tailieunhanh - Báo cáo hóa học: " Research Article Carrier Frequency Offset Estimation for Multiuser MIMO OFDM Uplink Using CAZAC Sequences: Performance and Sequence Optimization"

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: Research Article Carrier Frequency Offset Estimation for Multiuser MIMO OFDM Uplink Using CAZAC Sequences: Performance and Sequence Optimization | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2011 Article ID 570680 11 pages doi 2011 570680 Research Article Carrier Frequency Offset Estimation for Multiuser MIMO OFDM Uplink Using CAZAC Sequences Performance and Sequence Optimization Yan Wu 1 J. W. M. Bergmans 1 and Samir Attallah2 1 Signal Processing Systems Group Department of Electrical Engineering Technische Universiteit Eindhoven . Box 513 5600 MB Eindhoven The Netherlands 2 School of Science and Technology SIM University Singapore 599491 Correspondence should be addressed to Yan Wu Received 12 November 2010 Accepted 15 February 2011 Academic Editor Claudio Sacchi Copyright 2011 Yan Wu et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper studies carrier frequency offset CFO estimation in the uplink of multi-user multiple-input multiple-output MIMO orthogonal frequency division multiplexing OFDM systems. Conventional maximum likelihood estimator requires computational complexity that increases exponentially with the number of users. To reduce the complexity we propose a sub-optimal estimation algorithm using constant amplitude zero autocorrelation CAZAC training sequences. The complexity of the proposed algorithm increases only linearly with the number of users. In this algorithm the different CFOs from different users destroy the orthogonality among training sequences and introduce multiple access interference MAI which causes an irreducible error floor in the CFO estimation. To reduce the effect of the MAI we find the CAZAC sequence that maximizes the signal to interference ratio SIR . The optimal training sequence is dependent on the CFOs of all users which are unknown. To solve this problem we propose a new cost function which closely approximates the .

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