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.
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