Semi-Blind Channel Estimation Using Superimposed Training Sequences with Constant Magnitude in Dual Domain for OFDM Systems

A superimposed training scheme is proposed for channel estimation in OFDM systems. The generalized chirp-like (GCL) sequence is adopted since it has a constant magnitude in both the time domain and the frequency domain. Although the derived channel estimator has a slightly worse performance since the unknown data contributes extra noise, the effective data throughput is substantially increased. In addition, the proposed scheme is shown to have a much better peak-to-average power ratio (PAPR) because the added GCL sequence has a constant magnitude in the time domain.