Video Lectures

Lecture 1: Introduction

Lecture 2: Discrete Source Encoding

Lecture 3: Memory-less Sources

Lecture 4: Entropy and Asymptotic Equipartition Property

Lecture 5: Markov Sources

Lecture 6: Quantization

Lecture 7: High Rate Quantizers and Waveform Encoding

Lecture 8: Measure

Lecture 9: Discrete-Time Fourier Transforms

Lecture 10: Degrees of Freedom

Lecture 11: Signal Space

Lecture 12: Nyquist Theory

Lecture 13: Random Processes

Lecture 14: Jointly Gaussian Random Vectors

Lecture 15: Linear Functionals

Lecture 16: Review; Introduction to Detection

Lecture 17: Detection for Random Vectors and Processes

Lecture 18: Theory of Irrelevance

Lecture 19: Baseband Detection

Lecture 20: Introduction of Wireless Communication

Lecture 21: Doppler Spread

Lecture 22: Discrete-Time Baseband Models for Wireless Channels

Lecture 23: Detection for Flat Rayleigh Fading and Incoherent Channels

Lecture 24: Case Study on Code Division Multiple Access