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Báo cáo hóa học: "Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems"

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: Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems | EURASIP Journal on Applied Signal Processing 2005 4 532-540 2005 Hindawi Publishing Corporation Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems Tiejun Lv School of Information Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Email lvtiejun@tsinghua.org.cn Jie Chen Division of Engineering Brown University Providence RI02912 USA Email jie_chen@brown.edu Hua Li Division of Engineering Brown University Providence RI02912 USA Email hua_li_1@brown.edu Received 19 February 2004 Revised 4 October 2004 Recommended for Publication by Marc Moonen A low-complexity blind timing algorithm is proposed to estimate timing offset in OFDM systems when multiple symbols are received the timing offset estimation is independent of the frequency offset one . Though the maximum-likelihood estimation MLE using two or three symbols is good in offset estimation its performance can be significantly improved by including more symbols in our previous work. However timing offset estimation requires exhaustive search and a priori knowledge of the probability distribution of the received data. The method we propose utilizes the second-order statistics embedded in a cyclic prefix. An information vector IVR with the same length as the cyclic prefix is formed based on an autocorrelation matrix AM . The modulus of elements in the IVR is first quantized based on a threshold that is defined by the variance of OFDM symbols. The timing offset is then estimated based on the binary sequence of the IVR. Because the exhaustive search used in the MLE can be avoided computational complexity is significantly reduced. In practice the proposed scheme can be used as a coarse synchronization estimation that can rapidly provide a rough and contractible estimation range which serves as the basis for a fine estimation like the MLE. The proposed estimator will be proved theoretically to be asymptotically unbiased and mean-squared consistent. Simulations and .