{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:26:36Z","timestamp":1769019996755,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The large number of estimated parameters in a reconfigurable intelligent surface (RIS) makes it difficult to achieve accurate channel estimation accuracy in 6G. Therefore, we suggest a novel two-phase channel estimation framework for uplink multiuser communication. In this context, we propose an orthogonal matching pursuit (OMP)-based linear minimum mean square error (LMMSE) channel estimation approach. The OMP algorithm is used in the proposed algorithm to update the support set and pick the columns of the sensing matrix that are most correlated with the residual signal, which successfully reduces pilot overhead by removing redundancy. Here, we use LMMSE\u2019s advantages for handling noise to address the problem of inadequate channel estimation accuracy when the signal-to-noise ratio (SNR) is low. Simulation findings demonstrate that the proposed approach outperforms least-squares (LS), traditional OMP, and other OMP-based algorithms in terms of estimate accuracy.<\/jats:p>","DOI":"10.3390\/s23125516","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T02:00:45Z","timestamp":1686621645000},"page":"5516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3363-8684","authenticated-orcid":false,"given":"Ying","family":"Liu","sequence":"first","affiliation":[{"name":"School of Physics and Electronics, Central South University, Lushan South Road, Changsha 410083, China"}]},{"given":"Honggui","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Central South University, Lushan South Road, Changsha 410083, China"}]},{"given":"Chengzuo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Central South University, Lushan South Road, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.23919\/JCIN.2019.8917871","article-title":"Large intelligent surface\/antennas (LISA): Making reflective radios smart","volume":"4","author":"Liang","year":"2019","journal-title":"J. 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