Lectures related to Noisy-channel coding theorem

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.

Information Coding: Source, Cryptography, Channel

Covers source coding, cryptography, and channel coding for communication systems.

Channel Coding: Convolutional Codes

Explores channel coding with a focus on convolutional codes, emphasizing error detection, correction, and decoding processes.

Lecture: Shannon

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

Data Compression and Shannon's Theorem: Huffman Codes

Explores the performance of Shannon-Fano algorithm and introduces Huffman codes for efficient data compression.

Advanced Wireless Communications

Covers the fundamentals of wireless communication systems, advanced algorithms, coding, and multi-antenna systems.

Information Theory: Source Coding & Channel Coding

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Achievable Rate & Capacity

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

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 and BICM (LLRs)

Explores channel coding, BICM, and LLRs in wireless communication systems, emphasizing the importance of error detection and correction.

Channel Coding: Theory & Coding

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

Data Compression and Shannon's Theorem: Performance Analysis

Explores Shannon's theorem on data compression and the performance of Shannon Fano codes.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Binary Coding: Channel Decoding

Explores binary channel decoding and vector spaces in coding theory.

Soft-Output MIMO Detection for BICM

Covers Soft-Output MIMO Detection for BICM, LLRs computation, BICM system model, and challenges of channel assumptions.

Information Coding: Size Impact

Explores information coding, emphasizing the impact of size on processing and transmission.

Secret Key Generation: Polar Coding

Explores secret key generation using polar coding for short blocklengths, discussing key capacity, rate-leakage pairs, and practical implementation.

Data Compression and Shannon's Theorem: Entropy Calculation Example

Demonstrates the calculation of entropy for a specific example, resulting in an entropy value of 2.69.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.