Superimposed Training for Data Detection and Channel Estimation in OFDM Systems without Cyclic Prefix

Bandwidth efficiency is a critical concern in wireless communications. To fully utilize the available bandwidth, this paper adopts the superimposed training (ST) scheme in orthogonal frequency division multiplexing (OFDM) systems without using cyclic prefix (CP) and guard interval (GI). If the pilot pattern is fixed, it is shown that the performance of the channel estimation using the ST scheme is the same for both the proposed architecture, denoted as OFDM-ST, and the OFDM system with CP and ST scheme, denoted as CP-OFDM-ST. In addition, since the CP is not adopted in the proposed system, the performances of channel estimation and data detection are degraded because of the inter-symbol interference (ISI) and the inter-carrier interference (ICI). Therefore, a novel data detector, which includes ICI cancellation, is proposed to improve the channel estimation in OFDM-ST systems. The simulation results show that the proposed algorithm substantially enhances the systems performance.