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  2. 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.

  3. Channel capacity - Wikipedia

    en.wikipedia.org/wiki/Channel_capacity

    An application of the channel capacity concept to an additive white Gaussian noise (AWGN) channel with B Hz bandwidth and signal-to-noise ratio S/N is the Shannon–Hartley theorem: C = B log 2 ⁡ ( 1 + S N ) {\displaystyle C=B\log _{2}\left(1+{\frac {S}{N}}\right)\ }

  4. 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.

  5. 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 ...

  6. 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 ...

  7. 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]

  8. James P. Gordon - Wikipedia

    en.wikipedia.org/wiki/James_P._Gordon

    James Power Gordon (March 20, 1928 – June 21, 2013) was an American physicist known for his work in the fields of optics and quantum electronics.His contributions include the design, analysis and construction of the first maser in 1954 as a doctoral student at Columbia University under the supervision of C. H. Townes, development of the quantal equivalent of Shannon's information capacity ...

  9. A Mathematical Theory of Communication - Wikipedia

    en.wikipedia.org/wiki/A_Mathematical_Theory_of...

    Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise) This work is known for introducing the concepts of channel capacity as well as the noisy channel coding theorem. Shannon's article laid out the basic elements of communication: