Publication

Coding in the Presence of Semantic Value of Information: Unequal Error Protection Using Poset Decoders

2011
Reports

Abstract

In this work we explore possibilities for coding when information worlds have different (semantic) values. We introduce a loss function that expresses the overall performance of a coding scheme for discrete channels and exchange the usual goal of minimizing the error probability to that of minimizing the expected loss. In this environment we explore the possibilities of using poset-decoders to make a message-wise unequal error protection (UEP), where the most valuable information is protected by placing in its proximity information words that differ by small valued information. Similar definitions and results are shortly presented also for signal constellations in Euclidean space.

Official source

https://infoscience.epfl.ch/entities/publication/3f598713-69dc-4f20-8c9f-9278d749baff

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2011
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3f598713-69dc-4f20-8c9f-9278d749baff

About this result

Related concepts (32)

Loss function

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.

Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy.

Mutual information

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons (bits), nats or hartleys) obtained about one random variable by observing the other random variable. The concept of mutual information is intimately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable.

Related publications (2)

Coding in the Presence of Semantic Value of Information: Unequal Error Protection Using Poset Decoders

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