An Optimal Superimposed Training Sequence for Channel Estimation in OFDM Systems

in this work an iterative time domain Least Squares (LS) based channel estimation method using superimposed training (ST) for an Orthogonal Frequency Division Multiplexing (OFDM) system over frequency selective fading channels is proposed. The performance of the channel estimator is analyzed in terms of the Mean Square Estimation Error (MSEE) and its impact on the uncoded Bit Error Rate (BER) of the OFDM system is studied. The selection criterion for the training sequences is proposed by jointly optimizing the MSEE and the BER of the OFDM system. A chirp based sequence satisfying the selection criterion is proposed and compared with the other sequences found in the literature. The sequence is generalized to incorporate the use of guard subcarriers in the OFDM based system. The proposed sequence offers fairness through providing equal interference in all the data carriers unlike earlier proposals. Experimental studies are carried to study the performance of the proposed scheme and also its comparison with the existing schemes is presented. Also, the proposed scheme is applied to the IEEE 802.16e OFDM standard, its suitability is examined and a case is made with the required design of the sequence.