{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:51:05Z","timestamp":1776282665862,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T00:00:00Z","timestamp":1503360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e19080432","type":"journal-article","created":{"date-parts":[[2017,8,22]],"date-time":"2017-08-22T11:08:25Z","timestamp":1503400105000},"page":"432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels"],"prefix":"10.3390","volume":"19","author":[{"given":"Yanyan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-6028","authenticated-orcid":false,"given":"Yingsong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9850-6004","authenticated-orcid":false,"given":"Felix","family":"Albu","sequence":"additional","affiliation":[{"name":"Department of Electronics, Valahia University of Targoviste, Targoviste 130082, Romania"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huazhong Agricultural University, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chong, C.C., Watanabe, F., and Inamura, H. 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