SPWiCOM Lab

Software Defined Radio Lab

Instructor: Prof. Sudhan Majhi
Venue: ECE Lab
First Class: Aug 01
Timings: M,W,F (4-5 PM)

Syllabus

  • OFDM: Design and implementation on Pluto SDR.
  • BPSK/QPSK/16-QAM: Design and implementation on Pluto SDR.
  • Introduction to Simulink: Basic understanding of simulink model design.
  • Introduction to SDR: Features, adavantages, basic elements of SDR.

Pre-requisites: Basics of digital Communications


E2 216: Emerging Wireless Communication Technology

Instructor: Prof. Sudhan Majhi
Venue: ECE1.07
First Class: Jan.
Timings: M,W,F (4-5 PM)

Syllabus

  • Full-Duplex (FD): Single carrier-based FD system, OFDM-based FD systems, MIMO-based FD systems, self-Interference cancelation.
  • Intelligent reflecting surface (IRS): IRS system model, MIMO, and NOMA-based IRS systems, PLS for IRS systems, Index modulation for IRS systems.
  • Non-orthogonal multiple access (NOMA): Power domain NOMA, code domain NOMA, sparse code multiple access systems, interference cancelation techniques.
  • Physical layer security (PLS): Artificial noise-based PLS, beamforming-based PLS, Secrecy capacity and Secrecy outage probability for MIMO systems, Transmit and receiver beamforming-based PLS.
  • Outage probability: Basics of the probability distribution function and cumulative distribution function, channel capacity over Rayleigh and Rician channel, outage probability for the cooperative communication system.

Pre-requisites: Wireless Communications


References

  1. Hongliang Zhang, Boya Di, Lingyang Song, “Reconfigurable Intelligent Surface-Empowered 6G,” Springer International Publishing (2021).
  2. Yuanwei Liu, Zhijin Qin, Zhiguo Ding, “Non-Orthogonal Multiple Access for Massive Connectivity,” Springer International Publishing (2020).
  3. Xiangyun Zhou, Lingyang Song, and Yan Zhang , “Physical Layer Security in Wireless Communications,” CRC Press, 1st edition (19 April 2016).
  4. A. Kumar, S. Majhi and H. -C. Wu, "Physical-Layer Security of Underlay MIMO-D2D Communications by Null Steering Method Over Nakagami-m and Norton Fading Channels," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2022.3178758.

E2-217 (AUG) 3: 1: Machine Learning for Wireless Communication

Instructor: Prof. Sudhan Majhi
Venue: ECE1.07
First Class: Aug 03
Timings: Tue,Thu. (11:30 AM- 1 PM)

Syllabus

  • Spectrum sharing and resource allocation: Resource allocation, Spectrum sharing, Power allocation using reinforcement learning (RL) and deep RL.
  • Interference: Interference classification and mitigation for wireless communication, Self-interference cancellation for in-band full duplex radios.
  • Signal Estimation and Detection: AI/ML based Parameter estimation, STO and CFO estimation, Channel estimation, MIMO/OFDM/OTFS detectors.
  • Wireless Communications: AI/ML-based source coding and channel coding, PAPR reduction for the OTFS and OFDM modulation scheme, Autoencoder, Classification of wireless signals, Modulation classification, and deep unfolding methods.
  • Introduction to Machine Learning: Overview of supervised, semi-supervised and unsupervised.

Pre-requisites: Basics of Machine python


References

  1. I. 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. 4. Y. C. Eldar, A. Goldsmith, D. Gündüz, and H. V. Poor, Machine Learning and Wireless Communications, Cambridge University Press, 1st edition, 2022.