Semiblind iterative data detection for FDM systems with CFO and doubly selective channels

Authors:
Lanlan He;Shaodan Ma;Yik-Chung Wu;Tung-Sang Ng
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong
Venue:
IEEE Transactions on Communications
Year:
2010

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Abstract

Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information.