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However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC\/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.<\/jats:p>","DOI":"10.1155\/2020\/2389673","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T23:30:58Z","timestamp":1591313458000},"page":"1-9","source":"Crossref","is-referenced-by-count":6,"title":["Channel Estimation Performance Analysis of FBMC\/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7347-3763","authenticated-orcid":true,"given":"Han","family":"Wang","sequence":"first","affiliation":[{"name":"College of Physical Science and Engineering, Yichun University, 576 Xuefu Road, Yichun, Jiangxi 336000, China"},{"name":"Institute of Data Science, City University of Macau, Avenida Padretomas Pereira, Taipa, Macau 999078, 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