Deep Learning-Based Phase Noise Compensation in Multicarrier Systems
Amirhossein Mohammadian, Chintha Tellambura, Geoffrey Ye Li
Abstract
This letter presents a deep learning (DL) algorithm for compensating the impacts of phase noise in channel estimation and data detection for multicarrier systems. The proposed DL algorithm replaces conventional estimators’ iterative process and therefore reduces complexity. The input to this algorithm is the first iteration of the conventional least squares (LS) estimator, which requires no knowledge of channel and phase-noise statistics. The proposed algorithm is trained offline using simulation data and then exploited for online phase noise compensation. The simulation results show that the proposed DL algorithm outperforms the conventional estimators and achieves a 6 dB reduction in the mean-squared error (MSE) of channel estimation and a 45% improvement in the bit-error rate (BER).