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.
翻译:振荡器输出通常具有相位噪音,导致输出功率光谱密度(PSD)围绕Dirac 三角洲函数分散。本文考虑了AWGN 频道,该频道将伴随相位噪音的信号添加到Gaussian 频道,接收器接收到该频道。常规频道估算算法,如最小正方形(LMS)和中MSE标准,不适于此频道估算。我们(i) 结合信息理论学习标准(ITL)分析该阶段噪音频道估计,即最大可调合性标准(MCC),导致频道估计器的稳健状态行为;以及(ii) 将MSE和MCC合并为新型混合LMS算法,提高汇合率。