Lectures related to Space–time block code

Coding Theorem: Proof and Properties

Covers the proof and properties of the coding theorem, focusing on maximizing the properties of lx and the achievable rate.

Channel Coding: Theory & Coding

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

Information Theory: Entropy and Capacity

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

Information Theory and Coding: Source Coding

Covers source coding, encoder design, and error probability analysis in information theory and coding.

Random Coding: Achievability and Proof Variants

Explores random coding achievability and proof variants in information theory, emphasizing achievable rates and architectural principles.

Communication Channels: Encoding and Decoding

Explores encoding and decoding techniques in communication systems, focusing on fundamental limits and mutual information computations.

Information Theory: Source Coding

Covers source coding, typical sequences, stationarity, and efficient encoding in information theory.

Communication Channels: Gaussian Noise and Capacity

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

Error Correction Codes: Theory and Applications

Covers error correction codes theory and applications, emphasizing the importance of minimizing distance for reliable communication.

Information Theory and Coding

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

Complex Envelope: Signal Processing

Delves into the computation of bandpass and analytic signals using complex envelopes.

Gaussian Random Vectors

Explores Gaussian random vectors and their statistical properties, emphasizing the importance of specifying statistical properties in complex valued random vectors.

Convolutional Codes: Encoding and Decoding

Explains the encoding and decoding process of convolutional codes using constellation prints.

Rate-Distortion Theory

Covers Rate-Distortion Theory, optimizing encoding and decoding for efficient data transmission.

Passband Communications Design

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

Convolutional Codes: Mapping Symbols and Paths

Covers Convolutional Codes, focusing on mapping symbols and paths.

Mutual Information and Entropy

Explores mutual information and entropy calculation between random variables.

Binary Coding: Channel Decoding

Explores binary channel decoding and vector spaces in coding theory.

Power Spectral Density Computation

Covers the computation of power spectral density and the design of communication systems.

Source Coding and Prefix-Free Codes

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