ML EM Estimation of Fast Time-Varying OFDM-Type Channels

In this paper, we investigate the problem of fast time-varying multipath channel estimation over orthogonal frequency-division multiplexing (OFDM)-type transmissions. We do so by tracking each complex gain variation using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) in the non-data-aided (NDA) case. Since the LLF is extremely nonlinear, we opt for the expectation maximization (EM) concept to find its global maximum. Simulation results show that the new estimator is able to converge to the global maximum within few iterations only and to provide accurate estimates for all multipath gains, thereby resulting in significant BER and link-level throughput gains.