Estimation of MC-DS-CDMA Fading Channels Based on Kalman Filtering with High Order Autoregressive Models

Hassasneh, Walid; Jamoos, Ali; Grivel, Éric; Nour, Hanna Abdel

doi:10.1109/mcwc.2006.4375212

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…In [9] and [18] there is proposed a correction of the CM criterion by the addition of a very small regularization term to the diagonal of the correlation matrix, to enhance its conditioning. This correction considerably improves the performance, but the parameter is only set by simulation and high orders are still required in order to get closer to the BCRB, as can be seen in Fig.…”

Section: Existing Techniques Limitations and Open Questionsmentioning

confidence: 99%

“…This criterion is known as the correlation matching (CM) criterion [9,[11][12][13] 1 and the solution is obtained by solving the Yule-Walker equations [14]. Over the past two decades, an extensive literature on Rayleigh Channel estimation was based on an AR(p)-KF tuned using the CM criterion [12,[15][16][17][18]11] and still continues nowadays [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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Second-order autoregressive modelbased Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation. Digital Signal Processing, Elsevier, 2019, 90, pp. AbstractThis paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer-Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p = 2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their ap-$ plicability. A general solution for any scenario is simply missing for p = 2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behaviour of the KF with respect to the channel state (Doppler frequency and signal to noise ratio).Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state.Analytic and numerical comparisons with first-and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p ≥ 15.

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Section: Existing Techniques Limitations and Open Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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“…One of the main problems in this area that still needs to be solved is the channel tracking and equalization of such multiantenna systems in highly time-varying fading environ ments. A solution that is proposed in literature to address this problem is to use state space approach [1][2][3][4]. The state space model developes a channel estimator based on the valid assumption that time-varying fading channel is markovian in nature.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], the problem of channel tracking for MIMO time varying frequency-selective channels is addressed by using low order AR model. In [3], the authors have used higher order AR model to estimate MC-CDMA fading channels based on Kalman filtering. In [4], authors consider the estimation of rapidly time varying DS-CDMA channels in more realistic non isotropic Rayleigh fading based on high order AR model with Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

Channel tracking and equalization using Kalman estimation for MIMO systems in non-isotropic Ricean fading environment

Kazmi

Khan

2010

2010 International Conference on Information and Emerging Technologies

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The problem of channel tracking and equaliza tion for multi-input mUlti-output (MIMO) time-varying non isotropic Ricean fading channels is investigated. A Kalman filter based on high order autoregressive (AR) model with statistics closely matching those of the directional Ricean fading process is proposed to track channels variations. A finite length MIMO minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE) is used to perform equalization task. The proposed algorithm works much better in tracking time varying inter-symbol interference (lSI) channels at the expense of high computational cost.

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Model based approach for time-varying channel estimation in DS-CDMA systems using non-isotropic scattering environment

Ahmed

Khan

2009

2009 International Conference on Emerging Technologies

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Estimation of MC-DS-CDMA Fading Channels Based on Kalman Filtering with High Order Autoregressive Models

Cited by 3 publications

References 13 publications

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Channel tracking and equalization using Kalman estimation for MIMO systems in non-isotropic Ricean fading environment

Model based approach for time-varying channel estimation in DS-CDMA systems using non-isotropic scattering environment

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