Parametric Modeling and Pilot-Aided Estimation of the Wireless Multipath Channel in OFDM Systems

Jakobsen, Morten; Laugesen, Kasper; Manchón, Carles Navarro; Kirkelund, Gunvor Elisabeth; Rom, Christian; Fleury, Bernard Henri

doi:10.1109/icc.2010.5501779

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…For instance, the number of propagation paths and the path delays are often modeled as fixed, although the transmitter or the receiver can be moving. Channel estimators based on such static channel model are no longer suitable in the dynamic channel environment, especially the high-speed mobile communication system [10].…”

Section: Introductionmentioning

confidence: 99%

A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

Chen

Cui

Yang

et al. 2014

2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP)

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In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate the channel estimation as an iteration problem. During the iteration, the path delays are estimated through a simple reconstruction algorithm. After the path delays are estimated, the Kalman filter is performed on the path delays to obtain the minimum mean squared error (MMSE) estimation of the channel impulse response (CIR). Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing (CS) based channel estimators which perform CS at each time separately, the method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).

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Section: Introductionmentioning

confidence: 99%

A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

Chen

Cui

Yang

et al. 2014

2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP)

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“…[7] and the references therein. Subspace-based methods and preprocessing techniques have also been applied for orthogonal frequency-division multiplexing (OFDM) pilot-aided channel estimation [8], [9]. As shown by [9], selecting a too large (or small) window size relative to the available observation window leads to a severe drop in performance of the subspaced-based channel estimator.…”

Section: Introductionmentioning

confidence: 99%

“…In a next step, we infer how SS and FB affects the performance of a particular channel estimator. We consider an OFDM system with the channel estimation performed as in [8] and [9] operating in a multipath environment.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Smoothing Techniques for Subspace Estimation with Application to Channel Estimation

Pedersen

Jakobsen

Rom

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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In this paper, we present an investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not chosen properly, leads to dramatic performance breakdown of subspace-based methods. We provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.

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“…However, in time-varying channel scenarios the propagation delays, the number of delays and the tap coefficients vary over the time, and the static parametric channel model does not represent such a dynamic channel environment. A more realistic multipath channel model that allows the path number and the path delays to vary over the time is presented in [9]. In this work, we consider such a dynamic parametric channel model.…”

mentioning

confidence: 99%

“…Consequently, parametric methods enable to reduce the dimension of the estimation task and the amount of pilot symbols needed in channel estimation, and therefore, as compared to the non-parametric channel model, the parametric channel model based estimator can achieve better performance [5]. Parametric channel modeling based channel estimation methods proposed in [5,9,10] use minimum description length (MDL) to detect the number of paths and then apply subspace methods such as the estimation of signal parameters using rotational invariance techniques (ESPRIT) and the multiple signal classification (MUSIC) to estimate the channel path delays. However, in time-varying channel scenarios the propagation delays, the number of delays and the tap coefficients vary over the time, and the static parametric channel model does not represent such a dynamic channel environment.…”

mentioning

confidence: 99%

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

Şenol

2015

Wireless Pers Commun

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In this paper, we propose a space-alternating generalized expectation maximization (SAGE) based joint channel estimation and data detection algorithm in compressive sensing (CS) framework for orthogonal frequency-division multiplexing (OFDM) systems in rapidly time-varying sparse multipath channels. Using dynamic parametric channel model, the sparse multipath channel is parameterized by a small number of distinct paths, each represented by the path delays and path gains. In our model, we assume that the path gains rapidly vary within the OFDM symbol duration while the number of paths and path delays vary symbol by symbol. Since the convergency of the SAGE algorithm needs statistically independent parameter set of interest to be estimated, we specifically choose the discrete orthonormal Karhunen-Loeve basis expansion model (DKL-BEM) to provide statistically independent BEM coefficients within one OFDM symbol duration and use just a few significant BEM coefficients to represent the rapidly time-varying path gains. The resulting SAGE algorithm that also incorporates inter-channel interference cancellation updates the data sequences and the channel parameters serially. The computer simulations show that our proposed algorithm has better channel estimation and symbol error rate performance than that of the orthogonal matching pursuit algorithm that is commonly proposed in the CS literature.

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Parametric Modeling and Pilot-Aided Estimation of the Wireless Multipath Channel in OFDM Systems

Cited by 14 publications

References 15 publications

A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system

Analysis of Smoothing Techniques for Subspace Estimation with Application to Channel Estimation

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

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