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Lecture

Convolutional Codes: Mapping Symbols and Paths

In course

COM-302: Principles of digital communications

This course is on the foundations of digital communication. The focus is on the transmission problem (rather than being on source coding). ...

Description

This lecture covers the concept of Convolutional Codes, focusing on the method of mapping symbols and paths. Emre Telatar explains the process of encoding and decoding, analyzing the efficiency properties and the nature of different paths.

Instructor

Emre Telatar

I. Emre Telatar received the B.Sc. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1986. He received the S.M. and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1988 and 1992 respectively. In 1992, he joined the Communications Analysis Research Department at AT&T Bell Laboratories (later Lucent Technologies), Murray Hill, NJ. He has been at the EPFL since 2000. Emre Telatar was the recipient of the IEEE Information Theory Society Paper Award in 2001. He was a program co-chair for the IEEE International Symposium on Information Theory in 2002, and associate editor for Shannon Theory for the IEEE Information Theory Transactions from 2001 to 2004. He was awarded the EPFL Agepoly teaching prize in 2005. Emre Telatar's research interests are in communication and information theories.

Official source

Ontological neighbourhood

Information engineering

Information theory: Coding theory

Related concepts (32)

Convolutional code

In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates trellis decoding using a time-invariant trellis. Time invariant trellis decoding allows convolutional codes to be maximum-likelihood soft-decision decoded with reasonable complexity.

Error correction code

In computing, telecommunication, information theory, and coding theory, forward error correction (FEC) or channel coding is a technique used for controlling errors in data transmission over unreliable or noisy communication channels. The central idea is that the sender encodes the message in a redundant way, most often by using an error correction code or error correcting code (ECC). The redundancy allows the receiver not only to detect errors that may occur anywhere in the message, but often to correct a limited number of errors.

Turbo code

In information theory, turbo codes (originally in French Turbocodes) are a class of high-performance forward error correction (FEC) codes developed around 1990–91, but first published in 1993. They were the first practical codes to closely approach the maximum channel capacity or Shannon limit, a theoretical maximum for the code rate at which reliable communication is still possible given a specific noise level. Turbo codes are used in 3G/4G mobile communications (e.g.

Coding theory

Coding theory is the study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage. Codes are studied by various scientific disciplines—such as information theory, electrical engineering, mathematics, linguistics, and computer science—for the purpose of designing efficient and reliable data transmission methods.

2G is a short notation for second-generation cellular network, a group of technology standards employed for cellular networks. 2G was commercially launched on the GSM standard in Finland by Radiolinja (now part of Elisa Oyj) in 1991. After 2G was launched, the previous mobile wireless network systems were retroactively dubbed 1G. While radio signals on 1G networks are analog, radio signals on 2G networks are digital, though both systems use digital signaling to connect cellular radio towers to the rest of the mobile network system.

Related lectures (27)

Convolutional Codes: Encoding and Decoding

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

Convolutional Codes: Decoding and Performance Analysis

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

Convolutional Codes: Basics and Applications

Explores the basics and applications of convolutional codes in communication systems.

Convolutional Codes: Decoding and Eye Diagrams

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

Convolutional Codes: Analysis and Applications

Delves into the analysis and applications of convolutional codes, highlighting the significance of correct implementation for optimal results.

Related concepts (32)

Convolutional code

Error correction code

Turbo code

Coding theory