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In the channel considered by the Shannon–Hartley theorem, noise and signal are combined by addition. That is, the receiver measures a signal that is equal to the sum of the signal encoding the desired information and a continuous random variable that represents the noise. This addition creates uncertainty as to the original signal's value.
The computational complexity of finding the Shannon capacity of such a channel remains open, ... (SNR). This result is known as the Shannon–Hartley theorem. [11]
The channel capacity can be calculated from the physical properties of a channel; for a band-limited channel with Gaussian noise, using the Shannon–Hartley theorem. Simple schemes such as "send the message 3 times and use a best 2 out of 3 voting scheme if the copies differ" are inefficient error-correction methods, unable to asymptotically ...
the mutual information, and the channel capacity of a noisy channel, including the promise of perfect loss-free communication given by the noisy-channel coding theorem; the practical result of the Shannon–Hartley law for the channel capacity of a Gaussian channel; as well as; the bit—a new way of seeing the most fundamental unit of information.
In graph theory, the Shannon capacity of a graph is a graph invariant defined from the number of independent sets of strong graph products. It is named after American mathematician Claude Shannon . It measures the Shannon capacity of a communications channel defined from the graph, and is upper bounded by the Lovász number , which can be ...
the mutual information, and the channel capacity of a noisy channel, including the promise of perfect loss-free communication given by the noisy-channel coding theorem; the practical result of the Shannon–Hartley law for the channel capacity of a Gaussian channel; and of course; the bit - a new way of seeing the most fundamental unit of ...
In telecommunications, the channel capacity is equal to the mutual information, maximized over all input distributions. Discriminative training procedures for hidden Markov models have been proposed based on the maximum mutual information (MMI) criterion. RNA secondary structure prediction from a multiple sequence alignment.
C is the channel capacity in bits per second; B is the bandwidth of the channel in hertz; S is the total signal power over the bandwidth and N is the total noise power over the bandwidth. S/N is the signal-to-noise ratio of the communication signal to the Gaussian noise interference expressed as a straight power ratio (not as decibels).