Information theory

Channel capacity

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. (Wikipedia).

Channel capacity
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Related pages

Spectral efficiency | Decibel | Communication channel | Noisy-channel coding theorem | Lovász number | Mutual information | Hertz | Logarithm | Nyquist rate | Marginal distribution | Infimum and supremum | Information theory | Negentropy | MIMO | Redundancy (information theory) | Shannon–Hartley theorem | Joint probability distribution | Alphabet (formal languages) | Error exponent | Water filling algorithm | Additive white Gaussian noise | Bandwidth (signal processing) | Error correction code | Natural logarithm | Bandwidth (computing) | Signal-to-noise ratio | Nat (unit) | Code rate | Entropy (information theory) | Conditional probability distribution