{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:05:23Z","timestamp":1768557923342,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2016R1D1A1B03934653"],"award-info":[{"award-number":["NRF-2016R1D1A1B03934653"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1005920"],"award-info":[{"award-number":["NRF-2019R1A2C1005920"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space\u2013time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.<\/jats:p>","DOI":"10.3390\/sym11050713","type":"journal-article","created":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T23:07:27Z","timestamp":1558912047000},"page":"713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5154-2382","authenticated-orcid":false,"given":"Omar A.","family":"Saraereh","sequence":"first","affiliation":[{"name":"Communications Engineering Department, King Abdullah II School of Engineering, Princess Sumaya University for Technology PSUT, Amman 11941, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8900-0888","authenticated-orcid":false,"given":"Imran","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering &amp; Technology, Peshawar 814, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qais","family":"Alsafasfeh","sequence":"additional","affiliation":[{"name":"Department of Electrical Power and Mechatronics, Tafila Technical University, Tafila 11183, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salem","family":"Alemaishat","sequence":"additional","affiliation":[{"name":"School of Computing &amp; Informatics, Al-Hussein Technical University KHBP, Amman 11855, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1762-5915","authenticated-orcid":false,"given":"Sunghwan","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.aeue.2018.03.038","article-title":"Efficient Compressive Sensing Based Sparse Channel Estimation for 5G Massive MIMO Systems","volume":"89","author":"Khan","year":"2018","journal-title":"AEU Int. 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