| dc.contributor.advisor | Lizhong Zheng. | en_US |
| dc.contributor.author | AlHajri, Mohamed Ibrahim | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2019-02-14T15:21:43Z | |
| dc.date.available | 2019-02-14T15:21:43Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/120367 | |
| dc.description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 77-86). | en_US |
| dc.description.abstract | This thesis develops a novel technique for low-complexity MIMO detection known as Constellation Shift Binary Classification (CSBC). The proposed method utilizes the constellation structure to deduce a constellation shift that is used to reduce the problem of detecting the symbol into binary classification. The proposed method is proven to outperform other common techniques such as ZF and LMMSE. The proposed CSBC is a novel MIMO detection technique since it utilizes the constellation structure together with a binary classifier that is based on neural networks. | en_US |
| dc.description.statementofresponsibility | by Mohamed Ibrahim AlHajri. | en_US |
| dc.format.extent | 86 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Novel low-complexity MIMO detection based on constellation shift binary classification | en_US |
| dc.title.alternative | Novel low-complexity Multiple Input Multiple Output detection based on constellation shift binary classification | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | S.M. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 1083762686 | en_US |
