{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:57:37Z","timestamp":1760241457896,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T00:00:00Z","timestamp":1521417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, an improved set-membership proportionate normalized least mean square (SM-PNLMS) algorithm is proposed for block-sparse systems. The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed l 2 , 1 norm of weight-taps into the cost function of the SM-PNLMS. Furthermore, an improved BS-SMPNLMS algorithm (the (BS-SMIPNLMS algorithm) is also derived and analyzed. The proposed algorithms are well investigated in the framework of network echo cancellation. The results of simulations indicate that the devised BS-SMPNLMS and BS-SMIPNLMS algorithms converge faster and have smaller estimation errors compared with related algorithms.<\/jats:p>","DOI":"10.3390\/sym10030075","type":"journal-article","created":{"date-parts":[[2018,3,20]],"date-time":"2018-03-20T15:59:39Z","timestamp":1521561579000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System"],"prefix":"10.3390","volume":"10","author":[{"given":"Zhan","family":"Jin","sequence":"first","affiliation":[{"name":"College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-6028","authenticated-orcid":false,"given":"Yingsong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Liu","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Bellevue, WA 98004, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Benesty, J., Gaensler, T., Morgan, D.R., Sondhi, M.M., and Gay, S.L. 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