PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities [HEUGIP201707]
; National Key Research and Development Program of China-Government Corporation Special Program [2016YFE0111100]
; National Science Foundation of China [61571149]
; Science and Technology innovative Talents Foundation of Harbin [2016RAXXJ044]
; Projects for the Selected Returned Overseas Chinese Scholars of Heilongjiang Province
; MOHRSS of China
英文摘要:
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.
