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Microwave Circuits Lab

1. Radio-Frequency Characteristics of Components

2. Introduction to Microwave Measurements: Detection of RF Power and Development of a Scalar Reflectometer

3. Introduction to Network Analysis

4. Introduction to Microstrip Transmission Lines and Computer-Aided Design lab5.mdl - IC-CAP file for measurement control

5. Introduction to Microwave Transistors

6. Matching Network Design and Circuit Layout

7. Amplifier Design for Maximum Power Transfer

8. Amplifier Nonlinear Performance and Inter modulation

9. Low Noise Amplifier Design

Microwave Semiconductor Devices

Transient and ac behavior of p-n junctions, effect of doping profile on the capacitance of p-n junctions, noise in p-n junctions, high-frequency equivalent circuit, varactor diode and its applications; Schottky effect, Schottky barrier diode and its applications; Heterojunctions. Tunneling process in p-n junction and MIS tunnel diodes, V-I characteristics and device performance, backward diode. Impact ionization, IMPATT and other related diodes, small-signal analysis of IMPATT diodes.

Advanced Electromagnetic Engineering

Introduction to waves: The wave equation, waves in perfect dielectrics, lossy matter, reflection of waves, transmission line concepts, waveguide and resonator concepts, radiation and antenna concepts. Theorems and concepts: Duality, uniqueness, image theory, the equivalence principle, induction theorem, reciprocity theorem, Green’s function and integral equation. Plane wave functions: The wave function, plane waves, rectangular waveguide and cavity, partially filled waveguide, dielectric slab waveguide, surface guided waves, currents in waveguides.

Advanced Engineering Mathematics

Complex integration: Cauchy-Goursat Theorem (for convex region), Cauchy's integral formula, Higher order derivatives, Morera's Theorem, Cauchy's inequality and Liouville's theorem, Fundamental theorem of algebra, Maximum modulus principle, Taylor’s theorem, Schwarz lemma. Laurent's series, Isolated singularities, Meromorphic functions, Rouche's theorem, Residues, Cauchy's residue theorem, Evaluation of integrals, Riemann surfaces.

Machine learning for Digital Communication

Introduction and fundamentals of machine learning: Basics of supervised/unsupervised/reinforcement learning, Revision of probability and statistics revision, Revision of linear algebra, Fundamentals of numerical optimization, Machine learning for wireless communications, Machine learning for physical layer design Linear Modeling: A Least Squares Approach, Linear modeling Generalization and over fitting, Regularized least squares Wireless application - MIMO zero-forcing receiver design Linear Modeling: A Maximum Likelihood Approach, Errors as noise– thinking generatively, Maximum likelihood,

Case Studies in Signal Processing

Linear Prediction and its application - Modelling Time Series Data, Sigma-Delta Modulation, LPC in Speech Processing, Wireless Channel Prediction for Feedback Communication. Applications of Kalman Filter: OFDM Channel estimation, tracking a moving/Flying object using Radar, Lidar data and optical images, alpha-beta and alpha-beta-gamma trackers, State of Charge estimation of a Li-Ion Battery. Sensor fusion.

Applications of Wiener and Adaptive Filters: Deconvolution problem, Noise cancellation, System Identification.

Wireless Mesh Networks

Introduction: Introduction and overview of Wireless Mesh Networks, Evolution of Wireless Mesh Networks, Pros and Cons of WMNs, Architectural issues in Wireless Mesh Networks, Capacity of Wireless Mesh Networks, Layer-wise Protocol, Propagation models for WMNs, and Design issues in Wireless Mesh Networks.

Graph Theory

Introduction to Graphs and their applications. Finite and infinite graphs. History of graph theory. Paths and Circuits. Isomorphism, sub graphs. Walks, paths, and circuits. Hamiltonian paths and circuits. Trees and Fundamental Circuits. Cut-Sets and Cut-Vertices. Connected and disconnected graphs and components. Directed Graphs. Euler graphs. Operations on graphs. Graph-Theoretic Algorithms and Computer Programs. Applications of Graph theory in operations research. Distributed graph algorithms for computer networks. Complex networks.

Complex Networks

Graph Theory Preliminaries. Introduction to Complex Networks. Centrality Metrics. Community Detection in Complex Networks. Random Networks. E-R random networks. Properties of Random Network. Real- world examples of random networks. Small- World Networks. Creation of Deterministic Small- World Networks. Anchor Points in a String Topology Small- world Network. Routing in Small-World Networks. The capacity of small-world Networks. Scale-Free Networks. Characteristics of Scale-Free Networks. Real- world examples of Scale-free networks. Preferential Attachment-based Scale-Free Network Creation.

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