Estimation of Fast Time-Varying Channels in OFDM Systems Using Two-Dimensional Prolate

Pena-Campos, F.; Carrasco-Alvarez, Roberto; Longoria-Gándara, O.; Parra-Michel, R.

doi:10.1109/twc.2013.010413.120624

Cited by 50 publications

(28 citation statements)

References 21 publications

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“…For a given temporal blocklength N and a given maximum normalized Doppler frequency # max , they are the solutions u r ½n to the eigenvalue problem [22] X NÀ1 m¼0 sin 2p# max ðm À nÞ ð Þ pðm À nÞ u r ½m ¼ k r u r ½n; n ¼ 0; ... ; N À 1:…”

Section: Channel Estimation Based On Dpss-bemmentioning

confidence: 99%

“…Two basis functions are often used, that is complex exponential (CE) functions and discrete prolate spheroidal sequences (DPSS) [15,16]. A number of channel estimation methods based on CE-BEM [17,18] and DPSS-BEM have been proposed [6,[19][20][21][22] for the radio frequency wireless channel. Some of these algorithms are applied to the acoustic channel, e.g., [6,23], but not considering the clustered-sparsity structure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative estimation of doubly selective underwater acoustic channel using basis expansion models

Song

Badiey

et al. 2015

Ad Hoc Networks

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Section: Channel Estimation Based On Dpss-bemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative estimation of doubly selective underwater acoustic channel using basis expansion models

Song

Badiey

et al. 2015

Ad Hoc Networks

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“…whereŜ k c k ¼ H kxk is the received signals of detected data in the form of coefficients and S k can be obtained by the pilots and detected data using (8). Also, we use n k ¼ H k ðx k Àx k Þ þ n 0;k to represent the equivalent filtered noise.…”

Section: Channel Estimation Under Data Detection Errorsmentioning

confidence: 99%

“…Furthermore, an iterative channel estimation using Kalman filter and QR-detection is proposed in [6], and also extended to the resolution in MIMO-OFDM systems in [7]. To explore the time-frequency correlation in the channel model, an appropriate two-dimensional BEM using Prolate functions is evaluated in [8]. Furthermore, to reduce the computational complexity, channel estimation using banded structure of the channel matrix in the frequency domain is used to reduce the dimensionality of the parameter estimation space in [9].…”

Section: Introductionmentioning

confidence: 99%

Iterative Channel Estimation and Detection for High-Mobility MIMO-OFDM Systems: Mitigating Error Propagation by Exploiting Error Information

Teng

et al. 2015

Wireless Pers Commun

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Joint channel estimation and data detection are always challenging problems for high mobility multiple-input multiple-output orthogonal frequency division multiplexing systems. Existing iterative schemes focus on the performance improvement from the correct ones, ignoring error propagation from the incorrect ones. To deal with this problem, we propose a new iterative channel estimation and detection scheme that reduces the unexpected effects of both detection errors and channel estimation errors. The interference in channel estimation induced by detection errors is analyzed and transformed as part of the noise, which is filtered out by Kalman estimator using the derived covariance of both the channel and data errors in detection. Besides, to minimize the detection error caused by channel estimation errors, we propose a new detection algorithm with an optimized weight, which is obtained by the error covariance of the estimated channels and the detected data. Furthermore, matrix calculations and parameter approximations are performed to reduce computational complexity. Extensive simulation results are also presented to demonstrate the significant performance improvement in joint channel estimation and data detection with the proposed iterative schemes.

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“…Two basis functions are often used, that is complex exponential (CE) functions and discrete prolate spheroidal sequences (DPSS) [7,17]. A number of channel estimation methods based on CE-BEM [8,21] and DPSS-BEM have been proposed [12,14,22,26,27] for the radio frequency wireless channel. Some of these algorithms are applied to the acoustic channel, e.g., [13,22], but not considering the cluster-sparsity structure.…”

Section: Introductionmentioning

confidence: 99%

Iterative estimation of the time-varying underwater acoustic channel using basis expansion models

Song

Badiey

et al. 2013

Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems - WUWNet '13

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An iterative channel estimation scheme is proposed for orthogonal frequency division multiplexing (OFDM) systems over the time-varying underwater acoustic channel. The channel estimator is developed based on the basis expansion model (BEM) with two different types of basis functions, complex exponential (CE) basis and discrete prolate spheroidal sequences (DPSS). Considering the different Doppler characteristics for each cluster, we use cluster-specific parameters in the BEM for the most significant taps, such that the number of unknowns during channel estimation can be considerably reduced. The frequency domain receive equation is derived in terms of the model coefficients of the most significant taps. The channel estimator operates in an iterative, decision-directed fashion. At the first iteration, it utilizes only pilot symbols. After the first iteration, the estimator also uses the symbol decisions produced by a MMSE block equalizer, in addition to the pilot symbols. We show the bit-error-rate (BER) performance of the OFDM system over a simulated underwater acoustic channel. It is shown that the MMSE equalizer with the CE-BEM channel estimator and the one-tap equalizer with perfect channel state information have similar BER performances. The MMSE equalizer with the DPSS-BEM channel estimator outperforms the aforementioned two schemes.

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Estimation of Fast Time-Varying Channels in OFDM Systems Using Two-Dimensional Prolate

Cited by 50 publications

References 21 publications

Iterative estimation of doubly selective underwater acoustic channel using basis expansion models

Iterative estimation of doubly selective underwater acoustic channel using basis expansion models

Iterative Channel Estimation and Detection for High-Mobility MIMO-OFDM Systems: Mitigating Error Propagation by Exploiting Error Information

Iterative estimation of the time-varying underwater acoustic channel using basis expansion models

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