Document

Software Defined Radio Lab

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

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

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.

Hongliang Zhang, Boya Di, Lingyang Song, “Reconfigurable Intelligent Surface-Empowered 6G,” Springer International Publishing (2021).
Yuanwei Liu, Zhijin Qin, Zhiguo Ding, “Non-Orthogonal Multiple Access for Massive Connectivity,” Springer International Publishing (2020).
Xiangyun Zhou, Lingyang Song, and Yan Zhang , “Physical Layer Security in Wireless Communications,” CRC Press, 1st edition (19 April 2016).
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.

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

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.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
R.-S. He and Z.-G. Ding, Applications of Machine Learning in Wireless Communications, IET, 2019.
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.

Instructor: Prof. Sudhan Majhi
Venue: MP 30
Timings: Tue,Thu. (10:00 AM- 11:30 AM)

Vector Spaces & Linear Transformations: Vector spaces, Subspace, Span, Linear independence, Basis, Dimension, Linear transforms, Fundamental subspaces of a linear function, Spanning set of four fundamental subspaces, Rank Nullity Theorem, Subspace dimension, Sum of subspaces and dimension, Determinants.
Norms: Vector Norms, Inner product, Cauchy–Schwarz inequality, Types of Norms, Pre-norm and dual norm, Matrix Norms, Column sum matrix norm, Row sum matrix norm, Spectral radius of a matrix, Diagonally dominant matrices, Condition number of the matrix.
Eigenvalues, Canonical Forms & Structured Matrices: Eigenvalues and Eigenvectors of a matrix, Location and perturbation of eigenvalues, Canonical forms and properties, Minimal polynomial and the companion matrix, Algebraic multiplicity, Diagonalizability of a matrix, Hermitian Matrix, Unitary Matrix, Normal Matrix.
Factorizations, Definite Matrices & Applications: LU decomposition, Cholesky & Schur, QR factorization, Positive Definite Matrix and Positive Semi-definite Matrix, Positive and nonnegative matrices, Singular Value Decomposition, Computational methods and applications.