Superimposed Training for Channel Shortening Equalization in OFDM

In orthogonal frequency division multiplexing (OFDM) systems, the cyclic prefix (CP) is required to be greater than the length of the channel impulse response to avoid inter-block interference. However, a long CP decreases power and bandwidth efficiencies. Recently channel shortening equalizers (CSEs) have been introduced to enable the use of a shorter CP, but they either require perfect channel knowledge, or demand high complexity and long decoding delay when the channel is unknown. In this paper, we propose a low complexity CSE for OFDM with unknown channels by using superimposed training. Our unique design achieves bandwidth efficient channel estimation, and low complexity channel shortening equalization as well as symbol decoding. Simulation results demonstrate the effectiveness of the proposed scheme