BEM-based EKF-RTSS Channel Estimation for Non-stationary Doubly-selective Channel

Abstract

An extended Kalman filter and Rauch-Tung-Striebel Smoother (EKF-RTSS) based on Basis Expansion Model (BEM) is proposed in this paper to cope with the challenges of doubly-selective and non-stationary channel in high-speed environments. For doubly-selective channel, the BEM is adopted to reduce the estimation complexity. For non-stationary channel, a channel estimation based on EKF which is able to jointly estimate the time-varying time correlation coefficients and channel impulse response (CIR) is proposed. For further improving the channel estimation accuracy, a ‘filtering and smoothing’ channel estimator structure is designed by introducing the RTSS into channel estimation and interpolation. Simulation results illustrate that the proposed BEM-based EKF-RTSS method show better estimation accuracy, robustness and bit error rate (BER) performance than the traditional methods in high-speed scenarios.