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The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above\u2010mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.<\/jats:p>","DOI":"10.1155\/2018\/9783863","type":"journal-article","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T23:38:07Z","timestamp":1535067487000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms"],"prefix":"10.1155","volume":"2018","author":[{"given":"Lu","family":"Bai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9729-9592","authenticated-orcid":false,"given":"Cheng-Xiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Goussetis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0284-1930","authenticated-orcid":false,"given":"Jian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,8,23]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2014.2328098"},{"key":"e_1_2_10_2_2","unstructured":"Huawei 5G a technology vision White Paper https:\/\/www.huawei.com\/ilink\/en\/download\/HW_314849."},{"key":"e_1_2_10_3_2","unstructured":"Samsung 5G vision White Paper https:\/\/www.samsung.com\/global\/business\/networks\/insights\/5g-vision\/."},{"key":"e_1_2_10_4_2","unstructured":"GasparI.andWunderG. 5G cellular communications scenarios and system requirements 2013 https:\/\/www.5gnow.eu\/."},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2014.6736752"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/mwc.2016.7422408"},{"key":"e_1_2_10_7_2","volume-title":"Wireless Communications","author":"Molish A.","year":"2011"},{"key":"e_1_2_10_8_2","unstructured":"NurmelaV. 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