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• W2783709412 abstract "Non-stationarity is a key feature of wireless channels in high-mobility environments, such as high speed railway, which is one of the barriers on improving the communication quality if not being well treated in channel estimation. Traditional Kalman Filter (KF)- based channel estimation methods that assume constant time correlation coefficients based on Jakes model are no longer valid in non-stationary channel. To address the challenges of channel estimation in high-mobility OFDM communications, a joint design of channel estimation, detection and decoding is proposed on the basis of Extended Kalman Filter (EKF) and Iterative Detector & Decoder (IDD) technology in this paper. The dynamics of non-stationary channel is characterized as a time-varying autoregressive (AR) process and a Decision-directed EKF is developed to track both the channel frequency response (CFR) and the time correlation coefficients of the time-varying AR process. In order to mitigate the error propagation with Decision-directed at data symbols, the IDD is adopted and integrated with the EKF (IDD-EKF). And, instead of Decision-directed, this paper proposes a novel log-likelihood ratio (LLR)-directed mechanism, in which the a posteriori LLRs are utilized to update the weighting matrix of EKF. The simulation results of estimating a typical non-stationary WINNER-II channel show that, compared with traditional methods, the proposed method effectively overcomes the error propagation problems and improves the channel estimation accuracy. The overall system performance in terms of signal-to-noise ratio (SNR) gain is about 3dB at 200km/h." @default.
• W2783709412 created "2018-01-26" @default.
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• W2783709412 date "2017-12-01" @default.
• W2783709412 modified "2023-09-25" @default.
• W2783709412 title "EKF-Based Joint Channel Estimation and Decoding Design for Non-Stationary OFDM Channel" @default.
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• W2783709412 doi "https://doi.org/10.1109/glocom.2017.8254544" @default.
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