Time-Variant Channel Estimation and Symbol Detection for MIMO/OFDM Systems Using Superimposed Training

Channel estimation for Multiple-input multiple-output/Orthogonal frequency-division multiplexing (MIMO/OFDM) systems in Time-varying (TV) wireless channels using Superimposed training (ST) is considered. The TV channel coefficients are firstly modeled by truncated discrete Fourier bases. Based on this model, a two-step approach is adopted to estimate the TV channel over multiple OFDM symbols and the optimal training sequence is derived. We also present a performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variances. It is shown that the estimation variances, unlike that of the conventional ST schemes, approach to a fixed lower-bound as the training length increases, which is directly proportional to Information-pilot power ratios (IPPR). For the case that the training power is limited, we provide an iterative joint channel symbol detection scheme, where the recovered data symbol is utilized to enhance the performance by iteratively mitigate the information sequence interference to channel estimation. Simulations confirm that the proposed approach significantly outperforms the conventional ST, and compares well with that of frequency-division multiplexed trainings-based schemes.