Low-Complexity ML Doppler Spread Estimation for OFDM Systems

The time domain maximum likelihood (ML) Doppler spread estimation for OFDM systems provides an accurate estimation performance; however, it results in a much higher computation cost. We propose a low-complexity ML Doppler estimator based on a well-designed preamble sequence along with a suboptimal ML (SML) method to reduce the computational complexity of the optimal ML scheme. Because of the proposed preamble sequence, the received samples are able to be partitioned into uncorrelated subsets, yielding a substantial complexity reduction for the SML scheme. The simulation results show that the proposed estimator provides accurate and efficient Doppler spread estimation.