A Machine Learning Ensemble 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 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 70-72 | Volume-8 Issue-5, March 2019 | Retrieval Number: D2862028419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In the early days recognition of the errors in transmissions may diminish the time postponement of communications. The customary error recognition methods are not exact adequate. A machine learning based methodology is proposed to take care of this issue because of the ongoing momentous advancement. The machine learning technique acquires the transmission state is thought to be a component of the highlights of a channel situation like the impedance and the noise. The preparation dataset is produced by reproductions on the channel condition. The ensemble machine learning algorithms are namely AdaBoostM1, Attribute Selected Classifier, Bagging, Classification via Regression, and Random Committee implemented in this research work and found the best algorithm for giving best accuracy.
Keyword: Bagging, MIMO, Adaboostm1, OFDM, And Random Committee.
Scope of the Article: Classification