An Adaptive Channel Estimation Based on Fixed-Point Generalized Maximum Correntropy Criterion

Abstract

Many conventional adaptive channel estimation methods are based on minimum mean square error (MMSE) criterion, maximum correntropy criterion (MCC) or least p-norm criterion. However, these criteria are not always desirable in the presence of the various kinds of noises in different wireless channels. To further enhance the adaptability of the channel estimation to different noises, this paper introduces a new criterion named generalized maximum correntropy criterion (GMCC), and calculates the estimated channel parameters using the fixed-point iteration. The convergence analysis of the fixed-point iteration based on GMCC is discussed theoretically and is verified by experiments. More experiments are implemented to show the steady-state performance of the algorithm under different noise environments and the guide of parameter selection is provided.

Newtonian-Type Adaptive Filtering Based on the Maximum Correntropy Criterion

“… is the weighting function of RLS-type MCC-based adaptive filtering that values how much the n -th sample data influences the filtering. In Newtonian-type and RLS-type adaptive filtering, the weighting function can be calculated by [ 19 , 36 ]: where is the loss function or the error measurement. One can calculate that the error measurement and the weighting function are coincidentally in terms of MCC.…”

“…In recent years, information theory learning (ITL) was found suitable to deal with non-Gaussian noises [ 10 , 11 , 12 , 13 , 14 ]. Inspired by ITL, maximum correntropy criterion (MCC) or generalized maximum correntropy criterion (GMCC)-based adaptive filtering was studied [ 15 , 16 , 17 , 18 , 19 , 20 ]. Most of the aforementioned robust algorithms were LMS-like [ 21 , 22 ] or RLS like [ 23 , 24 , 25 ], which is to say that the optimization methods used were limited to gradient descent and fixed point iteration [ 26 ].…”

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

“…However, By increasing computation power, adaptive equalizers gained attraction. Reference [18] adopted MCC in sparse channel estimation, [19] used a recursive least square (RLS) algorithm that adopted a generalized form of MCC and [20] added MCC to the cost function of an adaptive filtering problem two fold. Firstly, MCC is the main term in the performance function and secondly, a Correntropy Induced Metric (CIM) mimics l0-norm as the penalization term.…”