Self-adaptive stochastic rayleigh flat fading channel estimation

Gerzaguet, Robin; Ros, Laurent; Brossier, Jean-Marc; Ghandour-Haidar, Soukayna; Belvèze, Fabrice

doi:10.1109/icdsp.2013.6622690

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…The RW model is also a Gauss-Markov model (when p = 1, or an integrated version if p > 1) but unlike the AR(p) model, the RW(p) model is not stationary: it has a variance that grows to infinity with the number of iterations, and it is why it is most often used for the (modulo-2π) phase estimation problem. Analytic solutions for the tuning of the RW(p) model have been found for p = 1 [35], p = 2 [33] and p = 3 [36]. We can conclude from these studies that the MSE performance of the RW(p)-KF is close to the BCRB for p ≥ 2.…”

Section: Existing Techniques Limitations and Open Questionsmentioning

confidence: 67%

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: 67%

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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“…The step-size update is based on the instantaneous square error |e(n)| 2 , to which a gradient descent is applied. We use the step-size update proposed in [25], which is based on a geometric update [26] with a forgetting factor γ < 1 [27]:…”

Section: Transient Mode and Adaptive Step Processmentioning

confidence: 99%

On the performance of digital adaptive spur cancellation for multi-standard radio frequency transceivers

Gerzaguet¹,

Ros²,

Belvèze³

et al. 2014

Digital Signal Processing

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15 pagesInternational audienceThis study deals with the asymptotic performance of a multiple-spur cancellation scheme. Radio frequency transceivers are now multi-standard and specific impairment can occur. The clock harmonics, called spurs, can leak into the signal band of the reception stage, and thus degrade the performance. The performance of a fully digital approach is presented here. A one-spur cancellation scheme is first described, for which we exploit the a priori knowledge of the spur frequency to create a reference of the polluting tone with the same frequency. A least-mean-square (LMS) algorithm block that uses this reference to mitigate the polluter is designed. However, due to imperfections in the physical components, there is a shift between the a priori frequency and the actual frequency of the spur, and the spur is affected by Brownian phase noise. Under these circumstances, we study the asymptotic and transient performance of the algorithm. We next improve the transient performance by adding a previously proposed adaptive-step-size process. In a second part of this paper, we present a multiple-spur parallel approach that is based on the one-spur cancellation scheme, for which we provide a closed-form expression of the asymptotic signal-plus-noise interference ratio in the presence of frequency shifts and phase noise

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“…The expressions of L(z) can be found in [15] [16] for r = 1, and in [17] [18] for r = 2, 3. It should be noted that L(z) depends on the elements of the Kalman gain vector K (l) (k) of size r×1 obtained for the steady-state mode (k → ∞).…”

Section: Asymptotic Mean-square Error Of the Per-path Kfmentioning

confidence: 99%

“…The resulting estimator is referred to as AR1 M AV -KF in the present study. Equivalent asymptotic performance can also be obtained by a first-order Random Walk (RW)-model-based KF (RW1-KF) ( [15], [16]). …”

Section: Introductionmentioning

confidence: 99%

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

Shu

Ros

Simon³

2014

IEEE Trans. Signal Process.

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Abstract-This study deals with multi-path channel estimation for orthogonal frequency division multiplexing systems under slow to moderate fading conditions. Advanced algorithms exploit the channel time-domain correlation by using Kalman Filters (KFs) based on an approximation of the time-varying channel. Recently, it was shown that under slow to moderate fading, near optimal channel multi-path complex amplitude estimation can be obtained by using the integrated Random Walk (RW) model as the channel approximation. To reduce the complexity of the high-dimensional RW-KF for joint estimation of the multi-path complex amplitudes, we propose using a lower dimensional RW-KF that estimates the complex amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman gain formulation through the Woodbury's identities. Hence, this new algorithm consists of a superposition of independent single-path single-carrier KFs, which were optimized in our previous studies. This observation allows us to adapt the optimization to the actual multi-path multi-carrier scenario, to provide analytic formulae for the mean-square error performance and the optimal tuning of the proposed estimator directly as a function of the physical parameters of the channel (Doppler frequency, Signal-to-Noise-Ratio, Power Delay Profile). These analytic formulae are given for the first-, second-, and thirdorder RW models used in the KF. The proposed per-path KF is shown to be as efficient as the exact KF (i.e., the joint multipath KF), and outperforms the autoregressive-model-based KFs proposed in the literature.

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Self-adaptive stochastic rayleigh flat fading channel estimation

Cited by 10 publications

References 19 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

On the performance of digital adaptive spur cancellation for multi-standard radio frequency transceivers

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

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