Kuo Guan Wu | National Chung Hsing University (国立中兴大学) (original) (raw)

Related Authors

Chintha Tellambura

usman  ali

usman ali

Federal Urdu University of Arts, Science and Technology,Islamabad, Pakistan

Jingxian Wu

Iman Taha

malihe Ahmadi

Domenico Ciuonzo

Uploads

Papers by Kuo Guan Wu

Research paper thumbnail of Efficient Decision-Directed Channel Estimation for OFDM Systems with Transmit Diversity

IEEE Communications Letters, 2000

To reduce the complexity involved in decisiondirected channel estimation (DDCE) in orthogonal fre... more To reduce the complexity involved in decisiondirected channel estimation (DDCE) in orthogonal frequencydivision multiplexing (OFDM) systems with transmit diversity, both data decoupling and direct cancellation of inter-antenna interference (IAI) suffer from residual IAI caused by channel frequency selectivity and time selectivity. In this paper, we propose a new algorithm to improve the performance of low complexity DDCE in OFDM systems with transmit diversity. The proposed algorithm includes a new data decoupling scheme with weaker assumptions regarding channel frequency response, and residual IAI cancellation using the results of the DDCE. Simulation results demonstrate that the proposed algorithm improves the performance of MSE and BER considerably.

Research paper thumbnail of Adaptively Regularized Least-Squares Estimator for Decision-Directed Channel Estimation in Transmit-Diversity OFDM Systems

Decision-directed channel estimation (DDCE) in orthogonal frequency-division multiplexing systems... more Decision-directed channel estimation (DDCE) in orthogonal frequency-division multiplexing systems with transmit diversity can be simplified by cancelling inter-antenna interference to separate each channel estimation. Conventional methods involve using the standard least-squares (LS) estimator and exhibit severe decision error propagation. This study proposes an adaptively regularized LS estimator to improve the DDCE performance. The proposed method adopts the latest DDCE estimate as a priori information in the regularized LS etimator, and adaptively determines the regularization parameter according to the mean-squared error of the a priori estimate and that of the standard LS estimate. Simulation results demonstrate the considerable improvement of the proposed algorithm.

Research paper thumbnail of Efficient Decision-Directed Channel Estimation for OFDM Systems with Transmit Diversity

IEEE Communications Letters, 2000

To reduce the complexity involved in decisiondirected channel estimation (DDCE) in orthogonal fre... more To reduce the complexity involved in decisiondirected channel estimation (DDCE) in orthogonal frequencydivision multiplexing (OFDM) systems with transmit diversity, both data decoupling and direct cancellation of inter-antenna interference (IAI) suffer from residual IAI caused by channel frequency selectivity and time selectivity. In this paper, we propose a new algorithm to improve the performance of low complexity DDCE in OFDM systems with transmit diversity. The proposed algorithm includes a new data decoupling scheme with weaker assumptions regarding channel frequency response, and residual IAI cancellation using the results of the DDCE. Simulation results demonstrate that the proposed algorithm improves the performance of MSE and BER considerably.

Research paper thumbnail of Adaptively Regularized Least-Squares Estimator for Decision-Directed Channel Estimation in Transmit-Diversity OFDM Systems

Decision-directed channel estimation (DDCE) in orthogonal frequency-division multiplexing systems... more Decision-directed channel estimation (DDCE) in orthogonal frequency-division multiplexing systems with transmit diversity can be simplified by cancelling inter-antenna interference to separate each channel estimation. Conventional methods involve using the standard least-squares (LS) estimator and exhibit severe decision error propagation. This study proposes an adaptively regularized LS estimator to improve the DDCE performance. The proposed method adopts the latest DDCE estimate as a priori information in the regularized LS etimator, and adaptively determines the regularization parameter according to the mean-squared error of the a priori estimate and that of the standard LS estimate. Simulation results demonstrate the considerable improvement of the proposed algorithm.

Log In