Work place: Computer Engineering Department, Delhi Technological University (DTU)
E-mail: kapil@ieee.org
Website:
Research Interests: Engineering, Computational Engineering, Computational Science and Engineering
Biography
Dr. Kapil Sharma was born in Haryana, India. In 2011,he has completed Doctors Degree in
Computer Science and Engineering under the Faculty of Engineering and Technology at the
M. D. University, Rohtak (Haryana), India. He has obtained his Bachelor of Engineering and
Master of Technology Degrees in Computer Science & Engineering and Information Technology from M. D. University, Rohtak and IASE, Rajasthan, India in 2000 and 2005 respectively. He has published various research papers in international conferences and journals in the domain of mobile communications along with several national and international patents. He is head of department of IT department and also Associate Professor in Department of Computer Engineering; Delhi Technological University. He previously worked as Assistant Professor with School of Information Communication & Technology; Department of Computer Science & Engineering; Greater Noida, Gautam Budh Nagar and as Reader and Head with Department of Information Technology; Guru Premsukh Memorial College of Engineering 245, Budhpur Villege, G.T Karnal Road; Delhi.
DOI: https://doi.org/10.5815/ijwmt.2018.02.02, Pub. Date: 8 Mar. 2018
In this paper, we present atmospheric effect on 5th Generation (5G) millimeter wave (MMWave) communication system. Atmospheric effects for Delhi (India) based 5G communication system is calculated as per Delhi atmospheric conditions. Atmospheric impairments are major cause of degrading mmWave signal power while mmWave propagation in wireless channel. Due to Atmospheric impairments attenuation takes place and major impairments are like water vapour, oxygen, rain and fog for Delhi (India). 5G mmWave attenuation calculations are performed for the mmWave band frequencies 28 GHz, 37 GHz and 39 GHz. In this paper intelligent adaptive transmitter based on trend of the atmospheric conditions tunes to machine learning (ML) based derivation of channel capacity. The ML based transmitter is a supervised ML device and it has provision of self teaching learning machine based on data. Results are graphed for the mentioned frequencies and also intelligently software defined (SD) Shannon channel capacity calculated for Delhi (India) based 5G mmWave communication system under different atmospheric conditions.
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