EM-EKF fast time-varying channel estimation based on superimposed pilot for high mobility OFDM systems

Abstract

The scarcity of wireless spectrum resources and high mobility have brought serious challenges to orthogonal frequency division multiplexing (OFDM) communication systems. To improve spectral efficiency and combat fast time-varying channel fading, this paper studies the OFDM system with high mobility based on superimposed pilots, and adopts a basis expansion model (BEM) to describe fast time-varying channels. An iterative channel estimation algorithm combining expectation–maximization (EM) and extended Kalman filtering (EKF) is proposed, which is called EM-EKF in this paper. It uses EM to calculate the parameter matrix, measured values and the covariance of the process noise of the unknown first-order time-varying autoregressive (TVAR) model to obtain the optimal estimation. To further enhance the system’s robustness for fast time-varying channel fading, EKF and smoothing algorithms are jointly used for time-varying channel tracking. The results show that compared with the existing representative channel estimation algorithms, the iterative EM-EKF algorithm proposed in this paper not only solves the problem of unknown prior information, but also significantly outperforms other non-iterative algorithms, and the performance of the proposed algorithm is similar to that of the iterative EKF algorithm, which is more suitable for the actual OFDM communication systems with high mobility.