In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFD... more In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFDM systems over the fast time-varying frequency-selective channel is explored within the framework of the expectation-maximization (EM) algorithm and parametric channel model. Assuming that the path delays are known, a novel iterative pilot-aided algorithm for joint estimation of the multipath Rayleigh channel complex gains (CG)
This paper deals with the estimation of the Amplify-and-Forward channel. Considering two widely a... more This paper deals with the estimation of the Amplify-and-Forward channel. Considering two widely accepted Rayleigh links with Jakes' spectrum, a first-order autoregressive model AR(1) is used to approximate the cascade of both links. A standard estimation algorithm is the Kalman filter. In this paper, we keep the choice of the AR(1)-Kalman filter, but we show that the method usually exploited in the literature to calculate the AR(1)-model parameter presents some disappointing results. We propose other values of the AR(1)-model parameter to improve the channel estimation, based on an off-line minimization of the asymptotic mean square error MSE for a given Doppler and signal to noise ratio. The simulation results show a considerable gain in terms of MSE of the well-tuned Kalman-based channel estimator, especially for the most common scenario of slow-fading channel.
This paper deals with the estimation of the flat fading Rayleigh channel with Jakes’ spectrum. A c... more This paper deals with the estimation of the flat fading Rayleigh channel with Jakes’ spectrum. A common method used in the literature consists in a Kalman Filter (KF) based on a first-order autoregressive (AR1) approximation of the channel, with a parameter of the AR1-model previously fixed by a Correlation Matching (CM) criterion. But in case of slow fading variations, this CM criterion is shown to be far from optimal in terms of distance to the Bayesian Cramer Rao Bound (BCRB). We propose three incremental improvements. The first one consists in using a more adequate criterion based on the Minimization of the Asymptotic Variance (MAV) of the estimator. The second one is to replace the KF by a lower complexity time-invariant order-one filter, with quasi same steady-state MSE performance after MAV optimization. Closed-form expression of the optimal filter-parameter (and corresponding MSE) are given versus the channel state (Doppler, SNR). Finally, we propose a self-adaptive version of the filter working without a priori knowledge of the channel state.
In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFD... more In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFDM systems over the fast time-varying frequency-selective channel is explored within the framework of the expectation-maximization (EM) algorithm and parametric channel model. Assuming that the path delays are known, a novel iterative pilot-aided algorithm for joint estimation of the multipath Rayleigh channel complex gains (CG)
This paper deals with the estimation of the Amplify-and-Forward channel. Considering two widely a... more This paper deals with the estimation of the Amplify-and-Forward channel. Considering two widely accepted Rayleigh links with Jakes' spectrum, a first-order autoregressive model AR(1) is used to approximate the cascade of both links. A standard estimation algorithm is the Kalman filter. In this paper, we keep the choice of the AR(1)-Kalman filter, but we show that the method usually exploited in the literature to calculate the AR(1)-model parameter presents some disappointing results. We propose other values of the AR(1)-model parameter to improve the channel estimation, based on an off-line minimization of the asymptotic mean square error MSE for a given Doppler and signal to noise ratio. The simulation results show a considerable gain in terms of MSE of the well-tuned Kalman-based channel estimator, especially for the most common scenario of slow-fading channel.
This paper deals with the estimation of the flat fading Rayleigh channel with Jakes’ spectrum. A c... more This paper deals with the estimation of the flat fading Rayleigh channel with Jakes’ spectrum. A common method used in the literature consists in a Kalman Filter (KF) based on a first-order autoregressive (AR1) approximation of the channel, with a parameter of the AR1-model previously fixed by a Correlation Matching (CM) criterion. But in case of slow fading variations, this CM criterion is shown to be far from optimal in terms of distance to the Bayesian Cramer Rao Bound (BCRB). We propose three incremental improvements. The first one consists in using a more adequate criterion based on the Minimization of the Asymptotic Variance (MAV) of the estimator. The second one is to replace the KF by a lower complexity time-invariant order-one filter, with quasi same steady-state MSE performance after MAV optimization. Closed-form expression of the optimal filter-parameter (and corresponding MSE) are given versus the channel state (Doppler, SNR). Finally, we propose a self-adaptive version of the filter working without a priori knowledge of the channel state.
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