Online Course on Machine Learning for 5G and 6G Wireless Communication (3:0)

Objectives

AI/ML has several applications in physical layer communication. It brings adaptiveness to the transmitter as well as the receiver and improves the performance and latency of the communication system. The 3GPP standards already adopted AI/ML as study material for 5G and 6G wireless communication.  6G AI Native radio also requires a solid knowledge of AI/ML. Most of the Telecom companies (network and modem) are looking for people who have knowledge in AI/ML for wireless communication; having this knowledge may help them find a job in these companies.

Syllabus

Introduction to Machine Learning: Overview of supervised, semi-supervised and unsupervised. Wireless Communications: AI/ML-based Modulation classification, channel estimation, Channel prediction, Classification of wireless signals Autoencoder (based on 3GPP Standard), CSI compression and feedback (based on 3GPP Standard), Beam forming and beam Management (based on 3GPP Standard). Signal Estimation and Detection: AL/ML based Parameter estimation, STO and CFO estimation, MIMO/OFDM/OTFS detectors. Spectrum sharing and resource allocation: Resource allocation, Spectrum sharing, Power allocation using reinforcement learning (RL) and deep RL.

Basic tools: Python, TensorFlow and PyTorch.

Target Group

IITs, NITs, IIIT, Samsung, Qualcomm, Nokia, Jio, MediaTek, Mavenir, Tejas Networks, Sasken Technologies, Tata Elxsi, Mistral Solutions, BEL, Sterlite Technologies Limited, CDOT, HFCL, Wipro Limited, DRDO, BSNL, ISRO, L&T Technology, and Tech Mahindra, ECE department of Local colleges in Bangalore 

Who Can apply?

BE/BTech, MSc/MS, ME/MTech, PhD in ECE can apply

Pre-requisites: Nil

Reference Books

1. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
2. R.-S. He and Z.-G. Ding, Applications of Machine Learning in Wireless Communications, IET, 2019.
3. F.-L. Luo, Machine Learning for Future Wireless Communications, Wiley-IEEE Press, 2020.
4. Y.C. Eldar, A. Goldsmith, D. Gündüz, and H. V. Poor, Machine Learning and Wireless Communications, Cambridge University Press, 1st edition, 2022.