Channel Estimation and Symbol Detection with ICI Cancellation Based on Superimposed Training for OFDM Systems
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概要
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Channel estimation using data-dependent superimposed training (DDST) is developed to doubly selective channels of Orthogonal Frequency Division Multiplexing (OFDM) systems; it consumes no extra bandwidth. An Inter-carrier interference (ICI) Self-cancelation method based on DDST scheme, IS-DDST, is designed which mitigates the interference from adjacent subcarriers to estimation. Moreover, a dual-iteration detection method is proposed to mitigate the ICI for IS-DDST scheme. Theoretical analysis and simulations show that the proposed scheme can achieve better Mean Square Error (MSE) and Bit Error Ratio (BER) performance than the existing DDST based scheme.
著者
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Zhang Rui
Laboratory of Ethnopharmacology, Institution for Nanobiomedical Technology and Membrane Biology, Regenerative Medicinal Research Center, West China Hospital, West China Medical School, Sichuan University
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ZHANG Rui
Laboratory of Network System Architecture and Convergence of Beijing University of Posts and Telecommunications
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WU Muqing
Laboratory of Network System Architecture and Convergence of Beijing University of Posts and Telecommunications
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ZHANG Qinjuan
Laboratory of Network System Architecture and Convergence of Beijing University of Posts and Telecommunications
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GUO Qilin
Laboratory of Network System Architecture and Convergence of Beijing University of Posts and Telecommunications
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ZHANG Chao
Laboratory of Network System Architecture and Convergence of Beijing University of Posts and Telecommunications
関連論文
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