A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estima...A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estimation of channel parameters and channel interpolation. The exponentially embedded family （EEF） criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors＇ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique （ESPRIT）. Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error （MSE）. It is demonstrated that this approach exhibits a superior performance over the existing algorithms.展开更多
文摘A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estimation of channel parameters and channel interpolation. The exponentially embedded family （EEF） criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors＇ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique （ESPRIT）. Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error （MSE）. It is demonstrated that this approach exhibits a superior performance over the existing algorithms.
