An novel ML estimator based on the Lagrange interpolation for the OFDM systems

Existing maximum likelihood (ML) channel estimation in orthogonal frequency division multiplexing (OFDM) systems requires knowledge of the effective length of the channel impulse response (CIR) to achieve the optimum performance. But it is very difficult to track effective length of the CIR in a practical system. By analyzing the relation between the performance of the ML estimation and the exactness of the CIR effective length estimation, we propose a novel ML channel estimator which combine the ML estimation with the successive approximation based on Lagrange interpolation polynomial and can track the variation of the effective length of CIR more easily. Simulation results show that the proposed ML channel estimation algorithm can provide nearly the same performance as the conventional ML channel estimation with the actual length of CIR.