Abstract
A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation performance in a mixed noise environment. As a result, a zero attraction term is obtained from the expected l 0 and l 1 penalty techniques. Furthermore, a reweighting factor is adopted and incorporated into the zero-attraction term of the GC-MCC algorithm which is denoted as the reweighted GC-MCC (RGC-MMC) algorithm to enhance the estimation performance. Both the GC-MCC and RGC-MCC algorithms are developed to exploit well the inherent sparseness properties of the sparse multi-path channels due to the expected zero-attraction terms in their iterations. The channel estimation behaviors are discussed and analyzed over sparse channels in mixed Gaussian noise environments. The computer simulation results show that the estimated steady-state error is smaller and the convergence is faster than those of the previously reported MCC and sparse MCC algorithms.
Highlights
With the rapid rise of various wireless technologies, wireless transmissions have been widely developed in various fields such as mobile communications and satellite communication systems [1,2].the signal might be distorted due to the diffraction, refraction, reflection or deviation from obstacles such as buildings, mountains and so on, resulting in transmission delays and other adverse effects
A group-constrained maximum correntropy criterion (GC-MCC) algorithm based on the compressive sensing (CS) concept and zero attracting (ZA) techniques is proposed in order to fully exploit the sparseness characteristics of the multi-path channels
Where θZA is a zero attracting ability controlling parameter that is used for giving a tradeoff between the estimation error and l1 -norm constraint, and β ZA is the step-size of the zero attracting MCC (ZA-MCC) algorithm
Summary
With the rapid rise of various wireless technologies, wireless transmissions have been widely developed in various fields such as mobile communications and satellite communication systems [1,2]. In order to reduce the effects of the ISS, several improved AF algorithms have been presented by using high order error criteria or mixed error norms such as least mean fourth (LMF), least mean squares-fourth (LMS/F) and so on [17,18,19,20] Their related sparse forms have been developed based on the above mentioned norm penalties [17,21,22,23,24,25,26,27]. A group-constrained maximum correntropy criterion (GC-MCC) algorithm based on the CS concept and zero attracting (ZA) techniques is proposed in order to fully exploit the sparseness characteristics of the multi-path channels.
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