Machine Learning for xG wireless Networks: Performance Analysis and Monte Carlo Simulations

The Big data and ultrahigh speed computing devices have transformed various sectors of economy across the globe, in particular a communication sector. As per Erickson mobility report, data traffic per active user is expected to increase five-fold from 1.4 GigaByte (GB) per month in 2015 to 7 GB per month by 2021. Utilization of data analytics tools on Large spectrum data gives a complete blend of two verticals (Data science and communication systems). Machine Learning and Deep Learning are the tools applied to solve varied domain research problems like computer vision systems, Natural Language processing, communication systems etc. in an efficient way. For Instance, in recently evolved research domains like molecular communication, underwater communication etc., channel modeling is difficult and thus Machine Learning and Deep Learning are utilized as a tools to model the wireless channel in such complex scenarios. Furthermore, modulation scheme recognition, channel modeling and identification, encoding and decoding, channel estimation etc. have been examined in the literature demonstrating the application of Machine Learning and Deep Learning at the physical layer of wireless networks.

This Faculty Development Program is especially intended to provide with an in-depth technical exposure of Machine Learning and Deep Learning implementation in python, Autoencoders, Long Short term memory (LSTM) network, hyperparameter tuning, Cognitive Radio, Vehicular Cyber Physical system and their utilities in wireless research to the faculty members of engineering colleges, research scholars in wireless communication, post-graduate students, and engineers across the nation. Furthermore, interested undergraduate students (UGRP scholars) with good academic record who would like to further explore the latest research in current wireless systems can also be a part of this program. The lecture session will be followed by three hours of respective lab sessions to further enhance and consolidate the understanding of the participants.

Please click here for the course Brochure.

Highlights of FDP 2017

Highlights of FDP 2018

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