We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST).The LTV channel is modeled by truncated discrete Fourier bases.Based on this model,a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols.We also present 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-based schemes,approach to a fixed lower-bound as the training length increases,which is directly proportional to information-pilot power ratios.For wireless communication systems with a limited transmission power,we optimize the ST power allocation by maximizing the lower bound of the average channel capacity.Simulation results show that the proposed approach outperforms the frequency-division multiplexed training schemes. We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM) / multiple-input multiple-output (MIMO) systems using superimposed training (ST). The LTV channel is modeled by truncated discrete Fourier bases.Based on this model, a two-step approach is estimated to the LTV channel over multiple OFDM symbols. We also present 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-based schemes, approach to a fixed lower-bound as the training length increases, which is directly proportional to information-pilot power ratios. For wireless communication systems with a limited transmission power, we optimize the ST power allocation by maximizing the lower bound of the average channel capacity. Simulation results show that the proposed approach outperforms the frequency-division multiplexed training schemes.