A low-complexity ML channel estimator for OFDM

Abstract : Orthogonal frequency-division multiplexing 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 channel estimator. In addition to being optimal (up to the modeling error), this estimator receives an elegant time-frequency interpretation. As a result, it has a significantly lower complexity than previously published methods.
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IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2003, 51 (2), pp.135-140. 〈10.1109/TCOMM.2003.809234〉
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Luc Deneire, P. Vandenameele, L. Van Der Perre, Bert Gyselinckx, M.G.E. Engels. A low-complexity ML channel estimator for OFDM. IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2003, 51 (2), pp.135-140. 〈10.1109/TCOMM.2003.809234〉. 〈hal-00187768〉

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