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 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.

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, Transfer learning.

Introduction to Wireless: Python code on Single carrier system, OFDM, MIMO, OTFS system

Wireless Communications: LDPC code decoding, Modulation classification, channel estimation, CSI compression and feedback (based on 3GPP Standard), Beamforming and beam Management (based on 3GPP Standard), PAPR reduction, and Spectrum sensing,

Signal Estimation and Detection: AL/ML based Parameter estimation, IF estimation, symbol rate estimation, STO and CFO estimation.

Resource allocation: Resource allocation, Spectrum sharing, Power allocation.

BE/B Tech ME/M Tech and Phd relevant discipline (4th year BE/B Tech also Eligible)

Wireless Communication and basic of python.

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

4. R. He and Z. Ding, Applications of Machine Learning in Wireless Communications. London, U.K.: The Institution of Engineering and Technology, 2019.

5. M. Viswanathan, Digital Modulations using Python. Independently published, 2019.