Channel estimation and prediction for adaptive OFDM downlinks [vehicular applications]

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 Kalman 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 are also 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.