Research Article
Adaptive Two-Dimensional Channel Estimation Scheme for OFDM Systems
@INPROCEEDINGS{10.1109/CROWNCOM.2008.4562554, author={Chia-Horng Liu}, title={Adaptive Two-Dimensional Channel Estimation Scheme for OFDM Systems}, proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2008}, month={7}, keywords={OFDM Doppler frequency channel estimation}, doi={10.1109/CROWNCOM.2008.4562554} }- Chia-Horng Liu
Year: 2008
Adaptive Two-Dimensional Channel Estimation Scheme for OFDM Systems
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2008.4562554
Abstract
This paper presents an adaptive low complexity channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the time-varying multipath fading channels. The proposed scheme is designed based on the pilots, which are arranged at different locations for different OFDM symbols. Based on the proposed channel estimation scheme, the channel frequency response of the current OFDM symbol is estimated from the current and neighboring pilots and no addition or multiplication is required. It is shown that the feasibility of the proposed scheme depends on the characteristics of the channels. We also analyze the performance of the proposed channel estimation scheme in the time-varying fading channels. Computer simulations are conducted to evaluate the performance of the proposed scheme. The simulation results are also compared with the ML algorithm and the system with ideal channel information (ICI). It is shown by the computer simulations that the performance of the proposed scheme is close to the ML algorithm in the slow fading channel. The implementation complexity is also discussed in this paper. It is shown that the complexity of the proposed channel estimation scheme is less than the linear interpolation and ML algorithm. Therefore, it is suitable for wireless local area network (WLAN) and wireless metropolitan area network (WMAN) applications.
