A Low Complexity Time-varying Channel Estimation Scheme Based on Superimposed Trainin

Compared to traditional Time Division Multiplexed (TDM) and Frequency Division Multiplexed (FDM) training sequence, Superimposed Training (ST) sequence can effectively improve the frequency spectrum efficiency. However, the interference between information and training sequences in ST causes severe degradation on the performance of the system. The crux of improving channel estimation performance is to cancel the information sequence interference effectively. This paper firstly proposes a new first-order statistic-based channel estimation algorithm for the time-varying channel. In this algorithm, the time-varying channel is approximated by the basis expansion model. The information sequence interference is suppressed by calculating mean of the partitioned sequence in time domain. On this basis, an iterative channel estimation and detection scheme is proposed according to that the information and training sequences undergo the identical fading channel. In the new scheme, the Deterministic Maximum Likelihood (DML) detector is substituted by a Kalman filtering detector. The detected symbols are seemed as additional training sequence, which increasing the channel estimation performance remarkably. The simulation results show that the new scheme not only cancels the information sequence interference effectively, but also include better performance and lower computation complexity compared to other schemes.