Reduced-Complexity Implementation Scheme for OFDM Channel Estimation

Orthogonal Frequency Division Multiplexing (OFDM) has been widely implemented in modern wireless communication systems because of its robustness against frequency selective wireless channels. To coherently detect signals, estimating the Channel State Information (CSI) is crucial for receiver design. Channel estimation is also necessary for Reference Signal Receive Quality (RSRQ) measurement, interference suppression, and diversity combining where there are multiple receive antennas. In this paper, we present a new channel estimation implementation scheme for OFDM communication systems. The proposed scheme is based on substituting Wiener filtering and interpolations across the OFDM time and frequency domain with Wiener filtering for pilot symbols only, and linear interpolate data symbols. Complexity analysis showed that the proposed scheme requires less than 30% of the computations needed by Wiener filtering and interpolation, while performance suffers by almost 3 dB. The best noise reduction method after Least-Square (LS) channel estimation is a combination of both averaging and Wiener filtering with linear interpolation, where the lower bound is a function of both SNR and channel statistics.