Recursive filter Based Channel Estimation and Correction

Communications through frequency-selective fading channels suffer inter-symbol interference (ISI), which limits the data rate. Complex equalizers are usually needed to compensate for the channel distortion. Orthogonal Frequency Division Multiplexing (OFDM) divides the channel spectrum into many sub-bands, each of which carries low-rate data. Since each subchannel is narrow band, communications through it experience only flat-fading. The sub-channels are separated with the minimum space required by channel Orthogonality. Therefore, a large number of low-rate data streams can be transmitted in parallel and aggregate to a high-rate one. Channel estimation and prediction algorithms are developed and evaluated for use in broadband adaptive OFDM downlinks over fading channels for vehicular users. Accurate channel estimation may be obtained by using a combined pilot aided and decision-directed approach based on Recursive filtering and prediction. The correlation properties of the channel in both time and space are taken into account. Kalman performance at much lower computational complexity is attained with recently developed constant gain adaptation laws. We present and evaluate a state-space realization of such an adaptation law, with computational complexity of the order of the square of the number of parallel tracked pilot subcarriers. In an adaptive OFDM system, prediction of the channel power a few milliseconds ahead will also be required. Frequency-domain channel estimates can be transformed to the time domain, and used as regressors in channel predictors based on linear regression. We also make a preliminary evaluation of the direct use of complex channel prediction in the frequency domain for channel power prediction.