blind identification of FIR communication channels. An interesting application of these ideas is in OFDM systems, which induce Cyclostationarity due to the cyclic prefix. We develop a novel subspace approach for blind channel identification using cyclic correlations at the OFDM receiver. Even channels with equispaced unit circle zeros are identifiable in the presence of any nonzero length cyclic prefix with adequate block length. Simulations of the proposed channel estimator along with its performance in OFDM systems combined with impulse response shortening and Reed-Solomon coding are presented.
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Blind channel identification and equalization with modulation-induced Cyclostationarity
IEEE Transactions on Signal Processing, 1998Co-Authors: E. Serpedin, G.b. GiannakisAbstract:Recent results have pointed out the importance of inducing Cyclostationarity at the transmitter for blind identification and equalization of communication channels. This paper addresses blind channel identification and equalization relying on the modulation-induced Cyclostationarity, without introducing redundancy at the transmitter. It is shown that single-input single-output channels can be identified uniquely from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors, provided that the period of modulation-induced Cyclostationarity is greater than half the channel length. Linear, closed-form, nonlinear correlation matching, and subspace-based approaches are developed for channel estimation and are tested using simulations. Necessary and sufficient blind channel identifiability conditions are presented. A Wiener cyclic equalizer is also proposed.
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Blind channel identification with modulation induced Cyclostationarity
Proceedings of 13th International Conference on Digital Signal Processing, 1997Co-Authors: G.b. Giannakis, E. SerpedinAbstract:Previous results have pointed out the importance of inducing Cyclostationarity at the transmitter for blind identification and equalization of communication channels. The present paper shows that by modulating the input sequence with a deterministic and periodic sequence, single-input single-output channels can be identified uniquely from cyclic second-order output statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order over-estimation errors, provided that the period of modulation induced Cyclostationarity is greater than half the channel length. Linear, closed-form, linear inverse, and nonlinear correlation matching approaches are developed for channel estimation and are tested using simulations.
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Cyclostationarity in partial response signaling: a novel framework for blind equalization
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: M K Tsatsanis, G.b. GiannakisAbstract:When fractional samples are available at the receiver, blind channel estimation methods can be developed exploiting the cyclostationary nature of the received signal. In this paper, we show that different solutions are possible if Cyclostationarity is introduced at the transmitter instead of the receiver. We propose specific coding and interleaving strategies at the transmitter which induce Cyclostationarity and facilitate the equalization task. Novel subspace equalization algorithms are derived which make no assumptions whatsoever on the channel zero locations. Synchronization issues are briefly discussed and some simulation examples are presented.
W.a. Gardner - One of the best experts on this subject based on the ideXlab platform.
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algorithms for analysis of signals with time warped Cyclostationarity
Asilomar Conference on Signals Systems and Computers, 2016Co-Authors: Antonio Napolitano, W.a. GardnerAbstract:Two philosophically different approaches to the analysis of signals with imperfect Cyclostationarity or polycy-clostationarity of the autocorrelation function due to warping are presented. The first approach consists of directly estimating the time-warping function (or its inverse) in a manner that transforms data having an empirical autocorrelation with irregular cyclicity into data having regular cyclicity. The second approach consists of modeling the signal as a time-warped poly-Cyclostationarity stochastic process, thereby providing a wide-sense probabilistic characterization-a time-varying probabilistic autocorrelation function-which is used to specify an estimator of the time-warping function that is intended to remove the impact of time-warping. From this estimate, an estimate of the autocorrelation function of the unwarped process is also obtained.
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Cyclostationarity half a century of research
Signal Processing, 2006Co-Authors: W.a. Gardner, Antonio Napolitano, Luigi PauraAbstract:In this paper, a concise survey of the literature on Cyclostationarity is presented and includes an extensive bibliography. The literature in all languages, in which a substantial amount of research has been published, is included. Seminal contributions are identified as such. Citations are classified into 22 categories and listed in chronological order. Both stochastic and nonstochastic approaches for signal analysis are treated. In the former, which is the classical one, signals are modelled as realizations of stochastic processes. In the latter, signals are modelled as single functions of time and statistical functions are defined through infinite-time averages instead of ensemble averages. Applications of Cyclostationarity in communications, signal processing, and many other research areas are considered.
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Cyclostationarity in communications and signal processing
1994Co-Authors: W.a. GardnerAbstract:By providing a comprehensive collection of contributions on the history and current state of the art in this rapidly emerging field, this book gives you a complete survey of the theory, applications, and mathematics of cyclo-stationarity. It brings together the latest work in the field by the foremost experts and presents it in a tutorial fashion. From this book, you will learn new concepts, methods, and algorithms for performing signal processing tasks and designing and analyzing communications systems. Cyclostationarity in Communications and Signal Processing features both broad chapters and more narrowly focused articles that provide in-depth surveys reviewing the newest developments in specific areas. The tutorial style, coupled with the comprehensive reference lists that are provided, make this book instrumental in furthering progress in understanding and using Cyclostationarity in all fields where it arises.
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Spectral characterization of n-th order Cyclostationarity
Fifth ASSP Workshop on Spectrum Estimation and Modeling, 1990Co-Authors: W.a. GardnerAbstract:The spectral characterization of second-order (or wide-sense) Cyclostationarity gives rise to a generalization of the Wiener relation between the power spectral density and the autocorrelation associated with second-order stationary time-series. This generalization, called the cyclic Wiener relation, is a Fourier transform relation between the spectral autocorrelation function and the cyclic temporal autocorrelation function, both defined in terms of time averages on a single time-series. The spectral characterization is generalized from second-order Cyclostationarity to n-th order Cyclostationarity for n=2,3,4,5,. . ., and some basic properties of the generalised spectral characterization are presented. These include a further generalization of the Wiener relation, called the n-th order cyclic Wiener relation, which relates the n-th order joint cyclic temporal moment function to the n-th order joint spectral moment function.
Yukitoshi Sanada - One of the best experts on this subject based on the ideXlab platform.
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Low-Complexity Cyclostationarity Feature Detection Scheme of Localized SC-FDMA Uplink System for Application to Detect and Avoid
Wireless Personal Communications, 2012Co-Authors: Wensheng Zhang, Yukitoshi SanadaAbstract:This paper proposes a low-complexity Cyclostationarity feature detection scheme for detect and avoid (DAA) of Ultra-Wideband (UWB) system in order to solve the coexistence issues between UWB system and Long Term Evolution-Advanced (LTE-Advanced) system. The proposed scheme is suitable for the detection of a localized Single-carrier Frequency Division Multiple Access (SC-FDMA) signal utilized in the uplink of LTE-Advanced system. Compared with conventional Cyclostationarity feature detection, the proposed scheme utilizes all possible cyclic-spectrums located in a distributed window function, which is decided by the frequency distribution of the Primary User (PU) signal. The computational complexity of the proposed scheme is low, due to only one window width instead of all occupied spectrum interval will be searched for the possible cyclic-spectrums. On the other hand, the proposed scheme can also avoid the estimation of the cyclic-spectrums when the type of PU signal is unclear or the cyclic-spectrums are hard to estimate. Simulation results indicate that the proposed scheme can make a tradeoff between detection performance and computational complexity. The low-complexity Cyclostationarity feature detection also provides a substitute for the energy detection when the later approach suffers from the noise uncertainty and cannot distinguish the target signal type.
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low complexity Cyclostationarity feature detection scheme of localized sc fdma uplink system for application to detect and avoid
International Symposium on Communications and Information Technologies, 2010Co-Authors: Wensheng Zhang, Yukitoshi SanadaAbstract:A low-complexity Cyclostationarity feature detection scheme is proposed in this paper. Such method is suitable for the detection of a localized Single-carrier Frequency Division Multiple Access (SC-FDMA) signal. SC-FDMA scheme is the candidate for the uplink of Long Term Evolution-Advanced (LTE-Advanced) system. The detection of the uplink signal is critical to the coexistence between UWB systems and LTE-Advanced systems. The proposed scheme requires low computational complexity at the cost of slight reduction in detection performance. Simulation results and computational complexity analysis indicate that the proposed scheme makes a tradeoff between detection performance and computational complexity. In a worse transmission environment, the low-complexity Cyclostationarity feature detection can be utilized as a substitute for the energy detection since the later approach is sensitive to noise uncertainty and can not work well in such circumstance.
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CrownCom - Cyclostationarity feature matched detection and application to IFDMA system
2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2009Co-Authors: Wensheng Zhang, Yukitoshi SanadaAbstract:Spectrum sensing is the key function of Cognitive Radios which act as secondary users and dynamically access vacant frequency bands. In practice, this sensing function is fulfilled by detecting the existence of primary users' signals. Almost all modulated signals show Cyclostationarity characteristics which are suitable for signal detection. Some specific signals (e.g. Interleaved Frequency Division Multiple Access (IFDMA) signal) regularly distributed in frequency domain possess specific Cyclostationarity distribution feature which can be utilized to construct a signal detector. In this paper, Cyclostationarity feature matched detection (Abbr. is the Cyclostationarity detection during the following parts) based on the correlation of signal inherent distribution in frequency domain is proposed. We give the theoretical deduction of the proposed method and an application example built on the practical IFDMA signal. The proposed detection scheme is simple and efficient because only parts of frequency bands are examined. Simulation results show that the detection performance of the proposed scheme precedes that of the conventional energy detection and so called suboptimum Cyclostationarity detection which is built without known signal distribution information. This paper gives the insight that signal inherent distribution information and signal (Cyclic Periodogram) CP distribution information are useful for the design of simple and efficient spectrum sensors.
