A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment

Abstract

Abstract-A sparsity-aware proportionate normalized maximum correntropy criterion (PNMCC) algorithm with lp-norm penalty, which is named as lp-norm constraint PNMCC (LP-PNMCC), is proposed and its crucial parameters, convergence speed rate and steady-state performance are discussed via estimating a typical sparse multipath channel and an typical echo channel. The LP-PNMCC algorithm is realized by integrating a lp-norm into the PNMCC's cost function to create an expected zero attraction term in the iterations of the presented LP-PNMCC algorithm, which aims to further exploit the sparsity property of the sparse channels. The presented LP-PNMCC algorithm has been derived and analyzed in detail. Experimental results obtained from sparse channel estimations demonstrate that the proposed LP-PNMCC algorithm is superior to the PNMCC, PNLMS, RZA-MCC, ZA-MCC, NMCC and MCC algorithms according to the convergence speed rate and steady-state mean square deviation.

An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations

“…Additionally, the sparsity is not given by a formula. Herein, the sparsity characteristic of the echo channel is measured by [40,45,49]. In this experiment, we use ϑ 12 = 0.8222 for the first 8000 iterations, while ϑ 12 = 0.7362 are set for the after 8000 iterations.…”

“…with κ and η positive constants with typical values of κ = 5/M and η = 0.01 [42][43][44][45]. Parameter κ is used to prevent the coefficients from stalling when they are much smaller than the largest one, while η avoids the stalling of all coefficients whenĝ (n) = 0 M×1 at initialization.…”

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

“…Finally, we set up an experiment to study the tracking behavior of our GZA-PNMCC algorithm for estimating a long-tap echo channel with two different sparsity levels and a length of 256. The sparsity measurement of the echo channel is ζ 12 [44][45][46][47][48]. A typical echo channel is described in Figure 6.…”

Adaptive blind equalization for multi‐level QAM signals in impulsive noise environment

“…Reference [18] proposed an adaptive equalization algorithm based on training sequence based on MCC criterion, which solved the problem of performance degradation of minimum MSE adaptive equalization algorithm in impulsive noise environment. For sparse system, the following MCC-based adaptive filtering algorithms with good anti-jamming capability to impulsive noise are proposed in references [19][20][21][22][23]. References [19,20] proposed the general zero attraction proportionate normalized MCC algorithm, reference [21] proposed the group-constrained MCC algorithm and reference [22] proposed the soft parameter function penalized normalized MCC algorithm.…”

“…For sparse system, the following MCC-based adaptive filtering algorithms with good anti-jamming capability to impulsive noise are proposed in references [19][20][21][22][23]. References [19,20] proposed the general zero attraction proportionate normalized MCC algorithm, reference [21] proposed the group-constrained MCC algorithm and reference [22] proposed the soft parameter function penalized normalized MCC algorithm. Reference [23] proposes a novel sparsity constrained recursive adaptive filtering algorithm via the generalized correntropy criterion with variable centre for sparse system parameters estimation.…”