An improved iterative minimum mean-squared error (MMSE) channel estimation method is proposed for joint coding and precoding OFDM systems. Compared with the traditional simplified estimator, the proposed scheme provides higher estimation quality with slight complexity increment at low signal-to-noise ratio (SNR) values. The performance of the iterative receiver including the proposed estimator approaches that of the perfect MMSE estimator without any simplification.

A method that jointly estimates the carrier frequency offset (CFO), channel and symbol timing for orthogonal frequency division multiplexing (OFDM) is proposed in this letter. Based on the characteristic of cyclic training symbols in the frequency domain, the joint estimation is divided into three separate estimations. The CFO and equivalent channel impulse response (CIR) are first estimated by an iterative joint maximum likelihood estimation (JMLE), then the symbol timing offset (STO) is obtained by the assistance of equivalent CIR, finally the CIR is calculated based on the equivalent CIR after known STO and CFO. In our proposed method, the effect of imperfect CIR is considered in the CFO estimator. Moveover, a procedure, which eliminates the inverse operation of a covariance matrix at each iterative process, was adopted to reduce the complexity of our proposed method. Simulations show that the proposed method is capable of retaining the same bit error rate as joint CFO and CIR maximum likelihood estimation without symbol timing error.

A scheme that jointly estimates carrier frequency offset and channel is proposed for the orthogonal frequency division multiplexing (OFDM) system. In the proposed scheme, the carrier frequency offset (CFO) and the channel state information (CSI) are first estimated by an minimum mean square error (MMSE) estimator and an maximum likelihood (ML) estimator, respectively. By exchanging the estimation information between these two estimators, the final estimation of CFO and CSI is then obtained by an iterative method. In the iterative process, the effect of imperfect CSI is considered. It can improve the estimation precision for a shorter preamble and accelerate the iterative convergence rate. To reduce the complexity of the proposed scheme, a procedure is adopted to eliminate the inverse operation of covariance matrix that is recalculated at each iteration. In addition, a sufficient condition for the convergence of the proposed method is deduced. The numerical simulation results show that the BER performance of our scheme is better than that of joint MLE for a shorter preamble and is comparable to that of joint MLE for a longer preamble. Furthermore, the average iterative time of our method is reduced by half as compared to the MLE methods without considering the effect of imperfect CSI.