Optimized Superimposed Training Aided Linear Time-Varying Channel Estimation Model for OFDM Systems
The authors propose an optimized time-varying channel estimation model for mobile Orthogonal Frequency Division Multiplexing (OFDM) systems applying superimposed training in time domain to assist the channel estimation. An extension line approximate method is proposed to fit the assumed linear time-varying channel. The performance of the proposed method is compared with the traditional one in terms of mean square error and results show better performance in scenarios of different SNR and maximum Doppler frequency. To satisfy the demand for real-time and high-rate multimedia services, Orthogonal Frequency Division Multiplexing (OFDM) technique is widely adopted in multiple systems such as 3GPP-LTE, WIMAX, IEEE 802.11e, etc.