Wiener filter preprocessing for OFDM systems in the presence of both nonstationary and stationary phase noises

Abstract

Statistics-based intercarrier interference (ICI) mitigation algorithm is proposed for orthogonal frequency division multiplexing systems in presence of both nonstationary and stationary phase noises. By utilizing the statistics of phase noise, which can be obtained from measurements or data sheets, a Wiener filter preprocessing algorithm for ICI mitigation is proposed. The proposed algorithm can be regarded as a performance-improving technique for the previous researches on phase noise cancelation. Simulation results show that the proposed algorithm can effectively mitigate ICI and lower the error floor, and therefore significantly improve the performances of previous researches on phase noise cancelation, especially in the presence of severe phase noise.

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“…The Wiener filtering method tracks a target on the basis of input signals and noises, and this method has significantly contributed to the application and development of filter theory. The effect of filtering degrades when the priori information deviates from the hypothesis [12]. Widrow and Hoff [13] of the Stanford University proposed the least mean squares adaptive algorithm based on the Wiener filter and Kalman filter (KF) theory and laid the foundation of adaptive filtering theory [14].…”