Online Course on Machine Learning for 5G and 6G Wireless Communication (3:0)
Last day to apply: 27 July 2025
Objectives
AI/ML has several applications in physical layer communication. It brings adaptive-ness 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 a study material for 5G and 6G wireless communications. 6G AI Native radio also requires a solid knowledge of AI/ML for wireless communication; having this knowledge may help them find a job in these companies.
Syllabus
Introduction to Python: Basic of Python programming Introduction to Machine Learning: Overview of supervised, semisupervised and unsupervised, Regression Model, SVM, KNN, CNN, DNN, RNN, LSTM, GANs, Transfer learning, RL. Introduction to Wireless: Python code on Single carrier system, OFDM, MIMO, OTFS system Wireless Communications: Source channel coding, channel coding, LDPC code decoding, odulation classification, channel estimation, Classification of wireless signals Autoencoder (based on 3GPP Standard), CSI compression and feedback (based on 3GPP Standard), Beamforming and beam Management (based on 3GPP Standard), PAPR reduction, Spectrum sensing, successive inference cancellation for NOMA Signal Estimation and Detection: AL/ML based Parameter estimation, IF estimation, symbol rate estimation, STO and CFO estimation, MIMO/OFDM/OTFS detectors, Denoising signals. Spectrum sharing and resource allocation: Resource allocation, Spectrum sharing, Power allocation using reinforcement learning (RL) and deep RL.
Course Plan
Week 1-6: Introduction to Python: Basic of Python programming Introduction to Machine Learning: Overview of supervised, semisupervised and unsupervised, Regression Model, SVM, KNN, CNN, DNN, RNN, LSTM, GANs, Transfer learning, RL.
Week 7-10: Introduction to Wireless: Python code on Single carrier system, OFDM, MIMO, OTFS system.
Week 11-14: Wireless Communications: Source channel coding, channel coding, LDPC code decoding, Modulation classification, channel estimation, Classification of wireless signals Autoencoder (based on 3GPP Standard), CSI compression and feedback (based on 3GPP Standard), Beamforming and beam Management (based on 3GPP Standard), PAPR reduction, Spectrum sensing, successive inference cancellation for NOMA
Week 15-18: Signal Estimation and Detection: AL/ML based Parameter estimation, IF estimation, symbol rate estimation, STO and CFO estimation, MIMO/OFDM/OTFS detectors, Denoising signals. Spectrum sharing and resource allocation: Resource allocation, Spectrum sharing, Power allocation using reinforcement learning (RL) and deep RL.
Who Can apply?
Suitable for B.Tech, M.Tech and PhD students, ( 4th year B.Tech with
ECE are eligible)
All IITs and NITs, Samsung, Qualcomm, Nokia, Mediatek, Mavenir,
Tejas Networks.
Pre-requisites
Wireless Communication
Number of credits – 3:0
Mode of Instruction
Online Classes
Duration
(AUG – DEC 2025)
Class Start Date
4th August 2025
Timings of the class
Monday and Wednesday
8 P.M. to 9:30 P.M.
Course Fee
₹ 15000 (Excluding 18% GST)
| Particulars | Amount (in ₹) |
| Course Fee | 15,000 |
| Application Fee | 300 |
| GST@18% | 2,754 |
| Total | 18,054 |
Reference Books
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT
Press, 2016
2. Y. C. Eldar, A. Goldsmith, D. Gündüz, and H. V. Poor, Machine
Learning and Wireless Communications, Cambridge University Press,
1st edition, 2022.
3. F.-L. Luo, Machine Learning for Future Wireless Communications,
Wiley-IEEE Press,
2020.