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CDMA Sparse Channel Estimation Using a GSIC/AM Algorithm for Radiolocation
"... This paper considers channel parameter estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) a ..."
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This paper considers channel parameter estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM
A matching pursuit/GSICbased algorithm for DSCDMA sparse channel estimation
 IEEE Signal Processing Letters
, 2004
"... A sparse channel estimation algorithm for multiuser environments is developed with application to time of arrival (TOA)based radiolocation. To eliminate multiple access interference, the Generalized Successive Interference Cancellation (GSIC) algorithm is used. At each GSIC stage, the matching purs ..."
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Cited by 8 (0 self)
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A sparse channel estimation algorithm for multiuser environments is developed with application to time of arrival (TOA)based radiolocation. To eliminate multiple access interference, the Generalized Successive Interference Cancellation (GSIC) algorithm is used. At each GSIC stage, the matching
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
, 2000
"... Introduction In blind source separation an Nchannel sensor signal x(t) arises from M unknown scalar source signals s i (t), linearly mixed together by an unknown N M matrix A, and possibly corrupted by additive noise (t) x(t) = As(t) + (t) (1.1) We wish to estimate the mixing matrix A and the M ..."
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Cited by 274 (34 self)
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Introduction In blind source separation an Nchannel sensor signal x(t) arises from M unknown scalar source signals s i (t), linearly mixed together by an unknown N M matrix A, and possibly corrupted by additive noise (t) x(t) = As(t) + (t) (1.1) We wish to estimate the mixing matrix A and the M
Single User Sparse Channel Acquisition in Multiuser DSCDMA Systems
"... Single user channel estimation for the case of sparse channels with large delay spreads is addressed. In addition, practical pulse shapes are considered. In sparse channels, the efficient way to estimate the parameters is to estimate the continuous delays of each path, instead of using the typical d ..."
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Single user channel estimation for the case of sparse channels with large delay spreads is addressed. In addition, practical pulse shapes are considered. In sparse channels, the efficient way to estimate the parameters is to estimate the continuous delays of each path, instead of using the typical
Estimation of Sparse MIMO Channels with Common Support
, 2012
"... We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a ..."
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Cited by 5 (3 self)
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We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share
ESTIMATING SPARSE MIMO CHANNELS HAVING COMMON SUPPORT
"... We propose an algorithm (SCSFRI) to estimate multipath channels with Sparse Common Support (SCS) based on Finite Rate of Innovation (FRI) sampling. In this setup, theoretical lowerbounds are derived, and simulation in a Rayleigh fading environment shows that SCSFRI gets very close to these bounds ..."
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Cited by 3 (3 self)
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to these bounds. We show how to apply SCSFRI to OFDM and CDMA downlinks. Recovery of a sparse common support is, among other, especially relevant for channel estimation in a multiple output system or beamforming from multiple input. The present algorithm is based on a multioutput extension of the Cadzow
Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria
 IEEE Trans. On Audio, Speech and Lang. Processing
, 2007
"... Abstractâ€”An unsupervised learning algorithm for the separation of sound sources in onechannel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a timevarying gain ..."
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Cited by 189 (30 self)
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whose gains are slowly varying and sparse. Temporal continuity is favored by using a cost term which is the sum of squared differences between the gains in adjacent frames, and sparseness is favored by penalizing nonzero gains. The proposed iterative estimation algorithm is initialized with random
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
"... Highrate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Trainingbased methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most co ..."
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Cited by 87 (9 self)
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commonly used to accomplish this task. Traditional trainingbased channel estimation methods, typically comprising of linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels
Sampling of sparse channels with common support
"... The present paper proposes and studies an algorithm to estimate channels with a sparse common support (SCS). It is a generalization of the classical sampling of signals with Finite Rate of Innovation (FRI) [1] and thus called SCSFRI. It is applicable to OFDM and WalshHadamard coded (CDMA downlink) ..."
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Cited by 3 (0 self)
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The present paper proposes and studies an algorithm to estimate channels with a sparse common support (SCS). It is a generalization of the classical sampling of signals with Finite Rate of Innovation (FRI) [1] and thus called SCSFRI. It is applicable to OFDM and WalshHadamard coded (CDMA downlink
Toeplitz compressed sensing matrices with applications to sparse channel estimation
, 2010
"... Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. In essence, CS enables the recovery of highdimensional sparse signals from relatively few linear observations in the form of projections onto a collection of test vectors. Existing results show that if the entri ..."
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Cited by 93 (12 self)
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statistical dependencies. It follows that CS can be effectively utilized in linear, timeinvariant system identification problems provided the impulse response of the system is (approximately or exactly) sparse. An immediate application is in wireless multipath channel estimation. It is shown here that time
Results 1  10
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