Analysis of Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing
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, Namakkal, (Tamil Nadu), India.
Manuscript received on 24 January 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 January 2019 | PP: 370-371 | Volume-7 Issue-6, March 2019 | Retrieval Number: E1921017519©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: Next-generation attractive air interface solution for wireless local area networks is combination of MIMO-OFDM (Multiple-input multiple-output) with (Orthogonal frequency division multiplexing). In this research paper provides a review of the existing research of MIMO-OFDM technology by using 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. In this research work concludes with a discussion of relevant open areas for further research.
Keywords: Machine Learning, Wireless Physical Layer, MIMO, Deep Learning, WLANS and OFDM.
Scope of the Article: Machine Learning