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Estimation of Kronecker structured channel covariances using training data
 IN PROCEEDINGS OF EUSIPCO
, 2007
"... The problem of estimating second order statistics for MIMO channels is treated. It is assumed that the so called Kronecker model holds. This implies that the channel covariance is the Kronecker product of two covariance matrices associated with the transmit and receive array, respectively. The propo ..."
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Cited by 6 (1 self)
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The problem of estimating second order statistics for MIMO channels is treated. It is assumed that the so called Kronecker model holds. This implies that the channel covariance is the Kronecker product of two covariance matrices associated with the transmit and receive array, respectively. The proposed estimator uses training data from a number of signal blocks to compute the estimate. This is in contrast to methods that assume that the channel realizations are directly available, or possible to estimate almost without error. It is also demonstrated how methods that make use of the training data indirectly via channel estimates can be biased. An estimator is derived that can, in an asymptotically optimal way, use, not only the structure implied by the Kronecker assumption, but also linear structure on the transmit and receive covariance matrices. The performance of the proposed estimator is analyzed and numerical simulations illustrate the results and also provide insight into the small sample behavior of the proposed method.
Low Complexity MLSE Equalization in Highly Dispersive Rayleigh Fading Channels
, 2010
"... A soft output low complexity maximum likelihood sequence estimation (MLSE) equalizer is proposed to equalize MQAM signals in systems with extremely long memory. The computational complexity of the proposed equalizer is quadratic in the data block length and approximately independent of the channel ..."
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Cited by 3 (2 self)
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A soft output low complexity maximum likelihood sequence estimation (MLSE) equalizer is proposed to equalize MQAM signals in systems with extremely long memory. The computational complexity of the proposed equalizer is quadratic in the data block length and approximately independent of the channel memory length, due to high parallelism of its underlying Hopfield neural network structure. The superior complexity of the proposed equalizer allows it to equalize signals with hundreds of memory elements at a fraction of the computational cost of conventional optimal equalizer, which has complexity linear in the data block length but exponential in die channel memory length. The proposed equalizer is evaluated in extremely long sparse and dense Rayleigh fading channels for uncoded BPSK and 16QAMmodulated systems and remarkable performance gains are achieved.
Linear MMSE MIMO Channel Estimation with Imperfect Channel Covariance Information
 in Proc. IEEE Int. Conf. Commun. (ICC
, 2009
"... Abstract—In this paper, we investigate the effects of imperfect knowledge of the channel covariance matrix on the performance of a linear minimum meansquareerror (MMSE) estimator for multipleinput multipleoutput (MIMO) channels. The estimation meansquareerror (MSE) is analytically analyzed by ..."
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Cited by 2 (2 self)
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Abstract—In this paper, we investigate the effects of imperfect knowledge of the channel covariance matrix on the performance of a linear minimum meansquareerror (MMSE) estimator for multipleinput multipleoutput (MIMO) channels. The estimation meansquareerror (MSE) is analytically analyzed by providing both a very tight lower bound and an upper bound. The proposed analysis is useful for the understanding of how estimation accuracy of the channel covariance matrix impacts on system performance, depending on the average signaltonoise ratio (SNR) and specific propagation conditions. Conclusions are fully supported by numerical results. I.
1 On the Robustness of MIMO LMMSE Channel Estimation
"... Abstract—The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multipleinput multipleoutput (MIMO) ..."
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Abstract—The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multipleinput multipleoutput (MIMO) systems, where a perfect estimate of the channel correlation matrix is not available. The channel estimation mean square error (MSE) is analytically analyzed assuming a general structure for the estimated channel correlation matrix used to implement the LMMSE channel estimator. The obtained results are successively detailed to the case of channel correlation matrices derived by sample correlation estimation methods. It is observed that the use of a coarse estimate of the channel correlation matrix can lead to a severe degradation on the LMMSE channel estimator performance, whereas the simpler leastsquare (LS) channel estimator may provide comparatively better results. Nevertheless, it is shown that a robust approach, although suboptimal, relies on implementing the LMMSE channel estimator by assuming transmissions over uncorrelated channels, since, with such an assumption, the resulting estimation MSE is certainly smaller than for the LS channel estimator. Index Terms—Channel estimation, MIMO systems, least mean square methods, correlation matrix, performance analysis. I.
A Statistical Theory for Measurement and Estimation of Rayleigh Fading Channels ∗
, 2007
"... In this paper, we propose a statistical theory on measurement and estimation of Rayleigh fading channels in wireless communications and provide complete solutions to the fundamental problems: What is the optimum estimator for the statistical parameters associated with the Rayleigh fading channel, an ..."
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In this paper, we propose a statistical theory on measurement and estimation of Rayleigh fading channels in wireless communications and provide complete solutions to the fundamental problems: What is the optimum estimator for the statistical parameters associated with the Rayleigh fading channel, and how many measurements are sufficient to estimate these parameters with the prescribed margin of error and confidence level? Our proposed statistical theory suggests that two testing signals of different strength be used. The maximum likelihood (ML) estimator is obtained for estimation of the statistical parameters of the Rayleigh fading channel that is both sufficient and complete statistic. Moreover, the ML estimator is the minimum variance (MV) estimator that in fact achieves the CramérRao lower bound. 1
Efficient NearOptimum Detection Algorithms for MIMO . . .
, 2006
"... Wireless communications continue to strive for higher data rates and a better link reliability in order to provide more advanced services. The use of multiple antennas at both the transmitter and receiver side, i.e., multipleinput multipleoutput (MIMO) communications, is one of the most promising ..."
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Wireless communications continue to strive for higher data rates and a better link reliability in order to provide more advanced services. The use of multiple antennas at both the transmitter and receiver side, i.e., multipleinput multipleoutput (MIMO) communications, is one of the most promising technologies to satisfy these demands. Indeed, MIMO systems are capable of achieving increased data rates and an improved link reliability compared to singleantenna systems without requiring additional bandwidth or transmit power. These improvements, however, necessitate the use of more computationally intensive data detection algorithms at the receiver side. In particular, optimum data detection can easily become prohibitively complex. Conventional suboptimum detection techniques have a low computational cost but their performance is in general significantly inferior to that of optimum data detection. Thus, there is a strong demand for computationally efficient data detection algorithms that are able to reduce this performance gap. In this thesis, novel algorithms for efficient nearoptimum data detection in MIMO systems are proposed and investigated. First, we show that specific “bad ” realizations of the MIMO channel are to a great extent responsible for the inferior performance of conventional suboptimum data detection
The 12thInternational SymposiumonWireless PersonalMultimediaCommunications (WPMC’09) EFFECT OFCHANNELCOVARIANCE ESTIMATIONERROR ONTHEMIMO LINEARMMSE CHANNELESTIMATOR
"... The performance of the linear MMSE MIMO channel estimator depends on the covariance matrix of the channel, which is estimatedatthereceiverduringaninitialtrainingphase. Inthis paper, we firstly study the conventional sample covariance estimator and then we consider an alterative improved estimator. A ..."
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The performance of the linear MMSE MIMO channel estimator depends on the covariance matrix of the channel, which is estimatedatthereceiverduringaninitialtrainingphase. Inthis paper, we firstly study the conventional sample covariance estimator and then we consider an alterative improved estimator. Analytic expressions for the linear MMSE channel estimation MSE are found, and detailed to a large sample set analysis, to take into account the effect of practical covariance matrix estimation. Numericalresultsconsiderthesystemperformancefor samplesetsoffinitesizeandcorroboratetheproposedanalysis. I
Research Article Low Complexity MLSE Equalization in Highly Dispersive Rayleigh Fading Channels
"... Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A soft output low complexity maximum likelihood sequence estimation (MLSE) equalizer is proposed to equalize MQAM signals in systems with extremely long m ..."
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Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A soft output low complexity maximum likelihood sequence estimation (MLSE) equalizer is proposed to equalize MQAM signals in systems with extremely long memory. The computational complexity of the proposed equalizer is quadratic in the data block length and approximately independent of the channel memory length, due to high parallelism of its underlying Hopfield neural network structure. The superior complexity of the proposed equalizer allows it to equalize signals with hundreds of memory elements at a fraction of the computational cost of conventional optimal equalizer, which has complexity linear in the data block length but exponential in die channel memory length. The proposed equalizer is evaluated in extremely long sparse and dense Rayleigh fading channels for uncoded BPSK and 16QAMmodulated systems and remarkable performance gains are achieved. 1.