Principles of Digital Communication Course 1

Lecture 1 - Introduction to digital communication

Lecture 2 - Discrete source encoding

Lecture 3 - Memory-less sources

Lecture 4 - Entropy and asymptotic equipartition property

Lecture 5 - Markov sources and Lempel-Ziv universal codes

Lecture 6 - Quantization

Lecture 7 - High rate quantizers and waveform encoding

Lecture 8 - Fourier Series

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 - Gaussian random vectors white Gaussian noise (WGN)

Lecture 15 - Linear functionals and filtering of random processes

Lecture 16 - Introduction to detection

Lecture 17 - Detection for random vectors and processes

Lecture 18 - Theorem of irrelevance

Lecture 19 - Baseband detection and complex Gaussian processes

Lecture 20 - Introduction of wireless communication

Lecture 21 - Doppler spread

Lecture 22 - Discrete-time baseband models for wireless channels

Lecture 23 - Rrayleigh fading and incoherent channels

Lecture 24 - Code division multiple access (CDMA)