Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Low-Complexity KL Expansion-Based Channel Estimator for OFDM Systems | EURASIP Journal on Wireless Communications and Networking 2005 2 163-174 2005 Hindawi Publishing Corporation A Low-Complexity KL Expansion-Based Channel Estimator for OFDM Systems Habib Senol Department of Computer Engineering Kadir Has University Cibali 34230 Istanbul Turkey Email hsenol@ Hakan A. Cirpan Department of Electrical-Electronics Engineering Istanbul University Avcilar 34850 Istanbul Turkey Email hcirpan@ Erdal Panayirci Department of Electronics Enginering I ik University Maslak 80670 Istanbul Turkey Email eepanay@ Received 23 April 2004 Revised 18 October 2004 This paper first proposes a computationally efficient pilot-aided linear minimum mean square error MMSE batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation ofthe discrete multipath fading channel based on the Karhunen-Loeve KL orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion no matrix inversion is required in the proposed MMSE estimator. Moreover optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We then consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. To further reduce the complexity we extend the batch linear MMSE to the sequential linear MMSE estimator. With the fast convergence property and the simple structure the sequential linear MMSE estimator provides an attractive alternative to the implementation of .
