Blind and nonblind turbo estimation for fast fading GSM channels

Kammeyer, Karl‐Dirk; Kühn, Volker; Petermann, T.

doi:10.1109/49.947036

Cited by 20 publications

(15 citation statements)

References 13 publications

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“…Tracking time-varying (TV) channels with a large Doppler spread is a critical task, regardless of whether a nonblind or blind approach is used [1]. In this situation, a nonblind approach in general requires to employ training more frequently since the channel estimate becomes obsolete shortly after the training period ends.…”

Section: Introductionmentioning

confidence: 99%

Subspace tracking of fast time-varying channels in precoded MIMO-OFDM systems

Champagne

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper presents a blind subspace-based tracking scheme for precoded MIMO-OFDM systems over rapidly time-varying wireless channels. Subspace-based tracking is normally considered for slow time-varying channels only. Thanks to the frequency correlation of the wireless channels, the proposed scheme is able to collect data not only from the time but also from the frequency domain to speed up the update of the required second-order statistics. After each update of the statistics, the subspace information is also updated using orthogonal iteration, and then a new channel estimate is computed. The proposed algorithm is verified in 3GPP-SCM suburban macro scenario, in which a mobile station is allowed to move in any direction with a constant speed of 100km/h. The simulation results show that the root mean square error of the channel estimates converges to the level of 2 × 10 −2 within less than 5 OFDM symbols even for such a high Doppler rate.Index Terms-Doppler effect, MIMO-OFDM systems, subspace tracking, 3GPP-SCM, precoding matrix.

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Section: Introductionmentioning

confidence: 99%

Subspace tracking of fast time-varying channels in precoded MIMO-OFDM systems

Champagne

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…In practical receivers, where the channel impulse response has to be estimated, it is convenient to have channel estimators capable of benefiting from the high performance of Turbo equalizers [15,16,17]. Moreover, secondand third-generation mobile standards consider the transmission of pilot sequences known by the receiver for channel estimation purposes.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Turbo Iterative Channel Estimation for Space-Time Coded Systems and Its Application to Radio Transmission in Subway Tunnels

Gonzalez-Lopez

Mı́guez

Castedo

2004

EURASIP J. Adv. Signal Process.

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This paper presents a novel channel estimation technique for space-time coded (STC) systems. It is based on applying the maximum likelihood (ML) principle not only over a known pilot sequence but also over the unknown symbols in a data frame. The resulting channel estimator gathers both the deterministic information corresponding to the pilot sequence and the statistical information, in terms of a posteriori probabilities, about the unknown symbols. The method is suitable for Turbo equalization schemes where those probabilities are computed with more and more precision at each iteration. Since the ML channel estimation problem does not have a closed-form solution, we employ the expectation-maximization (EM) algorithm in order to iteratively compute the ML estimate. The proposed channel estimator is first derived for a general time-dispersive MIMO channel and then is particularized to a realistic scenario consisting of a transmission system based on the global system mobile (GSM) standard performing in a subway tunnel. In this latter case, the channel is nondispersive but there exists controlled ISI introduced by the Gaussian minimum shift keying (GMSK) modulation format used in GSM. We demonstrate, using experimentally measured channels, that the training sequence length can be reduced from 26 bits as in the GSM standard to only 5 bits, thus achieving a 14% improvement in system throughput.

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“…An important class of algorithms for blind and semiblind channel estimation is based on the iterative strategy depicted in Figure 1 [3,4,5,6,7,8,9,10,11,12,13,14], which we call iterative channel-and-symbol estimation (ICSE). In these algorithms, an initial channel estimate is used by a symbol estimator to provide initial estimates of the firstorder (and possibly also the second-order) statistics of the transmitted symbol sequence.…”

Section: Introductionmentioning

confidence: 99%

The Extended-Window Channel Estimator for Iterative Channel-and-Symbol Estimation

Lopes

Barry

2005

J Wireless Com Network

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The application of the expectation-maximization (EM) algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE). The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer) and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW) estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE.

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Blind and nonblind turbo estimation for fast fading GSM channels

Cited by 20 publications

References 13 publications

Subspace tracking of fast time-varying channels in precoded MIMO-OFDM systems

Subspace tracking of fast time-varying channels in precoded MIMO-OFDM systems

Maximum Likelihood Turbo Iterative Channel Estimation for Space-Time Coded Systems and Its Application to Radio Transmission in Subway Tunnels

The Extended-Window Channel Estimator for Iterative Channel-and-Symbol Estimation

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