Comparative Analysis on Supervised Machine Learning Models for Future Wireless Communication Networks
Kishore Odugu1, B. Rajasekar2
1Kishore Odugu, PhD. Assistant Professor, Department of Electronics & Communication, Stayabhama Institute of Science and Technology, GMR Institute of Technology, Rajam (Andhra Pradesh), India.
2Dr. B. Rajasekar, Associate Professor, Department of Electronics & Communication, Stayabhama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 721-723 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3806048619/19©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: Future wireless communication networks are anticipated to backing the exceptional knowledge rates and revolutionary innovative applications, that needs replacement traditional radio technology wireless communication model. The remonstrance task is to help the wireless communication systems or networks for perspicacious dynamic learning and higher cognitive process, So disparate necessities of future wireless communication networks are often glad. Supervised Machine learning models were foremost reassuring computer science methods or algorithms, formed to backing sensible wireless communication system terminals. Upcoming sensible fifth generation (5G) mobile wireless terminals are anticipated to Independently access the foremost worthy different bands of spectrum depending on learning of elusive spectral efficiency , the transmission power is controlled, whereas impose on energy effective learning or inference and at the same time adjusting protocols for transmission depending on quality of service learning or inference. So, its needed to succinctly review the machine learning fundamental concepts and recommend their amenity within the enchanting applications of fifth generation networks, together with psychological feature radios, femto or small cells, massive MIMOs, smart grid, assorted networks, energy gathering, end-to end communications. The main aim is to backing the readers in refinement the enthusiasm, procedure of governing machine learning methods within the situation of next generation networks so that innovate into uncharted applications or services.
Keyword: Artificial Intelligence, Machine Learning, Massive MIMO, Diverse Networks, SVM, Bayesian Learning, Expectation Maximization, HMM.
Scope of the Article: Machine Learning