A Novel Classification Approach for MIMO-oFDM
R. A Veer1, L. C Siddanna Gowd2

1R.A Veer, Research Scholar, Department of Electronics and Communications Engineering, Bharath Institute of Higher Education and Research, Bharath University, Chennai (Tamil Nadu), India.
2L.C Siddanna Gowd, Professor, Department of Electronics and Communications Engineering, AMS Engineering College, Erumapatty, Namakkal (Tamil Nadu), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 318-320 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2790028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The expanding unpredictability of designing cellular networks recommends that machine learning (ML) can successfully enhance 5G advances. Machine learning has proven successful a performance that scales with the measure of accessible data. The absence of vast datasets restrains the twist of machine learning applications in remote interchanges. The transmission state is thought to be a component of the highlights of a channel situation like the obstruction and noise, the relative motion between the transmitter and the receiver and this capacity is acquired by the machine learning strategy. The preparation dataset is produced by recreations on the channel condition. The Jrip, J48 and Naïve Bayes are the three algorithms implemented in this research work. This research work test if machine learning methods can predict the transmission states with a high accuracy compared to conventional approaches.
Keyword: Machine Learning, Jrip, MIMO, J48, OFDM, CRC and Naïve Bayes.
Scope of the Article: Machine Learning