Approximate ML Doppler Spread Estimation Over Flat Rayleigh Fading Channels

The maximum likelihood (ML) Doppler spread estimator provides an accurate estimation performance; however, it results in a much higher computation cost. In this work, we propose a time-domain approximate-ML estimator that significantly reduces the computational complexity of the ML estimator over a flat Rayleigh fading channel. Simulation results show that the proposed method nearly achieves the exact ML performance. Moreover, by using a small number of observation samples, our proposed method gives almost unbiased Doppler spread estimation for low to medium user mobility.