**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

Concept# Channel capacity

Summary

Channel capacity, in electrical engineering, computer science, and information theory, is the tight upper bound on the rate at which information can be reliably transmitted over a communication channel.
Following the terms of the noisy-channel coding theorem, the channel capacity of a given channel is the highest information rate (in units of information per unit time) that can be achieved with arbitrarily small error probability.
Information theory, developed by Claude E. Shannon in 1948, defines the notion of channel capacity and provides a mathematical model by which it may be computed. The key result states that the capacity of the channel, as defined above, is given by the maximum of the mutual information between the input and output of the channel, where the maximization is with respect to the input distribution.
The notion of channel capacity has been central to the development of modern wireline and wireless communication systems, with the advent of novel error correction coding mechanisms that have resulted in achieving performance very close to the limits promised by channel capacity.
The basic mathematical model for a communication system is the following:
where:
is the message to be transmitted;
is the channel input symbol ( is a sequence of symbols) taken in an alphabet ;
is the channel output symbol ( is a sequence of symbols) taken in an alphabet ;
is the estimate of the transmitted message;
is the encoding function for a block of length ;
is the noisy channel, which is modeled by a conditional probability distribution; and,
is the decoding function for a block of length .
Let and be modeled as random variables. Furthermore, let be the conditional probability distribution function of given , which is an inherent fixed property of the communication channel. Then the choice of the marginal distribution completely determines the joint distribution due to the identity
which, in turn, induces a mutual information . The channel capacity is defined as
where the supremum is taken over all possible choices of .

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related courses (10)

Related MOOCs (2)

Related people (64)

Related units (8)

Related concepts (19)

Ontological neighbourhood

Related publications (454)

Related lectures (52)

EE-342: Systèmes de télécommunications

Maîtriser les notions de base d¿un système de transmission de l¿information et identifier les critères déterminants pour la planification d¿un système de télécommunication.
Évaluer les performances d¿

EE-543: Advanced wireless receivers

Students extend their knowledge on wireless communication systems to spread-spectrum communication and to multi-antenna systems. They also learn about the basic information theoretic concepts, about c

COM-404: Information theory and coding

The mathematical principles of communication that govern the compression and transmission of data and the design of efficient methods of doing so.

Information, Calcul, Communication: Introduction à la pensée informatique

Dans une première partie, nous étudierons d’abord comment résoudre de manière très concrète un problème au moyen d’un algorithme, ce qui nous amènera dans un second temps à une des grandes questions d

Information, Calcul, Communication: Introduction à la pensée informatique

Dans une première partie, nous étudierons d’abord comment résoudre de manière très concrète un problème au moyen d’un algorithme, ce qui nous amènera dans un second temps à une des grandes questions d

, , , , , , , , ,

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.

Bit rate

In telecommunications and computing, bit rate (bitrate or as a variable R) is the number of bits that are conveyed or processed per unit of time. The bit rate is expressed in the unit bit per second (symbol: bit/s), often in conjunction with an SI prefix such as kilo (1 kbit/s = 1,000 bit/s), mega (1 Mbit/s = 1,000 kbit/s), giga (1 Gbit/s = 1,000 Mbit/s) or tera (1 Tbit/s = 1,000 Gbit/s). The non-standard abbreviation bps is often used to replace the standard symbol bit/s, so that, for example, 1 Mbps is used to mean one million bits per second.

Information

Information is an abstract concept that refers to that which has the power to inform. At the most fundamental level, information pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artefacts such as analogue signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form.

Secret Key Generation: Polar Coding

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

Achievable Rate & Capacity

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

Information Theory: Source Coding & Channel Coding

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

Commitment is a key primitive which resides at the heart of several cryptographic protocols. Noisy channels can help realize information-theoretically secure commitment schemes; however, their imprecise statistical characterization can severely impair such ...

2023Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

We study the hitting probabilities of the solution to a system of d stochastic heat equations with additive noise subject to Dirichlet boundary conditions. We show that for any bounded Borel set with positive (d-6)\documentclass[12pt]{minimal} \usepackage{ ...