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  2. Channel capacity - Wikipedia

    en.wikipedia.org/wiki/Channel_capacity

    Shannon capacity of a graph. If G is an undirected graph, it can be used to define a communications channel in which the symbols are the graph vertices, and two ...

  3. Shannon–Hartley theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon–Hartley_theorem

    It connects Hartley's result with Shannon's channel capacity theorem in a form that is equivalent to specifying the M in Hartley's line rate formula in terms of a signal-to-noise ratio, but achieving reliability through error-correction coding rather than through reliably distinguishable pulse levels.

  4. Noisy-channel coding theorem - Wikipedia

    en.wikipedia.org/wiki/Noisy-channel_coding_theorem

    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 ...

  5. Shannon capacity of a graph - Wikipedia

    en.wikipedia.org/wiki/Shannon_capacity_of_a_graph

    The Shannon capacity of a graph G is bounded from below by α(G), and from above by ϑ(G). [5] In some cases, ϑ(G) and the Shannon capacity coincide; for instance, for the graph of a pentagon, both are equal to √ 5. However, there exist other graphs for which the Shannon capacity and the Lovász number differ. [6]

  6. Information theory - Wikipedia

    en.wikipedia.org/wiki/Information_theory

    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.

  7. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication", [2] [3] and is also referred to as Shannon entropy. Shannon's theory defines a data communication system composed of three elements: a source of data, a communication channel, and a receiver. The "fundamental problem ...

  8. Talk:Shannon–Hartley theorem - Wikipedia

    en.wikipedia.org/wiki/Talk:Shannon–Hartley_theorem

    Furthermore, later on in the Wiki article, the authors have set Hartley's expression equal to Shannon's channel capacity to find a relationship between M and the S/N ratio. If Hartley's is a rate, and Shannon's is a capacity, then it wouldn't make a lot of sense to set the two equal to each other. First Harmonic 12:04, 5 September 2006 (UTC)

  9. Shannon capacity - Wikipedia

    en.wikipedia.org/wiki/Shannon_capacity

    Shannon capacity may mean Channel capacity, the capacity of a channel in communications theory; Shannon capacity of a graph This page was last edited on 6 ...