| Type of Material: | Thesis |
| Title: | High Accuracy Estimation and Detection Of MIMO OFDM Machine Learning Ensemble Classification Approach |
| Researcher: | Rajkumar Arvind Veer |
| Guide: | Arul Selvi ,S |
| Department: | Department of Electronics and Telecommunication Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2021 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Telecommunication Engineering, Bharath University, Chennai, Chennai; 2021; D14EC512 |
| Fulltext: | Shodhganga |
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| 100 | __ | |aRajkumar Arvind Veer|eResearcher |
| 110 | __ | |aDepartment of Electronics and Telecommunication Engineering|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aHigh Accuracy Estimation and Detection Of MIMO OFDM Machine Learning Ensemble Classification Approach |
| 260 | __ | |aChennai|c2021|bBharath University, Chennai |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Electronics and Telecommunication Engineering, Bharath University, Chennai, Chennai|d2021|oD14EC512 |
| 520 | __ | |aNext-generation smart air interface solution for wireless local area network is mixture of MIMO-OFDM (Multiple-input multiple-output) with (Orthogonal frequency division multiplexing). To discuss and predict the use of machine learning and deep learning based on MIMO communications, channel estimation, signal detection and selection in OFDM systems, Opportunities and Challenges of Wireless Physical Layer, Physical layer channel authentication for 5G and MIMO data for machine learning application to beam selection. MIMO remote innovation in blend with MIMO-OFDM is an attractive airinterface response for cutting edge WLANs. The rudiments of MIMOOFDM spotlights and innovation on collector structure, multiuser frameworks, space-recurrence flagging, and equipment usage angles. The expanding unpredictability of designing cellular networks recommends that machine learning (ML) can successfully enhance 5G advances. Machine learning has proven successful performance that scales with the measure of accessible data. T |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aArul Selvi ,S|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/353292|yShodhganga |
| 905 | __ | |afromsg |
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