A Low-Complexity Kalman Approach for Channel Estimation in Doubly-Selective OFDM Systems

In this letter, we propose a vector state-scalar observation (VSSO) Kalman filter for channel estimation in doubly-selective orthogonal frequency division multiplexing (OFDM) systems. Vector state-vector observation (VSVO) Kalman filters have been reported before in the literature for this purpose. The proposed VSSO Kalman filter achieves the same performance as the VSVO Kalman filter and results in 92% complexity savings. The Kalman filter outperforms a recently proposed linear minimum mean square error (LMMSE) estimator and achieves a high spectral efficiency of 93% as compared to the LMMSE estimator of 68%. A key aspect of this paper is that we show how the observed pilot symbol vector can be decorrelated or decoupled into uncorrelated multipath scalars. This aspect (and the proposed Kalman filter) is similar in spirit to that of a quasi-static channel.