Channel Estimation and Symbol Detection with ICI Cancellation Based on Superimposed Training for OFDM Systems

摘要

Channel estimation using data-dependent superimposed training (DDST) is developed to doubly selective channels of Orthogonal Frequency Division Multiplexing (OFDM) systems; it consumes no extra bandwidth. An Inter-carrier interference (ICI) Self-cancelation method based on DDST scheme, IS-DDST, is designed which mitigates the interference from adjacent subcarriers to estimation. Moreover, a dual-iteration detection method is proposed to mitigate the ICI for IS-DDST scheme. Theoretical analysis and simulations show that the proposed scheme can achieve better Mean Square Error (MSE) and Bit Error Ratio (BER) performance than the existing DDST based scheme.