Publication

Systems and methods for broadcasting information additive codes

Mohammad Amin Shokrollahi
2006
Patents

Abstract

A broadcasting system for communicating data to multiple receivers using information additive code includes one or more information additive code transmitters (110) and one or more information additive code receivers (130). Each of the information additive code transmitters includes an encoder (312) configured to receive source data and to produce information additive code therefrom. Each of the information additive code receivers includes a decoder (732) configured to receive the information additive code and to reconstruct therefrom substantially a copy of the source data.

Official source

https://infoscience.epfl.ch/entities/publication/84db5da4-eb1d-44b4-931b-215a793d4b4a

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Mohammad Amin Shokrollahi
2006
Patents

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/84db5da4-eb1d-44b4-931b-215a793d4b4a

About this result

Related concepts (22)

Entropy (information theory)

In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent to the variable's possible outcomes. Given a discrete random variable , which takes values in the alphabet and is distributed according to : where denotes the sum over the variable's possible values. The choice of base for , the logarithm, varies for different applications. Base 2 gives the unit of bits (or "shannons"), while base e gives "natural units" nat, and base 10 gives units of "dits", "bans", or "hartleys".

Noisy-channel coding theorem

In information theory, the noisy-channel coding theorem (sometimes Shannon's theorem or Shannon's limit), establishes that for any given degree of noise contamination of a communication channel, it is possible to communicate discrete data (digital information) nearly error-free up to a computable maximum rate through the channel. This result was presented by Claude Shannon in 1948 and was based in part on earlier work and ideas of Harry Nyquist and Ralph Hartley.

Information content

In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable. It can be thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of information theory. The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome.

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