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This article puts forward a novel channel estimation and inter-carrier interference(ICI) suppression method for time-varying orthogonal frequency division multiplexing(OFDM) systems.The proposed adaptive finite impulse response(FIR) filter utilizes the time domain correlation of the subcarriers.Based on the one order auto-regressive(AR) model,a modified Kalman filter is exploited to track the channel variance.By solving the Yule-Walker equation,the coefficient of the AR process can be obtained from zero order Bessel function.However,when the channel is assumed not perfectly known,the above criterion will lead to an impractical application.To deal with it,the coefficient of the AR process is taken as a constant.Subsequently a new state space model is deduced and found.Simulation results show that the proposed method can work effectively at the speed of 144 km/h with a phase noise of -85 dBc/Hz at 100 kHz offset.The proposed method yields an improvement bit error rate(BER) compared with three of the existing algorithms with much lower complexity.
This article puts forward a novel channel estimation and inter-carrier interference (ICI) suppression method for time-varying orthogonal frequency division multiplexing (OFDM) systems. The proposed adaptive finite impulse response (FIR) filter utilizes the time domain correlation of the subcarriers. Based on the one order auto-regressive (AR) model, a modified Kalman filter is exploited to track the channel variance.By solving the Yule-Walker equation, the coefficient of the AR process can obtained from zero order Bessel function. However, when the channel is assumed not perfectly understood, the above criterion will lead to an impractical application. To deal with it, the coefficient of the AR process is taken as a constant. Submitted a new state space model is deduced and found. Simulation results show that the proposed method can work effectively at the speed of 144 km / h with a phase noise of -85 dBc / Hz at 100 kHz offset. The proposed method yields an improvement bit error rate (BER) compared with three of the existing algorithms with much lower complexity.