Phase Noise Estimation in Full-Duplex Orthogonal Frequency Division Multiplexing Systems

Abstract

The paper studies the estimation of phase noise (PHN) in a full-duplex (FD) orthogonal frequency division multiplexing (OFDM) system. Unlike the conventional half-duplex OFDM system, the receiver faces the challenge of estimating the PHN of the intended signals and self-interference (SI). To address this issue, the PHN estimation problem is transformed into a sparse signal detection problem, which can be solved using compressive sensing techniques. However, the performance of these techniques is limited by linear approximation, improper prior information, or improper structure of the sensing matrix. To overcome these limitations, the extended Kalman filter (EKF) is introduced for PHN estimation. The EKF utilizes the maximum a posteriori probability (MAP) criterion with an approximated linear observation model. Furthermore, a novel MAP estimator is developed that employs the original nonlinear observations. Numerical results validate the effectiveness of the proposed estimators and demonstrate that the proposed MAP estimator outperforms existing compressive sensing approaches due to the utilization of accurate posterior distribution.