Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels

Jamoos, Ali; Grivel, Éric; Bobillet, William; Guidorzi, Roberto

doi:10.1109/lsp.2007.901686

Cited by 17 publications

(11 citation statements)

References 15 publications

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“…We assume that the receiver has perfect knowledge of the variances of process and measurement noises, the spatial correlation matrix and the normalized Doppler rate f D T s . These parameters can be estimated as in [9], [17]. We also use the MATLAB function dare to numerically solve the Riccati equation (19).…”

Section: Simulation Resultsmentioning

confidence: 99%

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Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

Loiola

Lopes

Romano

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Abstract-In this paper we propose low-complexity algorithms for estimating flat, time-varying and spatially correlated MIMO channels. The proposed estimators employ Kalman filters to track the channel in orthogonal space-time block coded systems. After developing a state-space model for spatially correlated MIMO channels, we show that the proposed estimators can be simplified by using the orthogonality inherent to orthogonal space-time block codes. We also show that the channel estimates provided by the proposed algorithms correspond to weighted sums of instantaneous maximum likelihood channel estimates. For constant modulus signal constellations, we reduce the receiver complexity even more by proposing a steady-state Kalman filter. Simulation results indicate that there is no significant performance degradation of the steady-state filter compared to the first proposed algorithm and that both algorithms outperform conventional adaptive filters.

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Section: Simulation Resultsmentioning

confidence: 99%

“…These parameters can be estimated ahead of time using, for example, the methods proposed in [17] and in the references therein. Thus, we assume that the parameters in (12a)-(12c) and (12e) are known.…”

Section: Steady-state Kalman Channel Estimatormentioning

confidence: 99%

Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

Loiola

Lopes

Romano

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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“…In this case, (24a)-(24c) and (24e) are just functions of the initial estimate of P k|k , the normalized Doppler rate, the spatial correlation matrix, a constant equal to the energy of the constellation symbols and the variance of the measurement noise. These parameters can be estimated ahead of time using, for example, the methods proposed in (Jamoos et al, 2007) and in the references therein. Thus, we assume that the parameters in (24a)-(24c) and (24e) are known.…”

Section: Steady-state Kalman Channel Estimatormentioning

confidence: 99%

Adaptive Channel Estimation in Space-Time Coded MIMO Systems

Loiola¹,

Lopes²,

Romano³

2011

Adaptive Filtering Applications

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“…To avoid this drawback, we have recently investigated the relevance of Errors-In-Variables (EIV) approaches [29]. In that case, the estimation of the AR parameter vector consists in searching the null space of the autocorrelation matrix of the…”

Section: State-of-the-art On Ar Parameter Estimationmentioning

confidence: 99%

Estimation of autoregressive fading channels based on two cross-coupled H ∞ filters

Jamoos

Grivel

Christov

et al. 2008

SIViP

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This paper deals with the on-line estimation of time-varying frequency-flat Rayleigh fading channels based on training sequences and using H ∞ filtering. When the fading channel is approximated by an autoregressive (AR) process, the AR model parameters must be estimated. As their direct estimations from the available noisy observations at the receiver may yield biased values, the joint estimation of both the channel and its AR parameters must be addressed. Among the existing solutions to this joint estimation issue, Expectation Maximization (EM) algorithm or crosscoupled filter based approaches can be considered. They usually require Kalman filtering which is optimal in the H 2 sense provided that the initial state, the driving process and measurement noise are independent, white and Gaussian. However, in real cases, these assumptions may not be satisfied. In addition, the state-space matrices and the noise variances are not necessarily accurately estimated. To take into account the above problem, we propose to use two crosscoupled H ∞ filters. This method makes it possible to provide A. Jamoos (B)

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Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels

Cited by 17 publications

References 15 publications

Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

Adaptive Channel Estimation in Space-Time Coded MIMO Systems

Estimation of autoregressive fading channels based on two cross-coupled H ∞ filters

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