Superimposed-Training Based Channel Estimation for OFDM Systems

Superimposed training has raised lots of attentions due to its great spectral efficiency and its relatively fast channel estimation algorithms. In this paper, we present channel estimation methods for OFDM system using periodic superimposed training added in frequency domain. At the receiver, channel estimation is done both in time domain (pre-FFT) and frequency domain (post-FFT). We can see the pre-FFT method is relatively robust when SNR is low and data length is short. In addition, peak to average ratio (PAR) problem is analyzed with superimposed training. Simulations show that those methods are effective especially when SNR is lower.