Channel Parameter Estimation in the Presence of Phase Noise Based on Maximum Correntropy Criterion

TL;DR

This paper proposes a robust channel estimation method for phase noise channels using the maximum correntropy criterion, improving convergence speed by combining MCC with MSE.

Contribution

It introduces a new mixed-LMS algorithm that combines MCC and MSE for better convergence in phase noise channel estimation.

Findings

MCC provides robustness against phase noise.

The mixed-LMS algorithm converges faster than traditional methods.

Enhanced steady-state performance in phase noise environments.

Abstract

Oscillator output generally has phase noise causing the output power spectral density (PSD) to disperse around a Dirac delta function. In this paper, the AWGN channel is considered, where the sent signal accompanying with phase noise is added to the channel Gaussian noise and received at the receiver. Conventional channel estimation algorithms such as least mean square (LMS) and mean MSE criterion are not suitable for this channel estimation. We (i) analyze this phase noise channel estimation with information theoretic learning (ITL) criterion, i.e., maximum correntropy criterion (MCC), leading to robustness in the channel estimator's steady state behavior; and (ii) improve the convergence rate by combining MSE and MCC as a novel mixed-LMS algorithm.