Lectures related to Communication Channels: Encoding and Decoding

Information Theory and Coding

Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.

Communication Channels: Gaussian Noise and Capacity

Explores the capacity of communication channels with Gaussian noise and noise impact.

Achievable Rate & Capacity

Explores achievable rate, channel capacity, spectral efficiency, and fading channels in wireless communication systems.

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Mutual Information and Entropy

Explores mutual information and entropy calculation between random variables.

Lecture: Shannon

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Information Theory: Basics and Applications

Covers the basics of information theory and its applications in various fields.

Variational Formulation: Information Measures

Explores variational formulation for measuring information content and divergence between probability distributions.

Channel Coding: Theory & Coding

Covers the formation theory and coding, focusing on channel capacity and concave functions.

Information Measures

Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.

Passband Communications Design

Covers the design of passband communications and the use of recipies and complex-valued signals.

Designing Convolutional Codes

Covers the art of designing convolutional codes to improve communication rate.

Convolutional Codes: Decoding and Eye Diagrams

Covers the decoding process of convolutional codes and the analysis of eye diagrams.

Information Theory: Source Coding, Cryptography, Channel Coding

Covers source coding, cryptography, and channel coding in communication systems, exploring entropy, codes, error channels, and future related courses.

Signal Constellations: Design and Analysis

Covers the design and analysis of signal constellations in communication systems.

Source Coding and Prefix-Free Codes

Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.

Information Measures: Estimation & Detection

Covers information measures, entropy, mutual information, and data processing inequality in signal representation.

Convolutional Codes: Decoding and Performance Analysis

Covers the decoding and performance analysis of convolutional codes in communication systems.

Convolutional Coder Introduction

Covers the introduction of Convolutional Coder, explaining its structure and operation.

Binary Coding: Channel Decoding

Explores binary channel decoding and vector spaces in coding theory.