The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Alex B. Gershman - One of the best experts on this subject based on the ideXlab platform.
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blind channel estimation in ds cdma systems with unknown wide Sense Stationary noise using generalized correlation decomposition
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A novel blind subspace-based channel estimation technique is developed for direct-sequence code division multiple-access (DS-CDMA) systems operating in unknown wide-Sense Stationary noise environments. Unlike the existing blind algorithms designed for unknown noise environments, the proposed technique is applicable to any symbol constellation and does not require any auxiliary antennas at the receiver side. The proposed technique is based on the generalized correlation decomposition (GCD) that is used to obtain more accurate estimates of the noise subspace and the user-of-interest channel vector. Simulation results show that when the optimal GCD weighting matrices are used, the estimation performance is substantially improved as compared to the conventional singular value decomposition (SVD)-based blind channel estimation techniques.
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ICASSP - Blind channel estimation in DS-CDMA systems with unknown wide-Sense Stationary noise using generalized correlation decomposition
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A novel blind subspace-based channel estimation technique is developed for direct-sequence code division multiple-access (DS-CDMA) systems operating in unknown wide-Sense Stationary noise environments. Unlike the existing blind algorithms designed for unknown noise environments, the proposed technique is applicable to any symbol constellation and does not require any auxiliary antennas at the receiver side. The proposed technique is based on the generalized correlation decomposition (GCD) that is used to obtain more accurate estimates of the noise subspace and the user-of-interest channel vector. Simulation results show that when the optimal GCD weighting matrices are used, the estimation performance is substantially improved as compared to the conventional singular value decomposition (SVD)-based blind channel estimation techniques.
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Generalized Correlation Decomposition-Based Blind Channel Estimation in DS-CDMA Systems With Unknown Wide-Sense Stationary Noise
IEEE Transactions on Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A new blind subspace-based channel estimation technique is proposed for direct-sequence code-division multiple access (DS-CDMA) systems operating in the presence of unknown wide-Sense Stationary noise. Unlike most of the blind techniques developed for unknown correlated noise environments so far, the proposed algorithm is applicable to an arbitrary symbol constellation and does not require any auxiliary antennas at the receiver or any prior knowledge of the interfering user spreading codes. Our approach exploits the centro-Hermitian property of the unknown noise covariance matrix and makes use of the generalized correlation decomposition (GCD) to obtain an accurate estimate of the noise subspace and, consequently, of the user-of- interest channel vector. We also obtain optimal values of the GCD weighting matrices which maximally preserve the orthogonality of the estimated noise subspace to the actual signal subspace in the high signal-to-noise ratio (SNR) regime. It is shown that such an optimal choice of the weighting matrices transforms GCD to an extended form of the conventional canonical correlation decomposition (CCD). Simulation results further demonstrate that if such an extended CCD-based approach is used to estimate the user-of-interest channel vector, then the estimation performance can be substantially improved as compared to earlier SVD-based blind channel estimation techniques.
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Blind Subspace-Based Signature Estimation in DS-CDMA Systems With Unknown Wide-Sense Stationary Interference
IEEE Transactions on Signal Processing, 2007Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:In this paper, we propose a new blind subspace-based signature waveform estimation technique for direct-sequence code division multiple-access (DS-CDMA) communication systems operating in the presence of unknown wide-Sense Stationary interference. Unlike the existing algorithms, the proposed technique requires just a single receive antenna and is applicable to the case of arbitrary transmitted symbol constellations. Necessary and sufficient conditions for identifiability of the proposed technique are derived. For practical scenarios where the data covariance matrix is estimated using a finite number of samples, a closed form expression for the mean-squared error (MSE) of the estimated channel is obtained. For the high signal-to-interference ratio (SIR) regime and Gaussian interference, an approximation of the MSE expression is also derived and the effects of different parameters on the performance of the proposed algorithm are analyzed. Numerical examples validate our analytical results.
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Subspace-Based Blind Channel Estimation in DS-CDMA Systems with Unknown Wide-Sense Stationary Interference
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A new blind subspace-based channel and signature waveform estimation technique is proposed for DS-CDMA communication systems operating in the presence of unknown wide-Sense Stationary interference. Unlike the existing algorithms, our technique requires single receive antenna and is applicable to the general case of arbitrary transmitted symbol constellations. Necessary and sufficient conditions for identifiability of the proposed technique are derived. Closed-form expressions for the mean-squared error (MSE) of the estimated channel are obtained and verified by means of simulations
Keyvan Zarifi - One of the best experts on this subject based on the ideXlab platform.
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blind channel estimation in ds cdma systems with unknown wide Sense Stationary noise using generalized correlation decomposition
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A novel blind subspace-based channel estimation technique is developed for direct-sequence code division multiple-access (DS-CDMA) systems operating in unknown wide-Sense Stationary noise environments. Unlike the existing blind algorithms designed for unknown noise environments, the proposed technique is applicable to any symbol constellation and does not require any auxiliary antennas at the receiver side. The proposed technique is based on the generalized correlation decomposition (GCD) that is used to obtain more accurate estimates of the noise subspace and the user-of-interest channel vector. Simulation results show that when the optimal GCD weighting matrices are used, the estimation performance is substantially improved as compared to the conventional singular value decomposition (SVD)-based blind channel estimation techniques.
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ICASSP - Blind channel estimation in DS-CDMA systems with unknown wide-Sense Stationary noise using generalized correlation decomposition
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A novel blind subspace-based channel estimation technique is developed for direct-sequence code division multiple-access (DS-CDMA) systems operating in unknown wide-Sense Stationary noise environments. Unlike the existing blind algorithms designed for unknown noise environments, the proposed technique is applicable to any symbol constellation and does not require any auxiliary antennas at the receiver side. The proposed technique is based on the generalized correlation decomposition (GCD) that is used to obtain more accurate estimates of the noise subspace and the user-of-interest channel vector. Simulation results show that when the optimal GCD weighting matrices are used, the estimation performance is substantially improved as compared to the conventional singular value decomposition (SVD)-based blind channel estimation techniques.
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Generalized Correlation Decomposition-Based Blind Channel Estimation in DS-CDMA Systems With Unknown Wide-Sense Stationary Noise
IEEE Transactions on Signal Processing, 2008Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A new blind subspace-based channel estimation technique is proposed for direct-sequence code-division multiple access (DS-CDMA) systems operating in the presence of unknown wide-Sense Stationary noise. Unlike most of the blind techniques developed for unknown correlated noise environments so far, the proposed algorithm is applicable to an arbitrary symbol constellation and does not require any auxiliary antennas at the receiver or any prior knowledge of the interfering user spreading codes. Our approach exploits the centro-Hermitian property of the unknown noise covariance matrix and makes use of the generalized correlation decomposition (GCD) to obtain an accurate estimate of the noise subspace and, consequently, of the user-of- interest channel vector. We also obtain optimal values of the GCD weighting matrices which maximally preserve the orthogonality of the estimated noise subspace to the actual signal subspace in the high signal-to-noise ratio (SNR) regime. It is shown that such an optimal choice of the weighting matrices transforms GCD to an extended form of the conventional canonical correlation decomposition (CCD). Simulation results further demonstrate that if such an extended CCD-based approach is used to estimate the user-of-interest channel vector, then the estimation performance can be substantially improved as compared to earlier SVD-based blind channel estimation techniques.
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Blind Subspace-Based Signature Estimation in DS-CDMA Systems With Unknown Wide-Sense Stationary Interference
IEEE Transactions on Signal Processing, 2007Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:In this paper, we propose a new blind subspace-based signature waveform estimation technique for direct-sequence code division multiple-access (DS-CDMA) communication systems operating in the presence of unknown wide-Sense Stationary interference. Unlike the existing algorithms, the proposed technique requires just a single receive antenna and is applicable to the case of arbitrary transmitted symbol constellations. Necessary and sufficient conditions for identifiability of the proposed technique are derived. For practical scenarios where the data covariance matrix is estimated using a finite number of samples, a closed form expression for the mean-squared error (MSE) of the estimated channel is obtained. For the high signal-to-interference ratio (SIR) regime and Gaussian interference, an approximation of the MSE expression is also derived and the effects of different parameters on the performance of the proposed algorithm are analyzed. Numerical examples validate our analytical results.
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Subspace-Based Blind Channel Estimation in DS-CDMA Systems with Unknown Wide-Sense Stationary Interference
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, 2006Co-Authors: Keyvan Zarifi, Alex B. GershmanAbstract:A new blind subspace-based channel and signature waveform estimation technique is proposed for DS-CDMA communication systems operating in the presence of unknown wide-Sense Stationary interference. Unlike the existing algorithms, our technique requires single receive antenna and is applicable to the general case of arbitrary transmitted symbol constellations. Necessary and sufficient conditions for identifiability of the proposed technique are derived. Closed-form expressions for the mean-squared error (MSE) of the estimated channel are obtained and verified by means of simulations
Bruno Cernuschi-frias - One of the best experts on this subject based on the ideXlab platform.
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On the Papoulis Sampling Theorem: Some General Conditions
IEEE Transactions on Information Theory, 2018Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:Some general conditions for multichannel sampling are established for wide Sense Stationary sequences with spectral density. First, necessary and sufficient conditions are given for these processes so that they are linearly determined by the samples obtained from a multichannel sampling scheme. Some results are studied for Stationary sequences and then applied to the problem of sampling, not necessarily band limited, wide Sense Stationary processes. Conditions are also given for the existence of a frame sequence of the samples. In the case of frames, the condition that the spectral measure is absolutely continuous is proved to be necessary.
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Wide Sense Stationary Processes Forming Frames
IEEE Transactions on Information Theory, 2011Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:In this paper, we study the question of the representation of random variables by means of frames or Riesz basis generated by Stationary sequences. This concerns the representation of continuous time wide Sense Stationary random processes by means of discrete samples.
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On the Prediction of a Class of Wide-Sense Stationary Random Processes
IEEE Transactions on Signal Processing, 2011Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:We prove that under suitable conditions, a multi-band wide Sense Stationary stochastic process can be linearly predicted at time with arbitrarily small error using past samples taken at uniform rate. This result generalizes previous similar results for band-limited signals. Moreover we prove that the prediction problem from uniform past samples is equivalent to a disjoint translates condition on the spectrum together with the divergence of a logarithmic integral. We also show that, for the band-limited case, under similar conditions, non uniform samples can be taken.
Juan Miguel Medina - One of the best experts on this subject based on the ideXlab platform.
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On the Papoulis Sampling Theorem: Some General Conditions
IEEE Transactions on Information Theory, 2018Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:Some general conditions for multichannel sampling are established for wide Sense Stationary sequences with spectral density. First, necessary and sufficient conditions are given for these processes so that they are linearly determined by the samples obtained from a multichannel sampling scheme. Some results are studied for Stationary sequences and then applied to the problem of sampling, not necessarily band limited, wide Sense Stationary processes. Conditions are also given for the existence of a frame sequence of the samples. In the case of frames, the condition that the spectral measure is absolutely continuous is proved to be necessary.
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Wide Sense Stationary Processes Forming Frames
IEEE Transactions on Information Theory, 2011Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:In this paper, we study the question of the representation of random variables by means of frames or Riesz basis generated by Stationary sequences. This concerns the representation of continuous time wide Sense Stationary random processes by means of discrete samples.
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On the Prediction of a Class of Wide-Sense Stationary Random Processes
IEEE Transactions on Signal Processing, 2011Co-Authors: Juan Miguel Medina, Bruno Cernuschi-friasAbstract:We prove that under suitable conditions, a multi-band wide Sense Stationary stochastic process can be linearly predicted at time with arbitrarily small error using past samples taken at uniform rate. This result generalizes previous similar results for band-limited signals. Moreover we prove that the prediction problem from uniform past samples is equivalent to a disjoint translates condition on the spectrum together with the divergence of a logarithmic integral. We also show that, for the band-limited case, under similar conditions, non uniform samples can be taken.
Steven Kay - One of the best experts on this subject based on the ideXlab platform.
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Wide Sense Stationary Random Processes
Intuitive Probability and Random Processes Using MATLAB®, 2012Co-Authors: Steven KayAbstract:Having introduced the concept of a random process in the previous chapter, we now wish to explore an important subclass of Stationary random processes. This is motivated by the very restrictive nature of the stationarity condition, which although mathematically expedient, is almost never satisfied in practice. A somewhat weaker type of stat ionarity is based on requiring the mean to be a constant in time and the covariance sequence to depend only on the separation in time between the two samples. We have already encountered these types of random processes in Examples 16.9-16.11. Such a random process is said to be Stationary in the wide Sense or wide Sense Stationary (WSS). It is also termed a weakly Stationary random process to distinguish it from a Stationary process, which is said to be strictly Stationary. We will use the form er terminology to refer to such a process as a WSS random process. In addition, as we will see in Chapter 19, if the random process is Gaussian, then wide Sense stationarity implies stationarity. For this reason alone, it makes Sense to explore WSS random processes since the use of Gaussian random processes for modeling is ubiquitous.
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Linear Systems and Wide Sense Stationary Random Processes
Intuitive Probability and Random Processes Using MATLAB®, 2012Co-Authors: Steven KayAbstract:Most physical systems are conveniently modeled by a linear system. These include electrical circuits, mechanical machines, human biological functions, and chemical reactions, just to name a few. When the system is capable of responding to a continuous-time input, its effect can be described using a linear differential equation. For a system that responds to a discrete-time input a linear difference equation can be used to characterize the effect of the system. Furthermore, for systems whose characteristics do not change with time, the coefficients of the differential or difference equation are constants. Such a system is termed a linear time invariant (LTI) system for continuous-time inputs/outputs and a linear shift invariant (LSI) system for discrete-time inputs/outputs. In this chapter we explore the effect of these systems on wide Sense Stationary (WSS) random process inputs. The reader who is unfamiliar with the basic concepts of linear systems should first read Appendix D for a brief introduction. Many excellent books are available to supplement this material [Jackson 1991, Oppenheim, Willsky, and Nawab 1997, Poularikas and Seely 1985]. We will now consider only discrete-time systems and discrete-time WSS random processes. A summary of the analogous concepts for the continuous-time case is given in Section 18.6.
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Multiple Wide Sense Stationary Random Processes
Intuitive Probability and Random Processes Using MATLAB®, 2012Co-Authors: Steven KayAbstract:In Chapters 7 and 12 we defined multiple random variables X and Y as a mapping from the sample space S of the experiment to a point (x, y) in the x-y plane. We now extend that definition to be a mapping from S to a point in the x-y plane that evolves with time, and denote that point as (x[n], y[n]) for –∞ < n < ∞. The mapping, denoted either by (X[n], Y[n]) or equivalently by [X[n] Y[n]T, is called a jointly distributed random process. An example is the mapping from a point at some geographical location, where the possible choices for the location constitute S, to the daily temperature and pressure at that point or (T[n], P[n]). Instead of treating the random processes, which describe temperature and pressure, separately, it makes more Sense to analyze them jointly. This is especially true if the random processes are correlated. For example, a drop in barometric pressure usually indicates the onset of a storm, which in turn will cause a drop in the temperature.
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Representation and Generation of Non-Gaussian Wide-Sense Stationary Random Processes With Arbitrary PSDs and a Class of PDFs
IEEE Transactions on Signal Processing, 2010Co-Authors: Steven KayAbstract:A new method for representing and generating realizations of a wide-Sense Stationary non-Gaussian random process is described. The representation allows one to independently specify the power spectral density and the first-order probability density function of the random process. The only proviso is that the probability density function must be symmetric and infinitely divisible. The method proposed models the sinusoidal component frequencies as random variables, a key departure from the usual representation a of wide-Sense Stationary random process by the spectral theorem. Ergodicity in the mean and autocorrelation is also proven, under certain conditions. An example is given to illustrate its application to the K distribution, which is important in many physical modeling problems in radar and sonar.
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Intuitive Probability and Random Processes using MATLAB
2005Co-Authors: Steven KayAbstract:Computer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.
