Phase-Noise Compensation Based on Artificial Neural Network Optimization for OFDM Ranging Systems

Abstract

Phase noise (PN) plays a negative role in realizing the performance of orthogonal frequency division multiplexing for high-precision ranging. Many existing methods prioritize low complexity rather than efficiency. In this letter, a novel PN compensation method based on artificial neural network optimization (ANNO) is proposed, which can accurately compensate the effect of PN when ranging. We first establish an ANNO model for the PN and then use the artificial neural network algorithm to solve it. Moreover, a dimension reduction method is proposed, which can reduce the convergence time and complexity of the proposed method. Simulation results can further demonstrate that the proposed method can effectively estimate the PN and improve the ranging accuracy.