  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.
