On two-dimensional adaptive channel estimation in OFDM systems

In orthogonal frequency division multiplexing (OFDM) systems, two-dimensional (both time and frequency) minimum mean square error (2D-MMSE) channel estimation is optimum. However, accurate channel statistics are required to realize it, which are often unavailable in practice. In contrast, two-dimensional adaptive channel estimation based on a two-dimensional least mean square (2D-LMS) algorithm does not require any channel statistics, and at the same time can make full use of the time and frequency-domain correlations of the frequency response of time-varying dispersive fading channels. In this paper, we further study two-dimensional adaptive channel estimation in OFDM systems. We derive two-dimensional normalized least mean square (2D-NLMS) algorithm and find that the 2D-LMS algorithm with the optimum step-size parameter is just its special case. Furthermore, a parallel 2D-(N)LMS channel estimation scheme is proposed to solve the realtime realization problem due to high computational complexity encountered by two-dimensional adaptive channel estimation. Finally, we apply two-dimensional adaptive channel estimation to multiple-input multiple-output (MIMO) OFDM systems.