A low complexity ML channel estimator for OFDM

Orthogonal frequency-division multiplexing (OFDM) with cyclic prefix enables low cost frequency-domain mitigation of multipath distortion. However, to determine the equalizer coefficients, knowledge of the channel frequency response is required. While a straightforward approach is to measure the response to a known pilot symbol sequence, existing literature reports a significant performance gain when exploiting the frequency correlation properties of the channel. Expressing this correlation by the finite delay spread, we build a deterministic model parametrized by the channel impulse response and, based on this model, derive the maximum likelihood (ML) channel estimator. In addition to being optimal, this estimator receives an elegant time-frequency interpretation. As a result, it has a significantly lower complexity than previously published methods