Abstract
An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC algorithm is implemented by incorporating an adaptive combination function into the cost function of the proportionate normalized maximum correntropy criterion (PNMCC) algorithm to create a new penalty on the filter coefficients according to the devised threshold, which is based on the proportionate-type adaptive filter techniques and compressive sensing (CS) concept. The derivation of the proposed ACC-PNMCC algorithm is mathematically presented, and various simulation experiments have been carried out to investigate the performance of the proposed ACC-PNMCC algorithm. The experimental results show that our ACC-PNMCC algorithm outperforms the PNMCC and sparse PNMCC algorithms for sparse multi-path channel estimation applications.
Highlights
In recent years, wireless communication is the fastest growing and most widely used technology in the field of information communication
The performance of the proposed ACC-proportionate normalized version of the MCC algorithm (NMCC) (PNMCC) algorithm is evaluated through the steady-state mean square deviation (MSD), which is defined as MSD g(n) = E g − g(n) 2
From the updating Eq (23), we notice that there are several key parameters which may affect the ACC-PNMCC performance and must be properly selected. To better select these parameters, we experimentally analyzed their effect on the MSD performance of the ACC-PNMCC algorithm
Summary
Wireless communication is the fastest growing and most widely used technology in the field of information communication. The SPF-NMCC algorithm improved the steady-state performance of NMCC, but tends to converge slowly due to the large discrepancy between coefficient values in sparse channels. To remedy this drawback, proportionate-type [41, 42] NMCC algorithms were proposed. The performance of the proposed ACC-PNMCC algorithm is investigated through several different simulation experiments, and it is compared to the MCC, NMCC, PNMCC, and sparse PNMCC algorithms in terms of the SSE and convergence speed. The simulation results illustrate the potential of the ACC-PNMCC algorithm to provide a superior estimation performance for sparse multi-path channel estimation under non-Gaussian and impulsive noise environments.
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