Radial Basis Function Aided Space-Time Equalization in Dispersive Fading Uplink Environments

A novel Radial Basis Function Network (RBFN) assisted Decision-Feedback aided Space-Time Equalizer (DF-STE) designed for receivers employing multiple antennas is proposed. The Bit Error Rate (BER) performance of the RBFN aided DF-STE is evaluated when communicating over correlated Rayleigh fading channels, whose Channel Impulse Response (CIR) is estimated using a Kalman filtering based channel estimator. The proposed receiver structure outperforms the linear Minimum Mean-Squared Error benchmarker and it is less sensitive to both error propagation and channel estimation errors

3-D Mobile-to-Mobile channel tracking with first-order autoregressive model-based Kalman filter

International audienceThis paper deals with channel estimation in Mobile-to-Mobile communication assuming three-dimensional scattering environment. It approximates the channel by a first-order autoregressive (AR(1)) model and tracks it by a Kalman filter. The common method used in the literature to estimate the parameter of AR(1) model is based on a correlation matching criterion. We propose another criterion based on the Minimization of the Asymptotic Variance of the Kalman filter, and we justify why it is more appropriate for slow fading variations. This paper provides the closed-form expression of the optimal AR(1) parameter under minimum asymptotic variance criterion and the approximated expression of the estimation variance in output of the Kalman filter, both for Fixed-to-Mobile and Mobile-to-Mobile communication channels

Estimation of Autoregressive Fading Channels Based on Two Cross-Coupled H∞ Filters

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 H2 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 robust estimation of the fading channel and its AR parameters